diff --git a/.gitattributes b/.gitattributes
index 0e23286d95b5ff7b4bad2e3fb9658d7c85646a1b..a10e7d8e048b77c99553d63502f2edb101a8eb5a 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -36,3 +36,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
scannet200/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log filter=lfs diff=lfs merge=lfs -text
scannet200/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log filter=lfs diff=lfs merge=lfs -text
scannet200/semseg-pt-v3m1-1-ppt-extreme-with-alc-submit/train.log filter=lfs diff=lfs merge=lfs -text
+scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log filter=lfs diff=lfs merge=lfs -text
+scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log filter=lfs diff=lfs merge=lfs -text
diff --git a/scannet/.DS_Store b/scannet/.DS_Store
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index 0000000000000000000000000000000000000000..32f05c6a2aafa0fe2013d63f8d8444aa605721af
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/.DS_Store b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..fa03d47b705e647f05421a64728f25f86250a2d2
Binary files /dev/null and b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/.DS_Store differ
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/.DS_Store b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..a9361c324813216364a9c782892963e26d8eb554
Binary files /dev/null and b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/.DS_Store differ
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9cd6499201588526e04eec39619503d568936ff
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/__init__.py
@@ -0,0 +1,25 @@
+from .defaults import DefaultDataset, ConcatDataset
+from .builder import build_dataset
+from .utils import point_collate_fn, collate_fn
+
+# indoor scene
+from .s3dis import S3DISDataset
+from .scannet import ScanNetDataset, ScanNet200Dataset
+from .scannetpp import ScanNetPPDataset
+from .scannet_pair import ScanNetPairDataset
+from .arkitscenes import ArkitScenesDataset
+from .structure3d import Structured3DDataset
+from .alc import ARKitScenesLabelMakerConsensusDataset, ARKitScenesLabelMakerScanNet200Dataset
+from .scannetpp import ScanNetPPDataset
+
+# outdoor scene
+from .semantic_kitti import SemanticKITTIDataset
+from .nuscenes import NuScenesDataset
+from .waymo import WaymoDataset
+
+# object
+from .modelnet import ModelNetDataset
+from .shapenet_part import ShapeNetPartDataset
+
+# dataloader
+from .dataloader import MultiDatasetDataloader
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/alc.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/alc.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd939c8cb0f772a47708042c7872a36232c62130
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/alc.py
@@ -0,0 +1,161 @@
+import glob
+import os
+from collections.abc import Sequence
+from copy import deepcopy
+
+import numpy as np
+import torch
+from labelmaker.label_data import get_wordnet
+from torch.utils.data import Dataset
+
+from pointcept.utils.cache import shared_dict
+from pointcept.utils.logger import get_root_logger
+
+from .builder import DATASETS
+from .preprocessing.alc.preprocess_arkitscenes_labelmaker_consensus import get_wordnet_compact_mapping
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_20, VALID_CLASS_IDS_200
+from .transform import TRANSFORMS, Compose
+
+
+@DATASETS.register_module()
+class ARKitScenesLabelMakerConsensusDataset(Dataset):
+
+ label_key = "semantic_pseudo_gt_wn199"
+
+ def __init__(
+ self,
+ split="train",
+ data_root="data/alc",
+ transform=None,
+ ignore_index=-1,
+ test_mode=False,
+ test_cfg=None,
+ cache=False,
+ loop=1,
+ ):
+ super(ARKitScenesLabelMakerConsensusDataset, self).__init__()
+ self.get_class_to_id()
+
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.cache = cache
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+
+ self.ignore_index = ignore_index
+
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_list),
+ self.loop,
+ split,
+ )
+ )
+
+ def get_class_to_id(self):
+ self.class2id = get_wordnet_compact_mapping()[0]
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+
+ if not self.cache:
+ data = torch.load(data_path)
+ else:
+ data_name = data_path.replace(os.path.dirname(self.data_root), "").split(".")[0]
+ cache_name = "pointcept" + data_name.replace(os.path.sep, "-")
+ data = shared_dict(cache_name)
+
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ scene_id = data["scene_id"]
+ if self.label_key in data.keys():
+ segment = data[self.label_key].reshape(-1)
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ instance = np.ones(coord.shape[0]) * -1
+
+ data_dict = dict(
+ coord=coord,
+ color=color,
+ segment=segment,
+ instance=instance,
+ scene_id=scene_id,
+ )
+
+ if normal is not None:
+ data_dict["normal"] = normal
+
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ segment = data_dict.pop("segment")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ input_dict_list = []
+ for data in data_dict_list:
+ data_part_list = self.test_voxelize(data)
+ for data_part in data_part_list:
+ if self.test_crop:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ input_dict_list += data_part
+
+ for i in range(len(input_dict_list)):
+ input_dict_list[i] = self.post_transform(input_dict_list[i])
+ data_dict = dict(fragment_list=input_dict_list, segment=segment, name=self.get_data_name(idx))
+ return data_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ARKitScenesLabelMakerScanNet200Dataset(ARKitScenesLabelMakerConsensusDataset):
+ label_key = "semantic_pseudo_gt_scannet200"
+
+ def get_class_to_id(self):
+ self.class2id = np.array(VALID_CLASS_IDS_200)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/arkitscenes.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/arkitscenes.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5481bf553351b09c5f3081b95bcafc77c37f979
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/arkitscenes.py
@@ -0,0 +1,114 @@
+"""
+ArkitScenes Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_200
+
+
+@DATASETS.register_module()
+class ArkitScenesDataset(Dataset):
+ def __init__(
+ self,
+ split="Training",
+ data_root="data/ARKitScenesMesh",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super(ArkitScenesDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.loop = (
+ loop if not test_mode else 1
+ ) # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+ self.class2id = np.array(VALID_CLASS_IDS_200)
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop)
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_list), self.loop, split
+ )
+ )
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, list):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data = torch.load(self.data_list[idx % len(self.data_list)])
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ segment = np.zeros(coord.shape[0])
+ data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ data_idx = self.data_idx[idx % len(self.data_idx)]
+ return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ segment = data_dict.pop("segment")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ input_dict_list = []
+ for data in data_dict_list:
+ data_part_list = self.test_voxelize(data)
+ for data_part in data_part_list:
+ data_part_list = self.test_crop(data_part)
+ input_dict_list += data_part_list
+
+ for i in range(len(input_dict_list)):
+ input_dict_list[i] = self.post_transform(input_dict_list[i])
+ return input_dict_list, segment
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/builder.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa5f0ee71bf934d5c1bfe5c71446bfecba49f11
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/builder.py
@@ -0,0 +1,15 @@
+"""
+Dataset Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+DATASETS = Registry("datasets")
+
+
+def build_dataset(cfg):
+ """Build datasets."""
+ return DATASETS.build(cfg)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/dataloader.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3c8e1da41179896eb3443e91b2c49d94b62762a
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/dataloader.py
@@ -0,0 +1,112 @@
+from functools import partial
+import weakref
+import torch
+import torch.utils.data
+
+import pointcept.utils.comm as comm
+from pointcept.datasets.utils import point_collate_fn
+from pointcept.datasets import ConcatDataset
+from pointcept.utils.env import set_seed
+
+
+class MultiDatasetDummySampler:
+ def __init__(self):
+ self.dataloader = None
+
+ def set_epoch(self, epoch):
+ if comm.get_world_size() > 1:
+ for dataloader in self.dataloader.dataloaders:
+ dataloader.sampler.set_epoch(epoch)
+ return
+
+
+class MultiDatasetDataloader:
+ """
+ Multiple Datasets Dataloader, batch data from a same dataset and mix up ratio determined by loop of each sub dataset.
+ The overall length is determined by the main dataset (first) and loop of concat dataset.
+ """
+
+ def __init__(
+ self,
+ concat_dataset: ConcatDataset,
+ batch_size_per_gpu: int,
+ num_worker_per_gpu: int,
+ mix_prob=0,
+ seed=None,
+ ):
+ self.datasets = concat_dataset.datasets
+ self.ratios = [dataset.loop for dataset in self.datasets]
+ # reset data loop, original loop serve as ratios
+ for dataset in self.datasets:
+ dataset.loop = 1
+ # determine union training epoch by main dataset
+ self.datasets[0].loop = concat_dataset.loop
+ # build sub-dataloaders
+ num_workers = num_worker_per_gpu // len(self.datasets)
+ self.dataloaders = []
+ for dataset_id, dataset in enumerate(self.datasets):
+ if comm.get_world_size() > 1:
+ sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+ else:
+ sampler = None
+
+ init_fn = (
+ partial(
+ self._worker_init_fn,
+ dataset_id=dataset_id,
+ num_workers=num_workers,
+ num_datasets=len(self.datasets),
+ rank=comm.get_rank(),
+ seed=seed,
+ )
+ if seed is not None
+ else None
+ )
+ self.dataloaders.append(
+ torch.utils.data.DataLoader(
+ dataset,
+ batch_size=batch_size_per_gpu,
+ shuffle=(sampler is None),
+ num_workers=num_worker_per_gpu,
+ sampler=sampler,
+ collate_fn=partial(point_collate_fn, mix_prob=mix_prob),
+ pin_memory=True,
+ worker_init_fn=init_fn,
+ drop_last=True,
+ persistent_workers=True,
+ )
+ )
+ self.sampler = MultiDatasetDummySampler()
+ self.sampler.dataloader = weakref.proxy(self)
+
+ def __iter__(self):
+ iterator = [iter(dataloader) for dataloader in self.dataloaders]
+ while True:
+ for i in range(len(self.ratios)):
+ for _ in range(self.ratios[i]):
+ try:
+ batch = next(iterator[i])
+ except StopIteration:
+ if i == 0:
+ return
+ else:
+ iterator[i] = iter(self.dataloaders[i])
+ batch = next(iterator[i])
+ yield batch
+
+ def __len__(self):
+ main_data_loader_length = len(self.dataloaders[0])
+ return (
+ main_data_loader_length // self.ratios[0] * sum(self.ratios)
+ + main_data_loader_length % self.ratios[0]
+ )
+
+ @staticmethod
+ def _worker_init_fn(worker_id, num_workers, dataset_id, num_datasets, rank, seed):
+ worker_seed = (
+ num_workers * num_datasets * rank
+ + num_workers * dataset_id
+ + worker_id
+ + seed
+ )
+ set_seed(worker_seed)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/defaults.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9c94941ae5630a852846317614f0b00e39fdf68
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/defaults.py
@@ -0,0 +1,297 @@
+"""
+Default Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+from collections.abc import Sequence
+
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.cache import shared_dict
+from .builder import DATASETS, build_dataset
+from .transform import Compose, TRANSFORMS
+
+
+@DATASETS.register_module()
+class DefaultDataset(Dataset):
+ def __init__(
+ self,
+ split="train",
+ data_root="data/dataset",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super(DefaultDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize) if self.test_cfg.voxelize is not None else None
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop is not None else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data = torch.load(self.data_list[idx % len(self.data_list)])
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ if "semantic_gt" in data.keys():
+ segment = data["semantic_gt"].reshape([-1])
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ result_dict = dict(segment=data_dict.pop("segment"), name=self.get_data_name(idx))
+ if "origin_segment" in data_dict:
+ assert "inverse" in data_dict
+ result_dict["origin_segment"] = data_dict.pop("origin_segment")
+ result_dict["inverse"] = data_dict.pop("inverse")
+
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ fragment_list = []
+ for data in data_dict_list:
+ if self.test_voxelize is not None:
+ data_part_list = self.test_voxelize(data)
+ else:
+ data["index"] = np.arange(data["coord"].shape[0])
+ data_part_list = [data]
+ for data_part in data_part_list:
+ if self.test_crop is not None:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ fragment_list += data_part
+
+ for i in range(len(fragment_list)):
+ fragment_list[i] = self.post_transform(fragment_list[i])
+ result_dict["fragment_list"] = fragment_list
+ return result_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class DefaultDatasetV2(Dataset):
+ VALID_ASSETS = [
+ "coord",
+ "color",
+ "normal",
+ "strength",
+ "segment",
+ "instance",
+ "pose",
+ ]
+
+ def __init__(
+ self,
+ split="train",
+ data_root="data/dataset",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ cache=False,
+ ignore_index=-1,
+ loop=1,
+ ):
+ super(DefaultDatasetV2, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.cache = cache
+ self.ignore_index = ignore_index
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ name = self.get_data_name(idx)
+ if self.cache:
+ cache_name = f"pointcept-{name}"
+ return shared_dict(cache_name)
+
+ data_dict = {}
+ assets = os.listdir(data_path)
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ if asset[:-4] not in self.VALID_ASSETS:
+ continue
+ data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+ data_dict["name"] = name
+
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"].astype(np.float32)
+
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"].astype(np.float32)
+
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"].reshape([-1]).astype(np.int32)
+ else:
+ data_dict["segment"] = np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"].reshape([-1]).astype(np.int32)
+ else:
+ data_dict["instance"] = np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)])
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ result_dict = dict(segment=data_dict.pop("segment"), name=data_dict.pop("name"))
+ if "origin_segment" in data_dict:
+ assert "inverse" in data_dict
+ result_dict["origin_segment"] = data_dict.pop("origin_segment")
+ result_dict["inverse"] = data_dict.pop("inverse")
+
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ fragment_list = []
+ for data in data_dict_list:
+ if self.test_voxelize is not None:
+ data_part_list = self.test_voxelize(data)
+ else:
+ data["index"] = np.arange(data["coord"].shape[0])
+ data_part_list = [data]
+ for data_part in data_part_list:
+ if self.test_crop is not None:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ fragment_list += data_part
+
+ for i in range(len(fragment_list)):
+ fragment_list[i] = self.post_transform(fragment_list[i])
+ result_dict["fragment_list"] = fragment_list
+ return result_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ConcatDataset(Dataset):
+ def __init__(self, datasets, loop=1):
+ super(ConcatDataset, self).__init__()
+ self.datasets = [build_dataset(dataset) for dataset in datasets]
+ self.loop = loop
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in the concat set.".format(len(self.data_list), self.loop))
+
+ def get_data_list(self):
+ data_list = []
+ for i in range(len(self.datasets)):
+ data_list.extend(zip(np.ones(len(self.datasets[i])) * i, np.arange(len(self.datasets[i]))))
+ return data_list
+
+ def get_data(self, idx):
+ dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+ return self.datasets[dataset_idx][data_idx]
+
+ def get_data_name(self, idx):
+ dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+ return self.datasets[dataset_idx].get_data_name(data_idx)
+
+ def __getitem__(self, idx):
+ return self.get_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/modelnet.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/modelnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..213f3ed2dfe4d788380824b87355e6fcae1ee531
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/modelnet.py
@@ -0,0 +1,150 @@
+"""
+ModelNet40 Dataset
+
+get sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape)
+at "https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip"
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import pointops
+import torch
+from torch.utils.data import Dataset
+from copy import deepcopy
+
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ModelNetDataset(Dataset):
+ def __init__(
+ self,
+ split="train",
+ data_root="data/modelnet40",
+ class_names=None,
+ transform=None,
+ num_points=8192,
+ uniform_sampling=True,
+ save_record=True,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super().__init__()
+ self.data_root = data_root
+ self.class_names = dict(zip(class_names, range(len(class_names))))
+ self.split = split
+ self.num_point = num_points
+ self.uniform_sampling = uniform_sampling
+ self.transform = Compose(transform)
+ self.loop = (
+ loop if not test_mode else 1
+ ) # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+ if test_mode:
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_list), self.loop, split
+ )
+ )
+
+ # check, prepare record
+ record_name = f"modelnet40_{self.split}"
+ if num_points is not None:
+ record_name += f"_{num_points}points"
+ if uniform_sampling:
+ record_name += "_uniform"
+ record_path = os.path.join(self.data_root, f"{record_name}.pth")
+ if os.path.isfile(record_path):
+ logger.info(f"Loading record: {record_name} ...")
+ self.data = torch.load(record_path)
+ else:
+ logger.info(f"Preparing record: {record_name} ...")
+ self.data = {}
+ for idx in range(len(self.data_list)):
+ data_name = self.data_list[idx]
+ logger.info(f"Parsing data [{idx}/{len(self.data_list)}]: {data_name}")
+ self.data[data_name] = self.get_data(idx)
+ if save_record:
+ torch.save(self.data, record_path)
+
+ def get_data(self, idx):
+ data_idx = idx % len(self.data_list)
+ data_name = self.data_list[data_idx]
+ if data_name in self.data.keys():
+ return self.data[data_name]
+ else:
+ data_shape = "_".join(data_name.split("_")[0:-1])
+ data_path = os.path.join(
+ self.data_root, data_shape, self.data_list[data_idx] + ".txt"
+ )
+ data = np.loadtxt(data_path, delimiter=",").astype(np.float32)
+ if self.num_point is not None:
+ if self.uniform_sampling:
+ with torch.no_grad():
+ mask = pointops.farthest_point_sampling(
+ torch.tensor(data).float().cuda(),
+ torch.tensor([len(data)]).long().cuda(),
+ torch.tensor([self.num_point]).long().cuda(),
+ )
+ data = data[mask.cpu()]
+ else:
+ data = data[: self.num_point]
+ coord, normal = data[:, 0:3], data[:, 3:6]
+ category = np.array([self.class_names[data_shape]])
+ return dict(coord=coord, normal=normal, category=category)
+
+ def get_data_list(self):
+ assert isinstance(self.split, str)
+ split_path = os.path.join(
+ self.data_root, "modelnet40_{}.txt".format(self.split)
+ )
+ data_list = np.loadtxt(split_path, dtype="str")
+ return data_list
+
+ def get_data_name(self, idx):
+ data_idx = idx % len(self.data_list)
+ return self.data_list[data_idx]
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+ def prepare_train_data(self, idx):
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ assert idx < len(self.data_list)
+ data_dict = self.get_data(idx)
+ category = data_dict.pop("category")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+ for i in range(len(data_dict_list)):
+ data_dict_list[i] = self.post_transform(data_dict_list[i])
+ data_dict = dict(
+ voting_list=data_dict_list,
+ category=category,
+ name=self.get_data_name(idx),
+ )
+ return data_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/nuscenes.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/nuscenes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b126c4bbeaef5d73f6f0eb10decbee71d36f515a
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/nuscenes.py
@@ -0,0 +1,120 @@
+"""
+nuScenes Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Zheng Zhang
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+from collections.abc import Sequence
+import pickle
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class NuScenesDataset(DefaultDataset):
+ def __init__(self, sweeps=10, ignore_index=-1, **kwargs):
+ self.sweeps = sweeps
+ self.ignore_index = ignore_index
+ self.learning_map = self.get_learning_map(ignore_index)
+ super().__init__(ignore_index=ignore_index, **kwargs)
+
+ def get_info_path(self, split):
+ assert split in ["train", "val", "test"]
+ if split == "train":
+ return os.path.join(
+ self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_train.pkl"
+ )
+ elif split == "val":
+ return os.path.join(
+ self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_val.pkl"
+ )
+ elif split == "test":
+ return os.path.join(
+ self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_test.pkl"
+ )
+ else:
+ raise NotImplementedError
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ info_paths = [self.get_info_path(self.split)]
+ elif isinstance(self.split, Sequence):
+ info_paths = [self.get_info_path(s) for s in self.split]
+ else:
+ raise NotImplementedError
+ data_list = []
+ for info_path in info_paths:
+ with open(info_path, "rb") as f:
+ info = pickle.load(f)
+ data_list.extend(info)
+ return data_list
+
+ def get_data(self, idx):
+ data = self.data_list[idx % len(self.data_list)]
+ lidar_path = os.path.join(self.data_root, "raw", data["lidar_path"])
+ points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1).reshape(
+ [-1, 5]
+ )
+ coord = points[:, :3]
+ strength = points[:, 3].reshape([-1, 1]) / 255 # scale strength to [0, 1]
+
+ if "gt_segment_path" in data.keys():
+ gt_segment_path = os.path.join(
+ self.data_root, "raw", data["gt_segment_path"]
+ )
+ segment = np.fromfile(
+ str(gt_segment_path), dtype=np.uint8, count=-1
+ ).reshape([-1])
+ segment = np.vectorize(self.learning_map.__getitem__)(segment).astype(
+ np.int64
+ )
+ else:
+ segment = np.ones((points.shape[0],), dtype=np.int64) * self.ignore_index
+ data_dict = dict(coord=coord, strength=strength, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ # return data name for lidar seg, optimize the code when need to support detection
+ return self.data_list[idx % len(self.data_list)]["lidar_token"]
+
+ @staticmethod
+ def get_learning_map(ignore_index):
+ learning_map = {
+ 0: ignore_index,
+ 1: ignore_index,
+ 2: 6,
+ 3: 6,
+ 4: 6,
+ 5: ignore_index,
+ 6: 6,
+ 7: ignore_index,
+ 8: ignore_index,
+ 9: 0,
+ 10: ignore_index,
+ 11: ignore_index,
+ 12: 7,
+ 13: ignore_index,
+ 14: 1,
+ 15: 2,
+ 16: 2,
+ 17: 3,
+ 18: 4,
+ 19: ignore_index,
+ 20: ignore_index,
+ 21: 5,
+ 22: 8,
+ 23: 9,
+ 24: 10,
+ 25: 11,
+ 26: 12,
+ 27: 13,
+ 28: 14,
+ 29: ignore_index,
+ 30: 15,
+ 31: ignore_index,
+ }
+ return learning_map
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d8fac61417d1409379f2ad1dbdecda8a64791ad
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py
@@ -0,0 +1,375 @@
+import warnings
+
+import torch
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import argparse
+import glob
+import json
+import multiprocessing as mp
+import os
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plyfile
+from labelmaker import label_mappings
+from labelmaker.label_data import get_wordnet
+from labelmaker.scannet_200_labels import VALID_CLASS_IDS_200
+from tqdm import tqdm
+
+IGNORE_INDEX = -1
+
+
+def get_wordnet_to_scannet200_mapping():
+ table = pd.read_csv(Path(os.path.dirname(os.path.realpath(label_mappings.__file__))) / "mappings" / "label_mapping.csv")
+ wordnet = get_wordnet()
+ wordnet_keys = [x["name"] for x in wordnet]
+ mapping = {}
+ for row in table.index:
+ if table["wnsynsetkey"][row] not in wordnet_keys:
+ continue
+ scannet_id = table.loc[row, "id"]
+ wordnet199_id = next(x for x in wordnet if x["name"] == table["wnsynsetkey"][row])["id"]
+
+ if scannet_id in VALID_CLASS_IDS_200:
+ mapping.setdefault(wordnet199_id, set()).add(scannet_id)
+
+ wn199_size = np.array([x["id"] for x in wordnet]).max() + 1
+ mapping_array = np.zeros(shape=(wn199_size,), dtype=np.uint16)
+ for wordnet199_id in mapping.keys():
+ mapping_array[wordnet199_id] = min(mapping[wordnet199_id])
+
+ return mapping_array
+
+
+def get_wordnet_compact_mapping():
+ wordnet_info = get_wordnet()[1:]
+ wordnet_info = sorted(wordnet_info, key=lambda x: x["id"])
+
+ class2id = np.array([item["id"] for item in wordnet_info])
+ id2class = np.array([IGNORE_INDEX] * (class2id.max() + 1))
+ for class_, id_ in enumerate(class2id):
+ id2class[id_] = class_
+
+ return class2id, id2class
+
+
+def get_scannet200_compact_mapping():
+ class2id = np.array(VALID_CLASS_IDS_200)
+ id2class = np.array([IGNORE_INDEX] * (class2id.max() + 1))
+ for class_, id_ in enumerate(VALID_CLASS_IDS_200):
+ id2class[id_] = class_
+
+ return class2id, id2class
+
+
+def get_wordnet_names():
+ wordnet_info = get_wordnet()[1:]
+ wordnet_info = sorted(wordnet_info, key=lambda x: x["id"])
+
+ names = [item["name"].split(".")[0].replace("_", " ") for item in wordnet_info]
+
+ return names
+
+
+def read_plypcd(filepath):
+ """Read ply file and return it as numpy array. Returns None if emtpy."""
+
+ with open(filepath, "rb") as f:
+ plydata = plyfile.PlyData.read(f)
+ if plydata.elements:
+ data = plydata.elements[0].data
+ coords = np.array([data["x"], data["y"], data["z"]], dtype=np.float32).T
+
+ colors = None
+ if ({"red", "green", "blue"} - set(data.dtype.names)) == set():
+ colors = np.array([data["red"], data["green"], data["blue"]], dtype=np.uint8).T
+
+ normals = None
+ if ({"nx", "ny", "nz"} - set(data.dtype.names)) == set():
+ normals = np.array([data["nx"], data["ny"], data["nz"]], dtype=np.float32).T
+
+ return coords, colors, normals
+
+
+def handle_process(
+ scene_dir: str,
+ output_path: str,
+ label_mapping,
+ wn199_id2class,
+ scannet200_id2class,
+):
+ scene_dir = Path(scene_dir)
+
+ print(f"Processing: {scene_dir.name} in {scene_dir.parent.name}")
+
+ coords, colors, normals = read_plypcd(str(scene_dir / "pcd_downsampled.ply"))
+ save_dict = dict(
+ coord=coords,
+ color=colors,
+ scene_id=scene_dir.name,
+ normal=normals,
+ )
+
+ label_file = scene_dir / "labels_downsampled.txt"
+ wordnet_label = np.loadtxt(str(label_file), dtype=np.uint8).reshape(-1, 1)
+ scannet200_label = label_mapping[wordnet_label]
+ save_dict["semantic_pseudo_gt_wn199"] = wn199_id2class[wordnet_label]
+ save_dict["semantic_pseudo_gt_scannet200"] = scannet200_id2class[scannet200_label]
+
+ torch.save(save_dict, output_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ config = parser.parse_args()
+
+ # Create output directories
+ train_output_dir = os.path.join(config.output_root, "train")
+ os.makedirs(train_output_dir, exist_ok=True)
+ val_output_dir = os.path.join(config.output_root, "val")
+ os.makedirs(val_output_dir, exist_ok=True)
+
+ # Load label map
+ wn_scannet200_label_mapping = get_wordnet_to_scannet200_mapping()
+ _, wn199_id2class = get_wordnet_compact_mapping()
+ _, scannet200_id2class = get_scannet200_compact_mapping()
+
+ scene_dirs = []
+ output_paths = []
+
+ # Load train/val splits
+ train_folder = Path(config.dataset_root) / "Training"
+ train_scene_names = os.listdir(str(train_folder))
+ for scene in tqdm(train_scene_names):
+ file_path = train_folder / scene / "pcd_downsampled.ply"
+ if file_path.exists() and os.path.getsize(str(file_path)) <= 50 * 1024 * 1024:
+ scene_dirs.append(str(train_folder / scene))
+ output_paths.append(str(Path(config.output_root) / "train" / f"{scene}.pth"))
+
+ val_folder = Path(config.dataset_root) / "Validation"
+ val_scene_names = os.listdir(str(val_folder))
+ for scene in tqdm(val_scene_names):
+ file_path = val_folder / scene / "pcd_downsampled.ply"
+ if file_path.exists() and os.path.getsize(str(file_path)) <= 50 * 1024 * 1024:
+ scene_dirs.append(str(val_folder / scene))
+ output_paths.append(str(Path(config.output_root) / "val" / f"{scene}.pth"))
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+ print(f"Using {mp.cpu_count()} cores")
+ # pool = ProcessPoolExecutor(max_workers=1)
+ _ = list(
+ pool.map(
+ handle_process,
+ scene_dirs,
+ output_paths,
+ repeat(wn_scannet200_label_mapping),
+ repeat(wn199_id2class),
+ repeat(scannet200_id2class),
+ )
+ )
+
+
+WORDNET_NAMES = (
+ "wall",
+ "chair",
+ "book",
+ "cabinet",
+ "door",
+ "floor",
+ "ashcan",
+ "table",
+ "window",
+ "bookshelf",
+ "display",
+ "cushion",
+ "box",
+ "picture",
+ "ceiling",
+ "doorframe",
+ "desk",
+ "swivel chair",
+ "towel",
+ "sofa",
+ "sink",
+ "backpack",
+ "lamp",
+ "chest of drawers",
+ "apparel",
+ "armchair",
+ "bed",
+ "curtain",
+ "mirror",
+ "plant",
+ "radiator",
+ "toilet tissue",
+ "shoe",
+ "bag",
+ "bottle",
+ "countertop",
+ "coffee table",
+ "toilet",
+ "computer keyboard",
+ "fridge",
+ "stool",
+ "computer",
+ "mug",
+ "telephone",
+ "light",
+ "jacket",
+ "bathtub",
+ "shower curtain",
+ "microwave",
+ "footstool",
+ "baggage",
+ "laptop",
+ "printer",
+ "shower stall",
+ "soap dispenser",
+ "stove",
+ "fan",
+ "paper",
+ "stand",
+ "bench",
+ "wardrobe",
+ "blanket",
+ "booth",
+ "duplicator",
+ "bar",
+ "soap dish",
+ "switch",
+ "coffee maker",
+ "decoration",
+ "range hood",
+ "blackboard",
+ "clock",
+ "railing",
+ "mat",
+ "seat",
+ "bannister",
+ "container",
+ "mouse",
+ "person",
+ "stairway",
+ "basket",
+ "dumbbell",
+ "column",
+ "bucket",
+ "windowsill",
+ "signboard",
+ "dishwasher",
+ "loudspeaker",
+ "washer",
+ "paper towel",
+ "clothes hamper",
+ "piano",
+ "sack",
+ "handcart",
+ "blind",
+ "dish rack",
+ "mailbox",
+ "bag",
+ "bicycle",
+ "ladder",
+ "rack",
+ "tray",
+ "toaster",
+ "paper cutter",
+ "plunger",
+ "dryer",
+ "guitar",
+ "fire extinguisher",
+ "pitcher",
+ "pipe",
+ "plate",
+ "vacuum",
+ "bowl",
+ "hat",
+ "rod",
+ "water cooler",
+ "kettle",
+ "oven",
+ "scale",
+ "broom",
+ "hand blower",
+ "coatrack",
+ "teddy",
+ "alarm clock",
+ "ironing board",
+ "fire alarm",
+ "machine",
+ "music stand",
+ "fireplace",
+ "furniture",
+ "vase",
+ "vent",
+ "candle",
+ "crate",
+ "dustpan",
+ "earphone",
+ "jar",
+ "projector",
+ "gat",
+ "step",
+ "step stool",
+ "vending machine",
+ "coat",
+ "coat hanger",
+ "drinking fountain",
+ "hamper",
+ "thermostat",
+ "banner",
+ "iron",
+ "soap",
+ "chopping board",
+ "kitchen island",
+ "shirt",
+ "sleeping bag",
+ "tire",
+ "toothbrush",
+ "bathrobe",
+ "faucet",
+ "slipper",
+ "thermos",
+ "tripod",
+ "dispenser",
+ "heater",
+ "pool table",
+ "remote control",
+ "stapler",
+ "treadmill",
+ "beanbag",
+ "dartboard",
+ "metronome",
+ "rope",
+ "sewing machine",
+ "shredder",
+ "toolbox",
+ "water heater",
+ "brush",
+ "control",
+ "dais",
+ "dollhouse",
+ "envelope",
+ "food",
+ "frying pan",
+ "helmet",
+ "tennis racket",
+ "umbrella",
+)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bc9b3e47a35f5baa00bcf0f526d5d986b28494e
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py
@@ -0,0 +1,87 @@
+"""
+Preprocessing ArkitScenes
+"""
+
+import os
+import argparse
+import glob
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+import torch
+
+
+def read_plymesh(filepath):
+ """Read ply file and return it as numpy array. Returns None if emtpy."""
+ with open(filepath, "rb") as f:
+ plydata = plyfile.PlyData.read(f)
+ if plydata.elements:
+ vertices = pd.DataFrame(plydata["vertex"].data).values
+ faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+ return vertices, faces
+
+
+def face_normal(vertex, face):
+ v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+ v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+ vec = np.cross(v01, v02)
+ length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+ nf = vec / length
+ area = length * 0.5
+ return nf, area
+
+
+def vertex_normal(vertex, face):
+ nf, area = face_normal(vertex, face)
+ nf = nf * area
+
+ nv = np.zeros_like(vertex)
+ for i in range(face.shape[0]):
+ nv[face[i]] += nf[i]
+
+ length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+ nv = nv / length
+ return nv
+
+
+def parse_scene(scene_path, output_dir):
+ print(f"Parsing scene {scene_path}")
+ split = os.path.basename(os.path.dirname(os.path.dirname(scene_path)))
+ scene_id = os.path.basename(os.path.dirname(scene_path))
+ vertices, faces = read_plymesh(scene_path)
+ coords = vertices[:, :3]
+ colors = vertices[:, 3:6]
+ data_dict = dict(coord=coords, color=colors, scene_id=scene_id)
+ data_dict["normal"] = vertex_normal(coords, faces)
+ torch.save(data_dict, os.path.join(output_dir, split, f"{scene_id}.pth"))
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ opt = parser.parse_args()
+ # Create output directories
+ train_output_dir = os.path.join(opt.output_root, "Training")
+ os.makedirs(train_output_dir, exist_ok=True)
+ val_output_dir = os.path.join(opt.output_root, "Validation")
+ os.makedirs(val_output_dir, exist_ok=True)
+ # Load scene paths
+ scene_paths = sorted(glob.glob(opt.dataset_root + "/3dod/*/*/*_mesh.ply"))
+ # Preprocess data.
+ pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+ # pool = ProcessPoolExecutor(max_workers=1)
+ print("Processing scenes...")
+ _ = list(pool.map(parse_scene, scene_paths, repeat(opt.output_root)))
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ed106f193a488aa76385157aa33fb65e5944a6f
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py
@@ -0,0 +1,607 @@
+"""
+Preprocessing Script for nuScenes Informantion
+modified from OpenPCDet (https://github.com/open-mmlab/OpenPCDet)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from pathlib import Path
+import numpy as np
+import argparse
+import tqdm
+import pickle
+from functools import reduce
+from pyquaternion import Quaternion
+from nuscenes.nuscenes import NuScenes
+from nuscenes.utils import splits
+from nuscenes.utils.geometry_utils import transform_matrix
+
+
+map_name_from_general_to_detection = {
+ "human.pedestrian.adult": "pedestrian",
+ "human.pedestrian.child": "pedestrian",
+ "human.pedestrian.wheelchair": "ignore",
+ "human.pedestrian.stroller": "ignore",
+ "human.pedestrian.personal_mobility": "ignore",
+ "human.pedestrian.police_officer": "pedestrian",
+ "human.pedestrian.construction_worker": "pedestrian",
+ "animal": "ignore",
+ "vehicle.car": "car",
+ "vehicle.motorcycle": "motorcycle",
+ "vehicle.bicycle": "bicycle",
+ "vehicle.bus.bendy": "bus",
+ "vehicle.bus.rigid": "bus",
+ "vehicle.truck": "truck",
+ "vehicle.construction": "construction_vehicle",
+ "vehicle.emergency.ambulance": "ignore",
+ "vehicle.emergency.police": "ignore",
+ "vehicle.trailer": "trailer",
+ "movable_object.barrier": "barrier",
+ "movable_object.trafficcone": "traffic_cone",
+ "movable_object.pushable_pullable": "ignore",
+ "movable_object.debris": "ignore",
+ "static_object.bicycle_rack": "ignore",
+}
+
+
+cls_attr_dist = {
+ "barrier": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "bicycle": {
+ "cycle.with_rider": 2791,
+ "cycle.without_rider": 8946,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "bus": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 9092,
+ "vehicle.parked": 3294,
+ "vehicle.stopped": 3881,
+ },
+ "car": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 114304,
+ "vehicle.parked": 330133,
+ "vehicle.stopped": 46898,
+ },
+ "construction_vehicle": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 882,
+ "vehicle.parked": 11549,
+ "vehicle.stopped": 2102,
+ },
+ "ignore": {
+ "cycle.with_rider": 307,
+ "cycle.without_rider": 73,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 165,
+ "vehicle.parked": 400,
+ "vehicle.stopped": 102,
+ },
+ "motorcycle": {
+ "cycle.with_rider": 4233,
+ "cycle.without_rider": 8326,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "pedestrian": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 157444,
+ "pedestrian.sitting_lying_down": 13939,
+ "pedestrian.standing": 46530,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "traffic_cone": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "trailer": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 3421,
+ "vehicle.parked": 19224,
+ "vehicle.stopped": 1895,
+ },
+ "truck": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 21339,
+ "vehicle.parked": 55626,
+ "vehicle.stopped": 11097,
+ },
+}
+
+
+def get_available_scenes(nusc):
+ available_scenes = []
+ for scene in nusc.scene:
+ scene_token = scene["token"]
+ scene_rec = nusc.get("scene", scene_token)
+ sample_rec = nusc.get("sample", scene_rec["first_sample_token"])
+ sd_rec = nusc.get("sample_data", sample_rec["data"]["LIDAR_TOP"])
+ has_more_frames = True
+ scene_not_exist = False
+ while has_more_frames:
+ lidar_path, boxes, _ = nusc.get_sample_data(sd_rec["token"])
+ if not Path(lidar_path).exists():
+ scene_not_exist = True
+ break
+ else:
+ break
+ if scene_not_exist:
+ continue
+ available_scenes.append(scene)
+ return available_scenes
+
+
+def get_sample_data(nusc, sample_data_token, selected_anntokens=None):
+ """
+ Returns the data path as well as all annotations related to that sample_data.
+ Note that the boxes are transformed into the current sensor"s coordinate frame.
+ Args:
+ nusc:
+ sample_data_token: Sample_data token.
+ selected_anntokens: If provided only return the selected annotation.
+
+ Returns:
+
+ """
+ # Retrieve sensor & pose records
+ sd_record = nusc.get("sample_data", sample_data_token)
+ cs_record = nusc.get("calibrated_sensor", sd_record["calibrated_sensor_token"])
+ sensor_record = nusc.get("sensor", cs_record["sensor_token"])
+ pose_record = nusc.get("ego_pose", sd_record["ego_pose_token"])
+
+ data_path = nusc.get_sample_data_path(sample_data_token)
+
+ if sensor_record["modality"] == "camera":
+ cam_intrinsic = np.array(cs_record["camera_intrinsic"])
+ else:
+ cam_intrinsic = None
+
+ # Retrieve all sample annotations and map to sensor coordinate system.
+ if selected_anntokens is not None:
+ boxes = list(map(nusc.get_box, selected_anntokens))
+ else:
+ boxes = nusc.get_boxes(sample_data_token)
+
+ # Make list of Box objects including coord system transforms.
+ box_list = []
+ for box in boxes:
+ box.velocity = nusc.box_velocity(box.token)
+ # Move box to ego vehicle coord system
+ box.translate(-np.array(pose_record["translation"]))
+ box.rotate(Quaternion(pose_record["rotation"]).inverse)
+
+ # Move box to sensor coord system
+ box.translate(-np.array(cs_record["translation"]))
+ box.rotate(Quaternion(cs_record["rotation"]).inverse)
+
+ box_list.append(box)
+
+ return data_path, box_list, cam_intrinsic
+
+
+def quaternion_yaw(q: Quaternion) -> float:
+ """
+ Calculate the yaw angle from a quaternion.
+ Note that this only works for a quaternion that represents a box in lidar or global coordinate frame.
+ It does not work for a box in the camera frame.
+ :param q: Quaternion of interest.
+ :return: Yaw angle in radians.
+ """
+
+ # Project into xy plane.
+ v = np.dot(q.rotation_matrix, np.array([1, 0, 0]))
+
+ # Measure yaw using arctan.
+ yaw = np.arctan2(v[1], v[0])
+
+ return yaw
+
+
+def obtain_sensor2top(
+ nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type="lidar"
+):
+ """Obtain the info with RT matric from general sensor to Top LiDAR.
+
+ Args:
+ nusc (class): Dataset class in the nuScenes dataset.
+ sensor_token (str): Sample data token corresponding to the
+ specific sensor type.
+ l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3).
+ l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego
+ in shape (3, 3).
+ e2g_t (np.ndarray): Translation from ego to global in shape (1, 3).
+ e2g_r_mat (np.ndarray): Rotation matrix from ego to global
+ in shape (3, 3).
+ sensor_type (str): Sensor to calibrate. Default: "lidar".
+
+ Returns:
+ sweep (dict): Sweep information after transformation.
+ """
+ sd_rec = nusc.get("sample_data", sensor_token)
+ cs_record = nusc.get("calibrated_sensor", sd_rec["calibrated_sensor_token"])
+ pose_record = nusc.get("ego_pose", sd_rec["ego_pose_token"])
+ data_path = str(nusc.get_sample_data_path(sd_rec["token"]))
+ # if os.getcwd() in data_path: # path from lyftdataset is absolute path
+ # data_path = data_path.split(f"{os.getcwd()}/")[-1] # relative path
+ sweep = {
+ "data_path": data_path,
+ "type": sensor_type,
+ "sample_data_token": sd_rec["token"],
+ "sensor2ego_translation": cs_record["translation"],
+ "sensor2ego_rotation": cs_record["rotation"],
+ "ego2global_translation": pose_record["translation"],
+ "ego2global_rotation": pose_record["rotation"],
+ "timestamp": sd_rec["timestamp"],
+ }
+ l2e_r_s = sweep["sensor2ego_rotation"]
+ l2e_t_s = sweep["sensor2ego_translation"]
+ e2g_r_s = sweep["ego2global_rotation"]
+ e2g_t_s = sweep["ego2global_translation"]
+
+ # obtain the RT from sensor to Top LiDAR
+ # sweep->ego->global->ego'->lidar
+ l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
+ e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
+ R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
+ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+ )
+ T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
+ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+ )
+ T -= (
+ e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+ + l2e_t @ np.linalg.inv(l2e_r_mat).T
+ ).squeeze(0)
+ sweep["sensor2lidar_rotation"] = R.T # points @ R.T + T
+ sweep["sensor2lidar_translation"] = T
+ return sweep
+
+
+def fill_trainval_infos(
+ data_path, nusc, train_scenes, test=False, max_sweeps=10, with_camera=False
+):
+ train_nusc_infos = []
+ val_nusc_infos = []
+ progress_bar = tqdm.tqdm(
+ total=len(nusc.sample), desc="create_info", dynamic_ncols=True
+ )
+
+ ref_chan = "LIDAR_TOP" # The radar channel from which we track back n sweeps to aggregate the point cloud.
+ chan = "LIDAR_TOP" # The reference channel of the current sample_rec that the point clouds are mapped to.
+
+ for index, sample in enumerate(nusc.sample):
+ progress_bar.update()
+
+ ref_sd_token = sample["data"][ref_chan]
+ ref_sd_rec = nusc.get("sample_data", ref_sd_token)
+ ref_cs_rec = nusc.get(
+ "calibrated_sensor", ref_sd_rec["calibrated_sensor_token"]
+ )
+ ref_pose_rec = nusc.get("ego_pose", ref_sd_rec["ego_pose_token"])
+ ref_time = 1e-6 * ref_sd_rec["timestamp"]
+
+ ref_lidar_path, ref_boxes, _ = get_sample_data(nusc, ref_sd_token)
+
+ ref_cam_front_token = sample["data"]["CAM_FRONT"]
+ ref_cam_path, _, ref_cam_intrinsic = nusc.get_sample_data(ref_cam_front_token)
+
+ # Homogeneous transform from ego car frame to reference frame
+ ref_from_car = transform_matrix(
+ ref_cs_rec["translation"], Quaternion(ref_cs_rec["rotation"]), inverse=True
+ )
+
+ # Homogeneous transformation matrix from global to _current_ ego car frame
+ car_from_global = transform_matrix(
+ ref_pose_rec["translation"],
+ Quaternion(ref_pose_rec["rotation"]),
+ inverse=True,
+ )
+ info = {
+ "lidar_path": Path(ref_lidar_path).relative_to(data_path).__str__(),
+ "lidar_token": ref_sd_token,
+ "cam_front_path": Path(ref_cam_path).relative_to(data_path).__str__(),
+ "cam_intrinsic": ref_cam_intrinsic,
+ "token": sample["token"],
+ "sweeps": [],
+ "ref_from_car": ref_from_car,
+ "car_from_global": car_from_global,
+ "timestamp": ref_time,
+ }
+ if with_camera:
+ info["cams"] = dict()
+ l2e_r = ref_cs_rec["rotation"]
+ l2e_t = (ref_cs_rec["translation"],)
+ e2g_r = ref_pose_rec["rotation"]
+ e2g_t = ref_pose_rec["translation"]
+ l2e_r_mat = Quaternion(l2e_r).rotation_matrix
+ e2g_r_mat = Quaternion(e2g_r).rotation_matrix
+
+ # obtain 6 image's information per frame
+ camera_types = [
+ "CAM_FRONT",
+ "CAM_FRONT_RIGHT",
+ "CAM_FRONT_LEFT",
+ "CAM_BACK",
+ "CAM_BACK_LEFT",
+ "CAM_BACK_RIGHT",
+ ]
+ for cam in camera_types:
+ cam_token = sample["data"][cam]
+ cam_path, _, camera_intrinsics = nusc.get_sample_data(cam_token)
+ cam_info = obtain_sensor2top(
+ nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam
+ )
+ cam_info["data_path"] = (
+ Path(cam_info["data_path"]).relative_to(data_path).__str__()
+ )
+ cam_info.update(camera_intrinsics=camera_intrinsics)
+ info["cams"].update({cam: cam_info})
+
+ sample_data_token = sample["data"][chan]
+ curr_sd_rec = nusc.get("sample_data", sample_data_token)
+ sweeps = []
+ while len(sweeps) < max_sweeps - 1:
+ if curr_sd_rec["prev"] == "":
+ if len(sweeps) == 0:
+ sweep = {
+ "lidar_path": Path(ref_lidar_path)
+ .relative_to(data_path)
+ .__str__(),
+ "sample_data_token": curr_sd_rec["token"],
+ "transform_matrix": None,
+ "time_lag": curr_sd_rec["timestamp"] * 0,
+ }
+ sweeps.append(sweep)
+ else:
+ sweeps.append(sweeps[-1])
+ else:
+ curr_sd_rec = nusc.get("sample_data", curr_sd_rec["prev"])
+
+ # Get past pose
+ current_pose_rec = nusc.get("ego_pose", curr_sd_rec["ego_pose_token"])
+ global_from_car = transform_matrix(
+ current_pose_rec["translation"],
+ Quaternion(current_pose_rec["rotation"]),
+ inverse=False,
+ )
+
+ # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.
+ current_cs_rec = nusc.get(
+ "calibrated_sensor", curr_sd_rec["calibrated_sensor_token"]
+ )
+ car_from_current = transform_matrix(
+ current_cs_rec["translation"],
+ Quaternion(current_cs_rec["rotation"]),
+ inverse=False,
+ )
+
+ tm = reduce(
+ np.dot,
+ [ref_from_car, car_from_global, global_from_car, car_from_current],
+ )
+
+ lidar_path = nusc.get_sample_data_path(curr_sd_rec["token"])
+
+ time_lag = ref_time - 1e-6 * curr_sd_rec["timestamp"]
+
+ sweep = {
+ "lidar_path": Path(lidar_path).relative_to(data_path).__str__(),
+ "sample_data_token": curr_sd_rec["token"],
+ "transform_matrix": tm,
+ "global_from_car": global_from_car,
+ "car_from_current": car_from_current,
+ "time_lag": time_lag,
+ }
+ sweeps.append(sweep)
+
+ info["sweeps"] = sweeps
+
+ assert len(info["sweeps"]) == max_sweeps - 1, (
+ f"sweep {curr_sd_rec['token']} only has {len(info['sweeps'])} sweeps, "
+ f"you should duplicate to sweep num {max_sweeps - 1}"
+ )
+
+ if not test:
+ # processing gt bbox
+ annotations = [
+ nusc.get("sample_annotation", token) for token in sample["anns"]
+ ]
+
+ # the filtering gives 0.5~1 map improvement
+ num_lidar_pts = np.array([anno["num_lidar_pts"] for anno in annotations])
+ num_radar_pts = np.array([anno["num_radar_pts"] for anno in annotations])
+ mask = num_lidar_pts + num_radar_pts > 0
+
+ locs = np.array([b.center for b in ref_boxes]).reshape(-1, 3)
+ dims = np.array([b.wlh for b in ref_boxes]).reshape(-1, 3)[
+ :, [1, 0, 2]
+ ] # wlh == > dxdydz (lwh)
+ velocity = np.array([b.velocity for b in ref_boxes]).reshape(-1, 3)
+ rots = np.array([quaternion_yaw(b.orientation) for b in ref_boxes]).reshape(
+ -1, 1
+ )
+ names = np.array([b.name for b in ref_boxes])
+ tokens = np.array([b.token for b in ref_boxes])
+ gt_boxes = np.concatenate([locs, dims, rots, velocity[:, :2]], axis=1)
+
+ assert len(annotations) == len(gt_boxes) == len(velocity)
+
+ info["gt_boxes"] = gt_boxes[mask, :]
+ info["gt_boxes_velocity"] = velocity[mask, :]
+ info["gt_names"] = np.array(
+ [map_name_from_general_to_detection[name] for name in names]
+ )[mask]
+ info["gt_boxes_token"] = tokens[mask]
+ info["num_lidar_pts"] = num_lidar_pts[mask]
+ info["num_radar_pts"] = num_radar_pts[mask]
+
+ # processing gt segment
+ segment_path = nusc.get("lidarseg", ref_sd_token)["filename"]
+ info["gt_segment_path"] = segment_path
+
+ if sample["scene_token"] in train_scenes:
+ train_nusc_infos.append(info)
+ else:
+ val_nusc_infos.append(info)
+
+ progress_bar.close()
+ return train_nusc_infos, val_nusc_infos
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root", required=True, help="Path to the nuScenes dataset."
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where processed information located.",
+ )
+ parser.add_argument(
+ "--max_sweeps", default=10, type=int, help="Max number of sweeps. Default: 10."
+ )
+ parser.add_argument(
+ "--with_camera",
+ action="store_true",
+ default=False,
+ help="Whether use camera or not.",
+ )
+ config = parser.parse_args()
+
+ print(f"Loading nuScenes tables for version v1.0-trainval...")
+ nusc_trainval = NuScenes(
+ version="v1.0-trainval", dataroot=config.dataset_root, verbose=False
+ )
+ available_scenes_trainval = get_available_scenes(nusc_trainval)
+ available_scene_names_trainval = [s["name"] for s in available_scenes_trainval]
+ print("total scene num:", len(nusc_trainval.scene))
+ print("exist scene num:", len(available_scenes_trainval))
+ assert len(available_scenes_trainval) == len(nusc_trainval.scene) == 850
+
+ print(f"Loading nuScenes tables for version v1.0-test...")
+ nusc_test = NuScenes(
+ version="v1.0-test", dataroot=config.dataset_root, verbose=False
+ )
+ available_scenes_test = get_available_scenes(nusc_test)
+ available_scene_names_test = [s["name"] for s in available_scenes_test]
+ print("total scene num:", len(nusc_test.scene))
+ print("exist scene num:", len(available_scenes_test))
+ assert len(available_scenes_test) == len(nusc_test.scene) == 150
+
+ train_scenes = splits.train
+ train_scenes = set(
+ [
+ available_scenes_trainval[available_scene_names_trainval.index(s)]["token"]
+ for s in train_scenes
+ ]
+ )
+ test_scenes = splits.test
+ test_scenes = set(
+ [
+ available_scenes_test[available_scene_names_test.index(s)]["token"]
+ for s in test_scenes
+ ]
+ )
+ print(f"Filling trainval information...")
+ train_nusc_infos, val_nusc_infos = fill_trainval_infos(
+ config.dataset_root,
+ nusc_trainval,
+ train_scenes,
+ test=False,
+ max_sweeps=config.max_sweeps,
+ with_camera=config.with_camera,
+ )
+ print(f"Filling test information...")
+ test_nusc_infos, _ = fill_trainval_infos(
+ config.dataset_root,
+ nusc_test,
+ test_scenes,
+ test=True,
+ max_sweeps=config.max_sweeps,
+ with_camera=config.with_camera,
+ )
+
+ print(f"Saving nuScenes information...")
+ os.makedirs(os.path.join(config.output_root, "info"), exist_ok=True)
+ print(
+ f"train sample: {len(train_nusc_infos)}, val sample: {len(val_nusc_infos)}, test sample: {len(test_nusc_infos)}"
+ )
+ with open(
+ os.path.join(
+ config.output_root,
+ "info",
+ f"nuscenes_infos_{config.max_sweeps}sweeps_train.pkl",
+ ),
+ "wb",
+ ) as f:
+ pickle.dump(train_nusc_infos, f)
+ with open(
+ os.path.join(
+ config.output_root,
+ "info",
+ f"nuscenes_infos_{config.max_sweeps}sweeps_val.pkl",
+ ),
+ "wb",
+ ) as f:
+ pickle.dump(val_nusc_infos, f)
+ with open(
+ os.path.join(
+ config.output_root,
+ "info",
+ f"nuscenes_infos_{config.max_sweeps}sweeps_test.pkl",
+ ),
+ "wb",
+ ) as f:
+ pickle.dump(test_nusc_infos, f)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py
new file mode 100644
index 0000000000000000000000000000000000000000..d770ad6317996c8a53cd13b2e12af3a536b6dca4
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py
@@ -0,0 +1,233 @@
+"""
+Preprocessing Script for S3DIS
+Parsing normal vectors has a large consumption of memory. Please reduce max_workers if memory is limited.
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import glob
+import numpy as np
+
+try:
+ import open3d
+except ImportError:
+ import warnings
+
+ warnings.warn("Please install open3d for parsing normal")
+
+try:
+ import trimesh
+except ImportError:
+ import warnings
+
+ warnings.warn("Please install trimesh for parsing normal")
+
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+area_mesh_dict = {}
+
+
+def parse_room(
+ room, angle, dataset_root, output_root, align_angle=True, parse_normal=False
+):
+ print("Parsing: {}".format(room))
+ classes = [
+ "ceiling",
+ "floor",
+ "wall",
+ "beam",
+ "column",
+ "window",
+ "door",
+ "table",
+ "chair",
+ "sofa",
+ "bookcase",
+ "board",
+ "clutter",
+ ]
+ class2label = {cls: i for i, cls in enumerate(classes)}
+ source_dir = os.path.join(dataset_root, room)
+ save_path = os.path.join(output_root, room)
+ os.makedirs(save_path, exist_ok=True)
+ object_path_list = sorted(glob.glob(os.path.join(source_dir, "Annotations/*.txt")))
+
+ room_coords = []
+ room_colors = []
+ room_normals = []
+ room_semantic_gt = []
+ room_instance_gt = []
+
+ for object_id, object_path in enumerate(object_path_list):
+ object_name = os.path.basename(object_path).split("_")[0]
+ obj = np.loadtxt(object_path)
+ coords = obj[:, :3]
+ colors = obj[:, 3:6]
+ # note: in some room there is 'stairs' class
+ class_name = object_name if object_name in classes else "clutter"
+ semantic_gt = np.repeat(class2label[class_name], coords.shape[0])
+ semantic_gt = semantic_gt.reshape([-1, 1])
+ instance_gt = np.repeat(object_id, coords.shape[0])
+ instance_gt = instance_gt.reshape([-1, 1])
+
+ room_coords.append(coords)
+ room_colors.append(colors)
+ room_semantic_gt.append(semantic_gt)
+ room_instance_gt.append(instance_gt)
+
+ room_coords = np.ascontiguousarray(np.vstack(room_coords))
+
+ if parse_normal:
+ x_min, z_max, y_min = np.min(room_coords, axis=0)
+ x_max, z_min, y_max = np.max(room_coords, axis=0)
+ z_max = -z_max
+ z_min = -z_min
+ max_bound = np.array([x_max, y_max, z_max]) + 0.1
+ min_bound = np.array([x_min, y_min, z_min]) - 0.1
+ bbox = open3d.geometry.AxisAlignedBoundingBox(
+ min_bound=min_bound, max_bound=max_bound
+ )
+ # crop room
+ room_mesh = (
+ area_mesh_dict[os.path.dirname(room)]
+ .crop(bbox)
+ .transform(
+ np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
+ )
+ )
+ vertices = np.array(room_mesh.vertices)
+ faces = np.array(room_mesh.triangles)
+ vertex_normals = np.array(room_mesh.vertex_normals)
+ room_mesh = trimesh.Trimesh(
+ vertices=vertices, faces=faces, vertex_normals=vertex_normals
+ )
+ (closest_points, distances, face_id) = room_mesh.nearest.on_surface(room_coords)
+ room_normals = room_mesh.face_normals[face_id]
+
+ if align_angle:
+ angle = (2 - angle / 180) * np.pi
+ rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+ rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+ room_center = (np.max(room_coords, axis=0) + np.min(room_coords, axis=0)) / 2
+ room_coords = (room_coords - room_center) @ np.transpose(rot_t) + room_center
+ if parse_normal:
+ room_normals = room_normals @ np.transpose(rot_t)
+
+ room_colors = np.ascontiguousarray(np.vstack(room_colors))
+ room_semantic_gt = np.ascontiguousarray(np.vstack(room_semantic_gt))
+ room_instance_gt = np.ascontiguousarray(np.vstack(room_instance_gt))
+ np.save(os.path.join(save_path, "coord.npy"), room_coords.astype(np.float32))
+ np.save(os.path.join(save_path, "color.npy"), room_colors.astype(np.uint8))
+ np.save(os.path.join(save_path, "segment.npy"), room_semantic_gt.astype(np.int16))
+ np.save(os.path.join(save_path, "instance.npy"), room_instance_gt.astype(np.int16))
+
+ if parse_normal:
+ np.save(os.path.join(save_path, "normal.npy"), room_normals.astype(np.float32))
+
+
+def main_process():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--splits",
+ required=True,
+ nargs="+",
+ choices=["Area_1", "Area_2", "Area_3", "Area_4", "Area_5", "Area_6"],
+ help="Splits need to process ([Area_1, Area_2, Area_3, Area_4, Area_5, Area_6]).",
+ )
+ parser.add_argument(
+ "--dataset_root", required=True, help="Path to Stanford3dDataset_v1.2 dataset"
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where area folders will be located",
+ )
+ parser.add_argument(
+ "--raw_root",
+ default=None,
+ help="Path to Stanford2d3dDataset_noXYZ dataset (optional)",
+ )
+ parser.add_argument(
+ "--align_angle", action="store_true", help="Whether align room angles"
+ )
+ parser.add_argument(
+ "--parse_normal", action="store_true", help="Whether process normal"
+ )
+ parser.add_argument(
+ "--num_workers", default=1, type=int, help="Num workers for preprocessing."
+ )
+ args = parser.parse_args()
+
+ if args.parse_normal:
+ assert args.raw_root is not None
+
+ room_list = []
+ angle_list = []
+
+ # Load room information
+ print("Loading room information ...")
+ for split in args.splits:
+ area_info = np.loadtxt(
+ os.path.join(
+ args.dataset_root,
+ split,
+ f"{split}_alignmentAngle.txt",
+ ),
+ dtype=str,
+ )
+ room_list += [os.path.join(split, room_info[0]) for room_info in area_info]
+ angle_list += [int(room_info[1]) for room_info in area_info]
+
+ if args.parse_normal:
+ # load raw mesh file to extract normal
+ print("Loading raw mesh file ...")
+ for split in args.splits:
+ if split != "Area_5":
+ mesh_dir = os.path.join(args.raw_root, split, "3d", "rgb.obj")
+ mesh = open3d.io.read_triangle_mesh(mesh_dir)
+ mesh.triangle_uvs.clear()
+ else:
+ mesh_a_dir = os.path.join(args.raw_root, f"{split}a", "3d", "rgb.obj")
+ mesh_b_dir = os.path.join(args.raw_root, f"{split}b", "3d", "rgb.obj")
+ mesh_a = open3d.io.read_triangle_mesh(mesh_a_dir)
+ mesh_a.triangle_uvs.clear()
+ mesh_b = open3d.io.read_triangle_mesh(mesh_b_dir)
+ mesh_b.triangle_uvs.clear()
+ mesh_b = mesh_b.transform(
+ np.array(
+ [
+ [0, 0, -1, -4.09703582],
+ [0, 1, 0, 0],
+ [1, 0, 0, -6.22617759],
+ [0, 0, 0, 1],
+ ]
+ )
+ )
+ mesh = mesh_a + mesh_b
+ area_mesh_dict[split] = mesh
+ print(f"{split} mesh is loaded")
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(
+ max_workers=args.num_workers
+ ) # peak 110G memory when parsing normal.
+ _ = list(
+ pool.map(
+ parse_room,
+ room_list,
+ angle_list,
+ repeat(args.dataset_root),
+ repeat(args.output_root),
+ repeat(args.align_angle),
+ repeat(args.parse_normal),
+ )
+ )
+
+
+if __name__ == "__main__":
+ main_process()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/sampling_chunking_data.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/sampling_chunking_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..96536d415370bf28c0f1cc89312b2fde719c9a58
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/sampling_chunking_data.py
@@ -0,0 +1,149 @@
+"""
+Chunking Data
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+
+def chunking_scene(
+ name,
+ dataset_root,
+ split,
+ grid_size=None,
+ chunk_range=(6, 6),
+ chunk_stride=(3, 3),
+ chunk_minimum_size=10000,
+):
+ print(f"Chunking scene {name} in {split} split")
+ dataset_root = Path(dataset_root)
+ scene_path = dataset_root / split / name
+ assets = os.listdir(scene_path)
+ data_dict = dict()
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ data_dict[asset[:-4]] = np.load(scene_path / asset)
+ coord = data_dict["coord"] - data_dict["coord"].min(axis=0)
+
+ if grid_size is not None:
+ grid_coord = np.floor(coord / grid_size).astype(int)
+ _, idx = np.unique(grid_coord, axis=0, return_index=True)
+ coord = coord[idx]
+ for key in data_dict.keys():
+ data_dict[key] = data_dict[key][idx]
+
+ bev_range = coord.max(axis=0)[:2]
+ x, y = np.meshgrid(
+ np.arange(0, bev_range[0] + chunk_stride[0] - chunk_range[0], chunk_stride[0]),
+ np.arange(0, bev_range[0] + chunk_stride[0] - chunk_range[0], chunk_stride[0]),
+ indexing="ij",
+ )
+ chunks = np.concatenate([x.reshape([-1, 1]), y.reshape([-1, 1])], axis=-1)
+ chunk_idx = 0
+ for chunk in chunks:
+ mask = (
+ (coord[:, 0] >= chunk[0])
+ & (coord[:, 0] < chunk[0] + chunk_range[0])
+ & (coord[:, 1] >= chunk[1])
+ & (coord[:, 1] < chunk[1] + chunk_range[1])
+ )
+ if np.sum(mask) < chunk_minimum_size:
+ continue
+
+ chunk_data_name = f"{name}_{chunk_idx}"
+ if grid_size is not None:
+ chunk_split_name = (
+ f"{split}_"
+ f"grid{grid_size * 100:.0f}mm_"
+ f"chunk{chunk_range[0]}x{chunk_range[1]}_"
+ f"stride{chunk_stride[0]}x{chunk_stride[1]}"
+ )
+ else:
+ chunk_split_name = (
+ f"{split}_"
+ f"chunk{chunk_range[0]}x{chunk_range[1]}_"
+ f"stride{chunk_stride[0]}x{chunk_stride[1]}"
+ )
+
+ chunk_save_path = dataset_root / chunk_split_name / chunk_data_name
+ chunk_save_path.mkdir(parents=True, exist_ok=True)
+ for key in data_dict.keys():
+ np.save(chunk_save_path / f"{key}.npy", data_dict[key][mask])
+ chunk_idx += 1
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the Pointcept processed ScanNet++ dataset.",
+ )
+ parser.add_argument(
+ "--split",
+ required=True,
+ default="train",
+ type=str,
+ help="Split need to process.",
+ )
+ parser.add_argument(
+ "--grid_size",
+ default=None,
+ type=float,
+ help="Grid size for initial grid sampling",
+ )
+ parser.add_argument(
+ "--chunk_range",
+ default=[6, 6],
+ type=int,
+ nargs="+",
+ help="Range of each chunk, e.g. --chunk_range 6 6",
+ )
+ parser.add_argument(
+ "--chunk_stride",
+ default=[3, 3],
+ type=int,
+ nargs="+",
+ help="Stride of each chunk, e.g. --chunk_stride 3 3",
+ )
+ parser.add_argument(
+ "--chunk_minimum_size",
+ default=10000,
+ type=int,
+ help="Minimum number of points in each chunk",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+
+ config = parser.parse_args()
+ config.dataset_root = Path(config.dataset_root)
+ data_list = os.listdir(config.dataset_root / config.split)
+
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ chunking_scene,
+ data_list,
+ repeat(config.dataset_root),
+ repeat(config.split),
+ repeat(config.grid_size),
+ repeat(config.chunk_range),
+ repeat(config.chunk_stride),
+ repeat(config.chunk_minimum_size),
+ )
+ )
+ pool.shutdown()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e53f5bcb2c1480f42ee9327940246258aa434f88
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt
@@ -0,0 +1,17 @@
+1 trash
+3 basket
+4 bathtub
+5 bed
+9 shelf
+13 cabinet
+18 chair
+20 keyboard
+22 tv
+30 lamp
+31 laptop
+35 microwave
+39 pillow
+42 printer
+47 sofa
+48 stove
+49 table
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt
new file mode 100644
index 0000000000000000000000000000000000000000..48e228766391e0f0234c2eed086e31f738068a4b
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt
@@ -0,0 +1,20 @@
+1 wall
+2 floor
+3 cabinet
+4 bed
+5 chair
+6 sofa
+7 table
+8 door
+9 window
+10 bookshelf
+11 picture
+12 counter
+14 desk
+16 curtain
+24 refridgerator
+28 shower curtain
+33 toilet
+34 sink
+36 bathtub
+39 otherfurniture
\ No newline at end of file
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..0404fd6aa8ad14ad729354ce184d4b51834bfd1b
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py
@@ -0,0 +1,704 @@
+# ScanNet Benchmark constants
+VALID_CLASS_IDS_20 = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 14,
+ 16,
+ 24,
+ 28,
+ 33,
+ 34,
+ 36,
+ 39,
+)
+
+CLASS_LABELS_20 = (
+ "wall",
+ "floor",
+ "cabinet",
+ "bed",
+ "chair",
+ "sofa",
+ "table",
+ "door",
+ "window",
+ "bookshelf",
+ "picture",
+ "counter",
+ "desk",
+ "curtain",
+ "refrigerator",
+ "shower curtain",
+ "toilet",
+ "sink",
+ "bathtub",
+ "otherfurniture",
+)
+
+SCANNET_COLOR_MAP_20 = {
+ 0: (0.0, 0.0, 0.0),
+ 1: (174.0, 199.0, 232.0),
+ 2: (152.0, 223.0, 138.0),
+ 3: (31.0, 119.0, 180.0),
+ 4: (255.0, 187.0, 120.0),
+ 5: (188.0, 189.0, 34.0),
+ 6: (140.0, 86.0, 75.0),
+ 7: (255.0, 152.0, 150.0),
+ 8: (214.0, 39.0, 40.0),
+ 9: (197.0, 176.0, 213.0),
+ 10: (148.0, 103.0, 189.0),
+ 11: (196.0, 156.0, 148.0),
+ 12: (23.0, 190.0, 207.0),
+ 14: (247.0, 182.0, 210.0),
+ 15: (66.0, 188.0, 102.0),
+ 16: (219.0, 219.0, 141.0),
+ 17: (140.0, 57.0, 197.0),
+ 18: (202.0, 185.0, 52.0),
+ 19: (51.0, 176.0, 203.0),
+ 20: (200.0, 54.0, 131.0),
+ 21: (92.0, 193.0, 61.0),
+ 22: (78.0, 71.0, 183.0),
+ 23: (172.0, 114.0, 82.0),
+ 24: (255.0, 127.0, 14.0),
+ 25: (91.0, 163.0, 138.0),
+ 26: (153.0, 98.0, 156.0),
+ 27: (140.0, 153.0, 101.0),
+ 28: (158.0, 218.0, 229.0),
+ 29: (100.0, 125.0, 154.0),
+ 30: (178.0, 127.0, 135.0),
+ 32: (146.0, 111.0, 194.0),
+ 33: (44.0, 160.0, 44.0),
+ 34: (112.0, 128.0, 144.0),
+ 35: (96.0, 207.0, 209.0),
+ 36: (227.0, 119.0, 194.0),
+ 37: (213.0, 92.0, 176.0),
+ 38: (94.0, 106.0, 211.0),
+ 39: (82.0, 84.0, 163.0),
+ 40: (100.0, 85.0, 144.0),
+}
+
+# ScanNet200 Benchmark constants
+VALID_CLASS_IDS_200 = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 21,
+ 22,
+ 23,
+ 24,
+ 26,
+ 27,
+ 28,
+ 29,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 82,
+ 84,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 93,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 110,
+ 112,
+ 115,
+ 116,
+ 118,
+ 120,
+ 121,
+ 122,
+ 125,
+ 128,
+ 130,
+ 131,
+ 132,
+ 134,
+ 136,
+ 138,
+ 139,
+ 140,
+ 141,
+ 145,
+ 148,
+ 154,
+ 155,
+ 156,
+ 157,
+ 159,
+ 161,
+ 163,
+ 165,
+ 166,
+ 168,
+ 169,
+ 170,
+ 177,
+ 180,
+ 185,
+ 188,
+ 191,
+ 193,
+ 195,
+ 202,
+ 208,
+ 213,
+ 214,
+ 221,
+ 229,
+ 230,
+ 232,
+ 233,
+ 242,
+ 250,
+ 261,
+ 264,
+ 276,
+ 283,
+ 286,
+ 300,
+ 304,
+ 312,
+ 323,
+ 325,
+ 331,
+ 342,
+ 356,
+ 370,
+ 392,
+ 395,
+ 399,
+ 408,
+ 417,
+ 488,
+ 540,
+ 562,
+ 570,
+ 572,
+ 581,
+ 609,
+ 748,
+ 776,
+ 1156,
+ 1163,
+ 1164,
+ 1165,
+ 1166,
+ 1167,
+ 1168,
+ 1169,
+ 1170,
+ 1171,
+ 1172,
+ 1173,
+ 1174,
+ 1175,
+ 1176,
+ 1178,
+ 1179,
+ 1180,
+ 1181,
+ 1182,
+ 1183,
+ 1184,
+ 1185,
+ 1186,
+ 1187,
+ 1188,
+ 1189,
+ 1190,
+ 1191,
+)
+
+CLASS_LABELS_200 = (
+ "wall",
+ "chair",
+ "floor",
+ "table",
+ "door",
+ "couch",
+ "cabinet",
+ "shelf",
+ "desk",
+ "office chair",
+ "bed",
+ "pillow",
+ "sink",
+ "picture",
+ "window",
+ "toilet",
+ "bookshelf",
+ "monitor",
+ "curtain",
+ "book",
+ "armchair",
+ "coffee table",
+ "box",
+ "refrigerator",
+ "lamp",
+ "kitchen cabinet",
+ "towel",
+ "clothes",
+ "tv",
+ "nightstand",
+ "counter",
+ "dresser",
+ "stool",
+ "cushion",
+ "plant",
+ "ceiling",
+ "bathtub",
+ "end table",
+ "dining table",
+ "keyboard",
+ "bag",
+ "backpack",
+ "toilet paper",
+ "printer",
+ "tv stand",
+ "whiteboard",
+ "blanket",
+ "shower curtain",
+ "trash can",
+ "closet",
+ "stairs",
+ "microwave",
+ "stove",
+ "shoe",
+ "computer tower",
+ "bottle",
+ "bin",
+ "ottoman",
+ "bench",
+ "board",
+ "washing machine",
+ "mirror",
+ "copier",
+ "basket",
+ "sofa chair",
+ "file cabinet",
+ "fan",
+ "laptop",
+ "shower",
+ "paper",
+ "person",
+ "paper towel dispenser",
+ "oven",
+ "blinds",
+ "rack",
+ "plate",
+ "blackboard",
+ "piano",
+ "suitcase",
+ "rail",
+ "radiator",
+ "recycling bin",
+ "container",
+ "wardrobe",
+ "soap dispenser",
+ "telephone",
+ "bucket",
+ "clock",
+ "stand",
+ "light",
+ "laundry basket",
+ "pipe",
+ "clothes dryer",
+ "guitar",
+ "toilet paper holder",
+ "seat",
+ "speaker",
+ "column",
+ "bicycle",
+ "ladder",
+ "bathroom stall",
+ "shower wall",
+ "cup",
+ "jacket",
+ "storage bin",
+ "coffee maker",
+ "dishwasher",
+ "paper towel roll",
+ "machine",
+ "mat",
+ "windowsill",
+ "bar",
+ "toaster",
+ "bulletin board",
+ "ironing board",
+ "fireplace",
+ "soap dish",
+ "kitchen counter",
+ "doorframe",
+ "toilet paper dispenser",
+ "mini fridge",
+ "fire extinguisher",
+ "ball",
+ "hat",
+ "shower curtain rod",
+ "water cooler",
+ "paper cutter",
+ "tray",
+ "shower door",
+ "pillar",
+ "ledge",
+ "toaster oven",
+ "mouse",
+ "toilet seat cover dispenser",
+ "furniture",
+ "cart",
+ "storage container",
+ "scale",
+ "tissue box",
+ "light switch",
+ "crate",
+ "power outlet",
+ "decoration",
+ "sign",
+ "projector",
+ "closet door",
+ "vacuum cleaner",
+ "candle",
+ "plunger",
+ "stuffed animal",
+ "headphones",
+ "dish rack",
+ "broom",
+ "guitar case",
+ "range hood",
+ "dustpan",
+ "hair dryer",
+ "water bottle",
+ "handicap bar",
+ "purse",
+ "vent",
+ "shower floor",
+ "water pitcher",
+ "mailbox",
+ "bowl",
+ "paper bag",
+ "alarm clock",
+ "music stand",
+ "projector screen",
+ "divider",
+ "laundry detergent",
+ "bathroom counter",
+ "object",
+ "bathroom vanity",
+ "closet wall",
+ "laundry hamper",
+ "bathroom stall door",
+ "ceiling light",
+ "trash bin",
+ "dumbbell",
+ "stair rail",
+ "tube",
+ "bathroom cabinet",
+ "cd case",
+ "closet rod",
+ "coffee kettle",
+ "structure",
+ "shower head",
+ "keyboard piano",
+ "case of water bottles",
+ "coat rack",
+ "storage organizer",
+ "folded chair",
+ "fire alarm",
+ "power strip",
+ "calendar",
+ "poster",
+ "potted plant",
+ "luggage",
+ "mattress",
+)
+
+SCANNET_COLOR_MAP_200 = {
+ 0: (0.0, 0.0, 0.0),
+ 1: (174.0, 199.0, 232.0),
+ 2: (188.0, 189.0, 34.0),
+ 3: (152.0, 223.0, 138.0),
+ 4: (255.0, 152.0, 150.0),
+ 5: (214.0, 39.0, 40.0),
+ 6: (91.0, 135.0, 229.0),
+ 7: (31.0, 119.0, 180.0),
+ 8: (229.0, 91.0, 104.0),
+ 9: (247.0, 182.0, 210.0),
+ 10: (91.0, 229.0, 110.0),
+ 11: (255.0, 187.0, 120.0),
+ 13: (141.0, 91.0, 229.0),
+ 14: (112.0, 128.0, 144.0),
+ 15: (196.0, 156.0, 148.0),
+ 16: (197.0, 176.0, 213.0),
+ 17: (44.0, 160.0, 44.0),
+ 18: (148.0, 103.0, 189.0),
+ 19: (229.0, 91.0, 223.0),
+ 21: (219.0, 219.0, 141.0),
+ 22: (192.0, 229.0, 91.0),
+ 23: (88.0, 218.0, 137.0),
+ 24: (58.0, 98.0, 137.0),
+ 26: (177.0, 82.0, 239.0),
+ 27: (255.0, 127.0, 14.0),
+ 28: (237.0, 204.0, 37.0),
+ 29: (41.0, 206.0, 32.0),
+ 31: (62.0, 143.0, 148.0),
+ 32: (34.0, 14.0, 130.0),
+ 33: (143.0, 45.0, 115.0),
+ 34: (137.0, 63.0, 14.0),
+ 35: (23.0, 190.0, 207.0),
+ 36: (16.0, 212.0, 139.0),
+ 38: (90.0, 119.0, 201.0),
+ 39: (125.0, 30.0, 141.0),
+ 40: (150.0, 53.0, 56.0),
+ 41: (186.0, 197.0, 62.0),
+ 42: (227.0, 119.0, 194.0),
+ 44: (38.0, 100.0, 128.0),
+ 45: (120.0, 31.0, 243.0),
+ 46: (154.0, 59.0, 103.0),
+ 47: (169.0, 137.0, 78.0),
+ 48: (143.0, 245.0, 111.0),
+ 49: (37.0, 230.0, 205.0),
+ 50: (14.0, 16.0, 155.0),
+ 51: (196.0, 51.0, 182.0),
+ 52: (237.0, 80.0, 38.0),
+ 54: (138.0, 175.0, 62.0),
+ 55: (158.0, 218.0, 229.0),
+ 56: (38.0, 96.0, 167.0),
+ 57: (190.0, 77.0, 246.0),
+ 58: (208.0, 49.0, 84.0),
+ 59: (208.0, 193.0, 72.0),
+ 62: (55.0, 220.0, 57.0),
+ 63: (10.0, 125.0, 140.0),
+ 64: (76.0, 38.0, 202.0),
+ 65: (191.0, 28.0, 135.0),
+ 66: (211.0, 120.0, 42.0),
+ 67: (118.0, 174.0, 76.0),
+ 68: (17.0, 242.0, 171.0),
+ 69: (20.0, 65.0, 247.0),
+ 70: (208.0, 61.0, 222.0),
+ 71: (162.0, 62.0, 60.0),
+ 72: (210.0, 235.0, 62.0),
+ 73: (45.0, 152.0, 72.0),
+ 74: (35.0, 107.0, 149.0),
+ 75: (160.0, 89.0, 237.0),
+ 76: (227.0, 56.0, 125.0),
+ 77: (169.0, 143.0, 81.0),
+ 78: (42.0, 143.0, 20.0),
+ 79: (25.0, 160.0, 151.0),
+ 80: (82.0, 75.0, 227.0),
+ 82: (253.0, 59.0, 222.0),
+ 84: (240.0, 130.0, 89.0),
+ 86: (123.0, 172.0, 47.0),
+ 87: (71.0, 194.0, 133.0),
+ 88: (24.0, 94.0, 205.0),
+ 89: (134.0, 16.0, 179.0),
+ 90: (159.0, 32.0, 52.0),
+ 93: (213.0, 208.0, 88.0),
+ 95: (64.0, 158.0, 70.0),
+ 96: (18.0, 163.0, 194.0),
+ 97: (65.0, 29.0, 153.0),
+ 98: (177.0, 10.0, 109.0),
+ 99: (152.0, 83.0, 7.0),
+ 100: (83.0, 175.0, 30.0),
+ 101: (18.0, 199.0, 153.0),
+ 102: (61.0, 81.0, 208.0),
+ 103: (213.0, 85.0, 216.0),
+ 104: (170.0, 53.0, 42.0),
+ 105: (161.0, 192.0, 38.0),
+ 106: (23.0, 241.0, 91.0),
+ 107: (12.0, 103.0, 170.0),
+ 110: (151.0, 41.0, 245.0),
+ 112: (133.0, 51.0, 80.0),
+ 115: (184.0, 162.0, 91.0),
+ 116: (50.0, 138.0, 38.0),
+ 118: (31.0, 237.0, 236.0),
+ 120: (39.0, 19.0, 208.0),
+ 121: (223.0, 27.0, 180.0),
+ 122: (254.0, 141.0, 85.0),
+ 125: (97.0, 144.0, 39.0),
+ 128: (106.0, 231.0, 176.0),
+ 130: (12.0, 61.0, 162.0),
+ 131: (124.0, 66.0, 140.0),
+ 132: (137.0, 66.0, 73.0),
+ 134: (250.0, 253.0, 26.0),
+ 136: (55.0, 191.0, 73.0),
+ 138: (60.0, 126.0, 146.0),
+ 139: (153.0, 108.0, 234.0),
+ 140: (184.0, 58.0, 125.0),
+ 141: (135.0, 84.0, 14.0),
+ 145: (139.0, 248.0, 91.0),
+ 148: (53.0, 200.0, 172.0),
+ 154: (63.0, 69.0, 134.0),
+ 155: (190.0, 75.0, 186.0),
+ 156: (127.0, 63.0, 52.0),
+ 157: (141.0, 182.0, 25.0),
+ 159: (56.0, 144.0, 89.0),
+ 161: (64.0, 160.0, 250.0),
+ 163: (182.0, 86.0, 245.0),
+ 165: (139.0, 18.0, 53.0),
+ 166: (134.0, 120.0, 54.0),
+ 168: (49.0, 165.0, 42.0),
+ 169: (51.0, 128.0, 133.0),
+ 170: (44.0, 21.0, 163.0),
+ 177: (232.0, 93.0, 193.0),
+ 180: (176.0, 102.0, 54.0),
+ 185: (116.0, 217.0, 17.0),
+ 188: (54.0, 209.0, 150.0),
+ 191: (60.0, 99.0, 204.0),
+ 193: (129.0, 43.0, 144.0),
+ 195: (252.0, 100.0, 106.0),
+ 202: (187.0, 196.0, 73.0),
+ 208: (13.0, 158.0, 40.0),
+ 213: (52.0, 122.0, 152.0),
+ 214: (128.0, 76.0, 202.0),
+ 221: (187.0, 50.0, 115.0),
+ 229: (180.0, 141.0, 71.0),
+ 230: (77.0, 208.0, 35.0),
+ 232: (72.0, 183.0, 168.0),
+ 233: (97.0, 99.0, 203.0),
+ 242: (172.0, 22.0, 158.0),
+ 250: (155.0, 64.0, 40.0),
+ 261: (118.0, 159.0, 30.0),
+ 264: (69.0, 252.0, 148.0),
+ 276: (45.0, 103.0, 173.0),
+ 283: (111.0, 38.0, 149.0),
+ 286: (184.0, 9.0, 49.0),
+ 300: (188.0, 174.0, 67.0),
+ 304: (53.0, 206.0, 53.0),
+ 312: (97.0, 235.0, 252.0),
+ 323: (66.0, 32.0, 182.0),
+ 325: (236.0, 114.0, 195.0),
+ 331: (241.0, 154.0, 83.0),
+ 342: (133.0, 240.0, 52.0),
+ 356: (16.0, 205.0, 144.0),
+ 370: (75.0, 101.0, 198.0),
+ 392: (237.0, 95.0, 251.0),
+ 395: (191.0, 52.0, 49.0),
+ 399: (227.0, 254.0, 54.0),
+ 408: (49.0, 206.0, 87.0),
+ 417: (48.0, 113.0, 150.0),
+ 488: (125.0, 73.0, 182.0),
+ 540: (229.0, 32.0, 114.0),
+ 562: (158.0, 119.0, 28.0),
+ 570: (60.0, 205.0, 27.0),
+ 572: (18.0, 215.0, 201.0),
+ 581: (79.0, 76.0, 153.0),
+ 609: (134.0, 13.0, 116.0),
+ 748: (192.0, 97.0, 63.0),
+ 776: (108.0, 163.0, 18.0),
+ 1156: (95.0, 220.0, 156.0),
+ 1163: (98.0, 141.0, 208.0),
+ 1164: (144.0, 19.0, 193.0),
+ 1165: (166.0, 36.0, 57.0),
+ 1166: (212.0, 202.0, 34.0),
+ 1167: (23.0, 206.0, 34.0),
+ 1168: (91.0, 211.0, 236.0),
+ 1169: (79.0, 55.0, 137.0),
+ 1170: (182.0, 19.0, 117.0),
+ 1171: (134.0, 76.0, 14.0),
+ 1172: (87.0, 185.0, 28.0),
+ 1173: (82.0, 224.0, 187.0),
+ 1174: (92.0, 110.0, 214.0),
+ 1175: (168.0, 80.0, 171.0),
+ 1176: (197.0, 63.0, 51.0),
+ 1178: (175.0, 199.0, 77.0),
+ 1179: (62.0, 180.0, 98.0),
+ 1180: (8.0, 91.0, 150.0),
+ 1181: (77.0, 15.0, 130.0),
+ 1182: (154.0, 65.0, 96.0),
+ 1183: (197.0, 152.0, 11.0),
+ 1184: (59.0, 155.0, 45.0),
+ 1185: (12.0, 147.0, 145.0),
+ 1186: (54.0, 35.0, 219.0),
+ 1187: (210.0, 73.0, 181.0),
+ 1188: (221.0, 124.0, 77.0),
+ 1189: (149.0, 214.0, 66.0),
+ 1190: (72.0, 185.0, 134.0),
+ 1191: (42.0, 94.0, 198.0),
+}
+
+# For instance segmentation the non-object categories
+VALID_PANOPTIC_IDS = (1, 3)
+
+CLASS_LABELS_PANOPTIC = ("wall", "floor")
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..39ccc3c60bf289199342332e455fadb5b22129ee
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py
@@ -0,0 +1,625 @@
+# This file contains the HEAD - COMMON - TAIL split category ids for ScanNet 200
+
+HEAD_CATS_SCANNET_200 = [
+ "tv stand",
+ "curtain",
+ "blinds",
+ "shower curtain",
+ "bookshelf",
+ "tv",
+ "kitchen cabinet",
+ "pillow",
+ "lamp",
+ "dresser",
+ "monitor",
+ "object",
+ "ceiling",
+ "board",
+ "stove",
+ "closet wall",
+ "couch",
+ "office chair",
+ "kitchen counter",
+ "shower",
+ "closet",
+ "doorframe",
+ "sofa chair",
+ "mailbox",
+ "nightstand",
+ "washing machine",
+ "picture",
+ "book",
+ "sink",
+ "recycling bin",
+ "table",
+ "backpack",
+ "shower wall",
+ "toilet",
+ "copier",
+ "counter",
+ "stool",
+ "refrigerator",
+ "window",
+ "file cabinet",
+ "chair",
+ "wall",
+ "plant",
+ "coffee table",
+ "stairs",
+ "armchair",
+ "cabinet",
+ "bathroom vanity",
+ "bathroom stall",
+ "mirror",
+ "blackboard",
+ "trash can",
+ "stair rail",
+ "box",
+ "towel",
+ "door",
+ "clothes",
+ "whiteboard",
+ "bed",
+ "floor",
+ "bathtub",
+ "desk",
+ "wardrobe",
+ "clothes dryer",
+ "radiator",
+ "shelf",
+]
+COMMON_CATS_SCANNET_200 = [
+ "cushion",
+ "end table",
+ "dining table",
+ "keyboard",
+ "bag",
+ "toilet paper",
+ "printer",
+ "blanket",
+ "microwave",
+ "shoe",
+ "computer tower",
+ "bottle",
+ "bin",
+ "ottoman",
+ "bench",
+ "basket",
+ "fan",
+ "laptop",
+ "person",
+ "paper towel dispenser",
+ "oven",
+ "rack",
+ "piano",
+ "suitcase",
+ "rail",
+ "container",
+ "telephone",
+ "stand",
+ "light",
+ "laundry basket",
+ "pipe",
+ "seat",
+ "column",
+ "bicycle",
+ "ladder",
+ "jacket",
+ "storage bin",
+ "coffee maker",
+ "dishwasher",
+ "machine",
+ "mat",
+ "windowsill",
+ "bulletin board",
+ "fireplace",
+ "mini fridge",
+ "water cooler",
+ "shower door",
+ "pillar",
+ "ledge",
+ "furniture",
+ "cart",
+ "decoration",
+ "closet door",
+ "vacuum cleaner",
+ "dish rack",
+ "range hood",
+ "projector screen",
+ "divider",
+ "bathroom counter",
+ "laundry hamper",
+ "bathroom stall door",
+ "ceiling light",
+ "trash bin",
+ "bathroom cabinet",
+ "structure",
+ "storage organizer",
+ "potted plant",
+ "mattress",
+]
+TAIL_CATS_SCANNET_200 = [
+ "paper",
+ "plate",
+ "soap dispenser",
+ "bucket",
+ "clock",
+ "guitar",
+ "toilet paper holder",
+ "speaker",
+ "cup",
+ "paper towel roll",
+ "bar",
+ "toaster",
+ "ironing board",
+ "soap dish",
+ "toilet paper dispenser",
+ "fire extinguisher",
+ "ball",
+ "hat",
+ "shower curtain rod",
+ "paper cutter",
+ "tray",
+ "toaster oven",
+ "mouse",
+ "toilet seat cover dispenser",
+ "storage container",
+ "scale",
+ "tissue box",
+ "light switch",
+ "crate",
+ "power outlet",
+ "sign",
+ "projector",
+ "candle",
+ "plunger",
+ "stuffed animal",
+ "headphones",
+ "broom",
+ "guitar case",
+ "dustpan",
+ "hair dryer",
+ "water bottle",
+ "handicap bar",
+ "purse",
+ "vent",
+ "shower floor",
+ "water pitcher",
+ "bowl",
+ "paper bag",
+ "alarm clock",
+ "music stand",
+ "laundry detergent",
+ "dumbbell",
+ "tube",
+ "cd case",
+ "closet rod",
+ "coffee kettle",
+ "shower head",
+ "keyboard piano",
+ "case of water bottles",
+ "coat rack",
+ "folded chair",
+ "fire alarm",
+ "power strip",
+ "calendar",
+ "poster",
+ "luggage",
+]
+
+
+# Given the different size of the official train and val sets, not all ScanNet200 categories are present in the validation set.
+# Here we list of categories with labels and IDs present in both train and validation set, and the remaining categories those are present in train, but not in val
+# We dont evaluate on unseen validation categories in this benchmark
+
+VALID_CLASS_IDS_200_VALIDATION = (
+ "wall",
+ "chair",
+ "floor",
+ "table",
+ "door",
+ "couch",
+ "cabinet",
+ "shelf",
+ "desk",
+ "office chair",
+ "bed",
+ "pillow",
+ "sink",
+ "picture",
+ "window",
+ "toilet",
+ "bookshelf",
+ "monitor",
+ "curtain",
+ "book",
+ "armchair",
+ "coffee table",
+ "box",
+ "refrigerator",
+ "lamp",
+ "kitchen cabinet",
+ "towel",
+ "clothes",
+ "tv",
+ "nightstand",
+ "counter",
+ "dresser",
+ "stool",
+ "cushion",
+ "plant",
+ "ceiling",
+ "bathtub",
+ "end table",
+ "dining table",
+ "keyboard",
+ "bag",
+ "backpack",
+ "toilet paper",
+ "printer",
+ "tv stand",
+ "whiteboard",
+ "blanket",
+ "shower curtain",
+ "trash can",
+ "closet",
+ "stairs",
+ "microwave",
+ "stove",
+ "shoe",
+ "computer tower",
+ "bottle",
+ "bin",
+ "ottoman",
+ "bench",
+ "board",
+ "washing machine",
+ "mirror",
+ "copier",
+ "basket",
+ "sofa chair",
+ "file cabinet",
+ "fan",
+ "laptop",
+ "shower",
+ "paper",
+ "person",
+ "paper towel dispenser",
+ "oven",
+ "blinds",
+ "rack",
+ "plate",
+ "blackboard",
+ "piano",
+ "suitcase",
+ "rail",
+ "radiator",
+ "recycling bin",
+ "container",
+ "wardrobe",
+ "soap dispenser",
+ "telephone",
+ "bucket",
+ "clock",
+ "stand",
+ "light",
+ "laundry basket",
+ "pipe",
+ "clothes dryer",
+ "guitar",
+ "toilet paper holder",
+ "seat",
+ "speaker",
+ "column",
+ "ladder",
+ "bathroom stall",
+ "shower wall",
+ "cup",
+ "jacket",
+ "storage bin",
+ "coffee maker",
+ "dishwasher",
+ "paper towel roll",
+ "machine",
+ "mat",
+ "windowsill",
+ "bar",
+ "toaster",
+ "bulletin board",
+ "ironing board",
+ "fireplace",
+ "soap dish",
+ "kitchen counter",
+ "doorframe",
+ "toilet paper dispenser",
+ "mini fridge",
+ "fire extinguisher",
+ "ball",
+ "hat",
+ "shower curtain rod",
+ "water cooler",
+ "paper cutter",
+ "tray",
+ "shower door",
+ "pillar",
+ "ledge",
+ "toaster oven",
+ "mouse",
+ "toilet seat cover dispenser",
+ "furniture",
+ "cart",
+ "scale",
+ "tissue box",
+ "light switch",
+ "crate",
+ "power outlet",
+ "decoration",
+ "sign",
+ "projector",
+ "closet door",
+ "vacuum cleaner",
+ "plunger",
+ "stuffed animal",
+ "headphones",
+ "dish rack",
+ "broom",
+ "range hood",
+ "dustpan",
+ "hair dryer",
+ "water bottle",
+ "handicap bar",
+ "vent",
+ "shower floor",
+ "water pitcher",
+ "mailbox",
+ "bowl",
+ "paper bag",
+ "projector screen",
+ "divider",
+ "laundry detergent",
+ "bathroom counter",
+ "object",
+ "bathroom vanity",
+ "closet wall",
+ "laundry hamper",
+ "bathroom stall door",
+ "ceiling light",
+ "trash bin",
+ "dumbbell",
+ "stair rail",
+ "tube",
+ "bathroom cabinet",
+ "closet rod",
+ "coffee kettle",
+ "shower head",
+ "keyboard piano",
+ "case of water bottles",
+ "coat rack",
+ "folded chair",
+ "fire alarm",
+ "power strip",
+ "calendar",
+ "poster",
+ "potted plant",
+ "mattress",
+)
+
+CLASS_LABELS_200_VALIDATION = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 21,
+ 22,
+ 23,
+ 24,
+ 26,
+ 27,
+ 28,
+ 29,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 82,
+ 84,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 93,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 110,
+ 112,
+ 115,
+ 116,
+ 118,
+ 120,
+ 122,
+ 125,
+ 128,
+ 130,
+ 131,
+ 132,
+ 134,
+ 136,
+ 138,
+ 139,
+ 140,
+ 141,
+ 145,
+ 148,
+ 154,
+ 155,
+ 156,
+ 157,
+ 159,
+ 161,
+ 163,
+ 165,
+ 166,
+ 168,
+ 169,
+ 170,
+ 177,
+ 180,
+ 185,
+ 188,
+ 191,
+ 193,
+ 195,
+ 202,
+ 208,
+ 213,
+ 214,
+ 229,
+ 230,
+ 232,
+ 233,
+ 242,
+ 250,
+ 261,
+ 264,
+ 276,
+ 283,
+ 300,
+ 304,
+ 312,
+ 323,
+ 325,
+ 342,
+ 356,
+ 370,
+ 392,
+ 395,
+ 408,
+ 417,
+ 488,
+ 540,
+ 562,
+ 570,
+ 609,
+ 748,
+ 776,
+ 1156,
+ 1163,
+ 1164,
+ 1165,
+ 1166,
+ 1167,
+ 1168,
+ 1169,
+ 1170,
+ 1171,
+ 1172,
+ 1173,
+ 1175,
+ 1176,
+ 1179,
+ 1180,
+ 1181,
+ 1182,
+ 1184,
+ 1185,
+ 1186,
+ 1187,
+ 1188,
+ 1189,
+ 1191,
+)
+
+VALID_CLASS_IDS_200_TRAIN_ONLY = (
+ "bicycle",
+ "storage container",
+ "candle",
+ "guitar case",
+ "purse",
+ "alarm clock",
+ "music stand",
+ "cd case",
+ "structure",
+ "storage organizer",
+ "luggage",
+)
+
+CLASS_LABELS_200_TRAIN_ONLY = (
+ 121,
+ 221,
+ 286,
+ 331,
+ 399,
+ 572,
+ 581,
+ 1174,
+ 1178,
+ 1183,
+ 1190,
+)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz
new file mode 100644
index 0000000000000000000000000000000000000000..d9bbb4f7c3b72dbe81fbeb86f594066b883fafaf
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df5c2bd40e8518e982c7d7b4b39020b07ac774695038bf49cb28b44e5760457e
+size 676
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_test.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_test.txt
new file mode 100644
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--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_test.txt
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_train.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_train.txt
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_val.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..965ff258035f857446c30b10e9a6be49f71d3dc7
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_val.txt
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels-old.combined.tsv b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels-old.combined.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..05c006e98066aa78d126bebcfb3654200d351b93
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels-old.combined.tsv
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+1356 food bag food bag 1 37 7 bag bag Objects objects 39
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+1357 starbucks cup starbucks cup 1 40 7 cup otherprop Objects cup cup or mug 3797390 n03797390 mug.n.04 objects 39
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..cff61b132f3ebf4edd513445b76fd39db54462d2
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv
@@ -0,0 +1,608 @@
+id raw_category category count nyu40id eigen13id nyuClass nyu40class eigen13class ModelNet40 ModelNet10 ShapeNetCore55 synsetoffset wnsynsetid wnsynsetkey mpcat40 mpcat40index
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+2 chair chair 4646 5 4 chair chair Chair chair chair chair 3001627 n03001627 chair.n.01 chair 3
+22 books book 1678 23 2 book books Books n02870526 book.n.11 objects 39
+3 floor floor 1553 2 5 floor floor Floor n03365592 floor.n.01 floor 2
+5 door door 1483 8 12 door door Wall door n03221720 door.n.01 door 4
+1163 object object 1313 40 7 otherprop Objects objects 39
+16 window window 1209 9 13 window window Window n04587648 window.n.01 window 9
+4 table table 1170 7 10 table table Table table table table 4379243 n04379243 table.n.02 table 5
+56 trash can trash can 1090 39 6 garbage bin otherfurniture Furniture trash_bin 2747177 n02747177 ashcan.n.01 objects 39
+13 pillow pillow 937 18 7 pillow pillow Objects pillow 3938244 n03938244 pillow.n.01 cushion 8
+15 picture picture 862 11 8 picture picture Picture n03931044 picture.n.01 picture 6
+41 ceiling ceiling 806 22 3 ceiling ceiling Ceiling n02990373 ceiling.n.01 ceiling 17
+26 box box 775 29 7 box box Objects n02883344 box.n.01 objects 39
+161 doorframe doorframe 768 8 12 door door Wall door doorframe.n.01 door 4
+19 monitor monitor 765 40 7 monitor otherprop Objects monitor monitor tv or monitor 3211117 n03782190 monitor.n.04 objects 39
+7 cabinet cabinet 731 3 6 cabinet cabinet Furniture cabinet 2933112 n02933112 cabinet.n.01 cabinet 7
+9 desk desk 680 14 10 desk desk Table desk desk table 4379243 n03179701 desk.n.01 table 5
+8 shelf shelf 641 15 6 shelves shelves Furniture bookshelf bookshelf 2871439 n02871439 bookshelf.n.01 shelving 31
+10 office chair office chair 595 5 4 chair chair Chair chair chair chair 3001627 n04373704 swivel_chair.n.01 chair 3
+31 towel towel 570 27 7 towel towel Objects n04459362 towel.n.01 towel 20
+6 couch couch 502 6 9 sofa sofa Sofa sofa sofa sofa 4256520 n04256520 sofa.n.01 sofa 10
+14 sink sink 488 34 7 sink sink Objects sink n04223580 sink.n.01 sink 15
+48 backpack backpack 479 40 7 backpack otherprop Objects n02769748 backpack.n.01 objects 39
+28 lamp lamp 419 35 7 lamp lamp Objects lamp lamp 3636649 n03636649 lamp.n.02 lighting 28
+11 bed bed 370 4 1 bed bed Bed bed bed bed 2818832 n02818832 bed.n.01 bed 11
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+71 mirror mirror 349 19 7 mirror mirror Objects n03773035 mirror.n.01 mirror 21
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+40 plant plant 331 40 7 plant otherprop Objects plant n00017222 plant.n.02 plant 14
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+96 radiator radiator 322 39 6 radiator otherfurniture Furniture n04041069 radiator.n.02 misc 40
+22 book book 318 23 2 book books Books n02870526 book.n.11 objects 39
+29 kitchen cabinet kitchen cabinet 310 3 6 cabinet cabinet Furniture n02933112 cabinet.n.01 cabinet 7
+49 toilet paper toilet paper 291 40 7 toilet paper otherprop Objects n15075141 toilet_tissue.n.01 objects 39
+29 kitchen cabinets kitchen cabinet 289 3 6 cabinet cabinet Furniture cabinet 2933112 n02933112 cabinet.n.01 cabinet 7
+23 armchair armchair 281 5 4 chair chair Chair chair chair chair 3001627 n02738535 armchair.n.01 chair 3
+63 shoes shoe 272 40 7 shoe otherprop Objects n04199027 shoe.n.01 clothes 38
+24 coffee table coffee table 258 7 10 coffee table table Table table table table 4379243 n03063968 coffee_table.n.01 table 5
+17 toilet toilet 256 33 7 toilet toilet Objects toilet toilet n04446276 toilet.n.01 toilet 18
+47 bag bag 252 37 7 bag bag Objects suitcase 2773838 n02773838 bag.n.06 objects 39
+32 clothes clothes 248 21 7 clothes clothes Objects n02728440 apparel.n.01 clothes 38
+46 keyboard keyboard 246 40 7 keyboard otherprop Objects keyboard computer keyboard 3085013 n03085013 computer_keyboard.n.01 objects 39
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+38 stool stool 221 40 7 stool otherprop Objects stool n04326896 stool.n.01 stool 19
+33 tv tv 219 25 11 television television TV tv or monitor 3211117 n03211117 display.n.06 tv_monitor 22
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+44 end table end table 147 7 10 table table Table table table table 4379243 n04379243 table.n.02 table 5
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+1169 trash bin trash bin 52 39 6 garbage bin otherfurniture Furniture trash_bin 2747177 n02747177 ashcan.n.01 objects 39
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+118 speaker speaker 43 40 7 speaker otherprop Objects speaker 3691459 n03691459 loudspeaker.n.01 objects 39
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+106 laundry basket laundry basket 37 40 7 laundry basket otherprop Objects basket 2801938 n03050864 clothes_hamper.n.01 objects 39
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+276 closet door closet door 35 8 12 door door Wall door n03221720 door.n.01 door 4
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+58 stairs stairs 35 38 7 stairs otherstructure Objects n04298308 stairway.n.01 stairs 16
+86 blinds blinds 35 13 13 blinds blinds Window n02851099 blind.n.03 blinds 32
+2 stack of chairs chair 35 5 4 chair chair Chair chair chair chair 3001627 n03001627 chair.n.01 chair 3
+399 purse purse 34 40 7 purse otherprop Objects n02774152 bag.n.04 objects 39
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+283 vacuum cleaner vacuum cleaner 25 40 7 otherprop Objects n04517823 vacuum.n.04 objects 39
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+1175 closet rod closet rod 24 40 7 otherprop Objects n04100174 rod.n.01 misc 40
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+104 stand stand 23 39 6 stand otherfurniture Furniture table table table 4379243 n04301000 stand.n.04 table 5
+229 scale scale 23 40 7 scale otherprop Objects n04141975 scale.n.07 objects 39
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+417 shower floor shower floor 19 2 5 floor floor Floor n04208936 shower.n.01 floor 2
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+312 headphones headphones 12 40 7 otherprop Objects n03261776 earphone.n.01 objects 39
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+726 thermos thermos 7 40 7 flask otherprop Objects bottle bottle 2876657 n04422727 thermos.n.01 objects 39
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+39 couch cushions cushion 7 18 7 pillow pillow Objects n03151500 cushion.n.03 cushion 8
+1117 tripod tripod 7 39 6 stand otherfurniture Furniture n04485082 tripod.n.01 objects 39
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+1206 beer bottles beer bottle 6 40 7 bottle otherprop Objects bottle bottle 2876657 n02876657 bottle.n.01 objects 39
+153 treadmill treadmill 6 39 6 treadmill otherfurniture Furniture n04477387 treadmill.n.01 gym_equipment 33
+1207 salt salt 6 40 7 otherprop Objects objects 39
+129 chest chest 6 39 6 chest otherfurniture Furniture dresser dresser chest_of_drawers 13
+220 dispenser dispenser 6 40 7 otherprop Objects n03210683 dispenser.n.01 objects 39
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+1255 shaving cream shaving cream 3 40 7 otherprop Objects n04186051 shaving_cream.n.01 objects 39
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+1260 bear bear 3 40 7 otherprop Objects objects 39
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+1263 poster cutter poster cutter 3 40 7 otherprop Objects objects 39
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+28 table lamp lamp 3 35 7 lamp lamp Objects lamp lamp 3636649 n04380533 table_lamp.n.01 lighting 28
+1267 yoga mat yoga mat 3 20 5 floor mat floor mat Floor n03727837 mat.n.01 floor 2
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+188 shower doors shower door 3 38 7 otherstructure Objects n04208936 shower.n.01 door 4
+689 cardboard cardboard 3 40 7 otherprop Objects objects 39
+1270 rack stand rack stand 3 39 6 stand otherfurniture Furniture n04038440 rack.n.05 shelving 31
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+339 magazine magazine 3 40 7 magazine otherprop Objects n06595351 magazine.n.01 objects 39
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+15 pictures picture 3 11 8 picture picture Picture n03931044 picture.n.01 picture 6
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+1278 sponge sponge 3 40 7 otherprop Objects n01906749 sponge.n.04 objects 39
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+1184 folded chairs folded chair 2 5 4 chair chair Chair chair chair chair 3001627 n03001627 chair.n.01 chair 3
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+523 washcloth washcloth 2 40 7 otherprop Objects n04554523 washcloth.n.01 towel 20
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+29 open kitchen cabinet kitchen cabinet 2 3 6 cabinet cabinet Furniture n02933112 cabinet.n.01 cabinet 7
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+146 frame frame 2 38 7 otherstructure Objects misc 40
+130 cups cup 2 40 7 cup otherprop Objects cup cup or mug 3797390 n03797390 mug.n.04 objects 39
+372 exercise ball exercise ball 2 40 7 ball otherprop Objects n04285146 sports_equipment.n.01 gym_equipment 33
+289 easel easel 2 39 6 stand otherfurniture Furniture n03262809 easel.n.01 furniture 36
+440 garbage bag garbage bag 2 37 7 bag bag Objects suitcase 2773838 n02773838 bag.n.06 objects 39
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+976 garage door garage door 2 38 7 garage door otherstructure Objects door door 4
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+357 hand towel hand towel 2 27 7 towel towel Objects n03490006 hand_towel.n.01 towel 20
+1286 bath products bath product 2 40 7 otherprop Objects objects 39
+1287 star star 2 40 7 otherprop Objects n09444783 star.n.03 misc 40
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+81 headboard headboard 2 39 6 headboard otherfurniture Furniture n03502200 headboard.n.01 bed 11
+1289 ipad ipad 2 40 7 otherprop Objects objects 39
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+1300 boat boat 2 40 7 otherprop Objects misc 40
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+1051 kinect kinect 2 40 7 kinect otherprop Objects objects 39
+566 crib crib 2 39 6 crib otherfurniture Furniture furniture 36
+1302 plastic storage bin plastic storage bin 2 40 7 container otherprop Objects n03094503 container.n.01 objects 39
+1062 cooler cooler 2 24 6 refridgerator refridgerator Furniture n03102654 cooler.n.01 appliances 37
+1303 kitchen apron kitchen apron 2 21 7 clothes clothes Objects n02728440 apparel.n.01 clothes 38
+1304 dishwashing soap bottle dishwashing soap bottle 2 40 7 bottle otherprop Objects bottle bottle 2876657 n02876657 bottle.n.01 objects 39
+1305 xbox controller xbox controller 2 40 7 otherprop Objects objects 39
+1306 banana holder banana holder 2 40 7 otherprop Objects objects 39
+298 ping pong paddle ping pong paddle 2 40 7 otherprop Objects table 5
+1307 airplane airplane 2 40 7 otherprop Objects misc 40
+1308 conditioner bottle conditioner bottle 2 40 7 bottle otherprop Objects bottle bottle 2876657 n02876657 bottle.n.01 objects 39
+1309 tea kettle tea kettle 2 40 7 tea kettle otherprop Objects n04397768 teakettle.n.01 objects 39
+43 bedframe bedframe 2 39 6 otherfurniture Furniture n02822579 bedstead.n.01 bed 11
+1310 wood beam wood beam 2 38 7 otherstructure Objects beam 29
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+1311 wall mounted coat rack wall mounted coat rack 2 40 7 otherprop Objects n03059103 coatrack.n.01 shelving 31
+1312 film light film light 2 40 7 otherprop Objects lighting 28
+749 ceiling lamp ceiling lamp 1 35 7 lamp lamp Objects lamp lamp 3636649 n03636649 lamp.n.02 lighting 28
+623 chain chain 1 40 7 otherprop Objects chair 3
+1313 sofa sofa 1 6 9 sofa sofa Sofa sofa sofa sofa 4256520 n04256520 sofa.n.01 sofa 10
+99 closet wardrobe wardrobe 1 39 6 wardrobe otherfurniture Furniture wardrobe n04550184 wardrobe.n.01 furniture 36
+265 sweater sweater 1 40 7 otherprop Objects n04370048 sweater.n.01 clothes 38
+1314 kitchen mixer kitchen mixer 1 40 7 otherprop Objects appliances 37
+99 wardrobe wardrobe 1 39 6 wardrobe otherfurniture Furniture wardrobe n04550184 wardrobe.n.01 furniture 36
+1315 water softener water softener 1 40 7 otherprop Objects misc 40
+448 banister banister 1 38 7 banister otherstructure Objects n02788148 bannister.n.02 railing 30
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+786 sofa bed sofa bed 1 4 1 bed bed Bed bed bed bed 2818832 n02818832 bed.n.01 bed 11
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+972 shower faucet handle shower faucet handle 1 40 7 handle otherprop Objects shower 23
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+1318 fish fish 1 40 7 otherprop Objects n02512053 fish.n.01 objects 39
+75 file cabinets file cabinet 1 3 6 cabinet cabinet Furniture cabinet 2933112 n03337140 file.n.03 cabinet 7
+657 cat litter box cat litter box 1 29 7 box box Objects objects 39
+561 electric panel electric panel 1 40 7 otherprop Objects misc 40
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+513 curtain rod curtain rod 1 38 7 curtain rod otherstructure Objects curtain 12
+411 bunk bed bunk bed 1 39 6 bunk bed otherfurniture Furniture bed bed bed 2818832 n02920259 bunk_bed.n.01 bed 11
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+1319 toy dinosaur toy dinosaur 1 40 7 toy otherprop Objects n03964744 plaything.n.01 objects 39
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+1145 night lamp night lamp 1 35 7 lamp lamp Objects lamp lamp 3636649 n03636649 lamp.n.02 lighting 28
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+1174 cd cases cd case 1 40 7 otherprop Objects objects 39
+38 stools stool 1 40 7 stool otherprop Objects stool n04326896 stool.n.01 stool 19
+1326 hand sanitzer dispenser hand sanitzer dispenser 1 40 7 otherprop Objects n04254120 soap_dispenser.n.01 objects 39
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+1328 tray rack tray rack 1 40 7 otherprop Objects objects 39
+1329 wig wig 1 40 7 otherprop Objects n04584207 wig.n.01 objects 39
+182 switch switch 1 40 7 otherprop Objects n04372370 switch.n.01 misc 40
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+1332 mail bin mail bin 1 40 7 otherprop Objects misc 40
+1333 elevator button elevator button 1 40 7 otherprop Objects misc 40
+939 gaming wheel gaming wheel 1 40 7 otherprop Objects objects 39
+1334 drum set drum set 1 40 7 otherprop Objects objects 39
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+907 coffee mug coffee mug 1 40 7 vessel otherprop Objects cup or mug 3797390 n03063599 coffee_mug.n.01 objects 39
+1335 closet shelf closet shelf 1 15 6 shelves shelves Furniture bookshelf bookshelf 2871439 n02871439 bookshelf.n.01 shelving 31
+1336 baby mobile baby mobile 1 40 7 otherprop Objects objects 39
+829 diaper bin diaper bin 1 40 7 bin otherprop Objects objects 39
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+733 dress rack dress rack 1 40 7 otherprop Objects n03238762 dress_rack.n.01 misc 40
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+1338 recliner chair recliner chair 1 5 4 chair chair Chair chair chair chair 3001627 n03238762 dress_rack.n.01 chair 3
+1314 kitchenaid mixer kitchen mixer 1 40 7 otherprop Objects appliances 37
+1339 soda can soda can 1 40 7 can otherprop Objects can 2946921 n02946921 can.n.01 objects 39
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+142 tap tap 1 40 7 faucet otherprop Objects faucet 3325088 n04559451 water_faucet.n.01 objects 39
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+885 rocking chair rocking chair 1 5 4 chair chair Chair chair chair chair 3001627 n04099969 rocking_chair.n.01 chair 3
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+815 media center media center 1 3 6 cabinet cabinet Furniture cabinet 2933112 n02933112 cabinet.n.01 cabinet 7
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+1184 stack of folded chairs folded chair 1 5 4 chair chair Chair chair chair chair 3001627 n03001627 chair.n.01 chair 3
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+1126 baseball cap baseball cap 1 40 7 otherprop Objects cap 2954340 n02799323 baseball_cap.n.01 clothes 38
+1346 battery disposal jar battery disposal jar 1 40 7 jar otherprop Objects jar 3593526 n03593526 jar.n.01 objects 39
+332 mop mop 1 40 7 otherprop Objects n04367480 swab.n.02 objects 39
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+1163 object stick 1 40 7 stick otherprop Objects objects 39
+1347 closet floor closet floor 1 2 5 floor floor Floor n03365592 floor.n.01 floor 2
+1348 dryer sheets dryer sheets 1 40 7 otherprop Objects objects 39
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+1349 air mattress air mattress 1 4 1 bed bed Bed bed bed bed 2818832 n02690809 air_mattress.n.01 bed 11
+1350 clip clip 1 40 7 otherprop Objects objects 39
+222 side table side table 1 7 10 table table Table table table table 4379243 n04379243 table.n.02 table 5
+1253 pizza boxes pizza box 1 29 7 box box Objects n02883344 box.n.01 objects 39
+1351 display display 1 39 7 otherfurniture Furniture n03211117 display.n.06 misc 40
+1352 postcard postcard 1 40 7 otherprop Objects objects 39
+828 display sign display sign 1 40 7 sign otherprop Objects misc 40
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+1354 tennis racket bag tennis racket bag 1 40 7 otherprop Objects objects 39
+1355 air hockey table air hockey table 1 7 10 table table Table table table table 4379243 n04379243 table.n.02 table 5
+1301 socks sock 1 21 7 clothes clothes Objects n04254777 sock.n.01 clothes 38
+1356 food bag food bag 1 37 7 bag bag Objects objects 39
+1199 clothes hangers clothes hanger 1 40 7 otherprop Objects n03057920 coat_hanger.n.01 misc 40
+1357 starbucks cup starbucks cup 1 40 7 cup otherprop Objects cup cup or mug 3797390 n03797390 mug.n.04 objects 39
\ No newline at end of file
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_test.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..79d15b0ee4afa889883562a722b837b78ee8ce4b
--- /dev/null
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--- /dev/null
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9e7d9205321e8ca047a527466f4b7100c9c9d2c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaccf1f0ee5255749100dfd93cf31d886ec9c699
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py
@@ -0,0 +1,255 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+import argparse
+import glob
+import json
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+# Load external constants
+from meta_data.scannet200_constants import VALID_CLASS_IDS_200, VALID_CLASS_IDS_20
+
+CLOUD_FILE_PFIX = "_vh_clean_2"
+SEGMENTS_FILE_PFIX = ".0.010000.segs.json"
+AGGREGATIONS_FILE_PFIX = ".aggregation.json"
+CLASS_IDS200 = VALID_CLASS_IDS_200
+CLASS_IDS20 = VALID_CLASS_IDS_20
+IGNORE_INDEX = -1
+
+
+def read_plymesh(filepath):
+ """Read ply file and return it as numpy array. Returns None if emtpy."""
+ with open(filepath, "rb") as f:
+ plydata = plyfile.PlyData.read(f)
+ if plydata.elements:
+ vertices = pd.DataFrame(plydata["vertex"].data).values
+ faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+ return vertices, faces
+
+
+# Map the raw category id to the point cloud
+def point_indices_from_group(seg_indices, group, labels_pd):
+ group_segments = np.array(group["segments"])
+ label = group["label"]
+
+ # Map the category name to id
+ label_id20 = labels_pd[labels_pd["raw_category"] == label]["nyu40id"]
+ label_id20 = int(label_id20.iloc[0]) if len(label_id20) > 0 else 0
+ label_id200 = labels_pd[labels_pd["raw_category"] == label]["id"]
+ label_id200 = int(label_id200.iloc[0]) if len(label_id200) > 0 else 0
+
+ # Only store for the valid categories
+ if label_id20 in CLASS_IDS20:
+ label_id20 = CLASS_IDS20.index(label_id20)
+ else:
+ label_id20 = IGNORE_INDEX
+
+ if label_id200 in CLASS_IDS200:
+ label_id200 = CLASS_IDS200.index(label_id200)
+ else:
+ label_id200 = IGNORE_INDEX
+
+ # get points, where segment indices (points labelled with segment ids) are in the group segment list
+ point_idx = np.where(np.isin(seg_indices, group_segments))[0]
+ return point_idx, label_id20, label_id200
+
+
+def face_normal(vertex, face):
+ v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+ v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+ vec = np.cross(v01, v02)
+ length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+ nf = vec / length
+ area = length * 0.5
+ return nf, area
+
+
+def vertex_normal(vertex, face):
+ nf, area = face_normal(vertex, face)
+ nf = nf * area
+
+ nv = np.zeros_like(vertex)
+ for i in range(face.shape[0]):
+ nv[face[i]] += nf[i]
+
+ length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+ nv = nv / length
+ return nv
+
+
+def handle_process(
+ scene_path, output_path, labels_pd, train_scenes, val_scenes, parse_normals=True
+):
+ scene_id = os.path.basename(scene_path)
+ mesh_path = os.path.join(scene_path, f"{scene_id}{CLOUD_FILE_PFIX}.ply")
+ segments_file = os.path.join(
+ scene_path, f"{scene_id}{CLOUD_FILE_PFIX}{SEGMENTS_FILE_PFIX}"
+ )
+ aggregations_file = os.path.join(scene_path, f"{scene_id}{AGGREGATIONS_FILE_PFIX}")
+ info_file = os.path.join(scene_path, f"{scene_id}.txt")
+
+ if scene_id in train_scenes:
+ output_path = os.path.join(output_path, "train", f"{scene_id}")
+ split_name = "train"
+ elif scene_id in val_scenes:
+ output_path = os.path.join(output_path, "val", f"{scene_id}")
+ split_name = "val"
+ else:
+ output_path = os.path.join(output_path, "test", f"{scene_id}")
+ split_name = "test"
+
+ print(f"Processing: {scene_id} in {split_name}")
+
+ vertices, faces = read_plymesh(mesh_path)
+ coords = vertices[:, :3]
+ colors = vertices[:, 3:6]
+ save_dict = dict(
+ coord=coords.astype(np.float32),
+ color=colors.astype(np.uint8),
+ )
+
+ # # Rotating the mesh to axis aligned
+ # info_dict = {}
+ # with open(info_file) as f:
+ # for line in f:
+ # (key, val) = line.split(" = ")
+ # info_dict[key] = np.fromstring(val, sep=' ')
+ #
+ # if 'axisAlignment' not in info_dict:
+ # rot_matrix = np.identity(4)
+ # else:
+ # rot_matrix = info_dict['axisAlignment'].reshape(4, 4)
+ # r_coords = coords.transpose()
+ # r_coords = np.append(r_coords, np.ones((1, r_coords.shape[1])), axis=0)
+ # r_coords = np.dot(rot_matrix, r_coords)
+ # coords = r_coords
+
+ # Parse Normals
+ if parse_normals:
+ save_dict["normal"] = vertex_normal(coords, faces).astype(np.float32)
+
+ # Load segments file
+ if split_name != "test":
+ with open(segments_file) as f:
+ segments = json.load(f)
+ seg_indices = np.array(segments["segIndices"])
+
+ # Load Aggregations file
+ with open(aggregations_file) as f:
+ aggregation = json.load(f)
+ seg_groups = np.array(aggregation["segGroups"])
+
+ # Generate new labels
+ semantic_gt20 = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+ semantic_gt200 = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+ instance_ids = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+ for group in seg_groups:
+ point_idx, label_id20, label_id200 = point_indices_from_group(
+ seg_indices, group, labels_pd
+ )
+
+ semantic_gt20[point_idx] = label_id20
+ semantic_gt200[point_idx] = label_id200
+ instance_ids[point_idx] = group["id"]
+
+ semantic_gt20 = semantic_gt20.astype(int)
+ semantic_gt200 = semantic_gt200.astype(int)
+ instance_ids = instance_ids.astype(int)
+
+ save_dict["segment20"] = semantic_gt20
+ save_dict["segment200"] = semantic_gt200
+ save_dict["instance"] = instance_ids
+
+ # Concatenate with original cloud
+ processed_vertices = np.hstack((semantic_gt200, instance_ids))
+
+ if np.any(np.isnan(processed_vertices)) or not np.all(
+ np.isfinite(processed_vertices)
+ ):
+ raise ValueError(f"Find NaN in Scene: {scene_id}")
+
+ # Save processed data
+ os.makedirs(output_path, exist_ok=True)
+ for key in save_dict.keys():
+ np.save(os.path.join(output_path, f"{key}.npy"), save_dict[key])
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ parser.add_argument(
+ "--parse_normals", default=True, type=bool, help="Whether parse point normals"
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ config = parser.parse_args()
+
+ # Load label map
+ labels_pd = pd.read_csv(
+ "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv",
+ sep="\t",
+ header=0,
+ )
+
+ # Load train/val splits
+ with open(
+ "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_train.txt"
+ ) as train_file:
+ train_scenes = train_file.read().splitlines()
+ with open(
+ "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt"
+ ) as val_file:
+ val_scenes = val_file.read().splitlines()
+
+ # Create output directories
+ train_output_dir = os.path.join(config.output_root, "train")
+ os.makedirs(train_output_dir, exist_ok=True)
+ val_output_dir = os.path.join(config.output_root, "val")
+ os.makedirs(val_output_dir, exist_ok=True)
+ test_output_dir = os.path.join(config.output_root, "test")
+ os.makedirs(test_output_dir, exist_ok=True)
+
+ # Load scene paths
+ scene_paths = sorted(glob.glob(config.dataset_root + "/scans*/scene*"))
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ handle_process,
+ scene_paths,
+ repeat(config.output_root),
+ repeat(labels_pd),
+ repeat(train_scenes),
+ repeat(val_scenes),
+ repeat(config.parse_normals),
+ )
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py
new file mode 100644
index 0000000000000000000000000000000000000000..d90c8770e812f782e4735cc7095c100cd6258bf6
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py
@@ -0,0 +1,183 @@
+import os, struct
+import numpy as np
+import zlib
+import imageio
+import cv2
+
+COMPRESSION_TYPE_COLOR = {-1: "unknown", 0: "raw", 1: "png", 2: "jpeg"}
+COMPRESSION_TYPE_DEPTH = {
+ -1: "unknown",
+ 0: "raw_ushort",
+ 1: "zlib_ushort",
+ 2: "occi_ushort",
+}
+
+
+class RGBDFrame:
+ def load(self, file_handle):
+ self.camera_to_world = np.asarray(
+ struct.unpack("f" * 16, file_handle.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.timestamp_color = struct.unpack("Q", file_handle.read(8))[0]
+ self.timestamp_depth = struct.unpack("Q", file_handle.read(8))[0]
+ self.color_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+ self.depth_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+ self.color_data = b"".join(
+ struct.unpack(
+ "c" * self.color_size_bytes, file_handle.read(self.color_size_bytes)
+ )
+ )
+ self.depth_data = b"".join(
+ struct.unpack(
+ "c" * self.depth_size_bytes, file_handle.read(self.depth_size_bytes)
+ )
+ )
+
+ def decompress_depth(self, compression_type):
+ if compression_type == "zlib_ushort":
+ return self.decompress_depth_zlib()
+ else:
+ raise
+
+ def decompress_depth_zlib(self):
+ return zlib.decompress(self.depth_data)
+
+ def decompress_color(self, compression_type):
+ if compression_type == "jpeg":
+ return self.decompress_color_jpeg()
+ else:
+ raise
+
+ def decompress_color_jpeg(self):
+ return imageio.imread(self.color_data)
+
+
+class SensorData:
+ def __init__(self, filename):
+ self.version = 4
+ self.load(filename)
+
+ def load(self, filename):
+ with open(filename, "rb") as f:
+ version = struct.unpack("I", f.read(4))[0]
+ assert self.version == version
+ strlen = struct.unpack("Q", f.read(8))[0]
+ self.sensor_name = b"".join(struct.unpack("c" * strlen, f.read(strlen)))
+ self.intrinsic_color = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.extrinsic_color = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.intrinsic_depth = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.extrinsic_depth = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.color_compression_type = COMPRESSION_TYPE_COLOR[
+ struct.unpack("i", f.read(4))[0]
+ ]
+ self.depth_compression_type = COMPRESSION_TYPE_DEPTH[
+ struct.unpack("i", f.read(4))[0]
+ ]
+ self.color_width = struct.unpack("I", f.read(4))[0]
+ self.color_height = struct.unpack("I", f.read(4))[0]
+ self.depth_width = struct.unpack("I", f.read(4))[0]
+ self.depth_height = struct.unpack("I", f.read(4))[0]
+ self.depth_shift = struct.unpack("f", f.read(4))[0]
+ num_frames = struct.unpack("Q", f.read(8))[0]
+ self.frames = []
+ for i in range(num_frames):
+ frame = RGBDFrame()
+ frame.load(f)
+ self.frames.append(frame)
+
+ def export_depth_images(self, output_path, image_size=None, frame_skip=1):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print(
+ "exporting", len(self.frames) // frame_skip, " depth frames to", output_path
+ )
+ for f in range(0, len(self.frames), frame_skip):
+ if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+ continue
+ if f % 100 == 0:
+ print(
+ "exporting",
+ f,
+ "th depth frames to",
+ os.path.join(output_path, str(f) + ".png"),
+ )
+
+ depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
+ depth = np.fromstring(depth_data, dtype=np.uint16).reshape(
+ self.depth_height, self.depth_width
+ )
+ if image_size is not None:
+ depth = cv2.resize(
+ depth,
+ (image_size[1], image_size[0]),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ imageio.imwrite(os.path.join(output_path, str(f) + ".png"), depth)
+
+ def export_color_images(self, output_path, image_size=None, frame_skip=1):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print(
+ "exporting", len(self.frames) // frame_skip, "color frames to", output_path
+ )
+ for f in range(0, len(self.frames), frame_skip):
+ if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+ continue
+ if f % 100 == 0:
+ print(
+ "exporting",
+ f,
+ "th color frames to",
+ os.path.join(output_path, str(f) + ".png"),
+ )
+ color = self.frames[f].decompress_color(self.color_compression_type)
+ if image_size is not None:
+ color = cv2.resize(
+ color,
+ (image_size[1], image_size[0]),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ # imageio.imwrite(os.path.join(output_path, str(f) + '.jpg'), color)
+ imageio.imwrite(os.path.join(output_path, str(f) + ".png"), color)
+
+ def save_mat_to_file(self, matrix, filename):
+ with open(filename, "w") as f:
+ for line in matrix:
+ np.savetxt(f, line[np.newaxis], fmt="%f")
+
+ def export_poses(self, output_path, frame_skip=1):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print(
+ "exporting", len(self.frames) // frame_skip, "camera poses to", output_path
+ )
+ for f in range(0, len(self.frames), frame_skip):
+ self.save_mat_to_file(
+ self.frames[f].camera_to_world,
+ os.path.join(output_path, str(f) + ".txt"),
+ )
+
+ def export_intrinsics(self, output_path):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print("exporting camera intrinsics to", output_path)
+ self.save_mat_to_file(
+ self.intrinsic_color, os.path.join(output_path, "intrinsic_color.txt")
+ )
+ self.save_mat_to_file(
+ self.extrinsic_color, os.path.join(output_path, "extrinsic_color.txt")
+ )
+ self.save_mat_to_file(
+ self.intrinsic_depth, os.path.join(output_path, "intrinsic_depth.txt")
+ )
+ self.save_mat_to_file(
+ self.extrinsic_depth, os.path.join(output_path, "extrinsic_depth.txt")
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6b407eebad280f2817805d15ec43b9f7f6afbf4
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py
@@ -0,0 +1,91 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import torch
+import numpy as np
+import math
+import glob, os
+import argparse
+import open3d as o3d
+
+
+def make_open3d_point_cloud(xyz, color=None, voxel_size=None):
+ if np.isnan(xyz).any():
+ return None
+
+ xyz = xyz[:, :3]
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(xyz)
+ if color is not None:
+ pcd.colors = o3d.utility.Vector3dVector(color)
+ if voxel_size is not None:
+ pcd = pcd.voxel_down_sample(voxel_size)
+
+ return pcd
+
+
+def compute_overlap_ratio(pcd0, pcd1, voxel_size):
+ pcd0_down = pcd0.voxel_down_sample(voxel_size)
+ pcd1_down = pcd1.voxel_down_sample(voxel_size)
+ matching01 = get_matching_indices(pcd0_down, pcd1_down, voxel_size * 1.5, 1)
+ matching10 = get_matching_indices(pcd1_down, pcd0_down, voxel_size * 1.5, 1)
+ overlap0 = float(len(matching01)) / float(len(pcd0_down.points))
+ overlap1 = float(len(matching10)) / float(len(pcd1_down.points))
+ return max(overlap0, overlap1)
+
+
+def get_matching_indices(source, pcd_tree, search_voxel_size, K=None):
+ match_inds = []
+ for i, point in enumerate(source.points):
+ [_, idx, _] = pcd_tree.search_radius_vector_3d(point, search_voxel_size)
+ if K is not None:
+ idx = idx[:K]
+ for j in idx:
+ match_inds.append((i, j))
+ return match_inds
+
+
+def compute_full_overlapping(data_root, scene_id, voxel_size=0.05):
+ _points = [
+ (
+ pcd_name,
+ make_open3d_point_cloud(
+ torch.load(pcd_name)["coord"], voxel_size=voxel_size
+ ),
+ )
+ for pcd_name in glob.glob(os.path.join(data_root, scene_id, "pcd", "*.pth"))
+ ]
+ points = [(pcd_name, pcd) for (pcd_name, pcd) in _points if pcd is not None]
+ print(
+ "load {} point clouds ({} invalid has been filtered), computing matching/overlapping".format(
+ len(points), len(_points) - len(points)
+ )
+ )
+
+ matching_matrix = np.zeros((len(points), len(points)))
+ for i, (pcd0_name, pcd0) in enumerate(points):
+ print("matching to...{}".format(pcd0_name))
+ pcd0_tree = o3d.geometry.KDTreeFlann(copy.deepcopy(pcd0))
+ for j, (pcd1_name, pcd1) in enumerate(points):
+ if i == j:
+ continue
+ matching_matrix[i, j] = float(
+ len(get_matching_indices(pcd1, pcd0_tree, 1.5 * voxel_size, 1))
+ ) / float(len(pcd1.points))
+
+ # write to file
+ with open(os.path.join(data_root, scene_id, "pcd", "overlap.txt"), "w") as f:
+ for i, (pcd0_name, pcd0) in enumerate(points):
+ for j, (pcd1_name, pcd1) in enumerate(points):
+ if i < j:
+ overlap = max(matching_matrix[i, j], matching_matrix[j, i])
+ f.write(
+ "{} {} {}\n".format(
+ pcd0_name.replace(data_root, ""),
+ pcd1_name.replace(data_root, ""),
+ overlap,
+ )
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8943ba040cc24fc8d3130bd8784052cb57ce6c9
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py
@@ -0,0 +1,33 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import argparse
+import glob, os, sys
+
+from SensorData import SensorData
+
+# params
+parser = argparse.ArgumentParser()
+# data paths
+parser.add_argument("--target_dir", required=True, help="path to the target dir")
+
+opt = parser.parse_args()
+print(opt)
+
+
+def main():
+ overlaps = glob.glob(os.path.join(opt.target_dir, "*/pcd/overlap.txt"))
+ with open(os.path.join(opt.target_dir, "overlap30.txt"), "w") as f:
+ for fo in overlaps:
+ for line in open(fo):
+ pcd0, pcd1, op = line.strip().split()
+ if float(op) >= 0.3:
+ print("{} {} {}".format(pcd0, pcd1, op), file=f)
+ print("done")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..17400c4bd28764829d248e90dc141182fa1d8f03
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py
@@ -0,0 +1,894 @@
+# Copyright 2014 Darsh Ranjan
+#
+# This file is part of python-plyfile.
+#
+# python-plyfile is free software: you can redistribute it and/or
+# modify it under the terms of the GNU General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# python-plyfile is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with python-plyfile. If not, see
+# .
+
+from itertools import islice as _islice
+
+import numpy as _np
+from sys import byteorder as _byteorder
+
+
+try:
+ _range = xrange
+except NameError:
+ _range = range
+
+
+# Many-many relation
+_data_type_relation = [
+ ("int8", "i1"),
+ ("char", "i1"),
+ ("uint8", "u1"),
+ ("uchar", "b1"),
+ ("uchar", "u1"),
+ ("int16", "i2"),
+ ("short", "i2"),
+ ("uint16", "u2"),
+ ("ushort", "u2"),
+ ("int32", "i4"),
+ ("int", "i4"),
+ ("uint32", "u4"),
+ ("uint", "u4"),
+ ("float32", "f4"),
+ ("float", "f4"),
+ ("float64", "f8"),
+ ("double", "f8"),
+]
+
+_data_types = dict(_data_type_relation)
+_data_type_reverse = dict((b, a) for (a, b) in _data_type_relation)
+
+_types_list = []
+_types_set = set()
+for _a, _b in _data_type_relation:
+ if _a not in _types_set:
+ _types_list.append(_a)
+ _types_set.add(_a)
+ if _b not in _types_set:
+ _types_list.append(_b)
+ _types_set.add(_b)
+
+
+_byte_order_map = {"ascii": "=", "binary_little_endian": "<", "binary_big_endian": ">"}
+
+_byte_order_reverse = {"<": "binary_little_endian", ">": "binary_big_endian"}
+
+_native_byte_order = {"little": "<", "big": ">"}[_byteorder]
+
+
+def _lookup_type(type_str):
+ if type_str not in _data_type_reverse:
+ try:
+ type_str = _data_types[type_str]
+ except KeyError:
+ raise ValueError("field type %r not in %r" % (type_str, _types_list))
+
+ return _data_type_reverse[type_str]
+
+
+def _split_line(line, n):
+ fields = line.split(None, n)
+ if len(fields) == n:
+ fields.append("")
+
+ assert len(fields) == n + 1
+
+ return fields
+
+
+def make2d(array, cols=None, dtype=None):
+ """
+ Make a 2D array from an array of arrays. The `cols' and `dtype'
+ arguments can be omitted if the array is not empty.
+
+ """
+ if (cols is None or dtype is None) and not len(array):
+ raise RuntimeError("cols and dtype must be specified for empty " "array")
+
+ if cols is None:
+ cols = len(array[0])
+
+ if dtype is None:
+ dtype = array[0].dtype
+
+ return _np.fromiter(array, [("_", dtype, (cols,))], count=len(array))["_"]
+
+
+class PlyParseError(Exception):
+ """
+ Raised when a PLY file cannot be parsed.
+
+ The attributes `element', `row', `property', and `message' give
+ additional information.
+
+ """
+
+ def __init__(self, message, element=None, row=None, prop=None):
+ self.message = message
+ self.element = element
+ self.row = row
+ self.prop = prop
+
+ s = ""
+ if self.element:
+ s += "element %r: " % self.element.name
+ if self.row is not None:
+ s += "row %d: " % self.row
+ if self.prop:
+ s += "property %r: " % self.prop.name
+ s += self.message
+
+ Exception.__init__(self, s)
+
+ def __repr__(self):
+ return (
+ "PlyParseError(%r, element=%r, row=%r, prop=%r)" % self.message,
+ self.element,
+ self.row,
+ self.prop,
+ )
+
+
+class PlyData(object):
+ """
+ PLY file header and data.
+
+ A PlyData instance is created in one of two ways: by the static
+ method PlyData.read (to read a PLY file), or directly from __init__
+ given a sequence of elements (which can then be written to a PLY
+ file).
+
+ """
+
+ def __init__(
+ self, elements=[], text=False, byte_order="=", comments=[], obj_info=[]
+ ):
+ """
+ elements: sequence of PlyElement instances.
+
+ text: whether the resulting PLY file will be text (True) or
+ binary (False).
+
+ byte_order: '<' for little-endian, '>' for big-endian, or '='
+ for native. This is only relevant if `text' is False.
+
+ comments: sequence of strings that will be placed in the header
+ between the 'ply' and 'format ...' lines.
+
+ obj_info: like comments, but will be placed in the header with
+ "obj_info ..." instead of "comment ...".
+
+ """
+ if byte_order == "=" and not text:
+ byte_order = _native_byte_order
+
+ self.byte_order = byte_order
+ self.text = text
+
+ self.comments = list(comments)
+ self.obj_info = list(obj_info)
+ self.elements = elements
+
+ def _get_elements(self):
+ return self._elements
+
+ def _set_elements(self, elements):
+ self._elements = tuple(elements)
+ self._index()
+
+ elements = property(_get_elements, _set_elements)
+
+ def _get_byte_order(self):
+ return self._byte_order
+
+ def _set_byte_order(self, byte_order):
+ if byte_order not in ["<", ">", "="]:
+ raise ValueError("byte order must be '<', '>', or '='")
+
+ self._byte_order = byte_order
+
+ byte_order = property(_get_byte_order, _set_byte_order)
+
+ def _index(self):
+ self._element_lookup = dict((elt.name, elt) for elt in self._elements)
+ if len(self._element_lookup) != len(self._elements):
+ raise ValueError("two elements with same name")
+
+ @staticmethod
+ def _parse_header(stream):
+ """
+ Parse a PLY header from a readable file-like stream.
+
+ """
+ lines = []
+ comments = {"comment": [], "obj_info": []}
+ while True:
+ line = stream.readline().decode("ascii").strip()
+ fields = _split_line(line, 1)
+
+ if fields[0] == "end_header":
+ break
+
+ elif fields[0] in comments.keys():
+ lines.append(fields)
+ else:
+ lines.append(line.split())
+
+ a = 0
+ if lines[a] != ["ply"]:
+ raise PlyParseError("expected 'ply'")
+
+ a += 1
+ while lines[a][0] in comments.keys():
+ comments[lines[a][0]].append(lines[a][1])
+ a += 1
+
+ if lines[a][0] != "format":
+ raise PlyParseError("expected 'format'")
+
+ if lines[a][2] != "1.0":
+ raise PlyParseError("expected version '1.0'")
+
+ if len(lines[a]) != 3:
+ raise PlyParseError("too many fields after 'format'")
+
+ fmt = lines[a][1]
+
+ if fmt not in _byte_order_map:
+ raise PlyParseError("don't understand format %r" % fmt)
+
+ byte_order = _byte_order_map[fmt]
+ text = fmt == "ascii"
+
+ a += 1
+ while a < len(lines) and lines[a][0] in comments.keys():
+ comments[lines[a][0]].append(lines[a][1])
+ a += 1
+
+ return PlyData(
+ PlyElement._parse_multi(lines[a:]),
+ text,
+ byte_order,
+ comments["comment"],
+ comments["obj_info"],
+ )
+
+ @staticmethod
+ def read(stream):
+ """
+ Read PLY data from a readable file-like object or filename.
+
+ """
+ (must_close, stream) = _open_stream(stream, "read")
+ try:
+ data = PlyData._parse_header(stream)
+ for elt in data:
+ elt._read(stream, data.text, data.byte_order)
+ finally:
+ if must_close:
+ stream.close()
+
+ return data
+
+ def write(self, stream):
+ """
+ Write PLY data to a writeable file-like object or filename.
+
+ """
+ (must_close, stream) = _open_stream(stream, "write")
+ try:
+ stream.write(self.header.encode("ascii"))
+ stream.write(b"\r\n")
+ for elt in self:
+ elt._write(stream, self.text, self.byte_order)
+ finally:
+ if must_close:
+ stream.close()
+
+ @property
+ def header(self):
+ """
+ Provide PLY-formatted metadata for the instance.
+
+ """
+ lines = ["ply"]
+
+ if self.text:
+ lines.append("format ascii 1.0")
+ else:
+ lines.append("format " + _byte_order_reverse[self.byte_order] + " 1.0")
+
+ # Some information is lost here, since all comments are placed
+ # between the 'format' line and the first element.
+ for c in self.comments:
+ lines.append("comment " + c)
+
+ for c in self.obj_info:
+ lines.append("obj_info " + c)
+
+ lines.extend(elt.header for elt in self.elements)
+ lines.append("end_header")
+ return "\r\n".join(lines)
+
+ def __iter__(self):
+ return iter(self.elements)
+
+ def __len__(self):
+ return len(self.elements)
+
+ def __contains__(self, name):
+ return name in self._element_lookup
+
+ def __getitem__(self, name):
+ return self._element_lookup[name]
+
+ def __str__(self):
+ return self.header
+
+ def __repr__(self):
+ return "PlyData(%r, text=%r, byte_order=%r, " "comments=%r, obj_info=%r)" % (
+ self.elements,
+ self.text,
+ self.byte_order,
+ self.comments,
+ self.obj_info,
+ )
+
+
+def _open_stream(stream, read_or_write):
+ if hasattr(stream, read_or_write):
+ return (False, stream)
+ try:
+ return (True, open(stream, read_or_write[0] + "b"))
+ except TypeError:
+ raise RuntimeError("expected open file or filename")
+
+
+class PlyElement(object):
+ """
+ PLY file element.
+
+ A client of this library doesn't normally need to instantiate this
+ directly, so the following is only for the sake of documenting the
+ internals.
+
+ Creating a PlyElement instance is generally done in one of two ways:
+ as a byproduct of PlyData.read (when reading a PLY file) and by
+ PlyElement.describe (before writing a PLY file).
+
+ """
+
+ def __init__(self, name, properties, count, comments=[]):
+ """
+ This is not part of the public interface. The preferred methods
+ of obtaining PlyElement instances are PlyData.read (to read from
+ a file) and PlyElement.describe (to construct from a numpy
+ array).
+
+ """
+ self._name = str(name)
+ self._check_name()
+ self._count = count
+
+ self._properties = tuple(properties)
+ self._index()
+
+ self.comments = list(comments)
+
+ self._have_list = any(isinstance(p, PlyListProperty) for p in self.properties)
+
+ @property
+ def count(self):
+ return self._count
+
+ def _get_data(self):
+ return self._data
+
+ def _set_data(self, data):
+ self._data = data
+ self._count = len(data)
+ self._check_sanity()
+
+ data = property(_get_data, _set_data)
+
+ def _check_sanity(self):
+ for prop in self.properties:
+ if prop.name not in self._data.dtype.fields:
+ raise ValueError("dangling property %r" % prop.name)
+
+ def _get_properties(self):
+ return self._properties
+
+ def _set_properties(self, properties):
+ self._properties = tuple(properties)
+ self._check_sanity()
+ self._index()
+
+ properties = property(_get_properties, _set_properties)
+
+ def _index(self):
+ self._property_lookup = dict((prop.name, prop) for prop in self._properties)
+ if len(self._property_lookup) != len(self._properties):
+ raise ValueError("two properties with same name")
+
+ def ply_property(self, name):
+ return self._property_lookup[name]
+
+ @property
+ def name(self):
+ return self._name
+
+ def _check_name(self):
+ if any(c.isspace() for c in self._name):
+ msg = "element name %r contains spaces" % self._name
+ raise ValueError(msg)
+
+ def dtype(self, byte_order="="):
+ """
+ Return the numpy dtype of the in-memory representation of the
+ data. (If there are no list properties, and the PLY format is
+ binary, then this also accurately describes the on-disk
+ representation of the element.)
+
+ """
+ return [(prop.name, prop.dtype(byte_order)) for prop in self.properties]
+
+ @staticmethod
+ def _parse_multi(header_lines):
+ """
+ Parse a list of PLY element definitions.
+
+ """
+ elements = []
+ while header_lines:
+ (elt, header_lines) = PlyElement._parse_one(header_lines)
+ elements.append(elt)
+
+ return elements
+
+ @staticmethod
+ def _parse_one(lines):
+ """
+ Consume one element definition. The unconsumed input is
+ returned along with a PlyElement instance.
+
+ """
+ a = 0
+ line = lines[a]
+
+ if line[0] != "element":
+ raise PlyParseError("expected 'element'")
+ if len(line) > 3:
+ raise PlyParseError("too many fields after 'element'")
+ if len(line) < 3:
+ raise PlyParseError("too few fields after 'element'")
+
+ (name, count) = (line[1], int(line[2]))
+
+ comments = []
+ properties = []
+ while True:
+ a += 1
+ if a >= len(lines):
+ break
+
+ if lines[a][0] == "comment":
+ comments.append(lines[a][1])
+ elif lines[a][0] == "property":
+ properties.append(PlyProperty._parse_one(lines[a]))
+ else:
+ break
+
+ return (PlyElement(name, properties, count, comments), lines[a:])
+
+ @staticmethod
+ def describe(data, name, len_types={}, val_types={}, comments=[]):
+ """
+ Construct a PlyElement from an array's metadata.
+
+ len_types and val_types can be given as mappings from list
+ property names to type strings (like 'u1', 'f4', etc., or
+ 'int8', 'float32', etc.). These can be used to define the length
+ and value types of list properties. List property lengths
+ always default to type 'u1' (8-bit unsigned integer), and value
+ types default to 'i4' (32-bit integer).
+
+ """
+ if not isinstance(data, _np.ndarray):
+ raise TypeError("only numpy arrays are supported")
+
+ if len(data.shape) != 1:
+ raise ValueError("only one-dimensional arrays are " "supported")
+
+ count = len(data)
+
+ properties = []
+ descr = data.dtype.descr
+
+ for t in descr:
+ if not isinstance(t[1], str):
+ raise ValueError("nested records not supported")
+
+ if not t[0]:
+ raise ValueError("field with empty name")
+
+ if len(t) != 2 or t[1][1] == "O":
+ # non-scalar field, which corresponds to a list
+ # property in PLY.
+
+ if t[1][1] == "O":
+ if len(t) != 2:
+ raise ValueError("non-scalar object fields not " "supported")
+
+ len_str = _data_type_reverse[len_types.get(t[0], "u1")]
+ if t[1][1] == "O":
+ val_type = val_types.get(t[0], "i4")
+ val_str = _lookup_type(val_type)
+ else:
+ val_str = _lookup_type(t[1][1:])
+
+ prop = PlyListProperty(t[0], len_str, val_str)
+ else:
+ val_str = _lookup_type(t[1][1:])
+ prop = PlyProperty(t[0], val_str)
+
+ properties.append(prop)
+
+ elt = PlyElement(name, properties, count, comments)
+ elt.data = data
+
+ return elt
+
+ def _read(self, stream, text, byte_order):
+ """
+ Read the actual data from a PLY file.
+
+ """
+ if text:
+ self._read_txt(stream)
+ else:
+ if self._have_list:
+ # There are list properties, so a simple load is
+ # impossible.
+ self._read_bin(stream, byte_order)
+ else:
+ # There are no list properties, so loading the data is
+ # much more straightforward.
+ self._data = _np.fromfile(stream, self.dtype(byte_order), self.count)
+
+ if len(self._data) < self.count:
+ k = len(self._data)
+ del self._data
+ raise PlyParseError("early end-of-file", self, k)
+
+ self._check_sanity()
+
+ def _write(self, stream, text, byte_order):
+ """
+ Write the data to a PLY file.
+
+ """
+ if text:
+ self._write_txt(stream)
+ else:
+ if self._have_list:
+ # There are list properties, so serialization is
+ # slightly complicated.
+ self._write_bin(stream, byte_order)
+ else:
+ # no list properties, so serialization is
+ # straightforward.
+ self.data.astype(self.dtype(byte_order), copy=False).tofile(stream)
+
+ def _read_txt(self, stream):
+ """
+ Load a PLY element from an ASCII-format PLY file. The element
+ may contain list properties.
+
+ """
+ self._data = _np.empty(self.count, dtype=self.dtype())
+
+ k = 0
+ for line in _islice(iter(stream.readline, b""), self.count):
+ fields = iter(line.strip().split())
+ for prop in self.properties:
+ try:
+ self._data[prop.name][k] = prop._from_fields(fields)
+ except StopIteration:
+ raise PlyParseError("early end-of-line", self, k, prop)
+ except ValueError:
+ raise PlyParseError("malformed input", self, k, prop)
+ try:
+ next(fields)
+ except StopIteration:
+ pass
+ else:
+ raise PlyParseError("expected end-of-line", self, k)
+ k += 1
+
+ if k < self.count:
+ del self._data
+ raise PlyParseError("early end-of-file", self, k)
+
+ def _write_txt(self, stream):
+ """
+ Save a PLY element to an ASCII-format PLY file. The element may
+ contain list properties.
+
+ """
+ for rec in self.data:
+ fields = []
+ for prop in self.properties:
+ fields.extend(prop._to_fields(rec[prop.name]))
+
+ _np.savetxt(stream, [fields], "%.18g", newline="\r\n")
+
+ def _read_bin(self, stream, byte_order):
+ """
+ Load a PLY element from a binary PLY file. The element may
+ contain list properties.
+
+ """
+ self._data = _np.empty(self.count, dtype=self.dtype(byte_order))
+
+ for k in _range(self.count):
+ for prop in self.properties:
+ try:
+ self._data[prop.name][k] = prop._read_bin(stream, byte_order)
+ except StopIteration:
+ raise PlyParseError("early end-of-file", self, k, prop)
+
+ def _write_bin(self, stream, byte_order):
+ """
+ Save a PLY element to a binary PLY file. The element may
+ contain list properties.
+
+ """
+ for rec in self.data:
+ for prop in self.properties:
+ prop._write_bin(rec[prop.name], stream, byte_order)
+
+ @property
+ def header(self):
+ """
+ Format this element's metadata as it would appear in a PLY
+ header.
+
+ """
+ lines = ["element %s %d" % (self.name, self.count)]
+
+ # Some information is lost here, since all comments are placed
+ # between the 'element' line and the first property definition.
+ for c in self.comments:
+ lines.append("comment " + c)
+
+ lines.extend(list(map(str, self.properties)))
+
+ return "\r\n".join(lines)
+
+ def __getitem__(self, key):
+ return self.data[key]
+
+ def __setitem__(self, key, value):
+ self.data[key] = value
+
+ def __str__(self):
+ return self.header
+
+ def __repr__(self):
+ return "PlyElement(%r, %r, count=%d, comments=%r)" % (
+ self.name,
+ self.properties,
+ self.count,
+ self.comments,
+ )
+
+
+class PlyProperty(object):
+ """
+ PLY property description. This class is pure metadata; the data
+ itself is contained in PlyElement instances.
+
+ """
+
+ def __init__(self, name, val_dtype):
+ self._name = str(name)
+ self._check_name()
+ self.val_dtype = val_dtype
+
+ def _get_val_dtype(self):
+ return self._val_dtype
+
+ def _set_val_dtype(self, val_dtype):
+ self._val_dtype = _data_types[_lookup_type(val_dtype)]
+
+ val_dtype = property(_get_val_dtype, _set_val_dtype)
+
+ @property
+ def name(self):
+ return self._name
+
+ def _check_name(self):
+ if any(c.isspace() for c in self._name):
+ msg = "Error: property name %r contains spaces" % self._name
+ raise RuntimeError(msg)
+
+ @staticmethod
+ def _parse_one(line):
+ assert line[0] == "property"
+
+ if line[1] == "list":
+ if len(line) > 5:
+ raise PlyParseError("too many fields after " "'property list'")
+ if len(line) < 5:
+ raise PlyParseError("too few fields after " "'property list'")
+
+ return PlyListProperty(line[4], line[2], line[3])
+
+ else:
+ if len(line) > 3:
+ raise PlyParseError("too many fields after " "'property'")
+ if len(line) < 3:
+ raise PlyParseError("too few fields after " "'property'")
+
+ return PlyProperty(line[2], line[1])
+
+ def dtype(self, byte_order="="):
+ """
+ Return the numpy dtype description for this property (as a tuple
+ of strings).
+
+ """
+ return byte_order + self.val_dtype
+
+ def _from_fields(self, fields):
+ """
+ Parse from generator. Raise StopIteration if the property could
+ not be read.
+
+ """
+ return _np.dtype(self.dtype()).type(next(fields))
+
+ def _to_fields(self, data):
+ """
+ Return generator over one item.
+
+ """
+ yield _np.dtype(self.dtype()).type(data)
+
+ def _read_bin(self, stream, byte_order):
+ """
+ Read data from a binary stream. Raise StopIteration if the
+ property could not be read.
+
+ """
+ try:
+ return _np.fromfile(stream, self.dtype(byte_order), 1)[0]
+ except IndexError:
+ raise StopIteration
+
+ def _write_bin(self, data, stream, byte_order):
+ """
+ Write data to a binary stream.
+
+ """
+ _np.dtype(self.dtype(byte_order)).type(data).tofile(stream)
+
+ def __str__(self):
+ val_str = _data_type_reverse[self.val_dtype]
+ return "property %s %s" % (val_str, self.name)
+
+ def __repr__(self):
+ return "PlyProperty(%r, %r)" % (self.name, _lookup_type(self.val_dtype))
+
+
+class PlyListProperty(PlyProperty):
+ """
+ PLY list property description.
+
+ """
+
+ def __init__(self, name, len_dtype, val_dtype):
+ PlyProperty.__init__(self, name, val_dtype)
+
+ self.len_dtype = len_dtype
+
+ def _get_len_dtype(self):
+ return self._len_dtype
+
+ def _set_len_dtype(self, len_dtype):
+ self._len_dtype = _data_types[_lookup_type(len_dtype)]
+
+ len_dtype = property(_get_len_dtype, _set_len_dtype)
+
+ def dtype(self, byte_order="="):
+ """
+ List properties always have a numpy dtype of "object".
+
+ """
+ return "|O"
+
+ def list_dtype(self, byte_order="="):
+ """
+ Return the pair (len_dtype, val_dtype) (both numpy-friendly
+ strings).
+
+ """
+ return (byte_order + self.len_dtype, byte_order + self.val_dtype)
+
+ def _from_fields(self, fields):
+ (len_t, val_t) = self.list_dtype()
+
+ n = int(_np.dtype(len_t).type(next(fields)))
+
+ data = _np.loadtxt(list(_islice(fields, n)), val_t, ndmin=1)
+ if len(data) < n:
+ raise StopIteration
+
+ return data
+
+ def _to_fields(self, data):
+ """
+ Return generator over the (numerical) PLY representation of the
+ list data (length followed by actual data).
+
+ """
+ (len_t, val_t) = self.list_dtype()
+
+ data = _np.asarray(data, dtype=val_t).ravel()
+
+ yield _np.dtype(len_t).type(data.size)
+ for x in data:
+ yield x
+
+ def _read_bin(self, stream, byte_order):
+ (len_t, val_t) = self.list_dtype(byte_order)
+
+ try:
+ n = _np.fromfile(stream, len_t, 1)[0]
+ except IndexError:
+ raise StopIteration
+
+ data = _np.fromfile(stream, val_t, n)
+ if len(data) < n:
+ raise StopIteration
+
+ return data
+
+ def _write_bin(self, data, stream, byte_order):
+ """
+ Write data to a binary stream.
+
+ """
+ (len_t, val_t) = self.list_dtype(byte_order)
+
+ data = _np.asarray(data, dtype=val_t).ravel()
+
+ _np.array(data.size, dtype=len_t).tofile(stream)
+ data.tofile(stream)
+
+ def __str__(self):
+ len_str = _data_type_reverse[self.len_dtype]
+ val_str = _data_type_reverse[self.val_dtype]
+ return "property list %s %s %s" % (len_str, val_str, self.name)
+
+ def __repr__(self):
+ return "PlyListProperty(%r, %r, %r)" % (
+ self.name,
+ _lookup_type(self.len_dtype),
+ _lookup_type(self.val_dtype),
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cbff78d9453bac7efe8359448dabcd6edb60452
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py
@@ -0,0 +1,98 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import glob, os
+import numpy as np
+import cv2
+import torch
+
+
+def extractor(input_path, output_path):
+ if not os.path.exists(output_path):
+ os.mkdir(output_path)
+
+ # Load Depth Camera Intrinsic
+ depth_intrinsic = np.loadtxt(input_path + "/intrinsic/intrinsic_depth.txt")
+ print("Depth intrinsic: ")
+ print(depth_intrinsic)
+
+ # Compute Camrea Distance (just for demo, so you can choose the camera distance in frame sampling)
+ poses = sorted(
+ glob.glob(input_path + "/pose/*.txt"),
+ key=lambda a: int(os.path.basename(a).split(".")[0]),
+ )
+ depths = sorted(
+ glob.glob(input_path + "/depth/*.png"),
+ key=lambda a: int(os.path.basename(a).split(".")[0]),
+ )
+ colors = sorted(
+ glob.glob(input_path + "/color/*.png"),
+ key=lambda a: int(os.path.basename(a).split(".")[0]),
+ )
+
+ # # Get Aligned Point Clouds.
+ for ind, (pose, depth, color) in enumerate(zip(poses, depths, colors)):
+ name = os.path.basename(pose).split(".")[0]
+
+ if os.path.exists(output_path + "/{}.npz".format(name)):
+ continue
+
+ try:
+ print("=" * 50, ": {}".format(pose))
+ depth_img = cv2.imread(depth, -1) # read 16bit grayscale image
+ mask = depth_img != 0
+ color_image = cv2.imread(color)
+ color_image = cv2.resize(color_image, (640, 480))
+ color_image = np.reshape(color_image[mask], [-1, 3])
+ colors = np.zeros_like(color_image)
+ colors[:, 0] = color_image[:, 2]
+ colors[:, 1] = color_image[:, 1]
+ colors[:, 2] = color_image[:, 0]
+
+ pose = np.loadtxt(poses[ind])
+ print("Camera pose: ")
+ print(pose)
+
+ depth_shift = 1000.0
+ x, y = np.meshgrid(
+ np.linspace(0, depth_img.shape[1] - 1, depth_img.shape[1]),
+ np.linspace(0, depth_img.shape[0] - 1, depth_img.shape[0]),
+ )
+ uv_depth = np.zeros((depth_img.shape[0], depth_img.shape[1], 3))
+ uv_depth[:, :, 0] = x
+ uv_depth[:, :, 1] = y
+ uv_depth[:, :, 2] = depth_img / depth_shift
+ uv_depth = np.reshape(uv_depth, [-1, 3])
+ uv_depth = uv_depth[np.where(uv_depth[:, 2] != 0), :].squeeze()
+
+ intrinsic_inv = np.linalg.inv(depth_intrinsic)
+ fx = depth_intrinsic[0, 0]
+ fy = depth_intrinsic[1, 1]
+ cx = depth_intrinsic[0, 2]
+ cy = depth_intrinsic[1, 2]
+ bx = depth_intrinsic[0, 3]
+ by = depth_intrinsic[1, 3]
+ point_list = []
+ n = uv_depth.shape[0]
+ points = np.ones((n, 4))
+ X = (uv_depth[:, 0] - cx) * uv_depth[:, 2] / fx + bx
+ Y = (uv_depth[:, 1] - cy) * uv_depth[:, 2] / fy + by
+ points[:, 0] = X
+ points[:, 1] = Y
+ points[:, 2] = uv_depth[:, 2]
+ points_world = np.dot(points, np.transpose(pose))
+ print(points_world.shape)
+
+ pcd = dict(coord=points_world[:, :3], color=colors)
+ # pcd_save = np.zeros((points_world.shape[0], 7))
+ # pcd_save[:, :3] = points_world[:, :3]
+ # pcd_save[:, 3:6] = colors
+
+ # print('Saving npz file...')
+ # np.savez(output_path + '/{}.npz'.format(name), pcd=pcd_save)
+ torch.save(pcd, output_path + "/{}.pth".format(name))
+ except:
+ continue
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..818c369fa60b841736012950895c6dbaebdbef86
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py
@@ -0,0 +1,51 @@
+import os
+import argparse
+import glob
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from reader import reader
+from point_cloud_extractor import extractor
+from compute_full_overlapping import compute_full_overlapping
+
+
+frame_skip = 25
+
+
+def parse_sens(sens_dir, output_dir):
+ scene_id = os.path.basename(os.path.dirname(sens_dir))
+ print(f"Parsing sens data{sens_dir}")
+ reader(
+ sens_dir,
+ os.path.join(output_dir, scene_id),
+ frame_skip,
+ export_color_images=True,
+ export_depth_images=True,
+ export_poses=True,
+ export_intrinsics=True,
+ )
+ extractor(
+ os.path.join(output_dir, scene_id), os.path.join(output_dir, scene_id, "pcd")
+ )
+ compute_full_overlapping(output_dir, scene_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ opt = parser.parse_args()
+ sens_list = sorted(glob.glob(os.path.join(opt.dataset_root, "scans/scene*/*.sens")))
+ # Preprocess data.
+ pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+ # pool = ProcessPoolExecutor(max_workers=1)
+ print("Processing scenes...")
+ _ = list(pool.map(parse_sens, sens_list, repeat(opt.output_root)))
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..d21aa0ce88006f34775edc9d9aaf4e750d523197
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py
@@ -0,0 +1,33 @@
+import argparse
+import os, sys
+
+from SensorData import SensorData
+
+
+def reader(
+ filename,
+ output_path,
+ frame_skip,
+ export_color_images=False,
+ export_depth_images=False,
+ export_poses=False,
+ export_intrinsics=False,
+):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+
+ # load the data
+ print("loading %s..." % filename)
+ sd = SensorData(filename)
+ if export_depth_images:
+ sd.export_depth_images(
+ os.path.join(output_path, "depth"), frame_skip=frame_skip
+ )
+ if export_color_images:
+ sd.export_color_images(
+ os.path.join(output_path, "color"), frame_skip=frame_skip
+ )
+ if export_poses:
+ sd.export_poses(os.path.join(output_path, "pose"), frame_skip=frame_skip)
+ if export_intrinsics:
+ sd.export_intrinsics(os.path.join(output_path, "intrinsic"))
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad820029016ff16c53ab0ad872ede9449add6a7b
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py
@@ -0,0 +1,252 @@
+"""
+Preprocessing Script for ScanNet++
+modified from official preprocess code.
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import open3d as o3d
+import multiprocessing as mp
+from collections import OrderedDict
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+
+def parse_scene(
+ name,
+ split,
+ dataset_root,
+ output_root,
+ label_mapping,
+ class2idx,
+ ignore_index=-1,
+):
+ print(f"Parsing scene {name} in {split} split")
+ dataset_root = Path(dataset_root)
+ output_root = Path(output_root)
+ scene_path = dataset_root / "data" / name / "scans"
+ mesh_path = scene_path / "mesh_aligned_0.05.ply"
+ segs_path = scene_path / "segments.json"
+ anno_path = scene_path / "segments_anno.json"
+
+ # load mesh vertices and colors
+ mesh = o3d.io.read_triangle_mesh(str(mesh_path))
+
+ # extract mesh information
+ mesh.compute_vertex_normals(normalized=True)
+ coord = np.array(mesh.vertices).astype(np.float32)
+ color = (np.array(mesh.vertex_colors) * 255).astype(np.uint8)
+ normal = np.array(mesh.vertex_normals).astype(np.float32)
+
+ save_path = output_root / split / name
+ save_path.mkdir(parents=True, exist_ok=True)
+ np.save(save_path / "coord.npy", coord)
+ np.save(save_path / "color.npy", color)
+ np.save(save_path / "normal.npy", normal)
+
+ if split == "test":
+ return
+
+ # get label on vertices
+ # load segments = vertices per segment ID
+ with open(segs_path) as f:
+ segments = json.load(f)
+ # load anno = (instance, groups of segments)
+ with open(anno_path) as f:
+ anno = json.load(f)
+ seg_indices = np.array(segments["segIndices"], dtype=np.uint32)
+ num_vertices = len(seg_indices)
+ assert num_vertices == len(coord)
+ semantic_gt = np.ones((num_vertices, 3), dtype=np.int16) * ignore_index
+ instance_gt = np.ones((num_vertices, 3), dtype=np.int16) * ignore_index
+
+ # number of labels are used per vertex. initially 0
+ # increment each time a new label is added
+ instance_size = np.ones((num_vertices, 3), dtype=np.int16) * np.inf
+
+ # keep track of the size of the instance (#vertices) assigned to each vertex
+ # later, keep the label of the smallest instance for major label of vertices
+ # store inf initially so that we can pick the smallest instance
+ labels_used = np.zeros(num_vertices, dtype=np.int16)
+
+ for idx, instance in enumerate(anno["segGroups"]):
+ label = instance["label"]
+ instance["label_orig"] = label
+ # remap label
+ instance["label"] = label_mapping.get(label, None)
+ instance["label_index"] = class2idx.get(label, ignore_index)
+
+ if instance["label_index"] == ignore_index:
+ continue
+ # get all the vertices with segment index in this instance
+ # and max number of labels not yet applied
+ mask = np.isin(seg_indices, instance["segments"]) & (labels_used < 3)
+ size = mask.sum()
+ if size == 0:
+ continue
+
+ # get the position to add the label - 0, 1, 2
+ label_position = labels_used[mask]
+ semantic_gt[mask, label_position] = instance["label_index"]
+ # store all valid instance (include ignored instance)
+ instance_gt[mask, label_position] = instance["objectId"]
+ instance_size[mask, label_position] = size
+ labels_used[mask] += 1
+
+ # major label is the label of smallest instance for each vertex
+ # use major label for single class segmentation
+ # shift major label to the first column
+ mask = labels_used > 1
+ if mask.sum() > 0:
+ major_label_position = np.argmin(instance_size[mask], axis=1)
+
+ major_semantic_label = semantic_gt[mask, major_label_position]
+ semantic_gt[mask, major_label_position] = semantic_gt[:, 0][mask]
+ semantic_gt[:, 0][mask] = major_semantic_label
+
+ major_instance_label = instance_gt[mask, major_label_position]
+ instance_gt[mask, major_label_position] = instance_gt[:, 0][mask]
+ instance_gt[:, 0][mask] = major_instance_label
+
+ np.save(save_path / "segment.npy", semantic_gt)
+ np.save(save_path / "instance.npy", instance_gt)
+
+
+def filter_map_classes(mapping, count_thresh, count_type, mapping_type):
+ mapping = mapping[mapping[count_type] >= count_thresh]
+ if mapping_type == "semantic":
+ map_key = "semantic_map_to"
+ elif mapping_type == "instance":
+ map_key = "instance_map_to"
+ else:
+ raise NotImplementedError
+ # create a dict with classes to be mapped
+ # classes that don't have mapping are entered as x->x
+ # otherwise x->y
+ map_dict = OrderedDict()
+
+ for i in range(mapping.shape[0]):
+ row = mapping.iloc[i]
+ class_name = row["class"]
+ map_target = row[map_key]
+
+ # map to None or some other label -> don't add this class to the label list
+ try:
+ if len(map_target) > 0:
+ # map to None -> don't use this class
+ if map_target == "None":
+ pass
+ else:
+ # map to something else -> use this class
+ map_dict[class_name] = map_target
+ except TypeError:
+ # nan values -> no mapping, keep label as is
+ if class_name not in map_dict:
+ map_dict[class_name] = class_name
+
+ return map_dict
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet++ dataset containing data/metadata/splits.",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val/test folders will be located.",
+ )
+ parser.add_argument(
+ "--ignore_index",
+ default=-1,
+ type=int,
+ help="Default ignore index.",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ config = parser.parse_args()
+
+ print("Loading meta data...")
+ config.dataset_root = Path(config.dataset_root)
+ config.output_root = Path(config.output_root)
+
+ train_list = np.loadtxt(
+ config.dataset_root / "splits" / "nvs_sem_train.txt",
+ dtype=str,
+ )
+ print("Num samples in training split:", len(train_list))
+
+ val_list = np.loadtxt(
+ config.dataset_root / "splits" / "nvs_sem_val.txt",
+ dtype=str,
+ )
+ print("Num samples in validation split:", len(val_list))
+
+ test_list = np.loadtxt(
+ config.dataset_root / "splits" / "sem_test.txt",
+ dtype=str,
+ )
+ print("Num samples in testing split:", len(test_list))
+
+ data_list = np.concatenate([train_list, val_list, test_list])
+ split_list = np.concatenate(
+ [
+ np.full_like(train_list, "train"),
+ np.full_like(val_list, "val"),
+ np.full_like(test_list, "test"),
+ ]
+ )
+
+ # Parsing label information and mapping
+ segment_class_names = np.loadtxt(
+ config.dataset_root / "metadata" / "semantic_benchmark" / "top100.txt",
+ dtype=str,
+ delimiter=".", # dummy delimiter to replace " "
+ )
+ print("Num classes in segment class list:", len(segment_class_names))
+
+ instance_class_names = np.loadtxt(
+ config.dataset_root / "metadata" / "semantic_benchmark" / "top100_instance.txt",
+ dtype=str,
+ delimiter=".", # dummy delimiter to replace " "
+ )
+ print("Num classes in instance class list:", len(instance_class_names))
+
+ label_mapping = pd.read_csv(
+ config.dataset_root / "metadata" / "semantic_benchmark" / "map_benchmark.csv"
+ )
+ label_mapping = filter_map_classes(
+ label_mapping, count_thresh=0, count_type="count", mapping_type="semantic"
+ )
+ class2idx = {
+ class_name: idx for (idx, class_name) in enumerate(segment_class_names)
+ }
+
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ parse_scene,
+ data_list,
+ split_list,
+ repeat(config.dataset_root),
+ repeat(config.output_root),
+ repeat(label_mapping),
+ repeat(class2idx),
+ repeat(config.ignore_index),
+ )
+ )
+ pool.shutdown()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..6924dc9abf3a3c253ee80dd3b3d85454e3df35d2
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py
@@ -0,0 +1,420 @@
+"""
+Preprocessing Script for Structured3D
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import io
+import os
+import PIL
+from PIL import Image
+import cv2
+import zipfile
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+
+VALID_CLASS_IDS_25 = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 11,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 22,
+ 24,
+ 25,
+ 32,
+ 34,
+ 35,
+ 38,
+ 39,
+ 40,
+)
+CLASS_LABELS_25 = (
+ "wall",
+ "floor",
+ "cabinet",
+ "bed",
+ "chair",
+ "sofa",
+ "table",
+ "door",
+ "window",
+ "picture",
+ "desk",
+ "shelves",
+ "curtain",
+ "dresser",
+ "pillow",
+ "mirror",
+ "ceiling",
+ "refrigerator",
+ "television",
+ "nightstand",
+ "sink",
+ "lamp",
+ "otherstructure",
+ "otherfurniture",
+ "otherprop",
+)
+
+
+def normal_from_cross_product(points_2d: np.ndarray) -> np.ndarray:
+ xyz_points_pad = np.pad(points_2d, ((0, 1), (0, 1), (0, 0)), mode="symmetric")
+ xyz_points_ver = (xyz_points_pad[:, :-1, :] - xyz_points_pad[:, 1:, :])[:-1, :, :]
+ xyz_points_hor = (xyz_points_pad[:-1, :, :] - xyz_points_pad[1:, :, :])[:, :-1, :]
+ xyz_normal = np.cross(xyz_points_hor, xyz_points_ver)
+ xyz_dist = np.linalg.norm(xyz_normal, axis=-1, keepdims=True)
+ xyz_normal = np.divide(
+ xyz_normal, xyz_dist, out=np.zeros_like(xyz_normal), where=xyz_dist != 0
+ )
+ return xyz_normal
+
+
+class Structured3DReader:
+ def __init__(self, files):
+ super().__init__()
+ if isinstance(files, str):
+ files = [files]
+ self.readers = [zipfile.ZipFile(f, "r") for f in files]
+ self.names_mapper = dict()
+ for idx, reader in enumerate(self.readers):
+ for name in reader.namelist():
+ self.names_mapper[name] = idx
+
+ def filelist(self):
+ return list(self.names_mapper.keys())
+
+ def listdir(self, dir_name):
+ dir_name = dir_name.lstrip(os.path.sep).rstrip(os.path.sep)
+ file_list = list(
+ np.unique(
+ [
+ f.replace(dir_name + os.path.sep, "", 1).split(os.path.sep)[0]
+ for f in self.filelist()
+ if f.startswith(dir_name + os.path.sep)
+ ]
+ )
+ )
+ if "" in file_list:
+ file_list.remove("")
+ return file_list
+
+ def read(self, file_name):
+ split = self.names_mapper[file_name]
+ return self.readers[split].read(file_name)
+
+ def read_camera(self, camera_path):
+ z2y_top_m = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=np.float32)
+ cam_extr = np.fromstring(self.read(camera_path), dtype=np.float32, sep=" ")
+ cam_t = np.matmul(z2y_top_m, cam_extr[:3] / 1000)
+ if cam_extr.shape[0] > 3:
+ cam_front, cam_up = cam_extr[3:6], cam_extr[6:9]
+ cam_n = np.cross(cam_front, cam_up)
+ cam_r = np.stack((cam_front, cam_up, cam_n), axis=1).astype(np.float32)
+ cam_r = np.matmul(z2y_top_m, cam_r)
+ cam_f = cam_extr[9:11]
+ else:
+ cam_r = np.eye(3, dtype=np.float32)
+ cam_f = None
+ return cam_r, cam_t, cam_f
+
+ def read_depth(self, depth_path):
+ depth = cv2.imdecode(
+ np.frombuffer(self.read(depth_path), np.uint8), cv2.IMREAD_UNCHANGED
+ )[..., np.newaxis]
+ depth[depth == 0] = 65535
+ return depth
+
+ def read_color(self, color_path):
+ color = cv2.imdecode(
+ np.frombuffer(self.read(color_path), np.uint8), cv2.IMREAD_UNCHANGED
+ )[..., :3][..., ::-1]
+ return color
+
+ def read_segment(self, segment_path):
+ segment = np.array(PIL.Image.open(io.BytesIO(self.read(segment_path))))[
+ ..., np.newaxis
+ ]
+ return segment
+
+
+def parse_scene(
+ scene,
+ dataset_root,
+ output_root,
+ ignore_index=-1,
+ grid_size=None,
+ fuse_prsp=True,
+ fuse_pano=True,
+ vis=False,
+):
+ assert fuse_prsp or fuse_pano
+ reader = Structured3DReader(
+ [
+ os.path.join(dataset_root, f)
+ for f in os.listdir(dataset_root)
+ if f.endswith(".zip")
+ ]
+ )
+ scene_id = int(os.path.basename(scene).split("_")[-1])
+ if scene_id < 3000:
+ split = "train"
+ elif 3000 <= scene_id < 3250:
+ split = "val"
+ else:
+ split = "test"
+
+ print(f"Processing: {scene} in {split}")
+ rooms = reader.listdir(os.path.join("Structured3D", scene, "2D_rendering"))
+ for room in rooms:
+ room_path = os.path.join("Structured3D", scene, "2D_rendering", room)
+ coord_list = list()
+ color_list = list()
+ normal_list = list()
+ segment_list = list()
+ if fuse_prsp:
+ prsp_path = os.path.join(room_path, "perspective", "full")
+ frames = reader.listdir(prsp_path)
+
+ for frame in frames:
+ try:
+ cam_r, cam_t, cam_f = reader.read_camera(
+ os.path.join(prsp_path, frame, "camera_pose.txt")
+ )
+ depth = reader.read_depth(
+ os.path.join(prsp_path, frame, "depth.png")
+ )
+ color = reader.read_color(
+ os.path.join(prsp_path, frame, "rgb_rawlight.png")
+ )
+ segment = reader.read_segment(
+ os.path.join(prsp_path, frame, "semantic.png")
+ )
+ except:
+ print(
+ f"Skipping {scene}_room{room}_frame{frame} perspective view due to loading error"
+ )
+ else:
+ fx, fy = cam_f
+ height, width = depth.shape[0], depth.shape[1]
+ pixel = np.transpose(np.indices((width, height)), (2, 1, 0))
+ pixel = pixel.reshape((-1, 2))
+ pixel = np.hstack((pixel, np.ones((pixel.shape[0], 1))))
+ k = np.diag([1.0, 1.0, 1.0])
+
+ k[0, 2] = width / 2
+ k[1, 2] = height / 2
+
+ k[0, 0] = k[0, 2] / np.tan(fx)
+ k[1, 1] = k[1, 2] / np.tan(fy)
+ coord = (
+ depth.reshape((-1, 1)) * (np.linalg.inv(k) @ pixel.T).T
+ ).reshape(height, width, 3)
+ coord = coord @ np.array([[0, 0, 1], [0, -1, 0], [1, 0, 0]])
+ normal = normal_from_cross_product(coord)
+
+ # Filtering invalid points
+ view_dist = np.maximum(
+ np.linalg.norm(coord, axis=-1, keepdims=True), float(10e-5)
+ )
+ cosine_dist = np.sum(
+ (coord * normal / view_dist), axis=-1, keepdims=True
+ )
+ cosine_dist = np.abs(cosine_dist)
+ mask = ((cosine_dist > 0.15) & (depth < 65535) & (segment > 0))[
+ ..., 0
+ ].reshape(-1)
+
+ coord = np.matmul(coord / 1000, cam_r.T) + cam_t
+ normal = normal_from_cross_product(coord)
+
+ if sum(mask) > 0:
+ coord_list.append(coord.reshape(-1, 3)[mask])
+ color_list.append(color.reshape(-1, 3)[mask])
+ normal_list.append(normal.reshape(-1, 3)[mask])
+ segment_list.append(segment.reshape(-1, 1)[mask])
+ else:
+ print(
+ f"Skipping {scene}_room{room}_frame{frame} perspective view due to all points are filtered out"
+ )
+
+ if fuse_pano:
+ pano_path = os.path.join(room_path, "panorama")
+ try:
+ _, cam_t, _ = reader.read_camera(
+ os.path.join(pano_path, "camera_xyz.txt")
+ )
+ depth = reader.read_depth(os.path.join(pano_path, "full", "depth.png"))
+ color = reader.read_color(
+ os.path.join(pano_path, "full", "rgb_rawlight.png")
+ )
+ segment = reader.read_segment(
+ os.path.join(pano_path, "full", "semantic.png")
+ )
+ except:
+ print(f"Skipping {scene}_room{room} panorama view due to loading error")
+ else:
+ p_h, p_w = depth.shape[:2]
+ p_a = np.arange(p_w, dtype=np.float32) / p_w * 2 * np.pi - np.pi
+ p_b = np.arange(p_h, dtype=np.float32) / p_h * np.pi * -1 + np.pi / 2
+ p_a = np.tile(p_a[None], [p_h, 1])[..., np.newaxis]
+ p_b = np.tile(p_b[:, None], [1, p_w])[..., np.newaxis]
+ p_a_sin, p_a_cos, p_b_sin, p_b_cos = (
+ np.sin(p_a),
+ np.cos(p_a),
+ np.sin(p_b),
+ np.cos(p_b),
+ )
+ x = depth * p_a_cos * p_b_cos
+ y = depth * p_b_sin
+ z = depth * p_a_sin * p_b_cos
+ coord = np.concatenate([x, y, z], axis=-1) / 1000
+ normal = normal_from_cross_product(coord)
+
+ # Filtering invalid points
+ view_dist = np.maximum(
+ np.linalg.norm(coord, axis=-1, keepdims=True), float(10e-5)
+ )
+ cosine_dist = np.sum(
+ (coord * normal / view_dist), axis=-1, keepdims=True
+ )
+ cosine_dist = np.abs(cosine_dist)
+ mask = ((cosine_dist > 0.15) & (depth < 65535) & (segment > 0))[
+ ..., 0
+ ].reshape(-1)
+ coord = coord + cam_t
+
+ if sum(mask) > 0:
+ coord_list.append(coord.reshape(-1, 3)[mask])
+ color_list.append(color.reshape(-1, 3)[mask])
+ normal_list.append(normal.reshape(-1, 3)[mask])
+ segment_list.append(segment.reshape(-1, 1)[mask])
+ else:
+ print(
+ f"Skipping {scene}_room{room} panorama view due to all points are filtered out"
+ )
+
+ if len(coord_list) > 0:
+ coord = np.concatenate(coord_list, axis=0)
+ coord = coord @ np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+ color = np.concatenate(color_list, axis=0)
+ normal = np.concatenate(normal_list, axis=0)
+ normal = normal @ np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+ segment = np.concatenate(segment_list, axis=0)
+ segment25 = np.ones_like(segment, dtype=np.int64) * ignore_index
+ for idx, value in enumerate(VALID_CLASS_IDS_25):
+ mask = np.all(segment == value, axis=-1)
+ segment25[mask] = idx
+
+ data_dict = dict(
+ coord=coord.astype(np.float32),
+ color=color.astype(np.uint8),
+ normal=normal.astype(np.float32),
+ segment=segment25.astype(np.int16),
+ )
+ # Grid sampling data
+ if grid_size is not None:
+ grid_coord = np.floor(coord / grid_size).astype(int)
+ _, idx = np.unique(grid_coord, axis=0, return_index=True)
+ coord = coord[idx]
+ for key in data_dict.keys():
+ data_dict[key] = data_dict[key][idx]
+
+ # Save data
+ save_path = os.path.join(
+ output_root, split, os.path.basename(scene), f"room_{room}"
+ )
+ os.makedirs(save_path, exist_ok=True)
+ for key in data_dict.keys():
+ np.save(os.path.join(save_path, f"{key}.npy"), data_dict[key])
+
+ if vis:
+ from pointcept.utils.visualization import save_point_cloud
+
+ os.makedirs("./vis", exist_ok=True)
+ save_point_cloud(
+ coord, color / 255, f"./vis/{scene}_room{room}_color.ply"
+ )
+ save_point_cloud(
+ coord, (normal + 1) / 2, f"./vis/{scene}_room{room}_normal.ply"
+ )
+ else:
+ print(f"Skipping {scene}_room{room} due to no valid points")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders.",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located.",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ parser.add_argument(
+ "--grid_size", default=None, type=float, help="Grid size for grid sampling."
+ )
+ parser.add_argument("--ignore_index", default=-1, type=float, help="Ignore index.")
+ parser.add_argument(
+ "--fuse_prsp", action="store_true", help="Whether fuse perspective view."
+ )
+ parser.add_argument(
+ "--fuse_pano", action="store_true", help="Whether fuse panorama view."
+ )
+ config = parser.parse_args()
+
+ reader = Structured3DReader(
+ [
+ os.path.join(config.dataset_root, f)
+ for f in os.listdir(config.dataset_root)
+ if f.endswith(".zip")
+ ]
+ )
+
+ scenes_list = reader.listdir("Structured3D")
+ scenes_list = sorted(scenes_list)
+ os.makedirs(os.path.join(config.output_root, "train"), exist_ok=True)
+ os.makedirs(os.path.join(config.output_root, "val"), exist_ok=True)
+ os.makedirs(os.path.join(config.output_root, "test"), exist_ok=True)
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ parse_scene,
+ scenes_list,
+ repeat(config.dataset_root),
+ repeat(config.output_root),
+ repeat(config.ignore_index),
+ repeat(config.grid_size),
+ repeat(config.fuse_prsp),
+ repeat(config.fuse_pano),
+ )
+ )
+ pool.shutdown()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/3d_semseg_test_set_frames.txt b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/3d_semseg_test_set_frames.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e25dc615331aaff261c25bfc3e6b930fccf880ac
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/3d_semseg_test_set_frames.txt
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diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py
new file mode 100644
index 0000000000000000000000000000000000000000..33a309fc2a03c52b63a43863ac40632ad5daa0a6
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py
@@ -0,0 +1,387 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+import argparse
+import numpy as np
+import tensorflow.compat.v1 as tf
+from pathlib import Path
+from waymo_open_dataset.utils import frame_utils
+from waymo_open_dataset.utils import transform_utils
+from waymo_open_dataset.utils import range_image_utils
+from waymo_open_dataset import dataset_pb2 as open_dataset
+import glob
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+
+def create_lidar(frame):
+ """Parse and save the lidar data in psd format.
+ Args:
+ frame (:obj:`Frame`): Open dataset frame proto.
+ """
+ (
+ range_images,
+ camera_projections,
+ segmentation_labels,
+ range_image_top_pose,
+ ) = frame_utils.parse_range_image_and_camera_projection(frame)
+
+ points, cp_points, valid_masks = convert_range_image_to_point_cloud(
+ frame,
+ range_images,
+ camera_projections,
+ range_image_top_pose,
+ keep_polar_features=True,
+ )
+ points_ri2, cp_points_ri2, valid_masks_ri2 = convert_range_image_to_point_cloud(
+ frame,
+ range_images,
+ camera_projections,
+ range_image_top_pose,
+ ri_index=1,
+ keep_polar_features=True,
+ )
+
+ # 3d points in vehicle frame.
+ points_all = np.concatenate(points, axis=0)
+ points_all_ri2 = np.concatenate(points_ri2, axis=0)
+ # point labels.
+
+ points_all = np.concatenate([points_all, points_all_ri2], axis=0)
+
+ velodyne = np.c_[points_all[:, 3:6], points_all[:, 1]]
+ velodyne = velodyne.reshape((velodyne.shape[0] * velodyne.shape[1]))
+
+ valid_masks = [valid_masks, valid_masks_ri2]
+ return velodyne, valid_masks
+
+
+def create_label(frame):
+ (
+ range_images,
+ camera_projections,
+ segmentation_labels,
+ range_image_top_pose,
+ ) = frame_utils.parse_range_image_and_camera_projection(frame)
+
+ point_labels = convert_range_image_to_point_cloud_labels(
+ frame, range_images, segmentation_labels
+ )
+ point_labels_ri2 = convert_range_image_to_point_cloud_labels(
+ frame, range_images, segmentation_labels, ri_index=1
+ )
+
+ # point labels.
+ point_labels_all = np.concatenate(point_labels, axis=0)
+ point_labels_all_ri2 = np.concatenate(point_labels_ri2, axis=0)
+ point_labels_all = np.concatenate([point_labels_all, point_labels_all_ri2], axis=0)
+
+ labels = point_labels_all
+ return labels
+
+
+def convert_range_image_to_cartesian(
+ frame, range_images, range_image_top_pose, ri_index=0, keep_polar_features=False
+):
+ """Convert range images from polar coordinates to Cartesian coordinates.
+
+ Args:
+ frame: open dataset frame
+ range_images: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ range_image_top_pose: range image pixel pose for top lidar.
+ ri_index: 0 for the first return, 1 for the second return.
+ keep_polar_features: If true, keep the features from the polar range image
+ (i.e. range, intensity, and elongation) as the first features in the
+ output range image.
+
+ Returns:
+ dict of {laser_name, (H, W, D)} range images in Cartesian coordinates. D
+ will be 3 if keep_polar_features is False (x, y, z) and 6 if
+ keep_polar_features is True (range, intensity, elongation, x, y, z).
+ """
+ cartesian_range_images = {}
+ frame_pose = tf.convert_to_tensor(
+ value=np.reshape(np.array(frame.pose.transform), [4, 4])
+ )
+
+ # [H, W, 6]
+ range_image_top_pose_tensor = tf.reshape(
+ tf.convert_to_tensor(value=range_image_top_pose.data),
+ range_image_top_pose.shape.dims,
+ )
+ # [H, W, 3, 3]
+ range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(
+ range_image_top_pose_tensor[..., 0],
+ range_image_top_pose_tensor[..., 1],
+ range_image_top_pose_tensor[..., 2],
+ )
+ range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]
+ range_image_top_pose_tensor = transform_utils.get_transform(
+ range_image_top_pose_tensor_rotation, range_image_top_pose_tensor_translation
+ )
+
+ for c in frame.context.laser_calibrations:
+ range_image = range_images[c.name][ri_index]
+ if len(c.beam_inclinations) == 0: # pylint: disable=g-explicit-length-test
+ beam_inclinations = range_image_utils.compute_inclination(
+ tf.constant([c.beam_inclination_min, c.beam_inclination_max]),
+ height=range_image.shape.dims[0],
+ )
+ else:
+ beam_inclinations = tf.constant(c.beam_inclinations)
+
+ beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])
+ extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])
+
+ range_image_tensor = tf.reshape(
+ tf.convert_to_tensor(value=range_image.data), range_image.shape.dims
+ )
+ pixel_pose_local = None
+ frame_pose_local = None
+ if c.name == open_dataset.LaserName.TOP:
+ pixel_pose_local = range_image_top_pose_tensor
+ pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)
+ frame_pose_local = tf.expand_dims(frame_pose, axis=0)
+ range_image_cartesian = range_image_utils.extract_point_cloud_from_range_image(
+ tf.expand_dims(range_image_tensor[..., 0], axis=0),
+ tf.expand_dims(extrinsic, axis=0),
+ tf.expand_dims(tf.convert_to_tensor(value=beam_inclinations), axis=0),
+ pixel_pose=pixel_pose_local,
+ frame_pose=frame_pose_local,
+ )
+
+ range_image_cartesian = tf.squeeze(range_image_cartesian, axis=0)
+
+ if keep_polar_features:
+ # If we want to keep the polar coordinate features of range, intensity,
+ # and elongation, concatenate them to be the initial dimensions of the
+ # returned Cartesian range image.
+ range_image_cartesian = tf.concat(
+ [range_image_tensor[..., 0:3], range_image_cartesian], axis=-1
+ )
+
+ cartesian_range_images[c.name] = range_image_cartesian
+
+ return cartesian_range_images
+
+
+def convert_range_image_to_point_cloud(
+ frame,
+ range_images,
+ camera_projections,
+ range_image_top_pose,
+ ri_index=0,
+ keep_polar_features=False,
+):
+ """Convert range images to point cloud.
+
+ Args:
+ frame: open dataset frame
+ range_images: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ camera_projections: A dict of {laser_name,
+ [camera_projection_from_first_return,
+ camera_projection_from_second_return]}.
+ range_image_top_pose: range image pixel pose for top lidar.
+ ri_index: 0 for the first return, 1 for the second return.
+ keep_polar_features: If true, keep the features from the polar range image
+ (i.e. range, intensity, and elongation) as the first features in the
+ output range image.
+
+ Returns:
+ points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).
+ (NOTE: Will be {[N, 6]} if keep_polar_features is true.
+ cp_points: {[N, 6]} list of camera projections of length 5
+ (number of lidars).
+ """
+ calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+ points = []
+ cp_points = []
+ valid_masks = []
+
+ cartesian_range_images = convert_range_image_to_cartesian(
+ frame, range_images, range_image_top_pose, ri_index, keep_polar_features
+ )
+
+ for c in calibrations:
+ range_image = range_images[c.name][ri_index]
+ range_image_tensor = tf.reshape(
+ tf.convert_to_tensor(value=range_image.data), range_image.shape.dims
+ )
+ range_image_mask = range_image_tensor[..., 0] > 0
+
+ range_image_cartesian = cartesian_range_images[c.name]
+ points_tensor = tf.gather_nd(
+ range_image_cartesian, tf.compat.v1.where(range_image_mask)
+ )
+
+ cp = camera_projections[c.name][ri_index]
+ cp_tensor = tf.reshape(tf.convert_to_tensor(value=cp.data), cp.shape.dims)
+ cp_points_tensor = tf.gather_nd(cp_tensor, tf.compat.v1.where(range_image_mask))
+ points.append(points_tensor.numpy())
+ cp_points.append(cp_points_tensor.numpy())
+ valid_masks.append(range_image_mask.numpy())
+
+ return points, cp_points, valid_masks
+
+
+def convert_range_image_to_point_cloud_labels(
+ frame, range_images, segmentation_labels, ri_index=0
+):
+ """Convert segmentation labels from range images to point clouds.
+
+ Args:
+ frame: open dataset frame
+ range_images: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ segmentation_labels: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ ri_index: 0 for the first return, 1 for the second return.
+
+ Returns:
+ point_labels: {[N, 2]} list of 3d lidar points's segmentation labels. 0 for
+ points that are not labeled.
+ """
+ calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+ point_labels = []
+ for c in calibrations:
+ range_image = range_images[c.name][ri_index]
+ range_image_tensor = tf.reshape(
+ tf.convert_to_tensor(range_image.data), range_image.shape.dims
+ )
+ range_image_mask = range_image_tensor[..., 0] > 0
+
+ if c.name in segmentation_labels:
+ sl = segmentation_labels[c.name][ri_index]
+ sl_tensor = tf.reshape(tf.convert_to_tensor(sl.data), sl.shape.dims)
+ sl_points_tensor = tf.gather_nd(sl_tensor, tf.where(range_image_mask))
+ else:
+ num_valid_point = tf.math.reduce_sum(tf.cast(range_image_mask, tf.int32))
+ sl_points_tensor = tf.zeros([num_valid_point, 2], dtype=tf.int32)
+
+ point_labels.append(sl_points_tensor.numpy())
+ return point_labels
+
+
+def handle_process(file_path, output_root, test_frame_list):
+ file = os.path.basename(file_path)
+ split = os.path.basename(os.path.dirname(file_path))
+ print(f"Parsing {split}/{file}")
+ save_path = Path(output_root) / split / file.split(".")[0]
+
+ data_group = tf.data.TFRecordDataset(file_path, compression_type="")
+ for data in data_group:
+ frame = open_dataset.Frame()
+ frame.ParseFromString(bytearray(data.numpy()))
+ context_name = frame.context.name
+ timestamp = str(frame.timestamp_micros)
+
+ if split != "testing":
+ # for training and validation frame, extract labelled frame
+ if not frame.lasers[0].ri_return1.segmentation_label_compressed:
+ continue
+ else:
+ # for testing frame, extract frame in test_frame_list
+ if f"{context_name},{timestamp}" not in test_frame_list:
+ continue
+
+ os.makedirs(save_path / timestamp, exist_ok=True)
+
+ # extract frame pass above check
+ point_cloud, valid_masks = create_lidar(frame)
+ point_cloud = point_cloud.reshape(-1, 4)
+ coord = point_cloud[:, :3]
+ strength = np.tanh(point_cloud[:, -1].reshape([-1, 1]))
+ pose = np.array(frame.pose.transform, np.float32).reshape(4, 4)
+ mask = np.array(valid_masks, dtype=object)
+
+ np.save(save_path / timestamp / "coord.npy", coord)
+ np.save(save_path / timestamp / "strength.npy", strength)
+ np.save(save_path / timestamp / "pose.npy", pose)
+
+ # save mask for reverse prediction
+ if split != "training":
+ np.save(save_path / timestamp / "mask.npy", mask)
+
+ # save label
+ if split != "testing":
+ # ignore TYPE_UNDEFINED, ignore_index 0 -> -1
+ label = create_label(frame)[:, 1].reshape([-1]) - 1
+ np.save(save_path / timestamp / "segment.npy", label)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the Waymo dataset",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ parser.add_argument(
+ "--splits",
+ required=True,
+ nargs="+",
+ choices=["training", "validation", "testing"],
+ help="Splits need to process ([training, validation, testing]).",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ config = parser.parse_args()
+
+ # load file list
+ file_list = glob.glob(
+ os.path.join(os.path.abspath(config.dataset_root), "*", "*.tfrecord")
+ )
+ assert len(file_list) == 1150
+
+ # Create output directories
+ for split in config.splits:
+ os.makedirs(os.path.join(config.output_root, split), exist_ok=True)
+
+ file_list = [
+ file
+ for file in file_list
+ if os.path.basename(os.path.dirname(file)) in config.splits
+ ]
+
+ # Load test frame list
+ test_frame_file = os.path.join(
+ os.path.dirname(__file__), "3d_semseg_test_set_frames.txt"
+ )
+ test_frame_list = [x.rstrip() for x in (open(test_frame_file, "r").readlines())]
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ handle_process,
+ file_list,
+ repeat(config.output_root),
+ repeat(test_frame_list),
+ )
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/s3dis.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/s3dis.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cfc176380caaa42d35bbf517ae2f13bf5c86f90
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/s3dis.py
@@ -0,0 +1,18 @@
+"""
+S3DIS Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from .defaults import DefaultDataset
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class S3DISDataset(DefaultDataset):
+ def get_data_name(self, idx):
+ remain, room_name = os.path.split(self.data_list[idx % len(self.data_list)])
+ remain, area_name = os.path.split(remain)
+ return f"{area_name}-{room_name}"
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet.py
new file mode 100644
index 0000000000000000000000000000000000000000..5813c7650ce8336335ef84ed8459e59eac774497
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet.py
@@ -0,0 +1,290 @@
+"""
+ScanNet20 / ScanNet200 / ScanNet Data Efficient Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import glob
+import os
+from collections.abc import Sequence
+from copy import deepcopy
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from pointcept.utils.cache import shared_dict
+from pointcept.utils.logger import get_root_logger
+
+from .builder import DATASETS
+from .defaults import DefaultDatasetV2
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_20, VALID_CLASS_IDS_200
+from .transform import TRANSFORMS, Compose
+
+
+@DATASETS.register_module()
+class ScanNetDataset(Dataset):
+ class2id = np.array(VALID_CLASS_IDS_20)
+
+ def __init__(
+ self,
+ split="train",
+ data_root="data/scannet",
+ transform=None,
+ lr_file=None,
+ la_file=None,
+ ignore_index=-1,
+ test_mode=False,
+ test_cfg=None,
+ cache=False,
+ loop=1,
+ ):
+ super(ScanNetDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.cache = cache
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(
+ self.test_cfg.crop) if self.test_cfg.crop else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [
+ Compose(aug) for aug in self.test_cfg.aug_transform
+ ]
+
+ if lr_file:
+ self.data_list = [
+ os.path.join(data_root, "train", name + ".pth")
+ for name in np.loadtxt(lr_file, dtype=str)
+ ]
+ else:
+ self.data_list = self.get_data_list()
+ self.la = torch.load(la_file) if la_file else None
+ self.ignore_index = ignore_index
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in {} set.".format(
+ len(self.data_list), self.loop, split))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(
+ os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(
+ os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ if not self.cache:
+ data = torch.load(data_path)
+ else:
+ data_name = data_path.replace(os.path.dirname(self.data_root),
+ "").split(".")[0]
+ cache_name = "pointcept" + data_name.replace(os.path.sep, "-")
+ data = shared_dict(cache_name)
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ scene_id = data["scene_id"]
+ if "semantic_gt20" in data.keys():
+ segment = data["semantic_gt20"].reshape([-1])
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ if "instance_gt" in data.keys():
+ instance = data["instance_gt"].reshape([-1])
+ else:
+ instance = np.ones(coord.shape[0]) * -1
+ data_dict = dict(
+ coord=coord,
+ normal=normal,
+ color=color,
+ segment=segment,
+ instance=instance,
+ scene_id=scene_id,
+ )
+ if self.la:
+ sampled_index = self.la[self.get_data_name(idx)]
+ mask = np.ones_like(segment).astype(np.bool)
+ mask[sampled_index] = False
+ segment[mask] = self.ignore_index
+ data_dict["segment"] = segment
+ data_dict["sampled_index"] = sampled_index
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(
+ self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ segment = data_dict.pop("segment")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ input_dict_list = []
+ for data in data_dict_list:
+ data_part_list = self.test_voxelize(data)
+ for data_part in data_part_list:
+ if self.test_crop:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ input_dict_list += data_part
+
+ for i in range(len(input_dict_list)):
+ input_dict_list[i] = self.post_transform(input_dict_list[i])
+ data_dict = dict(fragment_list=input_dict_list,
+ segment=segment,
+ name=self.get_data_name(idx))
+ return data_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ScanNet200Dataset(ScanNetDataset):
+ class2id = np.array(VALID_CLASS_IDS_200)
+
+ def get_data(self, idx):
+ data = torch.load(self.data_list[idx % len(self.data_list)])
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ scene_id = data["scene_id"]
+ if "semantic_gt200" in data.keys():
+ segment = data["semantic_gt200"].reshape([-1])
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ if "instance_gt" in data.keys():
+ instance = data["instance_gt"].reshape([-1])
+ else:
+ instance = np.ones(coord.shape[0]) * -1
+ data_dict = dict(
+ coord=coord,
+ normal=normal,
+ color=color,
+ segment=segment,
+ instance=instance,
+ scene_id=scene_id,
+ )
+ if self.la:
+ sampled_index = self.la[self.get_data_name(idx)]
+ segment[sampled_index] = self.ignore_index
+ data_dict["segment"] = segment
+ data_dict["sampled_index"] = sampled_index
+ return data_dict
+
+
+@DATASETS.register_module()
+class ScanNetDatasetV2(DefaultDatasetV2):
+ VALID_ASSETS = [
+ "coord",
+ "color",
+ "normal",
+ "segment20",
+ "instance",
+ ]
+ class2id = np.array(VALID_CLASS_IDS_20)
+
+ def __init__(
+ self,
+ lr_file=None,
+ la_file=None,
+ **kwargs,
+ ):
+ self.lr = np.loadtxt(lr_file,
+ dtype=str) if lr_file is not None else None
+ self.la = torch.load(la_file) if la_file is not None else None
+ super().__init__(**kwargs)
+
+ def get_data_list(self):
+ if self.lr is None:
+ data_list = super().get_data_list()
+ else:
+ data_list = [
+ os.path.join(self.data_root, "train", name) for name in self.lr
+ ]
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ name = self.get_data_name(idx)
+ if self.cache:
+ cache_name = f"pointcept-{name}"
+ return shared_dict(cache_name)
+
+ data_dict = {}
+ assets = os.listdir(data_path)
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ if asset[:-4] not in self.VALID_ASSETS:
+ continue
+ data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+ data_dict["name"] = name
+ data_dict["coord"] = data_dict["coord"].astype(np.float32)
+ data_dict["color"] = data_dict["color"].astype(np.float32)
+ data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+ if "segment20" in data_dict.keys():
+ data_dict["segment"] = (data_dict.pop("segment20").reshape(
+ [-1]).astype(np.int32))
+ elif "segment200" in data_dict.keys():
+ data_dict["segment"] = (data_dict.pop("segment200").reshape(
+ [-1]).astype(np.int32))
+ else:
+ data_dict["segment"] = (
+ np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1)
+
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = (data_dict.pop("instance").reshape(
+ [-1]).astype(np.int32))
+ else:
+ data_dict["instance"] = (
+ np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1)
+ if self.la:
+ sampled_index = self.la[self.get_data_name(idx)]
+ mask = np.ones_like(data_dict["segment"], dtype=bool)
+ mask[sampled_index] = False
+ data_dict["segment"][mask] = self.ignore_index
+ data_dict["sampled_index"] = sampled_index
+ return data_dict
+
+
+@DATASETS.register_module()
+class ScanNet200DatasetV2(ScanNetDatasetV2):
+ VALID_ASSETS = [
+ "coord",
+ "color",
+ "normal",
+ "segment200",
+ "instance",
+ ]
+ class2id = np.array(VALID_CLASS_IDS_200)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet_pair.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet_pair.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7fdf199aa90b113bb4e8c7643e5549f62d0c51a
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet_pair.py
@@ -0,0 +1,89 @@
+"""
+ScanNet Pair Dataset (Frame-level contrastive view)
+
+Refer PointContrast
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+
+
+@DATASETS.register_module()
+class ScanNetPairDataset(Dataset):
+ def __init__(
+ self,
+ data_root="data/scannet_pair",
+ overlap_threshold=0.3,
+ view1_transform=None,
+ view2_transform=None,
+ loop=1,
+ **kwargs
+ ):
+ super(ScanNetPairDataset, self).__init__()
+ self.data_root = data_root
+ self.overlap_threshold = overlap_threshold
+ self.view1_transform = Compose(view1_transform)
+ self.view2_transform = Compose(view2_transform)
+ self.loop = loop
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples.".format(len(self.data_list), self.loop))
+
+ def get_data_list(self):
+ data_list = []
+ overlap_list = glob.glob(
+ os.path.join(self.data_root, "*", "pcd", "overlap.txt")
+ )
+ for overlap_file in overlap_list:
+ with open(overlap_file) as f:
+ overlap = f.readlines()
+ overlap = [pair.strip().split() for pair in overlap]
+ data_list.extend(
+ [
+ pair[:2]
+ for pair in overlap
+ if float(pair[2]) > self.overlap_threshold
+ ]
+ )
+ return data_list
+
+ def get_data(self, idx):
+ pair = self.data_list[idx % len(self.data_list)]
+ view1_dict = torch.load(self.data_root + pair[0])
+ view2_dict = torch.load(self.data_root + pair[1])
+ return view1_dict, view2_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ view1_dict, view2_dict = self.get_data(idx)
+ view1_dict = self.view1_transform(view1_dict)
+ view2_dict = self.view2_transform(view2_dict)
+ data_dict = dict()
+ for key, value in view1_dict.items():
+ data_dict["view1_" + key] = value
+ for key, value in view2_dict.items():
+ data_dict["view2_" + key] = value
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ raise NotImplementedError
+
+ def __getitem__(self, idx):
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannetpp.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannetpp.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e3ef3bf65bd4f81fb0f65652ead6fbff62337e9
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannetpp.py
@@ -0,0 +1,78 @@
+"""
+ScanNet++ dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import glob
+
+from pointcept.utils.cache import shared_dict
+
+from .builder import DATASETS
+from .defaults import DefaultDatasetV2
+
+
+@DATASETS.register_module()
+class ScanNetPPDataset(DefaultDatasetV2):
+ VALID_ASSETS = [
+ "coord",
+ "color",
+ "normal",
+ "segment",
+ "instance",
+ ]
+
+ def __init__(
+ self,
+ multilabel=False,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.multilabel = multilabel
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ name = self.get_data_name(idx)
+ if self.cache:
+ cache_name = f"pointcept-{name}"
+ return shared_dict(cache_name)
+
+ data_dict = {}
+ assets = os.listdir(data_path)
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ if asset[:-4] not in self.VALID_ASSETS:
+ continue
+ data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+ data_dict["name"] = name
+
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"].astype(np.float32)
+
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"].astype(np.float32)
+
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+ if not self.multilabel:
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][:, 0].astype(np.int32)
+ else:
+ data_dict["segment"] = (
+ np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+ )
+
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][:, 0].astype(np.int32)
+ else:
+ data_dict["instance"] = (
+ np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+ )
+ else:
+ raise NotImplementedError
+ return data_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/semantic_kitti.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/semantic_kitti.py
new file mode 100644
index 0000000000000000000000000000000000000000..3729b4163a113be69f3874cad646f4bc6255cfba
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/semantic_kitti.py
@@ -0,0 +1,139 @@
+"""
+Semantic KITTI dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class SemanticKITTIDataset(DefaultDataset):
+ def __init__(self, ignore_index=-1, **kwargs):
+ self.ignore_index = ignore_index
+ self.learning_map = self.get_learning_map(ignore_index)
+ self.learning_map_inv = self.get_learning_map_inv(ignore_index)
+ super().__init__(ignore_index=ignore_index, **kwargs)
+
+ def get_data_list(self):
+ split2seq = dict(
+ train=[0, 1, 2, 3, 4, 5, 6, 7, 9, 10],
+ val=[8],
+ test=[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
+ )
+ if isinstance(self.split, str):
+ seq_list = split2seq[self.split]
+ elif isinstance(self.split, list):
+ seq_list = []
+ for split in self.split:
+ seq_list += split2seq[split]
+ else:
+ raise NotImplementedError
+
+ data_list = []
+ for seq in seq_list:
+ seq = str(seq).zfill(2)
+ seq_folder = os.path.join(self.data_root, "dataset", "sequences", seq)
+ seq_files = sorted(os.listdir(os.path.join(seq_folder, "velodyne")))
+ data_list += [
+ os.path.join(seq_folder, "velodyne", file) for file in seq_files
+ ]
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ with open(data_path, "rb") as b:
+ scan = np.fromfile(b, dtype=np.float32).reshape(-1, 4)
+ coord = scan[:, :3]
+ strength = scan[:, -1].reshape([-1, 1])
+
+ label_file = data_path.replace("velodyne", "labels").replace(".bin", ".label")
+ if os.path.exists(label_file):
+ with open(label_file, "rb") as a:
+ segment = np.fromfile(a, dtype=np.int32).reshape(-1)
+ segment = np.vectorize(self.learning_map.__getitem__)(
+ segment & 0xFFFF
+ ).astype(np.int32)
+ else:
+ segment = np.zeros(scan.shape[0]).astype(np.int32)
+ data_dict = dict(coord=coord, strength=strength, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ dir_path, file_name = os.path.split(file_path)
+ sequence_name = os.path.basename(os.path.dirname(dir_path))
+ frame_name = os.path.splitext(file_name)[0]
+ data_name = f"{sequence_name}_{frame_name}"
+ return data_name
+
+ @staticmethod
+ def get_learning_map(ignore_index):
+ learning_map = {
+ 0: ignore_index, # "unlabeled"
+ 1: ignore_index, # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 0, # "car"
+ 11: 1, # "bicycle"
+ 13: 4, # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 2, # "motorcycle"
+ 16: 4, # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 3, # "truck"
+ 20: 4, # "other-vehicle"
+ 30: 5, # "person"
+ 31: 6, # "bicyclist"
+ 32: 7, # "motorcyclist"
+ 40: 8, # "road"
+ 44: 9, # "parking"
+ 48: 10, # "sidewalk"
+ 49: 11, # "other-ground"
+ 50: 12, # "building"
+ 51: 13, # "fence"
+ 52: ignore_index, # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 8, # "lane-marking" to "road" ---------------------------------mapped
+ 70: 14, # "vegetation"
+ 71: 15, # "trunk"
+ 72: 16, # "terrain"
+ 80: 17, # "pole"
+ 81: 18, # "traffic-sign"
+ 99: ignore_index, # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 0, # "moving-car" to "car" ------------------------------------mapped
+ 253: 6, # "moving-bicyclist" to "bicyclist" ------------------------mapped
+ 254: 5, # "moving-person" to "person" ------------------------------mapped
+ 255: 7, # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+ 256: 4, # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+ 257: 4, # "moving-bus" mapped to "other-vehicle" -------------------mapped
+ 258: 3, # "moving-truck" to "truck" --------------------------------mapped
+ 259: 4, # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+ }
+ return learning_map
+
+ @staticmethod
+ def get_learning_map_inv(ignore_index):
+ learning_map_inv = {
+ ignore_index: ignore_index, # "unlabeled"
+ 0: 10, # "car"
+ 1: 11, # "bicycle"
+ 2: 15, # "motorcycle"
+ 3: 18, # "truck"
+ 4: 20, # "other-vehicle"
+ 5: 30, # "person"
+ 6: 31, # "bicyclist"
+ 7: 32, # "motorcyclist"
+ 8: 40, # "road"
+ 9: 44, # "parking"
+ 10: 48, # "sidewalk"
+ 11: 49, # "other-ground"
+ 12: 50, # "building"
+ 13: 51, # "fence"
+ 14: 70, # "vegetation"
+ 15: 71, # "trunk"
+ 16: 72, # "terrain"
+ 17: 80, # "pole"
+ 18: 81, # "traffic-sign"
+ }
+ return learning_map_inv
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/shapenet_part.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/shapenet_part.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca0c3d50adf98b643233e28b56631654479fde60
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/shapenet_part.py
@@ -0,0 +1,160 @@
+"""
+ShapeNet Part Dataset (Unmaintained)
+
+get processed shapenet part dataset
+at "https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip"
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import json
+import torch
+import numpy as np
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ShapeNetPartDataset(Dataset):
+ def __init__(
+ self,
+ split="train",
+ data_root="data/shapenetcore_partanno_segmentation_benchmark_v0_normal",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super(ShapeNetPartDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.loop = (
+ loop if not test_mode else 1
+ ) # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+ self.cache = {}
+
+ # load categories file
+ self.categories = []
+ self.category2part = {
+ "Airplane": [0, 1, 2, 3],
+ "Bag": [4, 5],
+ "Cap": [6, 7],
+ "Car": [8, 9, 10, 11],
+ "Chair": [12, 13, 14, 15],
+ "Earphone": [16, 17, 18],
+ "Guitar": [19, 20, 21],
+ "Knife": [22, 23],
+ "Lamp": [24, 25, 26, 27],
+ "Laptop": [28, 29],
+ "Motorbike": [30, 31, 32, 33, 34, 35],
+ "Mug": [36, 37],
+ "Pistol": [38, 39, 40],
+ "Rocket": [41, 42, 43],
+ "Skateboard": [44, 45, 46],
+ "Table": [47, 48, 49],
+ }
+ self.token2category = {}
+ with open(os.path.join(self.data_root, "synsetoffset2category.txt"), "r") as f:
+ for line in f:
+ ls = line.strip().split()
+ self.token2category[ls[1]] = len(self.categories)
+ self.categories.append(ls[0])
+
+ if test_mode:
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ # load data list
+ if isinstance(self.split, str):
+ self.data_list = self.load_data_list(self.split)
+ elif isinstance(self.split, list):
+ self.data_list = []
+ for s in self.split:
+ self.data_list += self.load_data_list(s)
+ else:
+ raise NotImplementedError
+
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_idx), self.loop, split
+ )
+ )
+
+ def load_data_list(self, split):
+ split_file = os.path.join(
+ self.data_root,
+ "train_test_split",
+ "shuffled_{}_file_list.json".format(split),
+ )
+ if not os.path.isfile(split_file):
+ raise (RuntimeError("Split file do not exist: " + split_file + "\n"))
+ with open(split_file, "r") as f:
+ # drop "shape_data/" and append ".txt"
+ data_list = [
+ os.path.join(self.data_root, data[11:] + ".txt")
+ for data in json.load(f)
+ ]
+ return data_list
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_idx = idx % len(self.data_list)
+ if data_idx in self.cache:
+ coord, norm, segment, cls_token = self.cache[data_idx]
+ else:
+ data = np.loadtxt(self.data_list[data_idx]).astype(np.float32)
+ cls_token = self.token2category[
+ os.path.basename(os.path.dirname(self.data_list[data_idx]))
+ ]
+ coord, norm, segment = (
+ data[:, :3],
+ data[:, 3:6],
+ data[:, 6].astype(np.int32),
+ )
+ self.cache[data_idx] = (coord, norm, segment, cls_token)
+
+ data_dict = dict(coord=coord, norm=norm, segment=segment, cls_token=cls_token)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_idx = self.data_idx[idx % len(self.data_idx)]
+ data = np.loadtxt(self.data_list[data_idx]).astype(np.float32)
+ cls_token = self.token2category[
+ os.path.basename(os.path.dirname(self.data_list[data_idx]))
+ ]
+ coord, norm, segment = data[:, :3], data[:, 3:6], data[:, 6].astype(np.int32)
+
+ data_dict = dict(coord=coord, norm=norm, cls_token=cls_token)
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(self.post_transform(aug(deepcopy(data_dict))))
+ data_dict = dict(
+ fragment_list=data_dict_list, segment=segment, name=self.get_data_name(idx)
+ )
+ return data_dict
+
+ def get_data_name(self, idx):
+ data_idx = self.data_idx[idx % len(self.data_idx)]
+ return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_idx) * self.loop
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/structure3d.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/structure3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..b248ac453eed86688dfed36c676c0ca0245aaa96
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/structure3d.py
@@ -0,0 +1,61 @@
+"""
+Structured3D Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import glob
+import os
+from collections.abc import Sequence
+
+from .builder import DATASETS
+from .defaults import DefaultDataset, DefaultDatasetV2
+
+
+@DATASETS.register_module()
+class Structured3DDataset(DefaultDataset):
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(
+ os.path.join(self.data_root, self.split, "scene_*/room_*"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(
+ os.path.join(self.data_root, split, "scene_*/room_*"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ dir_path, room_name = os.path.split(file_path)
+ scene_name = os.path.basename(dir_path)
+ data_name = f"{scene_name}_{room_name}"
+ return data_name
+
+
+@DATASETS.register_module()
+class Structured3DDatasetV2(DefaultDatasetV2):
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(
+ os.path.join(self.data_root, self.split, "scene_*/room_*"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(
+ os.path.join(self.data_root, split, "scene_*/room_*"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ dir_path, room_name = os.path.split(file_path)
+ scene_name = os.path.basename(dir_path)
+ data_name = f"{scene_name}_{room_name}"
+ return data_name
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/transform.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1abfe4f3d88decdac17861b31647c34f9520292
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/transform.py
@@ -0,0 +1,1148 @@
+"""
+3D Point Cloud Augmentation
+
+Inspirited by chrischoy/SpatioTemporalSegmentation
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+import numbers
+import scipy
+import scipy.ndimage
+import scipy.interpolate
+import scipy.stats
+import numpy as np
+import torch
+import copy
+from collections.abc import Sequence, Mapping
+
+from pointcept.utils.registry import Registry
+
+TRANSFORMS = Registry("transforms")
+
+
+@TRANSFORMS.register_module()
+class Collect(object):
+ def __init__(self, keys, offset_keys_dict=None, **kwargs):
+ """
+ e.g. Collect(keys=[coord], feat_keys=[coord, color])
+ """
+ if offset_keys_dict is None:
+ offset_keys_dict = dict(offset="coord")
+ self.keys = keys
+ self.offset_keys = offset_keys_dict
+ self.kwargs = kwargs
+
+ def __call__(self, data_dict):
+ data = dict()
+ if isinstance(self.keys, str):
+ self.keys = [self.keys]
+ for key in self.keys:
+ data[key] = data_dict[key]
+ for key, value in self.offset_keys.items():
+ data[key] = torch.tensor([data_dict[value].shape[0]])
+ for name, keys in self.kwargs.items():
+ name = name.replace("_keys", "")
+ assert isinstance(keys, Sequence)
+ data[name] = torch.cat([data_dict[key].float() for key in keys], dim=1)
+ return data
+
+
+@TRANSFORMS.register_module()
+class Copy(object):
+ def __init__(self, keys_dict=None):
+ if keys_dict is None:
+ keys_dict = dict(coord="origin_coord", segment="origin_segment")
+ self.keys_dict = keys_dict
+
+ def __call__(self, data_dict):
+ for key, value in self.keys_dict.items():
+ if isinstance(data_dict[key], np.ndarray):
+ data_dict[value] = data_dict[key].copy()
+ elif isinstance(data_dict[key], torch.Tensor):
+ data_dict[value] = data_dict[key].clone().detach()
+ else:
+ data_dict[value] = copy.deepcopy(data_dict[key])
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ToTensor(object):
+ def __call__(self, data):
+ if isinstance(data, torch.Tensor):
+ return data
+ elif isinstance(data, str):
+ # note that str is also a kind of sequence, judgement should before sequence
+ return data
+ elif isinstance(data, int):
+ return torch.LongTensor([data])
+ elif isinstance(data, float):
+ return torch.FloatTensor([data])
+ elif isinstance(data, np.ndarray) and np.issubdtype(data.dtype, bool):
+ return torch.from_numpy(data)
+ elif isinstance(data, np.ndarray) and np.issubdtype(data.dtype, np.integer):
+ return torch.from_numpy(data).long()
+ elif isinstance(data, np.ndarray) and np.issubdtype(data.dtype, np.floating):
+ return torch.from_numpy(data).float()
+ elif isinstance(data, Mapping):
+ result = {sub_key: self(item) for sub_key, item in data.items()}
+ return result
+ elif isinstance(data, Sequence):
+ result = [self(item) for item in data]
+ return result
+ else:
+ raise TypeError(f"type {type(data)} cannot be converted to tensor.")
+
+
+@TRANSFORMS.register_module()
+class Add(object):
+ def __init__(self, keys_dict=None):
+ if keys_dict is None:
+ keys_dict = dict()
+ self.keys_dict = keys_dict
+
+ def __call__(self, data_dict):
+ for key, value in self.keys_dict.items():
+ data_dict[key] = value
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class NormalizeColor(object):
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"] / 127.5 - 1
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class NormalizeCoord(object):
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ # modified from pointnet2
+ centroid = np.mean(data_dict["coord"], axis=0)
+ data_dict["coord"] -= centroid
+ m = np.max(np.sqrt(np.sum(data_dict["coord"] ** 2, axis=1)))
+ data_dict["coord"] = data_dict["coord"] / m
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class PositiveShift(object):
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ coord_min = np.min(data_dict["coord"], 0)
+ data_dict["coord"] -= coord_min
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class CenterShift(object):
+ def __init__(self, apply_z=True):
+ self.apply_z = apply_z
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ x_min, y_min, z_min = data_dict["coord"].min(axis=0)
+ x_max, y_max, _ = data_dict["coord"].max(axis=0)
+ if self.apply_z:
+ shift = [(x_min + x_max) / 2, (y_min + y_max) / 2, z_min]
+ else:
+ shift = [(x_min + x_max) / 2, (y_min + y_max) / 2, 0]
+ data_dict["coord"] -= shift
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomShift(object):
+ def __init__(self, shift=((-0.2, 0.2), (-0.2, 0.2), (0, 0))):
+ self.shift = shift
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ shift_x = np.random.uniform(self.shift[0][0], self.shift[0][1])
+ shift_y = np.random.uniform(self.shift[1][0], self.shift[1][1])
+ shift_z = np.random.uniform(self.shift[2][0], self.shift[2][1])
+ data_dict["coord"] += [shift_x, shift_y, shift_z]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class PointClip(object):
+ def __init__(self, point_cloud_range=(-80, -80, -3, 80, 80, 1)):
+ self.point_cloud_range = point_cloud_range
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = np.clip(
+ data_dict["coord"],
+ a_min=self.point_cloud_range[:3],
+ a_max=self.point_cloud_range[3:],
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomDropout(object):
+ def __init__(self, dropout_ratio=0.2, dropout_application_ratio=0.5):
+ """
+ upright_axis: axis index among x,y,z, i.e. 2 for z
+ """
+ self.dropout_ratio = dropout_ratio
+ self.dropout_application_ratio = dropout_application_ratio
+
+ def __call__(self, data_dict):
+ if random.random() < self.dropout_application_ratio:
+ n = len(data_dict["coord"])
+ idx = np.random.choice(n, int(n * (1 - self.dropout_ratio)), replace=False)
+ if "sampled_index" in data_dict:
+ # for ScanNet data efficient, we need to make sure labeled point is sampled.
+ idx = np.unique(np.append(idx, data_dict["sampled_index"]))
+ mask = np.zeros_like(data_dict["segment"]).astype(bool)
+ mask[data_dict["sampled_index"]] = True
+ data_dict["sampled_index"] = np.where(mask[idx])[0]
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][idx]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][idx]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][idx]
+ if "strength" in data_dict.keys():
+ data_dict["strength"] = data_dict["strength"][idx]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][idx]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][idx]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomRotate(object):
+ def __init__(self, angle=None, center=None, axis="z", always_apply=False, p=0.5):
+ self.angle = [-1, 1] if angle is None else angle
+ self.axis = axis
+ self.always_apply = always_apply
+ self.p = p if not self.always_apply else 1
+ self.center = center
+
+ def __call__(self, data_dict):
+ if random.random() > self.p:
+ return data_dict
+ angle = np.random.uniform(self.angle[0], self.angle[1]) * np.pi
+ rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+ if self.axis == "x":
+ rot_t = np.array([[1, 0, 0], [0, rot_cos, -rot_sin], [0, rot_sin, rot_cos]])
+ elif self.axis == "y":
+ rot_t = np.array([[rot_cos, 0, rot_sin], [0, 1, 0], [-rot_sin, 0, rot_cos]])
+ elif self.axis == "z":
+ rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+ else:
+ raise NotImplementedError
+ if "coord" in data_dict.keys():
+ if self.center is None:
+ x_min, y_min, z_min = data_dict["coord"].min(axis=0)
+ x_max, y_max, z_max = data_dict["coord"].max(axis=0)
+ center = [(x_min + x_max) / 2, (y_min + y_max) / 2, (z_min + z_max) / 2]
+ else:
+ center = self.center
+ data_dict["coord"] -= center
+ data_dict["coord"] = np.dot(data_dict["coord"], np.transpose(rot_t))
+ data_dict["coord"] += center
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = np.dot(data_dict["normal"], np.transpose(rot_t))
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomRotateTargetAngle(object):
+ def __init__(
+ self, angle=(1 / 2, 1, 3 / 2), center=None, axis="z", always_apply=False, p=0.75
+ ):
+ self.angle = angle
+ self.axis = axis
+ self.always_apply = always_apply
+ self.p = p if not self.always_apply else 1
+ self.center = center
+
+ def __call__(self, data_dict):
+ if random.random() > self.p:
+ return data_dict
+ angle = np.random.choice(self.angle) * np.pi
+ rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+ if self.axis == "x":
+ rot_t = np.array([[1, 0, 0], [0, rot_cos, -rot_sin], [0, rot_sin, rot_cos]])
+ elif self.axis == "y":
+ rot_t = np.array([[rot_cos, 0, rot_sin], [0, 1, 0], [-rot_sin, 0, rot_cos]])
+ elif self.axis == "z":
+ rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+ else:
+ raise NotImplementedError
+ if "coord" in data_dict.keys():
+ if self.center is None:
+ x_min, y_min, z_min = data_dict["coord"].min(axis=0)
+ x_max, y_max, z_max = data_dict["coord"].max(axis=0)
+ center = [(x_min + x_max) / 2, (y_min + y_max) / 2, (z_min + z_max) / 2]
+ else:
+ center = self.center
+ data_dict["coord"] -= center
+ data_dict["coord"] = np.dot(data_dict["coord"], np.transpose(rot_t))
+ data_dict["coord"] += center
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = np.dot(data_dict["normal"], np.transpose(rot_t))
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomScale(object):
+ def __init__(self, scale=None, anisotropic=False):
+ self.scale = scale if scale is not None else [0.95, 1.05]
+ self.anisotropic = anisotropic
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ scale = np.random.uniform(
+ self.scale[0], self.scale[1], 3 if self.anisotropic else 1
+ )
+ data_dict["coord"] *= scale
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomFlip(object):
+ def __init__(self, p=0.5):
+ self.p = p
+
+ def __call__(self, data_dict):
+ if np.random.rand() < self.p:
+ if "coord" in data_dict.keys():
+ data_dict["coord"][:, 0] = -data_dict["coord"][:, 0]
+ if "normal" in data_dict.keys():
+ data_dict["normal"][:, 0] = -data_dict["normal"][:, 0]
+ if np.random.rand() < self.p:
+ if "coord" in data_dict.keys():
+ data_dict["coord"][:, 1] = -data_dict["coord"][:, 1]
+ if "normal" in data_dict.keys():
+ data_dict["normal"][:, 1] = -data_dict["normal"][:, 1]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomJitter(object):
+ def __init__(self, sigma=0.01, clip=0.05):
+ assert clip > 0
+ self.sigma = sigma
+ self.clip = clip
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ jitter = np.clip(
+ self.sigma * np.random.randn(data_dict["coord"].shape[0], 3),
+ -self.clip,
+ self.clip,
+ )
+ data_dict["coord"] += jitter
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ClipGaussianJitter(object):
+ def __init__(self, scalar=0.02, store_jitter=False):
+ self.scalar = scalar
+ self.mean = np.mean(3)
+ self.cov = np.identity(3)
+ self.quantile = 1.96
+ self.store_jitter = store_jitter
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ jitter = np.random.multivariate_normal(
+ self.mean, self.cov, data_dict["coord"].shape[0]
+ )
+ jitter = self.scalar * np.clip(jitter / 1.96, -1, 1)
+ data_dict["coord"] += jitter
+ if self.store_jitter:
+ data_dict["jitter"] = jitter
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ChromaticAutoContrast(object):
+ def __init__(self, p=0.2, blend_factor=None):
+ self.p = p
+ self.blend_factor = blend_factor
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ lo = np.min(data_dict["color"], 0, keepdims=True)
+ hi = np.max(data_dict["color"], 0, keepdims=True)
+ scale = 255 / (hi - lo)
+ contrast_feat = (data_dict["color"][:, :3] - lo) * scale
+ blend_factor = (
+ np.random.rand() if self.blend_factor is None else self.blend_factor
+ )
+ data_dict["color"][:, :3] = (1 - blend_factor) * data_dict["color"][
+ :, :3
+ ] + blend_factor * contrast_feat
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ChromaticTranslation(object):
+ def __init__(self, p=0.95, ratio=0.05):
+ self.p = p
+ self.ratio = ratio
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ tr = (np.random.rand(1, 3) - 0.5) * 255 * 2 * self.ratio
+ data_dict["color"][:, :3] = np.clip(tr + data_dict["color"][:, :3], 0, 255)
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ChromaticJitter(object):
+ def __init__(self, p=0.95, std=0.005):
+ self.p = p
+ self.std = std
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ noise = np.random.randn(data_dict["color"].shape[0], 3)
+ noise *= self.std * 255
+ data_dict["color"][:, :3] = np.clip(
+ noise + data_dict["color"][:, :3], 0, 255
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomColorGrayScale(object):
+ def __init__(self, p):
+ self.p = p
+
+ @staticmethod
+ def rgb_to_grayscale(color, num_output_channels=1):
+ if color.shape[-1] < 3:
+ raise TypeError(
+ "Input color should have at least 3 dimensions, but found {}".format(
+ color.shape[-1]
+ )
+ )
+
+ if num_output_channels not in (1, 3):
+ raise ValueError("num_output_channels should be either 1 or 3")
+
+ r, g, b = color[..., 0], color[..., 1], color[..., 2]
+ gray = (0.2989 * r + 0.587 * g + 0.114 * b).astype(color.dtype)
+ gray = np.expand_dims(gray, axis=-1)
+
+ if num_output_channels == 3:
+ gray = np.broadcast_to(gray, color.shape)
+
+ return gray
+
+ def __call__(self, data_dict):
+ if np.random.rand() < self.p:
+ data_dict["color"] = self.rgb_to_grayscale(data_dict["color"], 3)
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomColorJitter(object):
+ """
+ Random Color Jitter for 3D point cloud (refer torchvision)
+ """
+
+ def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, p=0.95):
+ self.brightness = self._check_input(brightness, "brightness")
+ self.contrast = self._check_input(contrast, "contrast")
+ self.saturation = self._check_input(saturation, "saturation")
+ self.hue = self._check_input(
+ hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False
+ )
+ self.p = p
+
+ @staticmethod
+ def _check_input(
+ value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True
+ ):
+ if isinstance(value, numbers.Number):
+ if value < 0:
+ raise ValueError(
+ "If {} is a single number, it must be non negative.".format(name)
+ )
+ value = [center - float(value), center + float(value)]
+ if clip_first_on_zero:
+ value[0] = max(value[0], 0.0)
+ elif isinstance(value, (tuple, list)) and len(value) == 2:
+ if not bound[0] <= value[0] <= value[1] <= bound[1]:
+ raise ValueError("{} values should be between {}".format(name, bound))
+ else:
+ raise TypeError(
+ "{} should be a single number or a list/tuple with length 2.".format(
+ name
+ )
+ )
+
+ # if value is 0 or (1., 1.) for brightness/contrast/saturation
+ # or (0., 0.) for hue, do nothing
+ if value[0] == value[1] == center:
+ value = None
+ return value
+
+ @staticmethod
+ def blend(color1, color2, ratio):
+ ratio = float(ratio)
+ bound = 255.0
+ return (
+ (ratio * color1 + (1.0 - ratio) * color2)
+ .clip(0, bound)
+ .astype(color1.dtype)
+ )
+
+ @staticmethod
+ def rgb2hsv(rgb):
+ r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
+ maxc = np.max(rgb, axis=-1)
+ minc = np.min(rgb, axis=-1)
+ eqc = maxc == minc
+ cr = maxc - minc
+ s = cr / (np.ones_like(maxc) * eqc + maxc * (1 - eqc))
+ cr_divisor = np.ones_like(maxc) * eqc + cr * (1 - eqc)
+ rc = (maxc - r) / cr_divisor
+ gc = (maxc - g) / cr_divisor
+ bc = (maxc - b) / cr_divisor
+
+ hr = (maxc == r) * (bc - gc)
+ hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc)
+ hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc)
+ h = hr + hg + hb
+ h = (h / 6.0 + 1.0) % 1.0
+ return np.stack((h, s, maxc), axis=-1)
+
+ @staticmethod
+ def hsv2rgb(hsv):
+ h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+ i = np.floor(h * 6.0)
+ f = (h * 6.0) - i
+ i = i.astype(np.int32)
+
+ p = np.clip((v * (1.0 - s)), 0.0, 1.0)
+ q = np.clip((v * (1.0 - s * f)), 0.0, 1.0)
+ t = np.clip((v * (1.0 - s * (1.0 - f))), 0.0, 1.0)
+ i = i % 6
+ mask = np.expand_dims(i, axis=-1) == np.arange(6)
+
+ a1 = np.stack((v, q, p, p, t, v), axis=-1)
+ a2 = np.stack((t, v, v, q, p, p), axis=-1)
+ a3 = np.stack((p, p, t, v, v, q), axis=-1)
+ a4 = np.stack((a1, a2, a3), axis=-1)
+
+ return np.einsum("...na, ...nab -> ...nb", mask.astype(hsv.dtype), a4)
+
+ def adjust_brightness(self, color, brightness_factor):
+ if brightness_factor < 0:
+ raise ValueError(
+ "brightness_factor ({}) is not non-negative.".format(brightness_factor)
+ )
+
+ return self.blend(color, np.zeros_like(color), brightness_factor)
+
+ def adjust_contrast(self, color, contrast_factor):
+ if contrast_factor < 0:
+ raise ValueError(
+ "contrast_factor ({}) is not non-negative.".format(contrast_factor)
+ )
+ mean = np.mean(RandomColorGrayScale.rgb_to_grayscale(color))
+ return self.blend(color, mean, contrast_factor)
+
+ def adjust_saturation(self, color, saturation_factor):
+ if saturation_factor < 0:
+ raise ValueError(
+ "saturation_factor ({}) is not non-negative.".format(saturation_factor)
+ )
+ gray = RandomColorGrayScale.rgb_to_grayscale(color)
+ return self.blend(color, gray, saturation_factor)
+
+ def adjust_hue(self, color, hue_factor):
+ if not (-0.5 <= hue_factor <= 0.5):
+ raise ValueError(
+ "hue_factor ({}) is not in [-0.5, 0.5].".format(hue_factor)
+ )
+ orig_dtype = color.dtype
+ hsv = self.rgb2hsv(color / 255.0)
+ h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+ h = (h + hue_factor) % 1.0
+ hsv = np.stack((h, s, v), axis=-1)
+ color_hue_adj = (self.hsv2rgb(hsv) * 255.0).astype(orig_dtype)
+ return color_hue_adj
+
+ @staticmethod
+ def get_params(brightness, contrast, saturation, hue):
+ fn_idx = torch.randperm(4)
+ b = (
+ None
+ if brightness is None
+ else np.random.uniform(brightness[0], brightness[1])
+ )
+ c = None if contrast is None else np.random.uniform(contrast[0], contrast[1])
+ s = (
+ None
+ if saturation is None
+ else np.random.uniform(saturation[0], saturation[1])
+ )
+ h = None if hue is None else np.random.uniform(hue[0], hue[1])
+ return fn_idx, b, c, s, h
+
+ def __call__(self, data_dict):
+ (
+ fn_idx,
+ brightness_factor,
+ contrast_factor,
+ saturation_factor,
+ hue_factor,
+ ) = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
+
+ for fn_id in fn_idx:
+ if (
+ fn_id == 0
+ and brightness_factor is not None
+ and np.random.rand() < self.p
+ ):
+ data_dict["color"] = self.adjust_brightness(
+ data_dict["color"], brightness_factor
+ )
+ elif (
+ fn_id == 1 and contrast_factor is not None and np.random.rand() < self.p
+ ):
+ data_dict["color"] = self.adjust_contrast(
+ data_dict["color"], contrast_factor
+ )
+ elif (
+ fn_id == 2
+ and saturation_factor is not None
+ and np.random.rand() < self.p
+ ):
+ data_dict["color"] = self.adjust_saturation(
+ data_dict["color"], saturation_factor
+ )
+ elif fn_id == 3 and hue_factor is not None and np.random.rand() < self.p:
+ data_dict["color"] = self.adjust_hue(data_dict["color"], hue_factor)
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class HueSaturationTranslation(object):
+ @staticmethod
+ def rgb_to_hsv(rgb):
+ # Translated from source of colorsys.rgb_to_hsv
+ # r,g,b should be a numpy arrays with values between 0 and 255
+ # rgb_to_hsv returns an array of floats between 0.0 and 1.0.
+ rgb = rgb.astype("float")
+ hsv = np.zeros_like(rgb)
+ # in case an RGBA array was passed, just copy the A channel
+ hsv[..., 3:] = rgb[..., 3:]
+ r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
+ maxc = np.max(rgb[..., :3], axis=-1)
+ minc = np.min(rgb[..., :3], axis=-1)
+ hsv[..., 2] = maxc
+ mask = maxc != minc
+ hsv[mask, 1] = (maxc - minc)[mask] / maxc[mask]
+ rc = np.zeros_like(r)
+ gc = np.zeros_like(g)
+ bc = np.zeros_like(b)
+ rc[mask] = (maxc - r)[mask] / (maxc - minc)[mask]
+ gc[mask] = (maxc - g)[mask] / (maxc - minc)[mask]
+ bc[mask] = (maxc - b)[mask] / (maxc - minc)[mask]
+ hsv[..., 0] = np.select(
+ [r == maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc
+ )
+ hsv[..., 0] = (hsv[..., 0] / 6.0) % 1.0
+ return hsv
+
+ @staticmethod
+ def hsv_to_rgb(hsv):
+ # Translated from source of colorsys.hsv_to_rgb
+ # h,s should be a numpy arrays with values between 0.0 and 1.0
+ # v should be a numpy array with values between 0.0 and 255.0
+ # hsv_to_rgb returns an array of uints between 0 and 255.
+ rgb = np.empty_like(hsv)
+ rgb[..., 3:] = hsv[..., 3:]
+ h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+ i = (h * 6.0).astype("uint8")
+ f = (h * 6.0) - i
+ p = v * (1.0 - s)
+ q = v * (1.0 - s * f)
+ t = v * (1.0 - s * (1.0 - f))
+ i = i % 6
+ conditions = [s == 0.0, i == 1, i == 2, i == 3, i == 4, i == 5]
+ rgb[..., 0] = np.select(conditions, [v, q, p, p, t, v], default=v)
+ rgb[..., 1] = np.select(conditions, [v, v, v, q, p, p], default=t)
+ rgb[..., 2] = np.select(conditions, [v, p, t, v, v, q], default=p)
+ return rgb.astype("uint8")
+
+ def __init__(self, hue_max=0.5, saturation_max=0.2):
+ self.hue_max = hue_max
+ self.saturation_max = saturation_max
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys():
+ # Assume color[:, :3] is rgb
+ hsv = HueSaturationTranslation.rgb_to_hsv(data_dict["color"][:, :3])
+ hue_val = (np.random.rand() - 0.5) * 2 * self.hue_max
+ sat_ratio = 1 + (np.random.rand() - 0.5) * 2 * self.saturation_max
+ hsv[..., 0] = np.remainder(hue_val + hsv[..., 0] + 1, 1)
+ hsv[..., 1] = np.clip(sat_ratio * hsv[..., 1], 0, 1)
+ data_dict["color"][:, :3] = np.clip(
+ HueSaturationTranslation.hsv_to_rgb(hsv), 0, 255
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomColorDrop(object):
+ def __init__(self, p=0.2, color_augment=0.0):
+ self.p = p
+ self.color_augment = color_augment
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ data_dict["color"] *= self.color_augment
+ return data_dict
+
+ def __repr__(self):
+ return "RandomColorDrop(color_augment: {}, p: {})".format(
+ self.color_augment, self.p
+ )
+
+
+@TRANSFORMS.register_module()
+class ElasticDistortion(object):
+ def __init__(self, distortion_params=None):
+ self.distortion_params = (
+ [[0.2, 0.4], [0.8, 1.6]] if distortion_params is None else distortion_params
+ )
+
+ @staticmethod
+ def elastic_distortion(coords, granularity, magnitude):
+ """
+ Apply elastic distortion on sparse coordinate space.
+ pointcloud: numpy array of (number of points, at least 3 spatial dims)
+ granularity: size of the noise grid (in same scale[m/cm] as the voxel grid)
+ magnitude: noise multiplier
+ """
+ blurx = np.ones((3, 1, 1, 1)).astype("float32") / 3
+ blury = np.ones((1, 3, 1, 1)).astype("float32") / 3
+ blurz = np.ones((1, 1, 3, 1)).astype("float32") / 3
+ coords_min = coords.min(0)
+
+ # Create Gaussian noise tensor of the size given by granularity.
+ noise_dim = ((coords - coords_min).max(0) // granularity).astype(int) + 3
+ noise = np.random.randn(*noise_dim, 3).astype(np.float32)
+
+ # Smoothing.
+ for _ in range(2):
+ noise = scipy.ndimage.filters.convolve(
+ noise, blurx, mode="constant", cval=0
+ )
+ noise = scipy.ndimage.filters.convolve(
+ noise, blury, mode="constant", cval=0
+ )
+ noise = scipy.ndimage.filters.convolve(
+ noise, blurz, mode="constant", cval=0
+ )
+
+ # Trilinear interpolate noise filters for each spatial dimensions.
+ ax = [
+ np.linspace(d_min, d_max, d)
+ for d_min, d_max, d in zip(
+ coords_min - granularity,
+ coords_min + granularity * (noise_dim - 2),
+ noise_dim,
+ )
+ ]
+ interp = scipy.interpolate.RegularGridInterpolator(
+ ax, noise, bounds_error=False, fill_value=0
+ )
+ coords += interp(coords) * magnitude
+ return coords
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys() and self.distortion_params is not None:
+ if random.random() < 0.95:
+ for granularity, magnitude in self.distortion_params:
+ data_dict["coord"] = self.elastic_distortion(
+ data_dict["coord"], granularity, magnitude
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class GridSample(object):
+ def __init__(
+ self,
+ grid_size=0.05,
+ hash_type="fnv",
+ mode="train",
+ keys=("coord", "color", "normal", "segment"),
+ return_inverse=False,
+ return_grid_coord=False,
+ return_min_coord=False,
+ return_displacement=False,
+ project_displacement=False,
+ ):
+ self.grid_size = grid_size
+ self.hash = self.fnv_hash_vec if hash_type == "fnv" else self.ravel_hash_vec
+ assert mode in ["train", "test"]
+ self.mode = mode
+ self.keys = keys
+ self.return_inverse = return_inverse
+ self.return_grid_coord = return_grid_coord
+ self.return_min_coord = return_min_coord
+ self.return_displacement = return_displacement
+ self.project_displacement = project_displacement
+
+ def __call__(self, data_dict):
+ assert "coord" in data_dict.keys()
+ scaled_coord = data_dict["coord"] / np.array(self.grid_size)
+ grid_coord = np.floor(scaled_coord).astype(int)
+ min_coord = grid_coord.min(0)
+ grid_coord -= min_coord
+ scaled_coord -= min_coord
+ min_coord = min_coord * np.array(self.grid_size)
+ key = self.hash(grid_coord)
+ idx_sort = np.argsort(key)
+ key_sort = key[idx_sort]
+ _, inverse, count = np.unique(key_sort, return_inverse=True, return_counts=True)
+ if self.mode == "train": # train mode
+ idx_select = (
+ np.cumsum(np.insert(count, 0, 0)[0:-1])
+ + np.random.randint(0, count.max(), count.size) % count
+ )
+ idx_unique = idx_sort[idx_select]
+ if "sampled_index" in data_dict:
+ # for ScanNet data efficient, we need to make sure labeled point is sampled.
+ idx_unique = np.unique(
+ np.append(idx_unique, data_dict["sampled_index"])
+ )
+ mask = np.zeros_like(data_dict["segment"]).astype(bool)
+ mask[data_dict["sampled_index"]] = True
+ data_dict["sampled_index"] = np.where(mask[idx_unique])[0]
+ if self.return_inverse:
+ data_dict["inverse"] = np.zeros_like(inverse)
+ data_dict["inverse"][idx_sort] = inverse
+ if self.return_grid_coord:
+ data_dict["grid_coord"] = grid_coord[idx_unique]
+ if self.return_min_coord:
+ data_dict["min_coord"] = min_coord.reshape([1, 3])
+ if self.return_displacement:
+ displacement = (
+ scaled_coord - grid_coord - 0.5
+ ) # [0, 1] -> [-0.5, 0.5] displacement to center
+ if self.project_displacement:
+ displacement = np.sum(
+ displacement * data_dict["normal"], axis=-1, keepdims=True
+ )
+ data_dict["displacement"] = displacement[idx_unique]
+ for key in self.keys:
+ data_dict[key] = data_dict[key][idx_unique]
+ return data_dict
+
+ elif self.mode == "test": # test mode
+ data_part_list = []
+ for i in range(count.max()):
+ idx_select = np.cumsum(np.insert(count, 0, 0)[0:-1]) + i % count
+ idx_part = idx_sort[idx_select]
+ data_part = dict(index=idx_part)
+ if self.return_inverse:
+ data_dict["inverse"] = np.zeros_like(inverse)
+ data_dict["inverse"][idx_sort] = inverse
+ if self.return_grid_coord:
+ data_part["grid_coord"] = grid_coord[idx_part]
+ if self.return_min_coord:
+ data_part["min_coord"] = min_coord.reshape([1, 3])
+ if self.return_displacement:
+ displacement = (
+ scaled_coord - grid_coord - 0.5
+ ) # [0, 1] -> [-0.5, 0.5] displacement to center
+ if self.project_displacement:
+ displacement = np.sum(
+ displacement * data_dict["normal"], axis=-1, keepdims=True
+ )
+ data_dict["displacement"] = displacement[idx_part]
+ for key in data_dict.keys():
+ if key in self.keys:
+ data_part[key] = data_dict[key][idx_part]
+ else:
+ data_part[key] = data_dict[key]
+ data_part_list.append(data_part)
+ return data_part_list
+ else:
+ raise NotImplementedError
+
+ @staticmethod
+ def ravel_hash_vec(arr):
+ """
+ Ravel the coordinates after subtracting the min coordinates.
+ """
+ assert arr.ndim == 2
+ arr = arr.copy()
+ arr -= arr.min(0)
+ arr = arr.astype(np.uint64, copy=False)
+ arr_max = arr.max(0).astype(np.uint64) + 1
+
+ keys = np.zeros(arr.shape[0], dtype=np.uint64)
+ # Fortran style indexing
+ for j in range(arr.shape[1] - 1):
+ keys += arr[:, j]
+ keys *= arr_max[j + 1]
+ keys += arr[:, -1]
+ return keys
+
+ @staticmethod
+ def fnv_hash_vec(arr):
+ """
+ FNV64-1A
+ """
+ assert arr.ndim == 2
+ # Floor first for negative coordinates
+ arr = arr.copy()
+ arr = arr.astype(np.uint64, copy=False)
+ hashed_arr = np.uint64(14695981039346656037) * np.ones(
+ arr.shape[0], dtype=np.uint64
+ )
+ for j in range(arr.shape[1]):
+ hashed_arr *= np.uint64(1099511628211)
+ hashed_arr = np.bitwise_xor(hashed_arr, arr[:, j])
+ return hashed_arr
+
+
+@TRANSFORMS.register_module()
+class SphereCrop(object):
+ def __init__(self, point_max=80000, sample_rate=None, mode="random"):
+ self.point_max = point_max
+ self.sample_rate = sample_rate
+ assert mode in ["random", "center", "all"]
+ self.mode = mode
+
+ def __call__(self, data_dict):
+ point_max = (
+ int(self.sample_rate * data_dict["coord"].shape[0])
+ if self.sample_rate is not None
+ else self.point_max
+ )
+
+ assert "coord" in data_dict.keys()
+ if self.mode == "all":
+ # TODO: Optimize
+ if "index" not in data_dict.keys():
+ data_dict["index"] = np.arange(data_dict["coord"].shape[0])
+ data_part_list = []
+ # coord_list, color_list, dist2_list, idx_list, offset_list = [], [], [], [], []
+ if data_dict["coord"].shape[0] > point_max:
+ coord_p, idx_uni = np.random.rand(
+ data_dict["coord"].shape[0]
+ ) * 1e-3, np.array([])
+ while idx_uni.size != data_dict["index"].shape[0]:
+ init_idx = np.argmin(coord_p)
+ dist2 = np.sum(
+ np.power(data_dict["coord"] - data_dict["coord"][init_idx], 2),
+ 1,
+ )
+ idx_crop = np.argsort(dist2)[:point_max]
+
+ data_crop_dict = dict()
+ if "coord" in data_dict.keys():
+ data_crop_dict["coord"] = data_dict["coord"][idx_crop]
+ if "grid_coord" in data_dict.keys():
+ data_crop_dict["grid_coord"] = data_dict["grid_coord"][idx_crop]
+ if "normal" in data_dict.keys():
+ data_crop_dict["normal"] = data_dict["normal"][idx_crop]
+ if "color" in data_dict.keys():
+ data_crop_dict["color"] = data_dict["color"][idx_crop]
+ if "displacement" in data_dict.keys():
+ data_crop_dict["displacement"] = data_dict["displacement"][
+ idx_crop
+ ]
+ if "strength" in data_dict.keys():
+ data_crop_dict["strength"] = data_dict["strength"][idx_crop]
+ data_crop_dict["weight"] = dist2[idx_crop]
+ data_crop_dict["index"] = data_dict["index"][idx_crop]
+ data_part_list.append(data_crop_dict)
+
+ delta = np.square(
+ 1 - data_crop_dict["weight"] / np.max(data_crop_dict["weight"])
+ )
+ coord_p[idx_crop] += delta
+ idx_uni = np.unique(
+ np.concatenate((idx_uni, data_crop_dict["index"]))
+ )
+ else:
+ data_crop_dict = data_dict.copy()
+ data_crop_dict["weight"] = np.zeros(data_dict["coord"].shape[0])
+ data_crop_dict["index"] = data_dict["index"]
+ data_part_list.append(data_crop_dict)
+ return data_part_list
+ # mode is "random" or "center"
+ elif data_dict["coord"].shape[0] > point_max:
+ if self.mode == "random":
+ center = data_dict["coord"][
+ np.random.randint(data_dict["coord"].shape[0])
+ ]
+ elif self.mode == "center":
+ center = data_dict["coord"][data_dict["coord"].shape[0] // 2]
+ else:
+ raise NotImplementedError
+ idx_crop = np.argsort(np.sum(np.square(data_dict["coord"] - center), 1))[
+ :point_max
+ ]
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][idx_crop]
+ if "origin_coord" in data_dict.keys():
+ data_dict["origin_coord"] = data_dict["origin_coord"][idx_crop]
+ if "grid_coord" in data_dict.keys():
+ data_dict["grid_coord"] = data_dict["grid_coord"][idx_crop]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][idx_crop]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][idx_crop]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][idx_crop]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][idx_crop]
+ if "displacement" in data_dict.keys():
+ data_dict["displacement"] = data_dict["displacement"][idx_crop]
+ if "strength" in data_dict.keys():
+ data_dict["strength"] = data_dict["strength"][idx_crop]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ShufflePoint(object):
+ def __call__(self, data_dict):
+ assert "coord" in data_dict.keys()
+ shuffle_index = np.arange(data_dict["coord"].shape[0])
+ np.random.shuffle(shuffle_index)
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][shuffle_index]
+ if "grid_coord" in data_dict.keys():
+ data_dict["grid_coord"] = data_dict["grid_coord"][shuffle_index]
+ if "displacement" in data_dict.keys():
+ data_dict["displacement"] = data_dict["displacement"][shuffle_index]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][shuffle_index]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][shuffle_index]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][shuffle_index]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][shuffle_index]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class CropBoundary(object):
+ def __call__(self, data_dict):
+ assert "segment" in data_dict
+ segment = data_dict["segment"].flatten()
+ mask = (segment != 0) * (segment != 1)
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][mask]
+ if "grid_coord" in data_dict.keys():
+ data_dict["grid_coord"] = data_dict["grid_coord"][mask]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][mask]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][mask]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][mask]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][mask]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ContrastiveViewsGenerator(object):
+ def __init__(
+ self,
+ view_keys=("coord", "color", "normal", "origin_coord"),
+ view_trans_cfg=None,
+ ):
+ self.view_keys = view_keys
+ self.view_trans = Compose(view_trans_cfg)
+
+ def __call__(self, data_dict):
+ view1_dict = dict()
+ view2_dict = dict()
+ for key in self.view_keys:
+ view1_dict[key] = data_dict[key].copy()
+ view2_dict[key] = data_dict[key].copy()
+ view1_dict = self.view_trans(view1_dict)
+ view2_dict = self.view_trans(view2_dict)
+ for key, value in view1_dict.items():
+ data_dict["view1_" + key] = value
+ for key, value in view2_dict.items():
+ data_dict["view2_" + key] = value
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class InstanceParser(object):
+ def __init__(self, segment_ignore_index=(-1, 0, 1), instance_ignore_index=-1):
+ self.segment_ignore_index = segment_ignore_index
+ self.instance_ignore_index = instance_ignore_index
+
+ def __call__(self, data_dict):
+ coord = data_dict["coord"]
+ segment = data_dict["segment"]
+ instance = data_dict["instance"]
+ mask = ~np.in1d(segment, self.segment_ignore_index)
+ # mapping ignored instance to ignore index
+ instance[~mask] = self.instance_ignore_index
+ # reorder left instance
+ unique, inverse = np.unique(instance[mask], return_inverse=True)
+ instance_num = len(unique)
+ instance[mask] = inverse
+ # init instance information
+ centroid = np.ones((coord.shape[0], 3)) * self.instance_ignore_index
+ bbox = np.ones((instance_num, 8)) * self.instance_ignore_index
+ vacancy = [
+ index for index in self.segment_ignore_index if index >= 0
+ ] # vacate class index
+
+ for instance_id in range(instance_num):
+ mask_ = instance == instance_id
+ coord_ = coord[mask_]
+ bbox_min = coord_.min(0)
+ bbox_max = coord_.max(0)
+ bbox_centroid = coord_.mean(0)
+ bbox_center = (bbox_max + bbox_min) / 2
+ bbox_size = bbox_max - bbox_min
+ bbox_theta = np.zeros(1, dtype=coord_.dtype)
+ bbox_class = np.array([segment[mask_][0]], dtype=coord_.dtype)
+ # shift class index to fill vacate class index caused by segment ignore index
+ bbox_class -= np.greater(bbox_class, vacancy).sum()
+
+ centroid[mask_] = bbox_centroid
+ bbox[instance_id] = np.concatenate(
+ [bbox_center, bbox_size, bbox_theta, bbox_class]
+ ) # 3 + 3 + 1 + 1 = 8
+ data_dict["instance"] = instance
+ data_dict["instance_centroid"] = centroid
+ data_dict["bbox"] = bbox
+ return data_dict
+
+
+class Compose(object):
+ def __init__(self, cfg=None):
+ self.cfg = cfg if cfg is not None else []
+ self.transforms = []
+ for t_cfg in self.cfg:
+ self.transforms.append(TRANSFORMS.build(t_cfg))
+
+ def __call__(self, data_dict):
+ for t in self.transforms:
+ data_dict = t(data_dict)
+ return data_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/utils.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3abb9bf88c81f5eae302468ffc91c62bd942a002
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/utils.py
@@ -0,0 +1,59 @@
+"""
+Utils for Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+from collections.abc import Mapping, Sequence
+import numpy as np
+import torch
+from torch.utils.data.dataloader import default_collate
+
+
+def collate_fn(batch):
+ """
+ collate function for point cloud which support dict and list,
+ 'coord' is necessary to determine 'offset'
+ """
+ if not isinstance(batch, Sequence):
+ raise TypeError(f"{batch.dtype} is not supported.")
+
+ if isinstance(batch[0], torch.Tensor):
+ return torch.cat(list(batch))
+ elif isinstance(batch[0], str):
+ # str is also a kind of Sequence, judgement should before Sequence
+ return list(batch)
+ elif isinstance(batch[0], Sequence):
+ for data in batch:
+ data.append(torch.tensor([data[0].shape[0]]))
+ batch = [collate_fn(samples) for samples in zip(*batch)]
+ batch[-1] = torch.cumsum(batch[-1], dim=0).int()
+ return batch
+ elif isinstance(batch[0], Mapping):
+ batch = {key: collate_fn([d[key] for d in batch]) for key in batch[0]}
+ for key in batch.keys():
+ if "offset" in key:
+ batch[key] = torch.cumsum(batch[key], dim=0)
+ return batch
+ else:
+ return default_collate(batch)
+
+
+def point_collate_fn(batch, mix_prob=0):
+ assert isinstance(
+ batch[0], Mapping
+ ) # currently, only support input_dict, rather than input_list
+ batch = collate_fn(batch)
+ if "offset" in batch.keys():
+ # Mix3d (https://arxiv.org/pdf/2110.02210.pdf)
+ if random.random() < mix_prob:
+ batch["offset"] = torch.cat(
+ [batch["offset"][1:-1:2], batch["offset"][-1].unsqueeze(0)], dim=0
+ )
+ return batch
+
+
+def gaussian_kernel(dist2: np.array, a: float = 1, c: float = 5):
+ return a * np.exp(-dist2 / (2 * c**2))
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/waymo.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/waymo.py
new file mode 100644
index 0000000000000000000000000000000000000000..93e7f7ee1a0f18f9c932248b5d1d192dcb78f5f5
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/waymo.py
@@ -0,0 +1,104 @@
+"""
+Waymo dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import glob
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class WaymoDataset(DefaultDataset):
+ def __init__(
+ self,
+ timestamp=(0,),
+ reference_label=True,
+ timing_embedding=False,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ assert timestamp[0] == 0
+ self.timestamp = timestamp
+ self.reference_label = reference_label
+ self.timing_embedding = timing_embedding
+ self.data_list = sorted(self.data_list)
+ _, self.sequence_offset, self.sequence_index = np.unique(
+ [os.path.dirname(data) for data in self.data_list],
+ return_index=True,
+ return_inverse=True,
+ )
+ self.sequence_offset = np.append(self.sequence_offset, len(self.data_list))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ self.split = [self.split]
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*", "*"))
+ return data_list
+
+ @staticmethod
+ def align_pose(coord, pose, target_pose):
+ coord = np.hstack((coord, np.ones_like(coord[:, :1])))
+ pose_align = np.matmul(np.linalg.inv(target_pose), pose)
+ coord = (pose_align @ coord.T).T[:, :3]
+ return coord
+
+ def get_single_frame(self, idx):
+ return super().get_data(idx)
+
+ def get_data(self, idx):
+ idx = idx % len(self.data_list)
+ if self.timestamp == (0,):
+ return self.get_single_frame(idx)
+
+ sequence_index = self.sequence_index[idx]
+ lower, upper = self.sequence_offset[[sequence_index, sequence_index + 1]]
+ major_frame = self.get_single_frame(idx)
+ name = major_frame.pop("name")
+ target_pose = major_frame.pop("pose")
+ for key in major_frame.keys():
+ major_frame[key] = [major_frame[key]]
+
+ for timestamp in self.timestamp[1:]:
+ refer_idx = timestamp + idx
+ if refer_idx < lower or upper <= refer_idx:
+ continue
+ refer_frame = self.get_single_frame(refer_idx)
+ refer_frame.pop("name")
+ pose = refer_frame.pop("pose")
+ refer_frame["coord"] = self.align_pose(
+ refer_frame["coord"], pose, target_pose
+ )
+ if not self.reference_label:
+ refer_frame["segment"] = (
+ np.ones_like(refer_frame["segment"]) * self.ignore_index
+ )
+
+ if self.timing_embedding:
+ refer_frame["strength"] = np.hstack(
+ (
+ refer_frame["strength"],
+ np.ones_like(refer_frame["strength"]) * timestamp,
+ )
+ )
+
+ for key in major_frame.keys():
+ major_frame[key].append(refer_frame[key])
+ for key in major_frame.keys():
+ major_frame[key] = np.concatenate(major_frame[key], axis=0)
+ major_frame["name"] = name
+ return major_frame
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ sequence_path, frame_name = os.path.split(file_path)
+ sequence_name = os.path.basename(sequence_path)
+ data_name = f"{sequence_name}_{frame_name}"
+ return data_name
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/defaults.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..d45e7925a50acb03bb510c46ec4c566f6815cc05
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/defaults.py
@@ -0,0 +1,152 @@
+"""
+Default training/testing logic
+
+modified from detectron2(https://github.com/facebookresearch/detectron2)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import sys
+import argparse
+import multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel
+
+
+import pointcept.utils.comm as comm
+from pointcept.utils.env import get_random_seed, set_seed
+from pointcept.utils.config import Config, DictAction
+
+
+def create_ddp_model(model, *, fp16_compression=False, **kwargs):
+ """
+ Create a DistributedDataParallel model if there are >1 processes.
+ Args:
+ model: a torch.nn.Module
+ fp16_compression: add fp16 compression hooks to the ddp object.
+ See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook
+ kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`.
+ """
+ if comm.get_world_size() == 1:
+ return model
+ # kwargs['find_unused_parameters'] = True
+ if "device_ids" not in kwargs:
+ kwargs["device_ids"] = [comm.get_local_rank()]
+ if "output_device" not in kwargs:
+ kwargs["output_device"] = [comm.get_local_rank()]
+ ddp = DistributedDataParallel(model, **kwargs)
+ if fp16_compression:
+ from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks
+
+ ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook)
+ return ddp
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+ """Worker init func for dataloader.
+
+ The seed of each worker equals to num_worker * rank + worker_id + user_seed
+
+ Args:
+ worker_id (int): Worker id.
+ num_workers (int): Number of workers.
+ rank (int): The rank of current process.
+ seed (int): The random seed to use.
+ """
+
+ worker_seed = num_workers * rank + worker_id + seed
+ set_seed(worker_seed)
+
+
+def default_argument_parser(epilog=None):
+ parser = argparse.ArgumentParser(
+ epilog=epilog
+ or f"""
+ Examples:
+ Run on single machine:
+ $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml
+ Change some config options:
+ $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001
+ Run on multiple machines:
+ (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags]
+ (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags]
+ """,
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ )
+ parser.add_argument(
+ "--config-file", default="", metavar="FILE", help="path to config file"
+ )
+ parser.add_argument(
+ "--num-gpus", type=int, default=1, help="number of gpus *per machine*"
+ )
+ parser.add_argument(
+ "--num-machines", type=int, default=1, help="total number of machines"
+ )
+ parser.add_argument(
+ "--machine-rank",
+ type=int,
+ default=0,
+ help="the rank of this machine (unique per machine)",
+ )
+ # PyTorch still may leave orphan processes in multi-gpu training.
+ # Therefore we use a deterministic way to obtain port,
+ # so that users are aware of orphan processes by seeing the port occupied.
+ # port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14
+ parser.add_argument(
+ "--dist-url",
+ # default="tcp://127.0.0.1:{}".format(port),
+ default="auto",
+ help="initialization URL for pytorch distributed backend. See "
+ "https://pytorch.org/docs/stable/distributed.html for details.",
+ )
+ parser.add_argument(
+ "--options", nargs="+", action=DictAction, help="custom options"
+ )
+ return parser
+
+
+def default_config_parser(file_path, options):
+ # config name protocol: dataset_name/model_name-exp_name
+ if os.path.isfile(file_path):
+ cfg = Config.fromfile(file_path)
+ else:
+ sep = file_path.find("-")
+ cfg = Config.fromfile(os.path.join(file_path[:sep], file_path[sep + 1 :]))
+
+ if options is not None:
+ cfg.merge_from_dict(options)
+
+ if cfg.seed is None:
+ cfg.seed = get_random_seed()
+
+ cfg.data.train.loop = cfg.epoch // cfg.eval_epoch
+
+ os.makedirs(os.path.join(cfg.save_path, "model"), exist_ok=True)
+ if not cfg.resume:
+ cfg.dump(os.path.join(cfg.save_path, "config.py"))
+ return cfg
+
+
+def default_setup(cfg):
+ # scalar by world size
+ world_size = comm.get_world_size()
+ cfg.num_worker = cfg.num_worker if cfg.num_worker is not None else mp.cpu_count()
+ cfg.num_worker_per_gpu = cfg.num_worker // world_size
+ assert cfg.batch_size % world_size == 0
+ assert cfg.batch_size_val is None or cfg.batch_size_val % world_size == 0
+ assert cfg.batch_size_test is None or cfg.batch_size_test % world_size == 0
+ cfg.batch_size_per_gpu = cfg.batch_size // world_size
+ cfg.batch_size_val_per_gpu = (
+ cfg.batch_size_val // world_size if cfg.batch_size_val is not None else 1
+ )
+ cfg.batch_size_test_per_gpu = (
+ cfg.batch_size_test // world_size if cfg.batch_size_test is not None else 1
+ )
+ # update data loop
+ assert cfg.epoch % cfg.eval_epoch == 0
+ # settle random seed
+ rank = comm.get_rank()
+ seed = None if cfg.seed is None else cfg.seed * cfg.num_worker_per_gpu + rank
+ set_seed(seed)
+ return cfg
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ab2c4beb7f1938d9703e572ad8619fe88bff223
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/__init__.py
@@ -0,0 +1,5 @@
+from .default import HookBase
+from .misc import *
+from .evaluator import *
+
+from .builder import build_hooks
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/builder.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f4cce4871b0e18f3adc1f7430a8d5410442c77c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/builder.py
@@ -0,0 +1,18 @@
+"""
+Hook Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+
+HOOKS = Registry("hooks")
+
+
+def build_hooks(cfg):
+ hooks = []
+ for hook_cfg in cfg:
+ hooks.append(HOOKS.build(hook_cfg))
+ return hooks
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/default.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..87a64415a5a66d2570dffbaa7b90707443be42e2
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/default.py
@@ -0,0 +1,32 @@
+"""
+Default Hook
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+
+class HookBase:
+ """
+ Base class for hooks that can be registered with :class:`TrainerBase`.
+ """
+
+ trainer = None # A weak reference to the trainer object.
+
+ def before_train(self):
+ pass
+
+ def before_epoch(self):
+ pass
+
+ def before_step(self):
+ pass
+
+ def after_step(self):
+ pass
+
+ def after_epoch(self):
+ pass
+
+ def after_train(self):
+ pass
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/evaluator.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..02b35b3abd83e0a7b59f532f1ca8aacf70afcce2
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/evaluator.py
@@ -0,0 +1,581 @@
+"""
+Evaluate Hook
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import pointops
+from uuid import uuid4
+
+import pointcept.utils.comm as comm
+from pointcept.utils.misc import intersection_and_union_gpu
+
+from .default import HookBase
+from .builder import HOOKS
+
+
+@HOOKS.register_module()
+class ClsEvaluator(HookBase):
+ def after_epoch(self):
+ if self.trainer.cfg.evaluate:
+ self.eval()
+
+ def eval(self):
+ self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ self.trainer.model.eval()
+ for i, input_dict in enumerate(self.trainer.val_loader):
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ output_dict = self.trainer.model(input_dict)
+ output = output_dict["cls_logits"]
+ loss = output_dict["loss"]
+ pred = output.max(1)[1]
+ label = input_dict["category"]
+ intersection, union, target = intersection_and_union_gpu(
+ pred,
+ label,
+ self.trainer.cfg.data.num_classes,
+ self.trainer.cfg.data.ignore_index,
+ )
+ if comm.get_world_size() > 1:
+ dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(
+ target
+ )
+ intersection, union, target = (
+ intersection.cpu().numpy(),
+ union.cpu().numpy(),
+ target.cpu().numpy(),
+ )
+ # Here there is no need to sync since sync happened in dist.all_reduce
+ self.trainer.storage.put_scalar("val_intersection", intersection)
+ self.trainer.storage.put_scalar("val_union", union)
+ self.trainer.storage.put_scalar("val_target", target)
+ self.trainer.storage.put_scalar("val_loss", loss.item())
+ self.trainer.logger.info(
+ "Test: [{iter}/{max_iter}] "
+ "Loss {loss:.4f} ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item()
+ )
+ )
+ loss_avg = self.trainer.storage.history("val_loss").avg
+ intersection = self.trainer.storage.history("val_intersection").total
+ union = self.trainer.storage.history("val_union").total
+ target = self.trainer.storage.history("val_target").total
+ iou_class = intersection / (union + 1e-10)
+ acc_class = intersection / (target + 1e-10)
+ m_iou = np.mean(iou_class)
+ m_acc = np.mean(acc_class)
+ all_acc = sum(intersection) / (sum(target) + 1e-10)
+ self.trainer.logger.info(
+ "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(
+ m_iou, m_acc, all_acc
+ )
+ )
+ for i in range(self.trainer.cfg.data.num_classes):
+ self.trainer.logger.info(
+ "Class_{idx}-{name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.trainer.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=acc_class[i],
+ )
+ )
+ current_epoch = self.trainer.epoch + 1
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch)
+ self.trainer.writer.add_scalar("val/mIoU", m_iou, current_epoch)
+ self.trainer.writer.add_scalar("val/mAcc", m_acc, current_epoch)
+ self.trainer.writer.add_scalar("val/allAcc", all_acc, current_epoch)
+ self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+ self.trainer.comm_info["current_metric_value"] = all_acc # save for saver
+ self.trainer.comm_info["current_metric_name"] = "allAcc" # save for saver
+
+ def after_train(self):
+ self.trainer.logger.info(
+ "Best {}: {:.4f}".format("allAcc", self.trainer.best_metric_value)
+ )
+
+
+@HOOKS.register_module()
+class SemSegEvaluator(HookBase):
+ def after_epoch(self):
+ if self.trainer.cfg.evaluate:
+ self.eval()
+
+ def eval(self):
+ self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ self.trainer.model.eval()
+ for i, input_dict in enumerate(self.trainer.val_loader):
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ output_dict = self.trainer.model(input_dict)
+ output = output_dict["seg_logits"]
+ loss = output_dict["loss"]
+ pred = output.max(1)[1]
+ segment = input_dict["segment"]
+ if "origin_coord" in input_dict.keys():
+ idx, _ = pointops.knn_query(
+ 1,
+ input_dict["coord"].float(),
+ input_dict["offset"].int(),
+ input_dict["origin_coord"].float(),
+ input_dict["origin_offset"].int(),
+ )
+ pred = pred[idx.flatten().long()]
+ segment = input_dict["origin_segment"]
+ intersection, union, target = intersection_and_union_gpu(
+ pred,
+ segment,
+ self.trainer.cfg.data.num_classes,
+ self.trainer.cfg.data.ignore_index,
+ )
+ if comm.get_world_size() > 1:
+ dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(
+ target
+ )
+ intersection, union, target = (
+ intersection.cpu().numpy(),
+ union.cpu().numpy(),
+ target.cpu().numpy(),
+ )
+ # Here there is no need to sync since sync happened in dist.all_reduce
+ self.trainer.storage.put_scalar("val_intersection", intersection)
+ self.trainer.storage.put_scalar("val_union", union)
+ self.trainer.storage.put_scalar("val_target", target)
+ self.trainer.storage.put_scalar("val_loss", loss.item())
+ info = "Test: [{iter}/{max_iter}] ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader)
+ )
+ if "origin_coord" in input_dict.keys():
+ info = "Interp. " + info
+ self.trainer.logger.info(
+ info
+ + "Loss {loss:.4f} ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item()
+ )
+ )
+ loss_avg = self.trainer.storage.history("val_loss").avg
+ intersection = self.trainer.storage.history("val_intersection").total
+ union = self.trainer.storage.history("val_union").total
+ target = self.trainer.storage.history("val_target").total
+ iou_class = intersection / (union + 1e-10)
+ acc_class = intersection / (target + 1e-10)
+ m_iou = np.mean(iou_class)
+ m_acc = np.mean(acc_class)
+ all_acc = sum(intersection) / (sum(target) + 1e-10)
+ self.trainer.logger.info(
+ "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(
+ m_iou, m_acc, all_acc
+ )
+ )
+ for i in range(self.trainer.cfg.data.num_classes):
+ self.trainer.logger.info(
+ "Class_{idx}-{name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.trainer.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=acc_class[i],
+ )
+ )
+ current_epoch = self.trainer.epoch + 1
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch)
+ self.trainer.writer.add_scalar("val/mIoU", m_iou, current_epoch)
+ self.trainer.writer.add_scalar("val/mAcc", m_acc, current_epoch)
+ self.trainer.writer.add_scalar("val/allAcc", all_acc, current_epoch)
+ self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+ self.trainer.comm_info["current_metric_value"] = m_iou # save for saver
+ self.trainer.comm_info["current_metric_name"] = "mIoU" # save for saver
+
+ def after_train(self):
+ self.trainer.logger.info(
+ "Best {}: {:.4f}".format("mIoU", self.trainer.best_metric_value)
+ )
+
+
+@HOOKS.register_module()
+class InsSegEvaluator(HookBase):
+ def __init__(self, segment_ignore_index=(-1,), instance_ignore_index=-1):
+ self.segment_ignore_index = segment_ignore_index
+ self.instance_ignore_index = instance_ignore_index
+
+ self.valid_class_names = None # update in before train
+ self.overlaps = np.append(np.arange(0.5, 0.95, 0.05), 0.25)
+ self.min_region_sizes = 100
+ self.distance_threshes = float("inf")
+ self.distance_confs = -float("inf")
+
+ def before_train(self):
+ self.valid_class_names = [
+ self.trainer.cfg.data.names[i]
+ for i in range(self.trainer.cfg.data.num_classes)
+ if i not in self.segment_ignore_index
+ ]
+
+ def after_epoch(self):
+ if self.trainer.cfg.evaluate:
+ self.eval()
+
+ def associate_instances(self, pred, segment, instance):
+ segment = segment.cpu().numpy()
+ instance = instance.cpu().numpy()
+ void_mask = np.in1d(segment, self.segment_ignore_index)
+
+ assert (
+ pred["pred_classes"].shape[0]
+ == pred["pred_scores"].shape[0]
+ == pred["pred_masks"].shape[0]
+ )
+ assert pred["pred_masks"].shape[1] == segment.shape[0] == instance.shape[0]
+ # get gt instances
+ gt_instances = dict()
+ for i in range(self.trainer.cfg.data.num_classes):
+ if i not in self.segment_ignore_index:
+ gt_instances[self.trainer.cfg.data.names[i]] = []
+ instance_ids, idx, counts = np.unique(
+ instance, return_index=True, return_counts=True
+ )
+ segment_ids = segment[idx]
+ for i in range(len(instance_ids)):
+ if instance_ids[i] == self.instance_ignore_index:
+ continue
+ if segment_ids[i] in self.segment_ignore_index:
+ continue
+ gt_inst = dict()
+ gt_inst["instance_id"] = instance_ids[i]
+ gt_inst["segment_id"] = segment_ids[i]
+ gt_inst["dist_conf"] = 0.0
+ gt_inst["med_dist"] = -1.0
+ gt_inst["vert_count"] = counts[i]
+ gt_inst["matched_pred"] = []
+ gt_instances[self.trainer.cfg.data.names[segment_ids[i]]].append(gt_inst)
+
+ # get pred instances and associate with gt
+ pred_instances = dict()
+ for i in range(self.trainer.cfg.data.num_classes):
+ if i not in self.segment_ignore_index:
+ pred_instances[self.trainer.cfg.data.names[i]] = []
+ instance_id = 0
+ for i in range(len(pred["pred_classes"])):
+ if pred["pred_classes"][i] in self.segment_ignore_index:
+ continue
+ pred_inst = dict()
+ pred_inst["uuid"] = uuid4()
+ pred_inst["instance_id"] = instance_id
+ pred_inst["segment_id"] = pred["pred_classes"][i]
+ pred_inst["confidence"] = pred["pred_scores"][i]
+ pred_inst["mask"] = np.not_equal(pred["pred_masks"][i], 0)
+ pred_inst["vert_count"] = np.count_nonzero(pred_inst["mask"])
+ pred_inst["void_intersection"] = np.count_nonzero(
+ np.logical_and(void_mask, pred_inst["mask"])
+ )
+ if pred_inst["vert_count"] < self.min_region_sizes:
+ continue # skip if empty
+ segment_name = self.trainer.cfg.data.names[pred_inst["segment_id"]]
+ matched_gt = []
+ for gt_idx, gt_inst in enumerate(gt_instances[segment_name]):
+ intersection = np.count_nonzero(
+ np.logical_and(
+ instance == gt_inst["instance_id"], pred_inst["mask"]
+ )
+ )
+ if intersection > 0:
+ gt_inst_ = gt_inst.copy()
+ pred_inst_ = pred_inst.copy()
+ gt_inst_["intersection"] = intersection
+ pred_inst_["intersection"] = intersection
+ matched_gt.append(gt_inst_)
+ gt_inst["matched_pred"].append(pred_inst_)
+ pred_inst["matched_gt"] = matched_gt
+ pred_instances[segment_name].append(pred_inst)
+ instance_id += 1
+ return gt_instances, pred_instances
+
+ def evaluate_matches(self, scenes):
+ overlaps = self.overlaps
+ min_region_sizes = [self.min_region_sizes]
+ dist_threshes = [self.distance_threshes]
+ dist_confs = [self.distance_confs]
+
+ # results: class x overlap
+ ap_table = np.zeros(
+ (len(dist_threshes), len(self.valid_class_names), len(overlaps)), float
+ )
+ for di, (min_region_size, distance_thresh, distance_conf) in enumerate(
+ zip(min_region_sizes, dist_threshes, dist_confs)
+ ):
+ for oi, overlap_th in enumerate(overlaps):
+ pred_visited = {}
+ for scene in scenes:
+ for _ in scene["pred"]:
+ for label_name in self.valid_class_names:
+ for p in scene["pred"][label_name]:
+ if "uuid" in p:
+ pred_visited[p["uuid"]] = False
+ for li, label_name in enumerate(self.valid_class_names):
+ y_true = np.empty(0)
+ y_score = np.empty(0)
+ hard_false_negatives = 0
+ has_gt = False
+ has_pred = False
+ for scene in scenes:
+ pred_instances = scene["pred"][label_name]
+ gt_instances = scene["gt"][label_name]
+ # filter groups in ground truth
+ gt_instances = [
+ gt
+ for gt in gt_instances
+ if gt["vert_count"] >= min_region_size
+ and gt["med_dist"] <= distance_thresh
+ and gt["dist_conf"] >= distance_conf
+ ]
+ if gt_instances:
+ has_gt = True
+ if pred_instances:
+ has_pred = True
+
+ cur_true = np.ones(len(gt_instances))
+ cur_score = np.ones(len(gt_instances)) * (-float("inf"))
+ cur_match = np.zeros(len(gt_instances), dtype=bool)
+ # collect matches
+ for gti, gt in enumerate(gt_instances):
+ found_match = False
+ for pred in gt["matched_pred"]:
+ # greedy assignments
+ if pred_visited[pred["uuid"]]:
+ continue
+ overlap = float(pred["intersection"]) / (
+ gt["vert_count"]
+ + pred["vert_count"]
+ - pred["intersection"]
+ )
+ if overlap > overlap_th:
+ confidence = pred["confidence"]
+ # if already have a prediction for this gt,
+ # the prediction with the lower score is automatically a false positive
+ if cur_match[gti]:
+ max_score = max(cur_score[gti], confidence)
+ min_score = min(cur_score[gti], confidence)
+ cur_score[gti] = max_score
+ # append false positive
+ cur_true = np.append(cur_true, 0)
+ cur_score = np.append(cur_score, min_score)
+ cur_match = np.append(cur_match, True)
+ # otherwise set score
+ else:
+ found_match = True
+ cur_match[gti] = True
+ cur_score[gti] = confidence
+ pred_visited[pred["uuid"]] = True
+ if not found_match:
+ hard_false_negatives += 1
+ # remove non-matched ground truth instances
+ cur_true = cur_true[cur_match]
+ cur_score = cur_score[cur_match]
+
+ # collect non-matched predictions as false positive
+ for pred in pred_instances:
+ found_gt = False
+ for gt in pred["matched_gt"]:
+ overlap = float(gt["intersection"]) / (
+ gt["vert_count"]
+ + pred["vert_count"]
+ - gt["intersection"]
+ )
+ if overlap > overlap_th:
+ found_gt = True
+ break
+ if not found_gt:
+ num_ignore = pred["void_intersection"]
+ for gt in pred["matched_gt"]:
+ if gt["segment_id"] in self.segment_ignore_index:
+ num_ignore += gt["intersection"]
+ # small ground truth instances
+ if (
+ gt["vert_count"] < min_region_size
+ or gt["med_dist"] > distance_thresh
+ or gt["dist_conf"] < distance_conf
+ ):
+ num_ignore += gt["intersection"]
+ proportion_ignore = (
+ float(num_ignore) / pred["vert_count"]
+ )
+ # if not ignored append false positive
+ if proportion_ignore <= overlap_th:
+ cur_true = np.append(cur_true, 0)
+ confidence = pred["confidence"]
+ cur_score = np.append(cur_score, confidence)
+
+ # append to overall results
+ y_true = np.append(y_true, cur_true)
+ y_score = np.append(y_score, cur_score)
+
+ # compute average precision
+ if has_gt and has_pred:
+ # compute precision recall curve first
+
+ # sorting and cumsum
+ score_arg_sort = np.argsort(y_score)
+ y_score_sorted = y_score[score_arg_sort]
+ y_true_sorted = y_true[score_arg_sort]
+ y_true_sorted_cumsum = np.cumsum(y_true_sorted)
+
+ # unique thresholds
+ (thresholds, unique_indices) = np.unique(
+ y_score_sorted, return_index=True
+ )
+ num_prec_recall = len(unique_indices) + 1
+
+ # prepare precision recall
+ num_examples = len(y_score_sorted)
+ # https://github.com/ScanNet/ScanNet/pull/26
+ # all predictions are non-matched but also all of them are ignored and not counted as FP
+ # y_true_sorted_cumsum is empty
+ # num_true_examples = y_true_sorted_cumsum[-1]
+ num_true_examples = (
+ y_true_sorted_cumsum[-1]
+ if len(y_true_sorted_cumsum) > 0
+ else 0
+ )
+ precision = np.zeros(num_prec_recall)
+ recall = np.zeros(num_prec_recall)
+
+ # deal with the first point
+ y_true_sorted_cumsum = np.append(y_true_sorted_cumsum, 0)
+ # deal with remaining
+ for idx_res, idx_scores in enumerate(unique_indices):
+ cumsum = y_true_sorted_cumsum[idx_scores - 1]
+ tp = num_true_examples - cumsum
+ fp = num_examples - idx_scores - tp
+ fn = cumsum + hard_false_negatives
+ p = float(tp) / (tp + fp)
+ r = float(tp) / (tp + fn)
+ precision[idx_res] = p
+ recall[idx_res] = r
+
+ # first point in curve is artificial
+ precision[-1] = 1.0
+ recall[-1] = 0.0
+
+ # compute average of precision-recall curve
+ recall_for_conv = np.copy(recall)
+ recall_for_conv = np.append(recall_for_conv[0], recall_for_conv)
+ recall_for_conv = np.append(recall_for_conv, 0.0)
+
+ stepWidths = np.convolve(
+ recall_for_conv, [-0.5, 0, 0.5], "valid"
+ )
+ # integrate is now simply a dot product
+ ap_current = np.dot(precision, stepWidths)
+
+ elif has_gt:
+ ap_current = 0.0
+ else:
+ ap_current = float("nan")
+ ap_table[di, li, oi] = ap_current
+ d_inf = 0
+ o50 = np.where(np.isclose(self.overlaps, 0.5))
+ o25 = np.where(np.isclose(self.overlaps, 0.25))
+ oAllBut25 = np.where(np.logical_not(np.isclose(self.overlaps, 0.25)))
+ ap_scores = dict()
+ ap_scores["all_ap"] = np.nanmean(ap_table[d_inf, :, oAllBut25])
+ ap_scores["all_ap_50%"] = np.nanmean(ap_table[d_inf, :, o50])
+ ap_scores["all_ap_25%"] = np.nanmean(ap_table[d_inf, :, o25])
+ ap_scores["classes"] = {}
+ for li, label_name in enumerate(self.valid_class_names):
+ ap_scores["classes"][label_name] = {}
+ ap_scores["classes"][label_name]["ap"] = np.average(
+ ap_table[d_inf, li, oAllBut25]
+ )
+ ap_scores["classes"][label_name]["ap50%"] = np.average(
+ ap_table[d_inf, li, o50]
+ )
+ ap_scores["classes"][label_name]["ap25%"] = np.average(
+ ap_table[d_inf, li, o25]
+ )
+ return ap_scores
+
+ def eval(self):
+ self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ self.trainer.model.eval()
+ scenes = []
+ for i, input_dict in enumerate(self.trainer.val_loader):
+ assert (
+ len(input_dict["offset"]) == 1
+ ) # currently only support bs 1 for each GPU
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ output_dict = self.trainer.model(input_dict)
+
+ loss = output_dict["loss"]
+
+ segment = input_dict["segment"]
+ instance = input_dict["instance"]
+ # map to origin
+ if "origin_coord" in input_dict.keys():
+ idx, _ = pointops.knn_query(
+ 1,
+ input_dict["coord"].float(),
+ input_dict["offset"].int(),
+ input_dict["origin_coord"].float(),
+ input_dict["origin_offset"].int(),
+ )
+ idx = idx.cpu().flatten().long()
+ output_dict["pred_masks"] = output_dict["pred_masks"][:, idx]
+ segment = input_dict["origin_segment"]
+ instance = input_dict["origin_instance"]
+
+ gt_instances, pred_instance = self.associate_instances(
+ output_dict, segment, instance
+ )
+ scenes.append(dict(gt=gt_instances, pred=pred_instance))
+
+ self.trainer.storage.put_scalar("val_loss", loss.item())
+ self.trainer.logger.info(
+ "Test: [{iter}/{max_iter}] "
+ "Loss {loss:.4f} ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item()
+ )
+ )
+
+ loss_avg = self.trainer.storage.history("val_loss").avg
+ comm.synchronize()
+ scenes_sync = comm.gather(scenes, dst=0)
+ scenes = [scene for scenes_ in scenes_sync for scene in scenes_]
+ ap_scores = self.evaluate_matches(scenes)
+ all_ap = ap_scores["all_ap"]
+ all_ap_50 = ap_scores["all_ap_50%"]
+ all_ap_25 = ap_scores["all_ap_25%"]
+ self.trainer.logger.info(
+ "Val result: mAP/AP50/AP25 {:.4f}/{:.4f}/{:.4f}.".format(
+ all_ap, all_ap_50, all_ap_25
+ )
+ )
+ for i, label_name in enumerate(self.valid_class_names):
+ ap = ap_scores["classes"][label_name]["ap"]
+ ap_50 = ap_scores["classes"][label_name]["ap50%"]
+ ap_25 = ap_scores["classes"][label_name]["ap25%"]
+ self.trainer.logger.info(
+ "Class_{idx}-{name} Result: AP/AP50/AP25 {AP:.4f}/{AP50:.4f}/{AP25:.4f}".format(
+ idx=i, name=label_name, AP=ap, AP50=ap_50, AP25=ap_25
+ )
+ )
+ current_epoch = self.trainer.epoch + 1
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch)
+ self.trainer.writer.add_scalar("val/mAP", all_ap, current_epoch)
+ self.trainer.writer.add_scalar("val/AP50", all_ap_50, current_epoch)
+ self.trainer.writer.add_scalar("val/AP25", all_ap_25, current_epoch)
+ self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+ self.trainer.comm_info["current_metric_value"] = all_ap_50 # save for saver
+ self.trainer.comm_info["current_metric_name"] = "AP50" # save for saver
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/misc.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..155bf5541fc8e5406618a801ba4ccb1e369d4308
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/hooks/misc.py
@@ -0,0 +1,464 @@
+"""
+Misc Hook
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import sys
+import glob
+import os
+import shutil
+import time
+import torch
+import torch.utils.data
+from collections import OrderedDict
+
+if sys.version_info >= (3, 10):
+ from collections.abc import Sequence
+else:
+ from collections import Sequence
+from pointcept.utils.timer import Timer
+from pointcept.utils.comm import is_main_process, synchronize, get_world_size
+from pointcept.utils.cache import shared_dict
+import pointcept.utils.comm as comm
+from pointcept.engines.test import TESTERS
+
+from .default import HookBase
+from .builder import HOOKS
+
+
+@HOOKS.register_module()
+class IterationTimer(HookBase):
+ def __init__(self, warmup_iter=1):
+ self._warmup_iter = warmup_iter
+ self._start_time = time.perf_counter()
+ self._iter_timer = Timer()
+ self._remain_iter = 0
+
+ def before_train(self):
+ self._start_time = time.perf_counter()
+ self._remain_iter = self.trainer.max_epoch * len(self.trainer.train_loader)
+
+ def before_epoch(self):
+ self._iter_timer.reset()
+
+ def before_step(self):
+ data_time = self._iter_timer.seconds()
+ self.trainer.storage.put_scalar("data_time", data_time)
+
+ def after_step(self):
+ batch_time = self._iter_timer.seconds()
+ self._iter_timer.reset()
+ self.trainer.storage.put_scalar("batch_time", batch_time)
+ self._remain_iter -= 1
+ remain_time = self._remain_iter * self.trainer.storage.history("batch_time").avg
+ t_m, t_s = divmod(remain_time, 60)
+ t_h, t_m = divmod(t_m, 60)
+ remain_time = "{:02d}:{:02d}:{:02d}".format(int(t_h), int(t_m), int(t_s))
+ if "iter_info" in self.trainer.comm_info.keys():
+ info = (
+ "Data {data_time_val:.3f} ({data_time_avg:.3f}) "
+ "Batch {batch_time_val:.3f} ({batch_time_avg:.3f}) "
+ "Remain {remain_time} ".format(
+ data_time_val=self.trainer.storage.history("data_time").val,
+ data_time_avg=self.trainer.storage.history("data_time").avg,
+ batch_time_val=self.trainer.storage.history("batch_time").val,
+ batch_time_avg=self.trainer.storage.history("batch_time").avg,
+ remain_time=remain_time,
+ )
+ )
+ self.trainer.comm_info["iter_info"] += info
+ if self.trainer.comm_info["iter"] <= self._warmup_iter:
+ self.trainer.storage.history("data_time").reset()
+ self.trainer.storage.history("batch_time").reset()
+
+
+@HOOKS.register_module()
+class InformationWriter(HookBase):
+ def __init__(self):
+ self.curr_iter = 0
+ self.model_output_keys = []
+
+ def before_train(self):
+ self.trainer.comm_info["iter_info"] = ""
+ self.curr_iter = self.trainer.start_epoch * len(self.trainer.train_loader)
+
+ def before_step(self):
+ self.curr_iter += 1
+ # MSC pretrain do not have offset information. Comment the code for support MSC
+ # info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] " \
+ # "Scan {batch_size} ({points_num}) ".format(
+ # epoch=self.trainer.epoch + 1, max_epoch=self.trainer.max_epoch,
+ # iter=self.trainer.comm_info["iter"], max_iter=len(self.trainer.train_loader),
+ # batch_size=len(self.trainer.comm_info["input_dict"]["offset"]),
+ # points_num=self.trainer.comm_info["input_dict"]["offset"][-1]
+ # )
+ info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] ".format(
+ epoch=self.trainer.epoch + 1,
+ max_epoch=self.trainer.max_epoch,
+ iter=self.trainer.comm_info["iter"] + 1,
+ max_iter=len(self.trainer.train_loader),
+ )
+ self.trainer.comm_info["iter_info"] += info
+
+ def after_step(self):
+ if "model_output_dict" in self.trainer.comm_info.keys():
+ model_output_dict = self.trainer.comm_info["model_output_dict"]
+ self.model_output_keys = model_output_dict.keys()
+ for key in self.model_output_keys:
+ self.trainer.storage.put_scalar(key, model_output_dict[key].item())
+
+ for key in self.model_output_keys:
+ self.trainer.comm_info["iter_info"] += "{key}: {value:.4f} ".format(
+ key=key, value=self.trainer.storage.history(key).val
+ )
+ lr = self.trainer.optimizer.state_dict()["param_groups"][0]["lr"]
+ self.trainer.comm_info["iter_info"] += "Lr: {lr:.5f}".format(lr=lr)
+ self.trainer.logger.info(self.trainer.comm_info["iter_info"])
+ self.trainer.comm_info["iter_info"] = "" # reset iter info
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("lr", lr, self.curr_iter)
+ for key in self.model_output_keys:
+ self.trainer.writer.add_scalar(
+ "train_batch/" + key,
+ self.trainer.storage.history(key).val,
+ self.curr_iter,
+ )
+
+ def after_epoch(self):
+ epoch_info = "Train result: "
+ for key in self.model_output_keys:
+ epoch_info += "{key}: {value:.4f} ".format(
+ key=key, value=self.trainer.storage.history(key).avg
+ )
+ self.trainer.logger.info(epoch_info)
+ if self.trainer.writer is not None:
+ for key in self.model_output_keys:
+ self.trainer.writer.add_scalar(
+ "train/" + key,
+ self.trainer.storage.history(key).avg,
+ self.trainer.epoch + 1,
+ )
+
+
+@HOOKS.register_module()
+class CheckpointSaver(HookBase):
+ def __init__(self, save_freq=None):
+ self.save_freq = save_freq # None or int, None indicate only save model last
+
+ def after_epoch(self):
+ if is_main_process():
+ is_best = False
+ if self.trainer.cfg.evaluate:
+ current_metric_value = self.trainer.comm_info["current_metric_value"]
+ current_metric_name = self.trainer.comm_info["current_metric_name"]
+ if current_metric_value > self.trainer.best_metric_value:
+ self.trainer.best_metric_value = current_metric_value
+ is_best = True
+ self.trainer.logger.info(
+ "Best validation {} updated to: {:.4f}".format(
+ current_metric_name, current_metric_value
+ )
+ )
+ self.trainer.logger.info(
+ "Currently Best {}: {:.4f}".format(
+ current_metric_name, self.trainer.best_metric_value
+ )
+ )
+
+ filename = os.path.join(
+ self.trainer.cfg.save_path, "model", "model_last.pth"
+ )
+ self.trainer.logger.info("Saving checkpoint to: " + filename)
+ torch.save(
+ {
+ "epoch": self.trainer.epoch + 1,
+ "state_dict": self.trainer.model.state_dict(),
+ "optimizer": self.trainer.optimizer.state_dict(),
+ "scheduler": self.trainer.scheduler.state_dict(),
+ "scaler": (
+ self.trainer.scaler.state_dict()
+ if self.trainer.cfg.enable_amp
+ else None
+ ),
+ "best_metric_value": self.trainer.best_metric_value,
+ },
+ filename + ".tmp",
+ )
+ os.replace(filename + ".tmp", filename)
+ if is_best:
+ shutil.copyfile(
+ filename,
+ os.path.join(self.trainer.cfg.save_path, "model", "model_best.pth"),
+ )
+ if self.save_freq and (self.trainer.epoch + 1) % self.save_freq == 0:
+ shutil.copyfile(
+ filename,
+ os.path.join(
+ self.trainer.cfg.save_path,
+ "model",
+ f"epoch_{self.trainer.epoch + 1}.pth",
+ ),
+ )
+
+
+@HOOKS.register_module()
+class CheckpointLoader(HookBase):
+ def __init__(self, keywords="", replacement=None, strict=False):
+ self.keywords = keywords
+ self.replacement = replacement if replacement is not None else keywords
+ self.strict = strict
+
+ def before_train(self):
+ self.trainer.logger.info("=> Loading checkpoint & weight ...")
+ if self.trainer.cfg.weight and os.path.isfile(self.trainer.cfg.weight):
+ self.trainer.logger.info(f"Loading weight at: {self.trainer.cfg.weight}")
+ checkpoint = torch.load(
+ self.trainer.cfg.weight,
+ map_location=lambda storage, loc: storage.cuda(),
+ )
+ self.trainer.logger.info(
+ f"Loading layer weights with keyword: {self.keywords}, "
+ f"replace keyword with: {self.replacement}"
+ )
+ weight = OrderedDict()
+ for key, value in checkpoint["state_dict"].items():
+ if not key.startswith("module."):
+ key = "module." + key # xxx.xxx -> module.xxx.xxx
+ # Now all keys contain "module." no matter DDP or not.
+ if self.keywords in key:
+ key = key.replace(self.keywords, self.replacement)
+ if comm.get_world_size() == 1:
+ key = key[7:] # module.xxx.xxx -> xxx.xxx
+ weight[key] = value
+ load_state_info = self.trainer.model.load_state_dict(
+ weight, strict=self.strict
+ )
+ self.trainer.logger.info(f"Missing keys: {load_state_info[0]}")
+ if self.trainer.cfg.resume:
+ self.trainer.logger.info(
+ f"Resuming train at eval epoch: {checkpoint['epoch']}"
+ )
+ self.trainer.start_epoch = checkpoint["epoch"]
+ self.trainer.best_metric_value = checkpoint["best_metric_value"]
+ self.trainer.optimizer.load_state_dict(checkpoint["optimizer"])
+ self.trainer.scheduler.load_state_dict(checkpoint["scheduler"])
+ if self.trainer.cfg.enable_amp:
+ self.trainer.scaler.load_state_dict(checkpoint["scaler"])
+ else:
+ self.trainer.logger.info(f"No weight found at: {self.trainer.cfg.weight}")
+
+
+@HOOKS.register_module()
+class PreciseEvaluator(HookBase):
+ def __init__(self, test_last=False):
+ self.test_last = test_last
+
+ def after_train(self):
+ self.trainer.logger.info(
+ ">>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>>"
+ )
+ torch.cuda.empty_cache()
+ cfg = self.trainer.cfg
+ tester = TESTERS.build(
+ dict(type=cfg.test.type, cfg=cfg, model=self.trainer.model)
+ )
+ if self.test_last:
+ self.trainer.logger.info("=> Testing on model_last ...")
+ else:
+ self.trainer.logger.info("=> Testing on model_best ...")
+ best_path = os.path.join(
+ self.trainer.cfg.save_path, "model", "model_best.pth"
+ )
+ checkpoint = torch.load(best_path)
+ state_dict = checkpoint["state_dict"]
+ tester.model.load_state_dict(state_dict, strict=True)
+ tester.test()
+
+
+@HOOKS.register_module()
+class DataCacheOperator(HookBase):
+ def __init__(self, data_root, split):
+ self.data_root = data_root
+ self.split = split
+ self.data_list = self.get_data_list()
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_cache_name(self, data_path):
+ data_name = data_path.replace(os.path.dirname(self.data_root), "")
+ return "pointcept" + data_name.replace(os.path.sep, "-")
+
+ def before_train(self):
+ self.trainer.logger.info(
+ f"=> Caching dataset: {self.data_root}, split: {self.split} ..."
+ )
+ if is_main_process():
+ dataset = self.trainer.train_loader.dataset
+ for i in range(len(dataset)):
+ data_dict = dataset[i]
+ name = data_dict["name"]
+ shared_dict(f"Pointcept-{name}", data_dict)
+ synchronize()
+
+
+@HOOKS.register_module()
+class RuntimeProfiler(HookBase):
+ def __init__(
+ self,
+ forward=True,
+ backward=True,
+ interrupt=False,
+ warm_up=2,
+ sort_by="cuda_time_total",
+ row_limit=30,
+ ):
+ self.forward = forward
+ self.backward = backward
+ self.interrupt = interrupt
+ self.warm_up = warm_up
+ self.sort_by = sort_by
+ self.row_limit = row_limit
+
+ def before_train(self):
+ self.trainer.logger.info("Profiling runtime ...")
+ from torch.profiler import profile, record_function, ProfilerActivity
+
+ for i, input_dict in enumerate(self.trainer.train_loader):
+ if i == self.warm_up + 1:
+ break
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ if self.forward:
+ with profile(
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ ) as forward_prof:
+ with record_function("model_inference"):
+ output_dict = self.trainer.model(input_dict)
+ else:
+ output_dict = self.trainer.model(input_dict)
+ loss = output_dict["loss"]
+ if self.backward:
+ with profile(
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ ) as backward_prof:
+ with record_function("model_inference"):
+ loss.backward()
+ self.trainer.logger.info(f"Profile: [{i + 1}/{self.warm_up + 1}]")
+ if self.forward:
+ self.trainer.logger.info(
+ "Forward profile: \n"
+ + str(
+ forward_prof.key_averages().table(
+ sort_by=self.sort_by, row_limit=self.row_limit
+ )
+ )
+ )
+ forward_prof.export_chrome_trace(
+ os.path.join(self.trainer.cfg.save_path, "forward_trace.json")
+ )
+
+ if self.backward:
+ self.trainer.logger.info(
+ "Backward profile: \n"
+ + str(
+ backward_prof.key_averages().table(
+ sort_by=self.sort_by, row_limit=self.row_limit
+ )
+ )
+ )
+ backward_prof.export_chrome_trace(
+ os.path.join(self.trainer.cfg.save_path, "backward_trace.json")
+ )
+ if self.interrupt:
+ sys.exit(0)
+
+
+@HOOKS.register_module()
+class RuntimeProfilerV2(HookBase):
+ def __init__(
+ self,
+ interrupt=False,
+ wait=1,
+ warmup=1,
+ active=10,
+ repeat=1,
+ sort_by="cuda_time_total",
+ row_limit=30,
+ ):
+ self.interrupt = interrupt
+ self.wait = wait
+ self.warmup = warmup
+ self.active = active
+ self.repeat = repeat
+ self.sort_by = sort_by
+ self.row_limit = row_limit
+
+ def before_train(self):
+ self.trainer.logger.info("Profiling runtime ...")
+ from torch.profiler import (
+ profile,
+ record_function,
+ ProfilerActivity,
+ schedule,
+ tensorboard_trace_handler,
+ )
+
+ prof = profile(
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+ schedule=schedule(
+ wait=self.wait,
+ warmup=self.warmup,
+ active=self.active,
+ repeat=self.repeat,
+ ),
+ on_trace_ready=tensorboard_trace_handler(self.trainer.cfg.save_path),
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ )
+ prof.start()
+ for i, input_dict in enumerate(self.trainer.train_loader):
+ if i >= (self.wait + self.warmup + self.active) * self.repeat:
+ break
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with record_function("model_forward"):
+ output_dict = self.trainer.model(input_dict)
+ loss = output_dict["loss"]
+ with record_function("model_backward"):
+ loss.backward()
+ prof.step()
+ self.trainer.logger.info(
+ f"Profile: [{i + 1}/{(self.wait + self.warmup + self.active) * self.repeat}]"
+ )
+ self.trainer.logger.info(
+ "Profile: \n"
+ + str(
+ prof.key_averages().table(
+ sort_by=self.sort_by, row_limit=self.row_limit
+ )
+ )
+ )
+ prof.stop()
+
+ if self.interrupt:
+ sys.exit(0)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/launch.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..99a8351fe5ab4393c1fab75c3bd546ba66641986
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/launch.py
@@ -0,0 +1,137 @@
+"""
+Launcher
+
+modified from detectron2(https://github.com/facebookresearch/detectron2)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import logging
+from datetime import timedelta
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from pointcept.utils import comm
+
+__all__ = ["DEFAULT_TIMEOUT", "launch"]
+
+DEFAULT_TIMEOUT = timedelta(minutes=60)
+
+
+def _find_free_port():
+ import socket
+
+ sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ # Binding to port 0 will cause the OS to find an available port for us
+ sock.bind(("", 0))
+ port = sock.getsockname()[1]
+ sock.close()
+ # NOTE: there is still a chance the port could be taken by other processes.
+ return port
+
+
+def launch(
+ main_func,
+ num_gpus_per_machine,
+ num_machines=1,
+ machine_rank=0,
+ dist_url=None,
+ cfg=(),
+ timeout=DEFAULT_TIMEOUT,
+):
+ """
+ Launch multi-gpu or distributed training.
+ This function must be called on all machines involved in the training.
+ It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine.
+ Args:
+ main_func: a function that will be called by `main_func(*args)`
+ num_gpus_per_machine (int): number of GPUs per machine
+ num_machines (int): the total number of machines
+ machine_rank (int): the rank of this machine
+ dist_url (str): url to connect to for distributed jobs, including protocol
+ e.g. "tcp://127.0.0.1:8686".
+ Can be set to "auto" to automatically select a free port on localhost
+ timeout (timedelta): timeout of the distributed workers
+ args (tuple): arguments passed to main_func
+ """
+ world_size = num_machines * num_gpus_per_machine
+ if world_size > 1:
+ if dist_url == "auto":
+ assert (
+ num_machines == 1
+ ), "dist_url=auto not supported in multi-machine jobs."
+ port = _find_free_port()
+ dist_url = f"tcp://127.0.0.1:{port}"
+ if num_machines > 1 and dist_url.startswith("file://"):
+ logger = logging.getLogger(__name__)
+ logger.warning(
+ "file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://"
+ )
+
+ mp.spawn(
+ _distributed_worker,
+ nprocs=num_gpus_per_machine,
+ args=(
+ main_func,
+ world_size,
+ num_gpus_per_machine,
+ machine_rank,
+ dist_url,
+ cfg,
+ timeout,
+ ),
+ daemon=False,
+ )
+ else:
+ main_func(*cfg)
+
+
+def _distributed_worker(
+ local_rank,
+ main_func,
+ world_size,
+ num_gpus_per_machine,
+ machine_rank,
+ dist_url,
+ cfg,
+ timeout=DEFAULT_TIMEOUT,
+):
+ assert (
+ torch.cuda.is_available()
+ ), "cuda is not available. Please check your installation."
+ global_rank = machine_rank * num_gpus_per_machine + local_rank
+ try:
+ dist.init_process_group(
+ backend="NCCL",
+ init_method=dist_url,
+ world_size=world_size,
+ rank=global_rank,
+ timeout=timeout,
+ )
+ except Exception as e:
+ logger = logging.getLogger(__name__)
+ logger.error("Process group URL: {}".format(dist_url))
+ raise e
+
+ # Setup the local process group (which contains ranks within the same machine)
+ assert comm._LOCAL_PROCESS_GROUP is None
+ num_machines = world_size // num_gpus_per_machine
+ for i in range(num_machines):
+ ranks_on_i = list(
+ range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine)
+ )
+ pg = dist.new_group(ranks_on_i)
+ if i == machine_rank:
+ comm._LOCAL_PROCESS_GROUP = pg
+
+ assert num_gpus_per_machine <= torch.cuda.device_count()
+ torch.cuda.set_device(local_rank)
+
+ # synchronize is needed here to prevent a possible timeout after calling init_process_group
+ # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172
+ comm.synchronize()
+
+ main_func(*cfg)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/test.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..04378738e269f432c8bf720b4e55fc80af3db280
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/test.py
@@ -0,0 +1,597 @@
+"""
+Tester
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import time
+import numpy as np
+from collections import OrderedDict
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import torch.utils.data
+
+from .defaults import create_ddp_model
+import pointcept.utils.comm as comm
+from pointcept.datasets import build_dataset, collate_fn
+from pointcept.models import build_model
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.registry import Registry
+from pointcept.utils.misc import (
+ AverageMeter,
+ intersection_and_union,
+ intersection_and_union_gpu,
+ make_dirs,
+)
+
+
+TESTERS = Registry("testers")
+
+
+class TesterBase:
+ def __init__(self, cfg, model=None, test_loader=None, verbose=False) -> None:
+ torch.multiprocessing.set_sharing_strategy("file_system")
+ self.logger = get_root_logger(
+ log_file=os.path.join(cfg.save_path, "test.log"),
+ file_mode="a" if cfg.resume else "w",
+ )
+ self.logger.info("=> Loading config ...")
+ self.cfg = cfg
+ self.verbose = verbose
+ if self.verbose:
+ self.logger.info(f"Save path: {cfg.save_path}")
+ self.logger.info(f"Config:\n{cfg.pretty_text}")
+ if model is None:
+ self.logger.info("=> Building model ...")
+ self.model = self.build_model()
+ else:
+ self.model = model
+ if test_loader is None:
+ self.logger.info("=> Building test dataset & dataloader ...")
+ self.test_loader = self.build_test_loader()
+ else:
+ self.test_loader = test_loader
+
+ def build_model(self):
+ model = build_model(self.cfg.model)
+ n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ self.logger.info(f"Num params: {n_parameters}")
+ model = create_ddp_model(
+ model.cuda(),
+ broadcast_buffers=False,
+ find_unused_parameters=self.cfg.find_unused_parameters,
+ )
+ if os.path.isfile(self.cfg.weight):
+ self.logger.info(f"Loading weight at: {self.cfg.weight}")
+ checkpoint = torch.load(self.cfg.weight)
+ weight = OrderedDict()
+ for key, value in checkpoint["state_dict"].items():
+ if key.startswith("module."):
+ if comm.get_world_size() == 1:
+ key = key[7:] # module.xxx.xxx -> xxx.xxx
+ else:
+ if comm.get_world_size() > 1:
+ key = "module." + key # xxx.xxx -> module.xxx.xxx
+ weight[key] = value
+ model.load_state_dict(weight, strict=True)
+ self.logger.info("=> Loaded weight '{}' (epoch {})".format(self.cfg.weight, checkpoint["epoch"]))
+ else:
+ raise RuntimeError("=> No checkpoint found at '{}'".format(self.cfg.weight))
+ return model
+
+ def build_test_loader(self):
+ test_dataset = build_dataset(self.cfg.data.test)
+ if comm.get_world_size() > 1:
+ test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset)
+ else:
+ test_sampler = None
+ test_loader = torch.utils.data.DataLoader(
+ test_dataset,
+ batch_size=self.cfg.batch_size_test_per_gpu,
+ shuffle=False,
+ num_workers=self.cfg.batch_size_test_per_gpu,
+ pin_memory=True,
+ sampler=test_sampler,
+ collate_fn=self.__class__.collate_fn,
+ )
+ return test_loader
+
+ def test(self):
+ raise NotImplementedError
+
+ @staticmethod
+ def collate_fn(batch):
+ raise collate_fn(batch)
+
+
+@TESTERS.register_module()
+class SemSegTester(TesterBase):
+
+ def test(self):
+ assert self.test_loader.batch_size == 1
+ logger = get_root_logger()
+ logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+
+ batch_time = AverageMeter()
+ intersection_meter = AverageMeter()
+ union_meter = AverageMeter()
+ target_meter = AverageMeter()
+ self.model.eval()
+
+ save_path = os.path.join(self.cfg.save_path, "result")
+ make_dirs(save_path)
+ # create submit folder only on main process
+ if (
+ self.cfg.data.test.type == "ScanNetDataset"
+ or self.cfg.data.test.type == "ScanNet200Dataset"
+ or self.cfg.data.test.type == "ScanNetPPDataset"
+ ) and comm.is_main_process():
+ make_dirs(os.path.join(save_path, "submit"))
+ elif self.cfg.data.test.type == "SemanticKITTIDataset" and comm.is_main_process():
+ make_dirs(os.path.join(save_path, "submit"))
+ elif self.cfg.data.test.type == "NuScenesDataset" and comm.is_main_process():
+ import json
+
+ make_dirs(os.path.join(save_path, "submit", "lidarseg", "test"))
+ make_dirs(os.path.join(save_path, "submit", "test"))
+ submission = dict(
+ meta=dict(
+ use_camera=False,
+ use_lidar=True,
+ use_radar=False,
+ use_map=False,
+ use_external=False,
+ )
+ )
+ with open(os.path.join(save_path, "submit", "test", "submission.json"), "w") as f:
+ json.dump(submission, f, indent=4)
+ comm.synchronize()
+ record = {}
+ # fragment inference
+ for idx, data_dict in enumerate(self.test_loader):
+ end = time.time()
+ data_dict = data_dict[0] # current assume batch size is 1
+ fragment_list = data_dict.pop("fragment_list")
+ segment = data_dict.pop("segment")
+ data_name = data_dict.pop("name")
+ pred_save_path = os.path.join(save_path, "{}_pred.npy".format(data_name))
+ if os.path.isfile(pred_save_path):
+ logger.info("{}/{}: {}, loaded pred and label.".format(idx + 1, len(self.test_loader), data_name))
+ pred = np.load(pred_save_path)
+ if "origin_segment" in data_dict.keys():
+ segment = data_dict["origin_segment"]
+ else:
+ pred = torch.zeros((segment.size, self.cfg.data.num_classes)).cuda()
+ for i in range(len(fragment_list)):
+ fragment_batch_size = 1
+ s_i, e_i = i * fragment_batch_size, min((i + 1) * fragment_batch_size, len(fragment_list))
+ input_dict = collate_fn(fragment_list[s_i:e_i])
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ idx_part = input_dict["index"]
+ with torch.no_grad():
+ pred_part = self.model(input_dict)["seg_logits"] # (n, k)
+ pred_part = F.softmax(pred_part, -1)
+ if self.cfg.empty_cache:
+ torch.cuda.empty_cache()
+ bs = 0
+ for be in input_dict["offset"]:
+ pred[idx_part[bs:be], :] += pred_part[bs:be]
+ bs = be
+
+ logger.info(
+ "Test: {}/{}-{data_name}, Batch: {batch_idx}/{batch_num}".format(
+ idx + 1,
+ len(self.test_loader),
+ data_name=data_name,
+ batch_idx=i,
+ batch_num=len(fragment_list),
+ )
+ )
+ if self.cfg.data.test.type == "ScanNetPPDataset":
+ pred = pred.topk(3, dim=1)[1].data.cpu().numpy()
+ else:
+ pred = pred.max(1)[1].data.cpu().numpy()
+ if "origin_segment" in data_dict.keys():
+ assert "inverse" in data_dict.keys()
+ pred = pred[data_dict["inverse"]]
+ segment = data_dict["origin_segment"]
+ np.save(pred_save_path, pred)
+ if (
+ self.cfg.data.test.type == "ScanNetDataset"
+ or self.cfg.data.test.type == "ScanNet200Dataset"
+ ):
+ np.savetxt(
+ os.path.join(save_path, "submit", "{}.txt".format(data_name)),
+ self.test_loader.dataset.class2id[pred].reshape([-1, 1]),
+ fmt="%d",
+ )
+ elif self.cfg.data.test.type == "ScanNetPPDataset":
+ np.savetxt(
+ os.path.join(save_path, "submit", "{}.txt".format(data_name)),
+ pred.astype(np.int32),
+ delimiter=",",
+ fmt="%d",
+ )
+ pred = pred[:, 0] # for mIoU, TODO: support top3 mIoU
+ elif self.cfg.data.test.type == "SemanticKITTIDataset":
+ # 00_000000 -> 00, 000000
+ sequence_name, frame_name = data_name.split("_")
+ os.makedirs(
+ os.path.join(
+ save_path, "submit", "sequences", sequence_name, "predictions"
+ ),
+ exist_ok=True,
+ )
+ pred = pred.astype(np.uint32)
+ pred = np.vectorize(
+ self.test_loader.dataset.learning_map_inv.__getitem__
+ )(pred).astype(np.uint32)
+ pred.tofile(
+ os.path.join(
+ save_path,
+ "submit",
+ "sequences",
+ sequence_name,
+ "predictions",
+ f"{frame_name}.label",
+ )
+ )
+ elif self.cfg.data.test.type == "NuScenesDataset":
+ np.array(pred + 1).astype(np.uint8).tofile(
+ os.path.join(
+ save_path,
+ "submit",
+ "lidarseg",
+ "test",
+ "{}_lidarseg.bin".format(data_name),
+ )
+ )
+
+ intersection, union, target = intersection_and_union(
+ pred, segment, self.cfg.data.num_classes, self.cfg.data.ignore_index
+ )
+ intersection_meter.update(intersection)
+ union_meter.update(union)
+ target_meter.update(target)
+ record[data_name] = dict(intersection=intersection, union=union, target=target)
+
+ mask = union != 0
+ iou_class = intersection / (union + 1e-10)
+ iou = np.mean(iou_class[mask])
+ acc = sum(intersection) / (sum(target) + 1e-10)
+
+ m_iou = np.mean(intersection_meter.sum / (union_meter.sum + 1e-10))
+ m_acc = np.mean(intersection_meter.sum / (target_meter.sum + 1e-10))
+
+ batch_time.update(time.time() - end)
+ logger.info(
+ "Test: {} [{}/{}]-{} "
+ "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
+ "Accuracy {acc:.4f} ({m_acc:.4f}) "
+ "mIoU {iou:.4f} ({m_iou:.4f})".format(
+ data_name,
+ idx + 1,
+ len(self.test_loader),
+ segment.size,
+ batch_time=batch_time,
+ acc=acc,
+ m_acc=m_acc,
+ iou=iou,
+ m_iou=m_iou,
+ )
+ )
+
+ logger.info("Syncing ...")
+ comm.synchronize()
+ record_sync = comm.gather(record, dst=0)
+
+ if comm.is_main_process():
+ record = {}
+ for _ in range(len(record_sync)):
+ r = record_sync.pop()
+ record.update(r)
+ del r
+ intersection = np.sum([meters["intersection"] for _, meters in record.items()], axis=0)
+ union = np.sum([meters["union"] for _, meters in record.items()], axis=0)
+ target = np.sum([meters["target"] for _, meters in record.items()], axis=0)
+
+ if self.cfg.data.test.type == "S3DISDataset":
+ torch.save(
+ dict(intersection=intersection, union=union, target=target),
+ os.path.join(save_path, f"{self.test_loader.dataset.split}.pth"),
+ )
+
+ iou_class = intersection / (union + 1e-10)
+ accuracy_class = intersection / (target + 1e-10)
+ mIoU = np.mean(iou_class)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection) / (sum(target) + 1e-10)
+
+ logger.info("Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}".format(mIoU, mAcc, allAcc))
+ for i in range(self.cfg.data.num_classes):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+ logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+
+ @staticmethod
+ def collate_fn(batch):
+ return batch
+
+
+@TESTERS.register_module()
+class ClsTester(TesterBase):
+ def test(self):
+ logger = get_root_logger()
+ logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ batch_time = AverageMeter()
+ intersection_meter = AverageMeter()
+ union_meter = AverageMeter()
+ target_meter = AverageMeter()
+ self.model.eval()
+
+ for i, input_dict in enumerate(self.test_loader):
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ end = time.time()
+ with torch.no_grad():
+ output_dict = self.model(input_dict)
+ output = output_dict["cls_logits"]
+ pred = output.max(1)[1]
+ label = input_dict["category"]
+ intersection, union, target = intersection_and_union_gpu(pred, label, self.cfg.data.num_classes, self.cfg.data.ignore_index)
+ if comm.get_world_size() > 1:
+ dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(target)
+ intersection, union, target = (
+ intersection.cpu().numpy(),
+ union.cpu().numpy(),
+ target.cpu().numpy(),
+ )
+ intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)
+
+ accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)
+ batch_time.update(time.time() - end)
+
+ logger.info(
+ "Test: [{}/{}] "
+ "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
+ "Accuracy {accuracy:.4f} ".format(
+ i + 1,
+ len(self.test_loader),
+ batch_time=batch_time,
+ accuracy=accuracy,
+ )
+ )
+
+ iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
+ accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
+ mIoU = np.mean(iou_class)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
+ logger.info("Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(mIoU, mAcc, allAcc))
+
+ for i in range(self.cfg.data.num_classes):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+ logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+
+ @staticmethod
+ def collate_fn(batch):
+ return collate_fn(batch)
+
+
+@TESTERS.register_module()
+class ClsVotingTester(TesterBase):
+ def __init__(
+ self,
+ num_repeat=100,
+ metric="allAcc",
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.num_repeat = num_repeat
+ self.metric = metric
+ self.best_idx = 0
+ self.best_record = None
+ self.best_metric = 0
+
+ def test(self):
+ for i in range(self.num_repeat):
+ logger = get_root_logger()
+ logger.info(f">>>>>>>>>>>>>>>> Start Evaluation {i + 1} >>>>>>>>>>>>>>>>")
+ record = self.test_once()
+ if comm.is_main_process():
+ if record[self.metric] > self.best_metric:
+ self.best_record = record
+ self.best_idx = i
+ self.best_metric = record[self.metric]
+ info = f"Current best record is Evaluation {i + 1}: "
+ for m in self.best_record.keys():
+ info += f"{m}: {self.best_record[m]:.4f} "
+ logger.info(info)
+
+ def test_once(self):
+ logger = get_root_logger()
+ batch_time = AverageMeter()
+ intersection_meter = AverageMeter()
+ target_meter = AverageMeter()
+ record = {}
+ self.model.eval()
+
+ for idx, data_dict in enumerate(self.test_loader):
+ end = time.time()
+ data_dict = data_dict[0] # current assume batch size is 1
+ voting_list = data_dict.pop("voting_list")
+ category = data_dict.pop("category")
+ data_name = data_dict.pop("name")
+ # pred = torch.zeros([1, self.cfg.data.num_classes]).cuda()
+ # for i in range(len(voting_list)):
+ # input_dict = voting_list[i]
+ # for key in input_dict.keys():
+ # if isinstance(input_dict[key], torch.Tensor):
+ # input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ # with torch.no_grad():
+ # pred += F.softmax(self.model(input_dict)["cls_logits"], -1)
+ input_dict = collate_fn(voting_list)
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ pred = F.softmax(self.model(input_dict)["cls_logits"], -1).sum(
+ 0, keepdim=True
+ )
+ pred = pred.max(1)[1].cpu().numpy()
+ intersection, union, target = intersection_and_union(
+ pred, category, self.cfg.data.num_classes, self.cfg.data.ignore_index
+ )
+ intersection_meter.update(intersection)
+ target_meter.update(target)
+ record[data_name] = dict(intersection=intersection, target=target)
+ acc = sum(intersection) / (sum(target) + 1e-10)
+ m_acc = np.mean(intersection_meter.sum / (target_meter.sum + 1e-10))
+ batch_time.update(time.time() - end)
+ logger.info(
+ "Test: {} [{}/{}] "
+ "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
+ "Accuracy {acc:.4f} ({m_acc:.4f}) ".format(
+ data_name,
+ idx + 1,
+ len(self.test_loader),
+ batch_time=batch_time,
+ acc=acc,
+ m_acc=m_acc,
+ )
+ )
+
+ logger.info("Syncing ...")
+ comm.synchronize()
+ record_sync = comm.gather(record, dst=0)
+
+ if comm.is_main_process():
+ record = {}
+ for _ in range(len(record_sync)):
+ r = record_sync.pop()
+ record.update(r)
+ del r
+ intersection = np.sum(
+ [meters["intersection"] for _, meters in record.items()], axis=0
+ )
+ target = np.sum([meters["target"] for _, meters in record.items()], axis=0)
+ accuracy_class = intersection / (target + 1e-10)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection) / (sum(target) + 1e-10)
+
+ logger.info("Val result: mAcc/allAcc {:.4f}/{:.4f}".format(mAcc, allAcc))
+ for i in range(self.cfg.data.num_classes):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {accuracy:.4f}".format(
+ idx=i,
+ name=self.cfg.data.names[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+ return dict(mAcc=mAcc, allAcc=allAcc)
+
+ @staticmethod
+ def collate_fn(batch):
+ return batch
+
+
+@TESTERS.register_module()
+class PartSegTester(TesterBase):
+ def test(self):
+ test_dataset = self.test_loader.dataset
+ logger = get_root_logger()
+ logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+
+ batch_time = AverageMeter()
+
+ num_categories = len(self.test_loader.dataset.categories)
+ iou_category, iou_count = np.zeros(num_categories), np.zeros(num_categories)
+ self.model.eval()
+
+ save_path = os.path.join(self.cfg.save_path, "result", "test_epoch{}".format(self.cfg.test_epoch))
+ make_dirs(save_path)
+
+ for idx in range(len(test_dataset)):
+ end = time.time()
+ data_name = test_dataset.get_data_name(idx)
+
+ data_dict_list, label = test_dataset[idx]
+ pred = torch.zeros((label.size, self.cfg.data.num_classes)).cuda()
+ batch_num = int(np.ceil(len(data_dict_list) / self.cfg.batch_size_test))
+ for i in range(batch_num):
+ s_i, e_i = i * self.cfg.batch_size_test, min((i + 1) * self.cfg.batch_size_test, len(data_dict_list))
+ input_dict = collate_fn(data_dict_list[s_i:e_i])
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ pred_part = self.model(input_dict)["cls_logits"]
+ pred_part = F.softmax(pred_part, -1)
+ if self.cfg.empty_cache:
+ torch.cuda.empty_cache()
+ pred_part = pred_part.reshape(-1, label.size, self.cfg.data.num_classes)
+ pred = pred + pred_part.total(dim=0)
+ logger.info(
+ "Test: {} {}/{}, Batch: {batch_idx}/{batch_num}".format(
+ data_name,
+ idx + 1,
+ len(test_dataset),
+ batch_idx=i,
+ batch_num=batch_num,
+ )
+ )
+ pred = pred.max(1)[1].data.cpu().numpy()
+
+ category_index = data_dict_list[0]["cls_token"]
+ category = self.test_loader.dataset.categories[category_index]
+ parts_idx = self.test_loader.dataset.category2part[category]
+ parts_iou = np.zeros(len(parts_idx))
+ for j, part in enumerate(parts_idx):
+ if (np.sum(label == part) == 0) and (np.sum(pred == part) == 0):
+ parts_iou[j] = 1.0
+ else:
+ i = (label == part) & (pred == part)
+ u = (label == part) | (pred == part)
+ parts_iou[j] = np.sum(i) / (np.sum(u) + 1e-10)
+ iou_category[category_index] += parts_iou.mean()
+ iou_count[category_index] += 1
+
+ batch_time.update(time.time() - end)
+ logger.info("Test: {} [{}/{}] " "Batch {batch_time.val:.3f} " "({batch_time.avg:.3f}) ".format(data_name, idx + 1, len(self.test_loader), batch_time=batch_time))
+
+ ins_mIoU = iou_category.sum() / (iou_count.sum() + 1e-10)
+ cat_mIoU = (iou_category / (iou_count + 1e-10)).mean()
+ logger.info("Val result: ins.mIoU/cat.mIoU {:.4f}/{:.4f}.".format(ins_mIoU, cat_mIoU))
+ for i in range(num_categories):
+ logger.info(
+ "Class_{idx}-{name} Result: iou_cat/num_sample {iou_cat:.4f}/{iou_count:.4f}".format(
+ idx=i,
+ name=self.test_loader.dataset.categories[i],
+ iou_cat=iou_category[i] / (iou_count[i] + 1e-10),
+ iou_count=int(iou_count[i]),
+ )
+ )
+ logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+
+ @staticmethod
+ def collate_fn(batch):
+ return collate_fn(batch)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/train.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0ca13e05743697c9bab6fcb63fb4d8c015c47e1
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/engines/train.py
@@ -0,0 +1,309 @@
+"""
+Trainer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import sys
+import weakref
+import torch
+import torch.nn as nn
+import torch.utils.data
+from functools import partial
+
+if sys.version_info >= (3, 10):
+ from collections.abc import Iterator
+else:
+ from collections import Iterator
+from tensorboardX import SummaryWriter
+
+from .defaults import create_ddp_model, worker_init_fn
+from .hooks import HookBase, build_hooks
+import pointcept.utils.comm as comm
+from pointcept.datasets import build_dataset, point_collate_fn, collate_fn
+from pointcept.models import build_model
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.optimizer import build_optimizer
+from pointcept.utils.scheduler import build_scheduler
+from pointcept.utils.events import EventStorage
+from pointcept.utils.registry import Registry
+
+
+TRAINERS = Registry("trainers")
+
+
+class TrainerBase:
+ def __init__(self) -> None:
+ self.hooks = []
+ self.epoch = 0
+ self.start_epoch = 0
+ self.max_epoch = 0
+ self.max_iter = 0
+ self.comm_info = dict()
+ self.data_iterator: Iterator = enumerate([])
+ self.storage: EventStorage
+ self.writer: SummaryWriter
+
+ def register_hooks(self, hooks) -> None:
+ hooks = build_hooks(hooks)
+ for h in hooks:
+ assert isinstance(h, HookBase)
+ # To avoid circular reference, hooks and trainer cannot own each other.
+ # This normally does not matter, but will cause memory leak if the
+ # involved objects contain __del__:
+ # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/
+ h.trainer = weakref.proxy(self)
+ self.hooks.extend(hooks)
+
+ def train(self):
+ with EventStorage() as self.storage:
+ # => before train
+ self.before_train()
+ for self.epoch in range(self.start_epoch, self.max_epoch):
+ # => before epoch
+ self.before_epoch()
+ # => run_epoch
+ for (
+ self.comm_info["iter"],
+ self.comm_info["input_dict"],
+ ) in self.data_iterator:
+ # => before_step
+ self.before_step()
+ # => run_step
+ self.run_step()
+ # => after_step
+ self.after_step()
+ # => after epoch
+ self.after_epoch()
+ # => after train
+ self.after_train()
+
+ def before_train(self):
+ for h in self.hooks:
+ h.before_train()
+
+ def before_epoch(self):
+ for h in self.hooks:
+ h.before_epoch()
+
+ def before_step(self):
+ for h in self.hooks:
+ h.before_step()
+
+ def run_step(self):
+ raise NotImplementedError
+
+ def after_step(self):
+ for h in self.hooks:
+ h.after_step()
+
+ def after_epoch(self):
+ for h in self.hooks:
+ h.after_epoch()
+ self.storage.reset_histories()
+
+ def after_train(self):
+ # Sync GPU before running train hooks
+ comm.synchronize()
+ for h in self.hooks:
+ h.after_train()
+ if comm.is_main_process():
+ self.writer.close()
+
+
+@TRAINERS.register_module("DefaultTrainer")
+class Trainer(TrainerBase):
+ def __init__(self, cfg):
+ super(Trainer, self).__init__()
+ self.epoch = 0
+ self.start_epoch = 0
+ self.max_epoch = cfg.eval_epoch
+ self.best_metric_value = -torch.inf
+ self.logger = get_root_logger(
+ log_file=os.path.join(cfg.save_path, "train.log"),
+ file_mode="a" if cfg.resume else "w",
+ )
+ self.logger.info("=> Loading config ...")
+ self.cfg = cfg
+ self.logger.info(f"Save path: {cfg.save_path}")
+ self.logger.info(f"Config:\n{cfg.pretty_text}")
+ self.logger.info("=> Building model ...")
+ self.model = self.build_model()
+ self.logger.info("=> Building writer ...")
+ self.writer = self.build_writer()
+ self.logger.info("=> Building train dataset & dataloader ...")
+ self.train_loader = self.build_train_loader()
+ self.logger.info("=> Building val dataset & dataloader ...")
+ self.val_loader = self.build_val_loader()
+ self.logger.info("=> Building optimize, scheduler, scaler(amp) ...")
+ self.optimizer = self.build_optimizer()
+ self.scheduler = self.build_scheduler()
+ self.scaler = self.build_scaler()
+ self.logger.info("=> Building hooks ...")
+ self.register_hooks(self.cfg.hooks)
+
+ def train(self):
+ with EventStorage() as self.storage:
+ # => before train
+ self.before_train()
+ self.logger.info(">>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>>")
+ for self.epoch in range(self.start_epoch, self.max_epoch):
+ # => before epoch
+ # TODO: optimize to iteration based
+ if comm.get_world_size() > 1:
+ self.train_loader.sampler.set_epoch(self.epoch)
+ self.model.train()
+ self.data_iterator = enumerate(self.train_loader)
+ self.before_epoch()
+ # => run_epoch
+ for (
+ self.comm_info["iter"],
+ self.comm_info["input_dict"],
+ ) in self.data_iterator:
+ # => before_step
+ self.before_step()
+ # => run_step
+ self.run_step()
+ # => after_step
+ self.after_step()
+ # => after epoch
+ self.after_epoch()
+ # => after train
+ self.after_train()
+
+ def run_step(self):
+ input_dict = self.comm_info["input_dict"]
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.cuda.amp.autocast(enabled=self.cfg.enable_amp):
+ output_dict = self.model(input_dict)
+ loss = output_dict["loss"]
+ self.optimizer.zero_grad()
+ if self.cfg.enable_amp:
+ self.scaler.scale(loss).backward()
+ self.scaler.step(self.optimizer)
+
+ # When enable amp, optimizer.step call are skipped if the loss scaling factor is too large.
+ # Fix torch warning scheduler step before optimizer step.
+ scaler = self.scaler.get_scale()
+ self.scaler.update()
+ if scaler <= self.scaler.get_scale():
+ self.scheduler.step()
+ else:
+ loss.backward()
+ self.optimizer.step()
+ self.scheduler.step()
+ if self.cfg.empty_cache:
+ torch.cuda.empty_cache()
+ self.comm_info["model_output_dict"] = output_dict
+
+ def after_epoch(self):
+ for h in self.hooks:
+ h.after_epoch()
+ self.storage.reset_histories()
+ if self.cfg.empty_cache_per_epoch:
+ torch.cuda.empty_cache()
+
+ def build_model(self):
+ model = build_model(self.cfg.model)
+ if self.cfg.sync_bn:
+ model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
+ n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ # logger.info(f"Model: \n{self.model}")
+ self.logger.info(f"Num params: {n_parameters}")
+ model = create_ddp_model(
+ model.cuda(),
+ broadcast_buffers=False,
+ find_unused_parameters=self.cfg.find_unused_parameters,
+ )
+ return model
+
+ def build_writer(self):
+ writer = SummaryWriter(self.cfg.save_path) if comm.is_main_process() else None
+ self.logger.info(f"Tensorboard writer logging dir: {self.cfg.save_path}")
+ return writer
+
+ def build_train_loader(self):
+ train_data = build_dataset(self.cfg.data.train)
+
+ if comm.get_world_size() > 1:
+ train_sampler = torch.utils.data.distributed.DistributedSampler(train_data)
+ else:
+ train_sampler = None
+
+ init_fn = (
+ partial(
+ worker_init_fn,
+ num_workers=self.cfg.num_worker_per_gpu,
+ rank=comm.get_rank(),
+ seed=self.cfg.seed,
+ )
+ if self.cfg.seed is not None
+ else None
+ )
+
+ train_loader = torch.utils.data.DataLoader(
+ train_data,
+ batch_size=self.cfg.batch_size_per_gpu,
+ shuffle=(train_sampler is None),
+ num_workers=self.cfg.num_worker_per_gpu,
+ sampler=train_sampler,
+ collate_fn=partial(point_collate_fn, mix_prob=self.cfg.mix_prob),
+ pin_memory=True,
+ worker_init_fn=init_fn,
+ drop_last=True,
+ persistent_workers=True,
+ )
+ return train_loader
+
+ def build_val_loader(self):
+ val_loader = None
+ if self.cfg.evaluate:
+ val_data = build_dataset(self.cfg.data.val)
+ if comm.get_world_size() > 1:
+ val_sampler = torch.utils.data.distributed.DistributedSampler(val_data)
+ else:
+ val_sampler = None
+ val_loader = torch.utils.data.DataLoader(
+ val_data,
+ batch_size=self.cfg.batch_size_val_per_gpu,
+ shuffle=False,
+ num_workers=self.cfg.num_worker_per_gpu,
+ pin_memory=True,
+ sampler=val_sampler,
+ collate_fn=collate_fn,
+ )
+ return val_loader
+
+ def build_optimizer(self):
+ return build_optimizer(self.cfg.optimizer, self.model, self.cfg.param_dicts)
+
+ def build_scheduler(self):
+ assert hasattr(self, "optimizer")
+ assert hasattr(self, "train_loader")
+ self.cfg.scheduler.total_steps = len(self.train_loader) * self.cfg.eval_epoch
+ return build_scheduler(self.cfg.scheduler, self.optimizer)
+
+ def build_scaler(self):
+ scaler = torch.cuda.amp.GradScaler() if self.cfg.enable_amp else None
+ return scaler
+
+
+@TRAINERS.register_module("MultiDatasetTrainer")
+class MultiDatasetTrainer(Trainer):
+ def build_train_loader(self):
+ from pointcept.datasets import MultiDatasetDataloader
+
+ train_data = build_dataset(self.cfg.data.train)
+ train_loader = MultiDatasetDataloader(
+ train_data,
+ self.cfg.batch_size_per_gpu,
+ self.cfg.num_worker_per_gpu,
+ self.cfg.mix_prob,
+ self.cfg.seed,
+ )
+ self.comm_info["iter_per_epoch"] = len(train_loader)
+ return train_loader
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b5b38b06de6d3629f9fbe09fb8773218b67bd99
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/__init__.py
@@ -0,0 +1,24 @@
+from .builder import build_model
+from .default import DefaultSegmentor, DefaultClassifier
+
+# Backbones
+from .sparse_unet import *
+from .point_transformer import *
+from .point_transformer_v2 import *
+from .point_transformer_v3 import *
+from .stratified_transformer import *
+from .spvcnn import *
+# from .octformer import *
+# from .oacnns import *
+
+# from .swin3d import *
+
+# Semantic Segmentation
+from .context_aware_classifier import *
+
+# Instance Segmentation
+from .point_group import *
+
+# Pretraining
+from .masked_scene_contrast import *
+from .point_prompt_training import *
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/builder.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbda24465a405a5a2094f8d0c420a53c50fe79cc
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/builder.py
@@ -0,0 +1,16 @@
+"""
+Model Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+MODELS = Registry("models")
+MODULES = Registry("modules")
+
+
+def build_model(cfg):
+ """Build models."""
+ return MODELS.build(cfg)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/context_aware_classifier/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/context_aware_classifier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8327900bbf92258fa242f4b99d5b235bc1ae44aa
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/context_aware_classifier/__init__.py
@@ -0,0 +1 @@
+from .context_aware_classifier_v1m1_base import CACSegmentor
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/context_aware_classifier/context_aware_classifier_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/context_aware_classifier/context_aware_classifier_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..893f4c8fc6d68a1fb7dc24e1ceb3b3ace11ebec6
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/context_aware_classifier/context_aware_classifier_v1m1_base.py
@@ -0,0 +1,275 @@
+"""
+Context-aware Classifier for Semantic Segmentation
+
+Author: Zhuotao Tian, Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pointcept.models.losses import build_criteria
+from pointcept.models.utils.structure import Point
+from pointcept.models.builder import MODELS, build_model
+
+
+@MODELS.register_module("CAC-v1m1")
+class CACSegmentor(nn.Module):
+ def __init__(
+ self,
+ num_classes,
+ backbone_out_channels,
+ backbone=None,
+ criteria=None,
+ cos_temp=15,
+ main_weight=1,
+ pre_weight=1,
+ pre_self_weight=1,
+ kl_weight=1,
+ conf_thresh=0,
+ detach_pre_logits=False,
+ ):
+ super().__init__()
+ self.num_classes = num_classes
+ self.cos_temp = cos_temp
+ self.main_weight = main_weight
+ self.pre_weight = pre_weight
+ self.pre_self_weight = pre_self_weight
+ self.kl_weight = kl_weight
+ self.conf_thresh = conf_thresh
+ self.detach_pre_logits = detach_pre_logits
+
+ # backbone
+ self.backbone = build_model(backbone)
+ # heads
+ self.seg_head = nn.Linear(backbone_out_channels, num_classes)
+ self.proj = nn.Sequential(
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels * 2, bias=False),
+ nn.ReLU(inplace=True),
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels),
+ )
+ self.apd_proj = nn.Sequential(
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels * 2, bias=False),
+ nn.ReLU(inplace=True),
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels),
+ )
+ self.feat_proj_layer = nn.Sequential(
+ nn.Linear(backbone_out_channels, backbone_out_channels, bias=False),
+ nn.BatchNorm1d(backbone_out_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(backbone_out_channels, backbone_out_channels),
+ )
+ # Criteria
+ self.criteria = build_criteria(criteria)
+
+ @staticmethod
+ def get_pred(x, proto):
+ # x: [n,c]; proto: [cls, c]
+ x = F.normalize(x, 2, 1)
+ proto = F.normalize(proto, 2, 1)
+ pred = x @ proto.permute(1, 0) # [n,c] x [c, cls] -> [n, cls]
+ return pred
+
+ def get_adaptive_perspective(self, feat, target, new_proto, proto):
+ raw_feat = feat.clone()
+ # target: [n]
+ # feat: [n,c]
+ # proto: [cls, c]
+ unique_y = list(target.unique())
+ if -1 in unique_y:
+ unique_y.remove(-1)
+ target = target.unsqueeze(-1) # [n, 1]
+
+ for tmp_y in unique_y:
+ tmp_mask = (target == tmp_y).float()
+ tmp_proto = (feat * tmp_mask).sum(0) / (tmp_mask.sum(0) + 1e-4) # c
+ onehot_vec = torch.zeros(new_proto.shape[0], 1).cuda() # cls, 1
+ onehot_vec[tmp_y.long()] = 1
+ new_proto = (
+ new_proto * (1 - onehot_vec) + tmp_proto.unsqueeze(0) * onehot_vec
+ )
+
+ new_proto = torch.cat([new_proto, proto], -1)
+ new_proto = self.apd_proj(new_proto)
+ raw_feat = self.feat_proj_layer(raw_feat)
+ pred = self.get_pred(raw_feat, new_proto)
+ return pred
+
+ def post_refine_proto_batch(self, feat, pred, proto, offset=None):
+ # x: [n, c]; pred: [n, cls]; proto: [cls, c]
+ pred_list = []
+ x = feat
+ raw_x = x.clone()
+ if self.detach_pre_logits:
+ pred = pred.detach()
+ raw_pred = pred.clone()
+
+ if offset is None:
+ raw_x = x.clone()
+ n, n_cls = pred.shape[:]
+ pred = pred.view(n, n_cls)
+ pred = F.softmax(pred, 1).permute(1, 0) # [n, cls] -> [cls, n]
+ if self.conf_thresh > 0:
+ max_pred = (
+ (pred.max(0)[0] >= self.conf_thresh).float().unsqueeze(0)
+ ) # 1, n
+ pred = pred * max_pred
+ pred_proto = (pred / (pred.sum(-1).unsqueeze(-1) + 1e-7)) @ raw_x # cls, c
+
+ pred_proto = torch.cat([pred_proto, proto], -1) # cls, 2c
+ pred_proto = self.proj(pred_proto)
+ raw_x = self.feat_proj_layer(raw_x)
+ new_pred = self.get_pred(raw_x, pred_proto)
+ else:
+ for i in range(len(offset)):
+ if i == 0:
+ start = 0
+ end = offset[i]
+ else:
+ start, end = offset[i - 1], offset[i]
+ tmp_x = raw_x[start:end]
+ pred = raw_pred[start:end]
+ n, n_cls = pred.shape[:]
+ pred = pred.view(n, n_cls)
+ pred = F.softmax(pred, 1).permute(1, 0) # [n, cls] -> [cls, n]
+ if self.conf_thresh > 0:
+ max_pred = (
+ (pred.max(0)[0] >= self.conf_thresh).float().unsqueeze(0)
+ ) # 1, n
+ pred = pred * max_pred
+ pred_proto = (
+ pred / (pred.sum(-1).unsqueeze(-1) + 1e-7)
+ ) @ tmp_x # cls, c
+
+ pred_proto = torch.cat([pred_proto, proto], -1) # cls, 2c
+ pred_proto = self.proj(pred_proto)
+ tmp_x = self.feat_proj_layer(tmp_x)
+ new_pred = self.get_pred(tmp_x, pred_proto)
+ pred_list.append(new_pred)
+ new_pred = torch.cat(pred_list, 0)
+ return new_pred
+
+ @staticmethod
+ def get_distill_loss(pred, soft, target, smoothness=0.5, eps=0):
+ """
+ knowledge distillation loss
+ """
+ n, c = soft.shape[:]
+ soft = soft.detach()
+ target = target.unsqueeze(-1) # n, 1
+ onehot = target.view(-1, 1) # n, 1
+ ignore_mask = (onehot == -1).float()
+ sm_soft = F.softmax(soft / 1, 1) # n, c
+
+ onehot = onehot * (1 - ignore_mask)
+ onehot = torch.zeros(n, c).cuda().scatter_(1, onehot.long(), 1) # n, c
+ smoothed_label = smoothness * sm_soft + (1 - smoothness) * onehot
+ if eps > 0:
+ smoothed_label = smoothed_label * (1 - eps) + (1 - smoothed_label) * eps / (
+ smoothed_label.shape[1] - 1
+ )
+
+ loss = torch.mul(-1 * F.log_softmax(pred, dim=1), smoothed_label) # b, n, h, w
+ loss = loss.sum(1)
+
+ sm_soft = F.softmax(soft / 1, 1) # n, c
+ entropy_mask = -1 * (sm_soft * torch.log(sm_soft + 1e-4)).sum(1)
+
+ # for class-wise entropy estimation
+ target = target.squeeze(-1)
+ unique_classes = list(target.unique())
+ if -1 in unique_classes:
+ unique_classes.remove(-1)
+ valid_mask = (target != -1).float()
+ entropy_mask = entropy_mask * valid_mask
+ loss_list = []
+ weight_list = []
+ for tmp_y in unique_classes:
+ tmp_mask = (target == tmp_y).float().squeeze()
+ tmp_entropy_mask = entropy_mask * tmp_mask
+ class_weight = 1
+ tmp_loss = (loss * tmp_entropy_mask).sum() / (tmp_entropy_mask.sum() + 1e-4)
+ loss_list.append(class_weight * tmp_loss)
+ weight_list.append(class_weight)
+
+ if len(weight_list) > 0:
+ loss = sum(loss_list) / (sum(weight_list) + 1e-4)
+ else:
+ loss = torch.zeros(1).cuda().mean()
+ return loss
+
+ def forward(self, data_dict):
+ offset = data_dict["offset"]
+ point = self.backbone(data_dict)
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ seg_logits = self.seg_head(feat)
+
+ if self.training:
+ target = data_dict["segment"]
+ pre_logits = seg_logits.clone()
+ refine_logits = (
+ self.post_refine_proto_batch(
+ feat=feat,
+ pred=seg_logits,
+ proto=self.seg_head.weight.squeeze(),
+ offset=offset,
+ )
+ * self.cos_temp
+ )
+
+ cac_pred = (
+ self.get_adaptive_perspective(
+ feat=feat,
+ target=target,
+ new_proto=self.seg_head.weight.detach().data.squeeze(),
+ proto=self.seg_head.weight.squeeze(),
+ )
+ * self.cos_temp
+ )
+
+ seg_loss = self.criteria(refine_logits, target) * self.main_weight
+ pre_loss = self.criteria(cac_pred, target) * self.pre_weight
+ pre_self_loss = self.criteria(pre_logits, target) * self.pre_self_weight
+ kl_loss = (
+ self.get_distill_loss(
+ pred=refine_logits, soft=cac_pred.detach(), target=target
+ )
+ * self.kl_weight
+ )
+ loss = seg_loss + pre_loss + pre_self_loss + kl_loss
+ return dict(
+ loss=loss,
+ seg_loss=seg_loss,
+ pre_loss=pre_loss,
+ pre_self_loss=pre_self_loss,
+ kl_loss=kl_loss,
+ )
+
+ elif "segment" in data_dict.keys():
+ refine_logits = (
+ self.post_refine_proto_batch(
+ feat=feat,
+ pred=seg_logits,
+ proto=self.seg_head.weight.squeeze(),
+ offset=offset,
+ )
+ * self.cos_temp
+ )
+
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss, seg_logits=refine_logits)
+
+ else:
+ refine_logits = (
+ self.post_refine_proto_batch(
+ feat=feat,
+ pred=seg_logits,
+ proto=self.seg_head.weight.squeeze(),
+ offset=offset,
+ )
+ * self.cos_temp
+ )
+ return dict(seg_logits=refine_logits)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/default.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..95aea1ff4405746dd0ed7f2d83c118e48e6283ec
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/default.py
@@ -0,0 +1,132 @@
+from contextlib import nullcontext
+
+import torch
+import torch.nn as nn
+import torch_scatter
+
+from pointcept.models.losses import build_criteria
+from pointcept.models.utils.structure import Point
+
+from .builder import MODELS, build_model
+
+
+@MODELS.register_module()
+class DefaultSegmentor(nn.Module):
+ def __init__(self, backbone=None, criteria=None):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.criteria = build_criteria(criteria)
+
+ def forward(self, input_dict):
+ if "condition" in input_dict.keys():
+ # PPT (https://arxiv.org/abs/2308.09718)
+ # currently, only support one batch one condition
+ input_dict["condition"] = input_dict["condition"][0]
+ seg_logits = self.backbone(input_dict)
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in input_dict.keys():
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
+
+
+@MODELS.register_module()
+class DefaultSegmentorV2(nn.Module):
+ def __init__(
+ self,
+ num_classes,
+ backbone_out_channels,
+ backbone=None,
+ criteria=None,
+ freeze_backbone=False,
+ ):
+ super().__init__()
+ self.seg_head = nn.Linear(backbone_out_channels, num_classes) if num_classes > 0 else nn.Identity()
+ self.backbone = build_model(backbone)
+ self.criteria = build_criteria(criteria)
+
+ if freeze_backbone:
+ self.optional_freeze = torch.no_grad
+ else:
+ self.optional_freeze = nullcontext
+
+ def forward(self, input_dict):
+ point = Point(input_dict)
+ with self.optional_freeze():
+ point = self.backbone(point)
+ # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ seg_logits = self.seg_head(feat)
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in input_dict.keys():
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
+
+
+@MODELS.register_module()
+class DefaultClassifier(nn.Module):
+ def __init__(
+ self,
+ backbone=None,
+ criteria=None,
+ num_classes=40,
+ backbone_embed_dim=256,
+ ):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.criteria = build_criteria(criteria)
+ self.num_classes = num_classes
+ self.backbone_embed_dim = backbone_embed_dim
+ self.cls_head = nn.Sequential(
+ nn.Linear(backbone_embed_dim, 256),
+ nn.BatchNorm1d(256),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(256, 128),
+ nn.BatchNorm1d(128),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(128, num_classes),
+ )
+
+ def forward(self, input_dict):
+ point = Point(input_dict)
+ point = self.backbone(point)
+ # Backbone added after v1.5.0 return Point instead of feat
+ # And after v1.5.0 feature aggregation for classification operated in classifier
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ point.feat = torch_scatter.segment_csr(
+ src=point.feat,
+ indptr=nn.functional.pad(point.offset, (1, 0)),
+ reduce="mean",
+ )
+ feat = point.feat
+ else:
+ feat = point
+ cls_logits = self.cls_head(feat)
+ if self.training:
+ loss = self.criteria(cls_logits, input_dict["category"])
+ return dict(loss=loss)
+ elif "category" in input_dict.keys():
+ loss = self.criteria(cls_logits, input_dict["category"])
+ return dict(loss=loss, cls_logits=cls_logits)
+ else:
+ return dict(cls_logits=cls_logits)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee44096e13acdf7d6cf7ba9ff764b4b6af3aafe5
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/__init__.py
@@ -0,0 +1,4 @@
+from .builder import build_criteria
+
+from .misc import CrossEntropyLoss, SmoothCELoss, DiceLoss, FocalLoss, BinaryFocalLoss
+from .lovasz import LovaszLoss
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/builder.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef642d9830622d847eb2b7e7013252a32c2b6368
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/builder.py
@@ -0,0 +1,31 @@
+"""
+Criteria Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+LOSSES = Registry("losses")
+
+
+class Criteria(object):
+ def __init__(self, cfg=None):
+ self.cfg = cfg if cfg is not None else []
+ self.criteria = []
+ for loss_cfg in self.cfg:
+ self.criteria.append(LOSSES.build(cfg=loss_cfg))
+
+ def __call__(self, pred, target):
+ if len(self.criteria) == 0:
+ # loss computation occur in model
+ return pred
+ loss = 0
+ for c in self.criteria:
+ loss += c(pred, target)
+ return loss
+
+
+def build_criteria(cfg):
+ return Criteria(cfg)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/lovasz.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/lovasz.py
new file mode 100644
index 0000000000000000000000000000000000000000..de01a181bce59ba8288171bb58c0ddc728adcb5b
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/lovasz.py
@@ -0,0 +1,257 @@
+"""
+Lovasz Loss
+refer https://arxiv.org/abs/1705.08790
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from typing import Optional
+from itertools import filterfalse
+import torch
+import torch.nn.functional as F
+from torch.nn.modules.loss import _Loss
+
+from .builder import LOSSES
+
+BINARY_MODE: str = "binary"
+MULTICLASS_MODE: str = "multiclass"
+MULTILABEL_MODE: str = "multilabel"
+
+
+def _lovasz_grad(gt_sorted):
+ """Compute gradient of the Lovasz extension w.r.t sorted errors
+ See Alg. 1 in paper
+ """
+ p = len(gt_sorted)
+ gts = gt_sorted.sum()
+ intersection = gts - gt_sorted.float().cumsum(0)
+ union = gts + (1 - gt_sorted).float().cumsum(0)
+ jaccard = 1.0 - intersection / union
+ if p > 1: # cover 1-pixel case
+ jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
+ return jaccard
+
+
+def _lovasz_hinge(logits, labels, per_image=True, ignore=None):
+ """
+ Binary Lovasz hinge loss
+ logits: [B, H, W] Logits at each pixel (between -infinity and +infinity)
+ labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
+ per_image: compute the loss per image instead of per batch
+ ignore: void class id
+ """
+ if per_image:
+ loss = mean(
+ _lovasz_hinge_flat(
+ *_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore)
+ )
+ for log, lab in zip(logits, labels)
+ )
+ else:
+ loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore))
+ return loss
+
+
+def _lovasz_hinge_flat(logits, labels):
+ """Binary Lovasz hinge loss
+ Args:
+ logits: [P] Logits at each prediction (between -infinity and +infinity)
+ labels: [P] Tensor, binary ground truth labels (0 or 1)
+ """
+ if len(labels) == 0:
+ # only void pixels, the gradients should be 0
+ return logits.sum() * 0.0
+ signs = 2.0 * labels.float() - 1.0
+ errors = 1.0 - logits * signs
+ errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
+ perm = perm.data
+ gt_sorted = labels[perm]
+ grad = _lovasz_grad(gt_sorted)
+ loss = torch.dot(F.relu(errors_sorted), grad)
+ return loss
+
+
+def _flatten_binary_scores(scores, labels, ignore=None):
+ """Flattens predictions in the batch (binary case)
+ Remove labels equal to 'ignore'
+ """
+ scores = scores.view(-1)
+ labels = labels.view(-1)
+ if ignore is None:
+ return scores, labels
+ valid = labels != ignore
+ vscores = scores[valid]
+ vlabels = labels[valid]
+ return vscores, vlabels
+
+
+def _lovasz_softmax(
+ probas, labels, classes="present", class_seen=None, per_image=False, ignore=None
+):
+ """Multi-class Lovasz-Softmax loss
+ Args:
+ @param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1).
+ Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
+ @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
+ @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
+ @param per_image: compute the loss per image instead of per batch
+ @param ignore: void class labels
+ """
+ if per_image:
+ loss = mean(
+ _lovasz_softmax_flat(
+ *_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore),
+ classes=classes
+ )
+ for prob, lab in zip(probas, labels)
+ )
+ else:
+ loss = _lovasz_softmax_flat(
+ *_flatten_probas(probas, labels, ignore),
+ classes=classes,
+ class_seen=class_seen
+ )
+ return loss
+
+
+def _lovasz_softmax_flat(probas, labels, classes="present", class_seen=None):
+ """Multi-class Lovasz-Softmax loss
+ Args:
+ @param probas: [P, C] Class probabilities at each prediction (between 0 and 1)
+ @param labels: [P] Tensor, ground truth labels (between 0 and C - 1)
+ @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
+ """
+ if probas.numel() == 0:
+ # only void pixels, the gradients should be 0
+ return probas.mean() * 0.0
+ C = probas.size(1)
+ losses = []
+ class_to_sum = list(range(C)) if classes in ["all", "present"] else classes
+ # for c in class_to_sum:
+ for c in labels.unique():
+ if class_seen is None:
+ fg = (labels == c).type_as(probas) # foreground for class c
+ if classes == "present" and fg.sum() == 0:
+ continue
+ if C == 1:
+ if len(classes) > 1:
+ raise ValueError("Sigmoid output possible only with 1 class")
+ class_pred = probas[:, 0]
+ else:
+ class_pred = probas[:, c]
+ errors = (fg - class_pred).abs()
+ errors_sorted, perm = torch.sort(errors, 0, descending=True)
+ perm = perm.data
+ fg_sorted = fg[perm]
+ losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted)))
+ else:
+ if c in class_seen:
+ fg = (labels == c).type_as(probas) # foreground for class c
+ if classes == "present" and fg.sum() == 0:
+ continue
+ if C == 1:
+ if len(classes) > 1:
+ raise ValueError("Sigmoid output possible only with 1 class")
+ class_pred = probas[:, 0]
+ else:
+ class_pred = probas[:, c]
+ errors = (fg - class_pred).abs()
+ errors_sorted, perm = torch.sort(errors, 0, descending=True)
+ perm = perm.data
+ fg_sorted = fg[perm]
+ losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted)))
+ return mean(losses)
+
+
+def _flatten_probas(probas, labels, ignore=None):
+ """Flattens predictions in the batch"""
+ if probas.dim() == 3:
+ # assumes output of a sigmoid layer
+ B, H, W = probas.size()
+ probas = probas.view(B, 1, H, W)
+
+ C = probas.size(1)
+ probas = torch.movedim(probas, 1, -1) # [B, C, Di, Dj, ...] -> [B, Di, Dj, ..., C]
+ probas = probas.contiguous().view(-1, C) # [P, C]
+
+ labels = labels.view(-1)
+ if ignore is None:
+ return probas, labels
+ valid = labels != ignore
+ vprobas = probas[valid]
+ vlabels = labels[valid]
+ return vprobas, vlabels
+
+
+def isnan(x):
+ return x != x
+
+
+def mean(values, ignore_nan=False, empty=0):
+ """Nan-mean compatible with generators."""
+ values = iter(values)
+ if ignore_nan:
+ values = filterfalse(isnan, values)
+ try:
+ n = 1
+ acc = next(values)
+ except StopIteration:
+ if empty == "raise":
+ raise ValueError("Empty mean")
+ return empty
+ for n, v in enumerate(values, 2):
+ acc += v
+ if n == 1:
+ return acc
+ return acc / n
+
+
+@LOSSES.register_module()
+class LovaszLoss(_Loss):
+ def __init__(
+ self,
+ mode: str,
+ class_seen: Optional[int] = None,
+ per_image: bool = False,
+ ignore_index: Optional[int] = None,
+ loss_weight: float = 1.0,
+ ):
+ """Lovasz loss for segmentation task.
+ It supports binary, multiclass and multilabel cases
+ Args:
+ mode: Loss mode 'binary', 'multiclass' or 'multilabel'
+ ignore_index: Label that indicates ignored pixels (does not contribute to loss)
+ per_image: If True loss computed per each image and then averaged, else computed per whole batch
+ Shape
+ - **y_pred** - torch.Tensor of shape (N, C, H, W)
+ - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
+ Reference
+ https://github.com/BloodAxe/pytorch-toolbelt
+ """
+ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
+ super().__init__()
+
+ self.mode = mode
+ self.ignore_index = ignore_index
+ self.per_image = per_image
+ self.class_seen = class_seen
+ self.loss_weight = loss_weight
+
+ def forward(self, y_pred, y_true):
+ if self.mode in {BINARY_MODE, MULTILABEL_MODE}:
+ loss = _lovasz_hinge(
+ y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index
+ )
+ elif self.mode == MULTICLASS_MODE:
+ y_pred = y_pred.softmax(dim=1)
+ loss = _lovasz_softmax(
+ y_pred,
+ y_true,
+ class_seen=self.class_seen,
+ per_image=self.per_image,
+ ignore=self.ignore_index,
+ )
+ else:
+ raise ValueError("Wrong mode {}.".format(self.mode))
+ return loss * self.loss_weight
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/misc.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec300a54b2d920d37882d25e8c7771bce01db97c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/losses/misc.py
@@ -0,0 +1,223 @@
+"""
+Misc Losses
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .builder import LOSSES
+
+
+@LOSSES.register_module()
+class CrossEntropyLoss(nn.Module):
+ def __init__(
+ self,
+ weight=None,
+ size_average=None,
+ reduce=None,
+ reduction="mean",
+ label_smoothing=0.0,
+ loss_weight=1.0,
+ ignore_index=-1,
+ ):
+ super(CrossEntropyLoss, self).__init__()
+ weight = torch.tensor(weight).cuda() if weight is not None else None
+ self.loss_weight = loss_weight
+ self.loss = nn.CrossEntropyLoss(
+ weight=weight,
+ size_average=size_average,
+ ignore_index=ignore_index,
+ reduce=reduce,
+ reduction=reduction,
+ label_smoothing=label_smoothing,
+ )
+
+ def forward(self, pred, target):
+ return self.loss(pred, target) * self.loss_weight
+
+
+@LOSSES.register_module()
+class SmoothCELoss(nn.Module):
+ def __init__(self, smoothing_ratio=0.1):
+ super(SmoothCELoss, self).__init__()
+ self.smoothing_ratio = smoothing_ratio
+
+ def forward(self, pred, target):
+ eps = self.smoothing_ratio
+ n_class = pred.size(1)
+ one_hot = torch.zeros_like(pred).scatter(1, target.view(-1, 1), 1)
+ one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
+ log_prb = F.log_softmax(pred, dim=1)
+ loss = -(one_hot * log_prb).total(dim=1)
+ loss = loss[torch.isfinite(loss)].mean()
+ return loss
+
+
+@LOSSES.register_module()
+class BinaryFocalLoss(nn.Module):
+ def __init__(self, gamma=2.0, alpha=0.5, logits=True, reduce=True, loss_weight=1.0):
+ """Binary Focal Loss
+ `
+ """
+ super(BinaryFocalLoss, self).__init__()
+ assert 0 < alpha < 1
+ self.gamma = gamma
+ self.alpha = alpha
+ self.logits = logits
+ self.reduce = reduce
+ self.loss_weight = loss_weight
+
+ def forward(self, pred, target, **kwargs):
+ """Forward function.
+ Args:
+ pred (torch.Tensor): The prediction with shape (N)
+ target (torch.Tensor): The ground truth. If containing class
+ indices, shape (N) where each value is 0≤targets[i]≤1, If containing class probabilities,
+ same shape as the input.
+ Returns:
+ torch.Tensor: The calculated loss
+ """
+ if self.logits:
+ bce = F.binary_cross_entropy_with_logits(pred, target, reduction="none")
+ else:
+ bce = F.binary_cross_entropy(pred, target, reduction="none")
+ pt = torch.exp(-bce)
+ alpha = self.alpha * target + (1 - self.alpha) * (1 - target)
+ focal_loss = alpha * (1 - pt) ** self.gamma * bce
+
+ if self.reduce:
+ focal_loss = torch.mean(focal_loss)
+ return focal_loss * self.loss_weight
+
+
+@LOSSES.register_module()
+class FocalLoss(nn.Module):
+ def __init__(
+ self, gamma=2.0, alpha=0.5, reduction="mean", loss_weight=1.0, ignore_index=-1
+ ):
+ """Focal Loss
+ `
+ """
+ super(FocalLoss, self).__init__()
+ assert reduction in (
+ "mean",
+ "sum",
+ ), "AssertionError: reduction should be 'mean' or 'sum'"
+ assert isinstance(
+ alpha, (float, list)
+ ), "AssertionError: alpha should be of type float"
+ assert isinstance(gamma, float), "AssertionError: gamma should be of type float"
+ assert isinstance(
+ loss_weight, float
+ ), "AssertionError: loss_weight should be of type float"
+ assert isinstance(ignore_index, int), "ignore_index must be of type int"
+ self.gamma = gamma
+ self.alpha = alpha
+ self.reduction = reduction
+ self.loss_weight = loss_weight
+ self.ignore_index = ignore_index
+
+ def forward(self, pred, target, **kwargs):
+ """Forward function.
+ Args:
+ pred (torch.Tensor): The prediction with shape (N, C) where C = number of classes.
+ target (torch.Tensor): The ground truth. If containing class
+ indices, shape (N) where each value is 0≤targets[i]≤C−1, If containing class probabilities,
+ same shape as the input.
+ Returns:
+ torch.Tensor: The calculated loss
+ """
+ # [B, C, d_1, d_2, ..., d_k] -> [C, B, d_1, d_2, ..., d_k]
+ pred = pred.transpose(0, 1)
+ # [C, B, d_1, d_2, ..., d_k] -> [C, N]
+ pred = pred.reshape(pred.size(0), -1)
+ # [C, N] -> [N, C]
+ pred = pred.transpose(0, 1).contiguous()
+ # (B, d_1, d_2, ..., d_k) --> (B * d_1 * d_2 * ... * d_k,)
+ target = target.view(-1).contiguous()
+ assert pred.size(0) == target.size(
+ 0
+ ), "The shape of pred doesn't match the shape of target"
+ valid_mask = target != self.ignore_index
+ target = target[valid_mask]
+ pred = pred[valid_mask]
+
+ if len(target) == 0:
+ return 0.0
+
+ num_classes = pred.size(1)
+ target = F.one_hot(target, num_classes=num_classes)
+
+ alpha = self.alpha
+ if isinstance(alpha, list):
+ alpha = pred.new_tensor(alpha)
+ pred_sigmoid = pred.sigmoid()
+ target = target.type_as(pred)
+ one_minus_pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
+ focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * one_minus_pt.pow(
+ self.gamma
+ )
+
+ loss = (
+ F.binary_cross_entropy_with_logits(pred, target, reduction="none")
+ * focal_weight
+ )
+ if self.reduction == "mean":
+ loss = loss.mean()
+ elif self.reduction == "sum":
+ loss = loss.total()
+ return self.loss_weight * loss
+
+
+@LOSSES.register_module()
+class DiceLoss(nn.Module):
+ def __init__(self, smooth=1, exponent=2, loss_weight=1.0, ignore_index=-1):
+ """DiceLoss.
+ This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
+ Volumetric Medical Image Segmentation `_.
+ """
+ super(DiceLoss, self).__init__()
+ self.smooth = smooth
+ self.exponent = exponent
+ self.loss_weight = loss_weight
+ self.ignore_index = ignore_index
+
+ def forward(self, pred, target, **kwargs):
+ # [B, C, d_1, d_2, ..., d_k] -> [C, B, d_1, d_2, ..., d_k]
+ pred = pred.transpose(0, 1)
+ # [C, B, d_1, d_2, ..., d_k] -> [C, N]
+ pred = pred.reshape(pred.size(0), -1)
+ # [C, N] -> [N, C]
+ pred = pred.transpose(0, 1).contiguous()
+ # (B, d_1, d_2, ..., d_k) --> (B * d_1 * d_2 * ... * d_k,)
+ target = target.view(-1).contiguous()
+ assert pred.size(0) == target.size(
+ 0
+ ), "The shape of pred doesn't match the shape of target"
+ valid_mask = target != self.ignore_index
+ target = target[valid_mask]
+ pred = pred[valid_mask]
+
+ pred = F.softmax(pred, dim=1)
+ num_classes = pred.shape[1]
+ target = F.one_hot(
+ torch.clamp(target.long(), 0, num_classes - 1), num_classes=num_classes
+ )
+
+ total_loss = 0
+ for i in range(num_classes):
+ if i != self.ignore_index:
+ num = torch.sum(torch.mul(pred[:, i], target[:, i])) * 2 + self.smooth
+ den = (
+ torch.sum(
+ pred[:, i].pow(self.exponent) + target[:, i].pow(self.exponent)
+ )
+ + self.smooth
+ )
+ dice_loss = 1 - num / den
+ total_loss += dice_loss
+ loss = total_loss / num_classes
+ return self.loss_weight * loss
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18733d94acc5c7ed0feb7c52c77cb040d53bee7e
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/__init__.py
@@ -0,0 +1,2 @@
+from .masked_scene_contrast_v1m1_base import MaskedSceneContrast
+from .masked_scene_contrast_v1m2_csc import MaskedSceneContrast
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b18bd563a36695eca4882a6620d4ddbe246ef80
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m1_base.py
@@ -0,0 +1,310 @@
+"""
+Masked Scene Contrast
+https://arxiv.org/abs/2303.14191
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+from itertools import chain
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch_geometric.nn.pool import voxel_grid
+
+from timm.models.layers import trunc_normal_
+import pointops
+
+from pointcept.models.builder import MODELS, build_model
+from pointcept.models.utils import offset2batch
+from pointcept.utils.comm import get_world_size
+
+
+@MODELS.register_module("MSC-v1m1")
+class MaskedSceneContrast(nn.Module):
+ def __init__(
+ self,
+ backbone,
+ backbone_in_channels,
+ backbone_out_channels,
+ mask_grid_size=0.1,
+ mask_rate=0.4,
+ view1_mix_prob=0,
+ view2_mix_prob=0,
+ matching_max_k=8,
+ matching_max_radius=0.03,
+ matching_max_pair=8192,
+ nce_t=0.4,
+ contrast_weight=1,
+ reconstruct_weight=1,
+ reconstruct_color=True,
+ reconstruct_normal=True,
+ ):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.mask_grid_size = mask_grid_size
+ self.mask_rate = mask_rate
+ self.view1_mix_prob = view1_mix_prob
+ self.view2_mix_prob = view2_mix_prob
+ self.matching_max_k = matching_max_k
+ self.matching_max_radius = matching_max_radius
+ self.matching_max_pair = matching_max_pair
+ self.nce_t = nce_t
+ self.contrast_weight = contrast_weight
+ self.reconstruct_weight = reconstruct_weight
+ self.reconstruct_color = reconstruct_color
+ self.reconstruct_normal = reconstruct_normal
+
+ self.mask_token = nn.Parameter(torch.zeros(1, backbone_in_channels))
+ trunc_normal_(self.mask_token, mean=0.0, std=0.02)
+ self.color_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_color else None
+ )
+ self.normal_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_normal else None
+ )
+ self.nce_criteria = torch.nn.CrossEntropyLoss(reduction="mean")
+
+ @torch.no_grad()
+ def generate_cross_masks(
+ self, view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ ):
+ # union origin coord
+ view1_batch = offset2batch(view1_offset)
+ view2_batch = offset2batch(view2_offset)
+
+ view1_batch_count = view1_batch.bincount()
+ view2_batch_count = view2_batch.bincount()
+ view1_origin_coord_split = view1_origin_coord.split(list(view1_batch_count))
+ view2_origin_coord_split = view2_origin_coord.split(list(view2_batch_count))
+ union_origin_coord = torch.cat(
+ list(
+ chain.from_iterable(
+ zip(view1_origin_coord_split, view2_origin_coord_split)
+ )
+ )
+ )
+ union_offset = torch.cat(
+ [view1_offset.unsqueeze(-1), view2_offset.unsqueeze(-1)], dim=-1
+ ).sum(-1)
+ union_batch = offset2batch(union_offset)
+
+ # grid partition
+ mask_patch_coord = union_origin_coord.div(self.mask_grid_size)
+ mask_patch_grid_coord = torch.floor(mask_patch_coord)
+ mask_patch_cluster = voxel_grid(
+ pos=mask_patch_grid_coord, size=1, batch=union_batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ mask_patch_cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ patch_num = unique.shape[0]
+ patch_max_point = counts.max().item()
+ patch2point_map = cluster.new_zeros(patch_num, patch_max_point)
+ patch2point_mask = torch.lt(
+ torch.arange(patch_max_point).cuda().unsqueeze(0), counts.unsqueeze(-1)
+ )
+ sorted_cluster_value, sorted_cluster_indices = torch.sort(cluster)
+ patch2point_map[patch2point_mask] = sorted_cluster_indices
+
+ # generate cross masks
+ assert self.mask_rate <= 0.5
+ patch_mask = torch.zeros(patch_num, device=union_origin_coord.device).int()
+ rand_perm = torch.randperm(patch_num)
+ mask_patch_num = int(patch_num * self.mask_rate)
+
+ # mask1 tag with 1, mask2 tag with 2
+ patch_mask[rand_perm[0:mask_patch_num]] = 1
+ patch_mask[rand_perm[mask_patch_num : mask_patch_num * 2]] = 2
+ point_mask = torch.zeros(
+ union_origin_coord.shape[0], device=union_origin_coord.device
+ ).int()
+ point_mask[
+ patch2point_map[patch_mask == 1][patch2point_mask[patch_mask == 1]]
+ ] = 1
+ point_mask[
+ patch2point_map[patch_mask == 2][patch2point_mask[patch_mask == 2]]
+ ] = 2
+
+ # separate mask to view1 and view2
+ point_mask_split = point_mask.split(
+ list(
+ torch.cat(
+ [view1_batch_count.unsqueeze(-1), view2_batch_count.unsqueeze(-1)],
+ dim=-1,
+ ).flatten()
+ )
+ )
+ view1_point_mask = torch.cat(point_mask_split[0::2]) == 1
+ view2_point_mask = torch.cat(point_mask_split[1::2]) == 2
+ return view1_point_mask, view2_point_mask
+
+ @torch.no_grad()
+ def match_contrastive_pair(
+ self, view1_coord, view1_offset, view2_coord, view2_offset, max_k, max_radius
+ ):
+ index, distance = pointops.knn_query(
+ max_k,
+ view2_coord.float(),
+ view2_offset.int(),
+ view1_coord.float(),
+ view1_offset.int(),
+ )
+ index = torch.cat(
+ [
+ torch.arange(index.shape[0], device=index.device, dtype=torch.long)
+ .view(-1, 1, 1)
+ .expand(-1, max_k, 1),
+ index.view(-1, max_k, 1),
+ ],
+ dim=-1,
+ )[distance.squeeze(-1) < max_radius]
+ unique, count = index[:, 0].unique(return_counts=True)
+ select = (
+ torch.cumsum(count, dim=0)
+ - torch.randint(count.max(), count.shape, device=count.device) % count
+ - 1
+ )
+ index = index[select]
+ if index.shape[0] > self.matching_max_pair:
+ index = index[torch.randperm(index.shape[0])[: self.matching_max_pair]]
+ return index
+
+ def compute_contrastive_loss(
+ self, view1_feat, view1_offset, view2_feat, view2_offset, match_index
+ ):
+ assert view1_offset.shape == view2_offset.shape
+
+ view1_feat = view1_feat[match_index[:, 0]]
+ view2_feat = view2_feat[match_index[:, 1]]
+ view1_feat = view1_feat / (
+ torch.norm(view1_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+ view2_feat = view2_feat / (
+ torch.norm(view2_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+ sim = torch.mm(view1_feat, view2_feat.transpose(1, 0))
+
+ with torch.no_grad():
+ pos_sim = torch.diagonal(sim).mean()
+ neg_sim = sim.mean(dim=-1).mean() - pos_sim / match_index.shape[0]
+ labels = torch.arange(sim.shape[0], device=view1_feat.device).long()
+ loss = self.nce_criteria(torch.div(sim, self.nce_t), labels)
+
+ if get_world_size() > 1:
+ dist.all_reduce(loss)
+ dist.all_reduce(pos_sim)
+ dist.all_reduce(neg_sim)
+ return (
+ loss / get_world_size(),
+ pos_sim / get_world_size(),
+ neg_sim / get_world_size(),
+ )
+
+ def forward(self, data_dict):
+ view1_origin_coord = data_dict["view1_origin_coord"]
+ view1_coord = data_dict["view1_coord"]
+ view1_feat = data_dict["view1_feat"]
+ view1_offset = data_dict["view1_offset"].int()
+
+ view2_origin_coord = data_dict["view2_origin_coord"]
+ view2_coord = data_dict["view2_coord"]
+ view2_feat = data_dict["view2_feat"]
+ view2_offset = data_dict["view2_offset"].int()
+
+ # mask generation by union original coord (without spatial augmentation)
+ view1_point_mask, view2_point_mask = self.generate_cross_masks(
+ view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ )
+
+ view1_mask_tokens = self.mask_token.expand(view1_coord.shape[0], -1)
+ view1_weight = view1_point_mask.unsqueeze(-1).type_as(view1_mask_tokens)
+ view1_feat = view1_feat * (1 - view1_weight) + view1_mask_tokens * view1_weight
+
+ view2_mask_tokens = self.mask_token.expand(view2_coord.shape[0], -1)
+ view2_weight = view2_point_mask.unsqueeze(-1).type_as(view2_mask_tokens)
+ view2_feat = view2_feat * (1 - view2_weight) + view2_mask_tokens * view2_weight
+
+ view1_data_dict = dict(
+ origin_coord=view1_origin_coord,
+ coord=view1_coord,
+ feat=view1_feat,
+ offset=view1_offset,
+ )
+ view2_data_dict = dict(
+ origin_coord=view2_origin_coord,
+ coord=view2_coord,
+ feat=view2_feat,
+ offset=view2_offset,
+ )
+
+ # SparseConv based method need grid coord
+ if "view1_grid_coord" in data_dict.keys():
+ view1_data_dict["grid_coord"] = data_dict["view1_grid_coord"]
+ if "view2_grid_coord" in data_dict.keys():
+ view2_data_dict["grid_coord"] = data_dict["view2_grid_coord"]
+
+ # view mixing strategy
+ if random.random() < self.view1_mix_prob:
+ view1_data_dict["offset"] = torch.cat(
+ [view1_offset[1:-1:2], view1_offset[-1].unsqueeze(0)], dim=0
+ )
+ if random.random() < self.view2_mix_prob:
+ view2_data_dict["offset"] = torch.cat(
+ [view2_offset[1:-1:2], view2_offset[-1].unsqueeze(0)], dim=0
+ )
+
+ view1_feat = self.backbone(view1_data_dict)
+ view2_feat = self.backbone(view2_data_dict)
+ match_index = self.match_contrastive_pair(
+ view1_origin_coord,
+ view1_offset,
+ view2_origin_coord,
+ view2_offset,
+ max_k=self.matching_max_k,
+ max_radius=self.matching_max_radius,
+ )
+ nce_loss, pos_sim, neg_sim = self.compute_contrastive_loss(
+ view1_feat, view1_offset, view2_feat, view2_offset, match_index
+ )
+ loss = nce_loss * self.contrast_weight
+ result_dict = dict(nce_loss=nce_loss, pos_sim=pos_sim, neg_sim=neg_sim)
+
+ if self.color_head is not None:
+ assert "view1_color" in data_dict.keys()
+ assert "view2_color" in data_dict.keys()
+ view1_color = data_dict["view1_color"]
+ view2_color = data_dict["view2_color"]
+ view1_color_pred = self.color_head(view1_feat[view1_point_mask])
+ view2_color_pred = self.color_head(view2_feat[view2_point_mask])
+ color_loss = (
+ torch.sum((view1_color_pred - view1_color[view1_point_mask]) ** 2)
+ + torch.sum((view2_color_pred - view2_color[view2_point_mask]) ** 2)
+ ) / (view1_color_pred.shape[0] + view2_color_pred.shape[0])
+ loss = loss + color_loss * self.reconstruct_weight
+ result_dict["color_loss"] = color_loss
+
+ if self.normal_head is not None:
+ assert "view1_normal" in data_dict.keys()
+ assert "view2_normal" in data_dict.keys()
+ view1_normal = data_dict["view1_normal"]
+ view2_normal = data_dict["view2_normal"]
+ view1_normal_pred = self.normal_head(view1_feat[view1_point_mask])
+ view2_normal_pred = self.normal_head(view2_feat[view2_point_mask])
+
+ view1_normal_pred = view1_normal_pred / (
+ torch.norm(view1_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ view2_normal_pred = view2_normal_pred / (
+ torch.norm(view2_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ normal_loss = (
+ torch.sum(view1_normal_pred * view1_normal[view1_point_mask])
+ + torch.sum(view2_normal_pred * view2_normal[view2_point_mask])
+ ) / (view1_normal_pred.shape[0] + view2_normal_pred.shape[0])
+ loss = loss + normal_loss * self.reconstruct_weight
+ result_dict["normal_loss"] = normal_loss
+
+ result_dict["loss"] = loss
+ return result_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m2_csc.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m2_csc.py
new file mode 100644
index 0000000000000000000000000000000000000000..139e26b89d88bbe8711fb9a162b5347d846fd399
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m2_csc.py
@@ -0,0 +1,377 @@
+"""
+Masked Scene Contrast v1m2
+contrastive learning backend with CSC (https://arxiv.org/abs/2012.09165)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Chengyao Wang (cywang22@cse.cuhk.edu.hk)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+from itertools import chain
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch_geometric.nn.pool import voxel_grid
+
+from timm.models.layers import trunc_normal_
+import pointops
+
+from pointcept.models.builder import MODELS, build_model
+from pointcept.models.utils import offset2batch
+from pointcept.utils.comm import get_world_size
+
+
+@MODELS.register_module("MSC-v1m2")
+class MaskedSceneContrast(nn.Module):
+ def __init__(
+ self,
+ backbone,
+ backbone_in_channels,
+ backbone_out_channels,
+ mask_grid_size=0.1,
+ mask_rate=0.4,
+ view1_mix_prob=0,
+ view2_mix_prob=0,
+ matching_max_k=8,
+ matching_max_radius=0.03,
+ matching_max_pair=8192,
+ nce_t=0.4,
+ contrast_weight=1,
+ reconstruct_weight=1,
+ reconstruct_color=True,
+ reconstruct_normal=True,
+ partitions=4,
+ r1=0.125,
+ r2=2,
+ ):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.mask_grid_size = mask_grid_size
+ self.mask_rate = mask_rate
+ self.view1_mix_prob = view1_mix_prob
+ self.view2_mix_prob = view2_mix_prob
+ self.matching_max_k = matching_max_k
+ self.matching_max_radius = matching_max_radius
+ self.matching_max_pair = matching_max_pair
+ self.nce_t = nce_t
+ self.contrast_weight = contrast_weight
+ self.reconstruct_weight = reconstruct_weight
+ self.reconstruct_color = reconstruct_color
+ self.reconstruct_normal = reconstruct_normal
+
+ # csc partition
+ self.partitions = partitions
+ self.r1 = r1
+ self.r2 = r2
+
+ self.mask_token = nn.Parameter(torch.zeros(1, backbone_in_channels))
+ trunc_normal_(self.mask_token, mean=0.0, std=0.02)
+ self.color_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_color else None
+ )
+ self.normal_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_normal else None
+ )
+ self.nce_criteria = torch.nn.CrossEntropyLoss(reduction="mean")
+
+ @torch.no_grad()
+ def generate_cross_masks(
+ self, view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ ):
+ # union origin coord
+ view1_batch = offset2batch(view1_offset)
+ view2_batch = offset2batch(view2_offset)
+
+ view1_batch_count = view1_batch.bincount()
+ view2_batch_count = view2_batch.bincount()
+ view1_origin_coord_split = view1_origin_coord.split(list(view1_batch_count))
+ view2_origin_coord_split = view2_origin_coord.split(list(view2_batch_count))
+ union_origin_coord = torch.cat(
+ list(
+ chain.from_iterable(
+ zip(view1_origin_coord_split, view2_origin_coord_split)
+ )
+ )
+ )
+ union_offset = torch.cat(
+ [view1_offset.unsqueeze(-1), view2_offset.unsqueeze(-1)], dim=-1
+ ).sum(-1)
+ union_batch = offset2batch(union_offset)
+
+ # grid partition
+ mask_patch_coord = union_origin_coord.div(self.mask_grid_size)
+ mask_patch_grid_coord = torch.floor(mask_patch_coord)
+ mask_patch_cluster = voxel_grid(
+ pos=mask_patch_grid_coord, size=1, batch=union_batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ mask_patch_cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ patch_num = unique.shape[0]
+ patch_max_point = counts.max().item()
+ patch2point_map = cluster.new_zeros(patch_num, patch_max_point)
+ patch2point_mask = torch.lt(
+ torch.arange(patch_max_point).cuda().unsqueeze(0), counts.unsqueeze(-1)
+ )
+ sorted_cluster_value, sorted_cluster_indices = torch.sort(cluster)
+ patch2point_map[patch2point_mask] = sorted_cluster_indices
+
+ # generate cross masks
+ assert self.mask_rate <= 0.5
+ patch_mask = torch.zeros(patch_num, device=union_origin_coord.device).int()
+ rand_perm = torch.randperm(patch_num)
+ mask_patch_num = int(patch_num * self.mask_rate)
+
+ # mask1 tag with 1, mask2 tag with 2
+ patch_mask[rand_perm[0:mask_patch_num]] = 1
+ patch_mask[rand_perm[mask_patch_num : mask_patch_num * 2]] = 2
+ point_mask = torch.zeros(
+ union_origin_coord.shape[0], device=union_origin_coord.device
+ ).int()
+ point_mask[
+ patch2point_map[patch_mask == 1][patch2point_mask[patch_mask == 1]]
+ ] = 1
+ point_mask[
+ patch2point_map[patch_mask == 2][patch2point_mask[patch_mask == 2]]
+ ] = 2
+
+ # separate mask to view1 and view2
+ point_mask_split = point_mask.split(
+ list(
+ torch.cat(
+ [view1_batch_count.unsqueeze(-1), view2_batch_count.unsqueeze(-1)],
+ dim=-1,
+ ).flatten()
+ )
+ )
+ view1_point_mask = torch.cat(point_mask_split[0::2]) == 1
+ view2_point_mask = torch.cat(point_mask_split[1::2]) == 2
+ return view1_point_mask, view2_point_mask
+
+ @torch.no_grad()
+ def match_contrastive_pair(
+ self, view1_coord, view1_offset, view2_coord, view2_offset, max_k, max_radius
+ ):
+ index, distance = pointops.knn_query(
+ max_k,
+ view2_coord.float(),
+ view2_offset.int(),
+ view1_coord.float(),
+ view1_offset.int(),
+ )
+ index = torch.cat(
+ [
+ torch.arange(index.shape[0], device=index.device, dtype=torch.long)
+ .view(-1, 1, 1)
+ .expand(-1, max_k, 1),
+ index.view(-1, max_k, 1),
+ ],
+ dim=-1,
+ )[distance.squeeze(-1) < max_radius]
+ unique, count = index[:, 0].unique(return_counts=True)
+ select = (
+ torch.cumsum(count, dim=0)
+ - torch.randint(count.max(), count.shape, device=count.device) % count
+ - 1
+ )
+ index = index[select]
+ if index.shape[0] > self.matching_max_pair:
+ index = index[torch.randperm(index.shape[0])[: self.matching_max_pair]]
+ return index
+
+ def compute_partitions(self, coord1, coord2):
+ partition_matrix = torch.zeros((coord1.shape[0], coord2.shape[0]))
+ partition_matrix = partition_matrix.cuda() - 1e7
+
+ rel_trans = coord1.unsqueeze(0) - coord2.unsqueeze(1)
+ mask_up = rel_trans[:, :, 2] > 0.0
+ mask_down = rel_trans[:, :, 2] < 0.0
+
+ distance_matrix = torch.sqrt(torch.sum(rel_trans.pow(2), 2).add(1e-7))
+
+ mask = (distance_matrix[:, :] > self.r1) & (distance_matrix[:, :] <= self.r2)
+ partition_matrix[mask & mask_up] = 0
+ partition_matrix[mask & mask_down] = 1
+
+ mask = distance_matrix[:, :] > self.r2
+ partition_matrix[mask & mask_up] = 2
+ partition_matrix[mask & mask_down] = 3
+
+ return partition_matrix
+
+ def compute_contrastive_loss(
+ self,
+ view1_feat,
+ view1_coord,
+ view1_offset,
+ view2_feat,
+ view2_coord,
+ view2_offset,
+ match_index,
+ ):
+ assert view1_offset.shape == view2_offset.shape
+ device = view1_feat.device
+ loss = torch.tensor(0.0, device=device)
+ pos_sim = torch.tensor(0.0, device=device)
+ neg_sim = torch.tensor(0.0, device=device)
+ large_num = 1e9
+
+ view1_feat = view1_feat[match_index[:, 0]]
+ view2_feat = view2_feat[match_index[:, 1]]
+ view1_feat = view1_feat / (
+ torch.norm(view1_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+ view2_feat = view2_feat / (
+ torch.norm(view2_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+
+ view1_coord = view1_coord[match_index[:, 0]]
+ view2_coord = view2_coord[match_index[:, 1]]
+
+ batch = offset2batch(view1_offset)[match_index[:, 0]]
+ for batch_id in batch.unique():
+ batch_mask = batch == batch_id
+ sim = torch.mm(view1_feat[batch_mask], view2_feat[batch_mask].T)
+
+ with torch.no_grad():
+ pos_sim += torch.diagonal(sim).mean()
+ neg_sim += sim.mean(dim=-1).mean() - pos_sim / batch_mask.sum()
+
+ labels = torch.arange(sim.shape[0], device=view1_feat.device).long()
+ part = self.compute_partitions(
+ view1_coord[batch_mask], view2_coord[batch_mask]
+ )
+ for part_id in part.unique():
+ part_mask = part == part_id
+ part_mask.fill_diagonal_(True)
+ loss += self.nce_criteria(
+ torch.div(sim, self.nce_t) - large_num * (~part_mask).float(),
+ labels,
+ )
+
+ loss /= len(view1_offset) * self.partitions
+ pos_sim /= len(view1_offset)
+ neg_sim /= len(view1_offset)
+
+ if get_world_size() > 1:
+ dist.all_reduce(loss)
+ dist.all_reduce(pos_sim)
+ dist.all_reduce(neg_sim)
+ return (
+ loss / get_world_size(),
+ pos_sim / get_world_size(),
+ neg_sim / get_world_size(),
+ )
+
+ def forward(self, data_dict):
+ view1_origin_coord = data_dict["view1_origin_coord"]
+ view1_coord = data_dict["view1_coord"]
+ view1_feat = data_dict["view1_feat"]
+ view1_offset = data_dict["view1_offset"].int()
+
+ view2_origin_coord = data_dict["view2_origin_coord"]
+ view2_coord = data_dict["view2_coord"]
+ view2_feat = data_dict["view2_feat"]
+ view2_offset = data_dict["view2_offset"].int()
+
+ # mask generation by union original coord (without spatial augmentation)
+ view1_point_mask, view2_point_mask = self.generate_cross_masks(
+ view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ )
+
+ view1_mask_tokens = self.mask_token.expand(view1_coord.shape[0], -1)
+ view1_weight = view1_point_mask.unsqueeze(-1).type_as(view1_mask_tokens)
+ view1_feat = view1_feat * (1 - view1_weight) + view1_mask_tokens * view1_weight
+
+ view2_mask_tokens = self.mask_token.expand(view2_coord.shape[0], -1)
+ view2_weight = view2_point_mask.unsqueeze(-1).type_as(view2_mask_tokens)
+ view2_feat = view2_feat * (1 - view2_weight) + view2_mask_tokens * view2_weight
+
+ view1_data_dict = dict(
+ origin_coord=view1_origin_coord,
+ coord=view1_coord,
+ feat=view1_feat,
+ offset=view1_offset,
+ )
+ view2_data_dict = dict(
+ origin_coord=view2_origin_coord,
+ coord=view2_coord,
+ feat=view2_feat,
+ offset=view2_offset,
+ )
+
+ # SparseConv based method need grid coord
+ if "view1_grid_coord" in data_dict.keys():
+ view1_data_dict["grid_coord"] = data_dict["view1_grid_coord"]
+ if "view2_grid_coord" in data_dict.keys():
+ view2_data_dict["grid_coord"] = data_dict["view2_grid_coord"]
+
+ # view mixing strategy
+ if random.random() < self.view1_mix_prob:
+ view1_data_dict["offset"] = torch.cat(
+ [view1_offset[1:-1:2], view1_offset[-1].unsqueeze(0)], dim=0
+ )
+ if random.random() < self.view2_mix_prob:
+ view2_data_dict["offset"] = torch.cat(
+ [view2_offset[1:-1:2], view2_offset[-1].unsqueeze(0)], dim=0
+ )
+
+ view1_feat = self.backbone(view1_data_dict)
+ view2_feat = self.backbone(view2_data_dict)
+ match_index = self.match_contrastive_pair(
+ view1_origin_coord,
+ view1_offset,
+ view2_origin_coord,
+ view2_offset,
+ max_k=self.matching_max_k,
+ max_radius=self.matching_max_radius,
+ )
+ nce_loss, pos_sim, neg_sim = self.compute_contrastive_loss(
+ view1_feat,
+ view1_origin_coord,
+ view1_offset,
+ view2_feat,
+ view2_origin_coord,
+ view2_offset,
+ match_index,
+ )
+ loss = nce_loss * self.contrast_weight
+ result_dict = dict(nce_loss=nce_loss, pos_sim=pos_sim, neg_sim=neg_sim)
+
+ if self.color_head is not None:
+ assert "view1_color" in data_dict.keys()
+ assert "view2_color" in data_dict.keys()
+ view1_color = data_dict["view1_color"]
+ view2_color = data_dict["view2_color"]
+ view1_color_pred = self.color_head(view1_feat[view1_point_mask])
+ view2_color_pred = self.color_head(view2_feat[view2_point_mask])
+ color_loss = (
+ torch.sum((view1_color_pred - view1_color[view1_point_mask]) ** 2)
+ + torch.sum((view2_color_pred - view2_color[view2_point_mask]) ** 2)
+ ) / (view1_color_pred.shape[0] + view2_color_pred.shape[0])
+ loss = loss + color_loss * self.reconstruct_weight
+ result_dict["color_loss"] = color_loss
+
+ if self.normal_head is not None:
+ assert "view1_normal" in data_dict.keys()
+ assert "view2_normal" in data_dict.keys()
+ view1_normal = data_dict["view1_normal"]
+ view2_normal = data_dict["view2_normal"]
+ view1_normal_pred = self.normal_head(view1_feat[view1_point_mask])
+ view2_normal_pred = self.normal_head(view2_feat[view2_point_mask])
+
+ view1_normal_pred = view1_normal_pred / (
+ torch.norm(view1_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ view2_normal_pred = view2_normal_pred / (
+ torch.norm(view2_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ normal_loss = (
+ torch.sum(view1_normal_pred * view1_normal[view1_point_mask])
+ + torch.sum(view2_normal_pred * view2_normal[view2_point_mask])
+ ) / (view1_normal_pred.shape[0] + view2_normal_pred.shape[0])
+ loss = loss + normal_loss * self.reconstruct_weight
+ result_dict["normal_loss"] = normal_loss
+
+ result_dict["loss"] = loss
+ return result_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/modules.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a737ae9e52620ffc4eb139a81e0bdd46cded0ed
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/modules.py
@@ -0,0 +1,83 @@
+import sys
+import torch.nn as nn
+import spconv.pytorch as spconv
+from collections import OrderedDict
+from pointcept.models.utils.structure import Point
+
+
+class PointModule(nn.Module):
+ r"""PointModule
+ placeholder, all module subclass from this will take Point in PointSequential.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+
+class PointSequential(PointModule):
+ r"""A sequential container.
+ Modules will be added to it in the order they are passed in the constructor.
+ Alternatively, an ordered dict of modules can also be passed in.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__()
+ if len(args) == 1 and isinstance(args[0], OrderedDict):
+ for key, module in args[0].items():
+ self.add_module(key, module)
+ else:
+ for idx, module in enumerate(args):
+ self.add_module(str(idx), module)
+ for name, module in kwargs.items():
+ if sys.version_info < (3, 6):
+ raise ValueError("kwargs only supported in py36+")
+ if name in self._modules:
+ raise ValueError("name exists.")
+ self.add_module(name, module)
+
+ def __getitem__(self, idx):
+ if not (-len(self) <= idx < len(self)):
+ raise IndexError("index {} is out of range".format(idx))
+ if idx < 0:
+ idx += len(self)
+ it = iter(self._modules.values())
+ for i in range(idx):
+ next(it)
+ return next(it)
+
+ def __len__(self):
+ return len(self._modules)
+
+ def add(self, module, name=None):
+ if name is None:
+ name = str(len(self._modules))
+ if name in self._modules:
+ raise KeyError("name exists")
+ self.add_module(name, module)
+
+ def forward(self, input):
+ for k, module in self._modules.items():
+ # Point module
+ if isinstance(module, PointModule):
+ input = module(input)
+ # Spconv module
+ elif spconv.modules.is_spconv_module(module):
+ if isinstance(input, Point):
+ input.sparse_conv_feat = module(input.sparse_conv_feat)
+ input.feat = input.sparse_conv_feat.features
+ else:
+ input = module(input)
+ # PyTorch module
+ else:
+ if isinstance(input, Point):
+ input.feat = module(input.feat)
+ if "sparse_conv_feat" in input.keys():
+ input.sparse_conv_feat = input.sparse_conv_feat.replace_feature(
+ input.feat
+ )
+ elif isinstance(input, spconv.SparseConvTensor):
+ if input.indices.shape[0] != 0:
+ input = input.replace_feature(module(input.features))
+ else:
+ input = module(input)
+ return input
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/oacnns/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/oacnns/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..654b767080457f3814142159a121a2bef9c682b7
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/oacnns/__init__.py
@@ -0,0 +1 @@
+from .oacnns_v1m1_base import OACNNs
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/oacnns/oacnns_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/oacnns/oacnns_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd8ee6d25ed152f04ab8b3905bbd8c2bdf127d06
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/oacnns/oacnns_v1m1_base.py
@@ -0,0 +1,345 @@
+from functools import partial
+import torch
+import torch.nn as nn
+from einops import rearrange
+import spconv.pytorch as spconv
+from timm.models.layers import trunc_normal_
+from ..builder import MODELS
+from ..utils import offset2batch
+from torch_geometric.nn.pool import voxel_grid
+from torch_geometric.utils import scatter
+
+
+class BasicBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ norm_fn=None,
+ indice_key=None,
+ depth=4,
+ groups=None,
+ grid_size=None,
+ bias=False,
+ ):
+ super().__init__()
+ assert embed_channels % groups == 0
+ self.groups = groups
+ self.embed_channels = embed_channels
+ self.proj = nn.ModuleList()
+ self.grid_size = grid_size
+ self.weight = nn.ModuleList()
+ self.l_w = nn.ModuleList()
+ self.proj.append(
+ nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ )
+ for _ in range(depth - 1):
+ self.proj.append(
+ nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ )
+ self.l_w.append(
+ nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ )
+ self.weight.append(nn.Linear(embed_channels, embed_channels, bias=False))
+
+ self.adaptive = nn.Linear(embed_channels, depth - 1, bias=False)
+ self.fuse = nn.Sequential(
+ nn.Linear(embed_channels * 2, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ self.voxel_block = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ indice_key=indice_key,
+ bias=bias,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ indice_key=indice_key,
+ bias=bias,
+ ),
+ norm_fn(embed_channels),
+ )
+ self.act = nn.ReLU()
+
+ def forward(self, x, clusters):
+ feat = x.features
+ feats = []
+ for i, cluster in enumerate(clusters):
+ pw = self.l_w[i](feat)
+ pw = pw - scatter(pw, cluster, reduce="mean")[cluster]
+ pw = self.weight[i](pw)
+ pw = torch.exp(pw - pw.max())
+ pw = pw / (scatter(pw, cluster, reduce="sum", dim=0)[cluster] + 1e-6)
+ pfeat = self.proj[i](feat) * pw
+ pfeat = scatter(pfeat, cluster, reduce="sum")[cluster]
+ feats.append(pfeat)
+ adp = self.adaptive(feat)
+ adp = torch.softmax(adp, dim=1)
+ feats = torch.stack(feats, dim=1)
+ feats = torch.einsum("l n, l n c -> l c", adp, feats)
+ feat = self.proj[-1](feat)
+ feat = torch.cat([feat, feats], dim=1)
+ feat = self.fuse(feat) + x.features
+ res = feat
+ x = x.replace_feature(feat)
+ x = self.voxel_block(x)
+ x = x.replace_feature(self.act(x.features + res))
+ return x
+
+
+class DonwBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ depth,
+ sp_indice_key,
+ point_grid_size,
+ num_ref=16,
+ groups=None,
+ norm_fn=None,
+ sub_indice_key=None,
+ ):
+ super().__init__()
+ self.num_ref = num_ref
+ self.depth = depth
+ self.point_grid_size = point_grid_size
+ self.down = spconv.SparseSequential(
+ spconv.SparseConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=2,
+ stride=2,
+ indice_key=sp_indice_key,
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ self.blocks = nn.ModuleList()
+ for _ in range(depth):
+ self.blocks.append(
+ BasicBlock(
+ in_channels=embed_channels,
+ embed_channels=embed_channels,
+ depth=len(point_grid_size) + 1,
+ groups=groups,
+ grid_size=point_grid_size,
+ norm_fn=norm_fn,
+ indice_key=sub_indice_key,
+ )
+ )
+
+ def forward(self, x):
+ x = self.down(x)
+ coord = x.indices[:, 1:].float()
+ batch = x.indices[:, 0]
+ clusters = []
+ for grid_size in self.point_grid_size:
+ cluster = voxel_grid(pos=coord, size=grid_size, batch=batch)
+ _, cluster = torch.unique(cluster, return_inverse=True)
+ clusters.append(cluster)
+ for block in self.blocks:
+ x = block(x, clusters)
+ return x
+
+
+class UpBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ depth,
+ sp_indice_key,
+ norm_fn=None,
+ down_ratio=2,
+ sub_indice_key=None,
+ ):
+ super().__init__()
+ assert depth > 0
+ self.up = spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=down_ratio,
+ indice_key=sp_indice_key,
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ self.blocks = nn.ModuleList()
+ self.fuse = nn.Sequential(
+ nn.Linear(skip_channels + embed_channels, embed_channels),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ nn.Linear(embed_channels, embed_channels),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+
+ def forward(self, x, skip_x):
+ x = self.up(x)
+ x = x.replace_feature(
+ self.fuse(torch.cat([x.features, skip_x.features], dim=1)) + x.features
+ )
+ return x
+
+
+@MODELS.register_module()
+class OACNNs(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ embed_channels=64,
+ enc_num_ref=[16, 16, 16, 16],
+ enc_channels=[64, 64, 128, 256],
+ groups=[2, 4, 8, 16],
+ enc_depth=[2, 3, 6, 4],
+ down_ratio=[2, 2, 2, 2],
+ dec_channels=[96, 96, 128, 256],
+ point_grid_size=[[16, 32, 64], [8, 16, 24], [4, 8, 12], [2, 4, 6]],
+ dec_depth=[2, 2, 2, 2],
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_channels)
+ self.embed_channels = embed_channels
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+ self.stem = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ padding=1,
+ indice_key="stem",
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ padding=1,
+ indice_key="stem",
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ padding=1,
+ indice_key="stem",
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList()
+ for i in range(self.num_stages):
+ self.enc.append(
+ DonwBlock(
+ in_channels=embed_channels if i == 0 else enc_channels[i - 1],
+ embed_channels=enc_channels[i],
+ depth=enc_depth[i],
+ norm_fn=norm_fn,
+ groups=groups[i],
+ point_grid_size=point_grid_size[i],
+ num_ref=enc_num_ref[i],
+ sp_indice_key=f"spconv{i}",
+ sub_indice_key=f"subm{i + 1}",
+ )
+ )
+ self.dec.append(
+ UpBlock(
+ in_channels=(
+ enc_channels[-1]
+ if i == self.num_stages - 1
+ else dec_channels[i + 1]
+ ),
+ skip_channels=embed_channels if i == 0 else enc_channels[i - 1],
+ embed_channels=dec_channels[i],
+ depth=dec_depth[i],
+ norm_fn=norm_fn,
+ sp_indice_key=f"spconv{i}",
+ sub_indice_key=f"subm{i}",
+ )
+ )
+
+ self.final = spconv.SubMConv3d(dec_channels[0], num_classes, kernel_size=1)
+ self.apply(self._init_weights)
+
+ def forward(self, input_dict):
+ discrete_coord = input_dict["grid_coord"]
+ feat = input_dict["feat"]
+ offset = input_dict["offset"]
+ batch = offset2batch(offset)
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat([batch.unsqueeze(-1), discrete_coord], dim=1)
+ .int()
+ .contiguous(),
+ spatial_shape=torch.add(
+ torch.max(discrete_coord, dim=0).values, 1
+ ).tolist(),
+ batch_size=batch[-1].tolist() + 1,
+ )
+
+ x = self.stem(x)
+ skips = [x]
+ for i in range(self.num_stages):
+ x = self.enc[i](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ for i in reversed(range(self.num_stages)):
+ skip = skips.pop(-1)
+ x = self.dec[i](x, skip)
+ x = self.final(x)
+ return x.features
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/octformer/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/octformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06bea370d10808b0bad2b68189e957765fc46081
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/octformer/__init__.py
@@ -0,0 +1 @@
+from .octformer_v1m1_base import OctFormer
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/octformer/octformer_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/octformer/octformer_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c0faf700126b0d589867811e871025dc9782b76
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/octformer/octformer_v1m1_base.py
@@ -0,0 +1,629 @@
+"""
+Octree Transformer
+
+Modified from https://github.com/octree-nn/octformer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from typing import Optional, List, Dict
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+try:
+ import ocnn
+ from ocnn.octree import Octree, Points
+except ImportError:
+ from pointcept.utils.misc import DummyClass
+
+ ocnn = None
+ Octree = DummyClass
+ Points = DummyClass
+
+try:
+ import dwconv
+except ImportError:
+ dwconv = None
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch
+
+
+class OctreeT(Octree):
+ def __init__(
+ self,
+ octree: Octree,
+ patch_size: int = 24,
+ dilation: int = 4,
+ nempty: bool = True,
+ max_depth: Optional[int] = None,
+ start_depth: Optional[int] = None,
+ **kwargs
+ ):
+ super().__init__(octree.depth, octree.full_depth)
+ self.__dict__.update(octree.__dict__)
+
+ self.patch_size = patch_size
+ self.dilation = dilation
+ self.nempty = nempty
+ self.max_depth = max_depth or self.depth
+ self.start_depth = start_depth or self.full_depth
+ self.invalid_mask_value = -1e3
+ assert self.start_depth > 1
+
+ self.block_num = patch_size * dilation
+ self.nnum_t = self.nnum_nempty if nempty else self.nnum
+ self.nnum_a = ((self.nnum_t / self.block_num).ceil() * self.block_num).int()
+
+ num = self.max_depth + 1
+ self.batch_idx = [None] * num
+ self.patch_mask = [None] * num
+ self.dilate_mask = [None] * num
+ self.rel_pos = [None] * num
+ self.dilate_pos = [None] * num
+ self.build_t()
+
+ def build_t(self):
+ for d in range(self.start_depth, self.max_depth + 1):
+ self.build_batch_idx(d)
+ self.build_attn_mask(d)
+ self.build_rel_pos(d)
+
+ def build_batch_idx(self, depth: int):
+ batch = self.batch_id(depth, self.nempty)
+ self.batch_idx[depth] = self.patch_partition(batch, depth, self.batch_size)
+
+ def build_attn_mask(self, depth: int):
+ batch = self.batch_idx[depth]
+ mask = batch.view(-1, self.patch_size)
+ self.patch_mask[depth] = self._calc_attn_mask(mask)
+
+ mask = batch.view(-1, self.patch_size, self.dilation)
+ mask = mask.transpose(1, 2).reshape(-1, self.patch_size)
+ self.dilate_mask[depth] = self._calc_attn_mask(mask)
+
+ def _calc_attn_mask(self, mask: torch.Tensor):
+ attn_mask = mask.unsqueeze(2) - mask.unsqueeze(1)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, self.invalid_mask_value)
+ return attn_mask
+
+ def build_rel_pos(self, depth: int):
+ key = self.key(depth, self.nempty)
+ key = self.patch_partition(key, depth)
+ x, y, z, _ = ocnn.octree.key2xyz(key, depth)
+ xyz = torch.stack([x, y, z], dim=1)
+
+ xyz = xyz.view(-1, self.patch_size, 3)
+ self.rel_pos[depth] = xyz.unsqueeze(2) - xyz.unsqueeze(1)
+
+ xyz = xyz.view(-1, self.patch_size, self.dilation, 3)
+ xyz = xyz.transpose(1, 2).reshape(-1, self.patch_size, 3)
+ self.dilate_pos[depth] = xyz.unsqueeze(2) - xyz.unsqueeze(1)
+
+ def patch_partition(self, data: torch.Tensor, depth: int, fill_value=0):
+ num = self.nnum_a[depth] - self.nnum_t[depth]
+ tail = data.new_full((num,) + data.shape[1:], fill_value)
+ return torch.cat([data, tail], dim=0)
+
+ def patch_reverse(self, data: torch.Tensor, depth: int):
+ return data[: self.nnum_t[depth]]
+
+
+class MLP(torch.nn.Module):
+ def __init__(
+ self,
+ in_features: int,
+ hidden_features: Optional[int] = None,
+ out_features: Optional[int] = None,
+ activation=torch.nn.GELU,
+ drop: float = 0.0,
+ **kwargs
+ ):
+ super().__init__()
+ self.in_features = in_features
+ self.out_features = out_features or in_features
+ self.hidden_features = hidden_features or in_features
+
+ self.fc1 = torch.nn.Linear(self.in_features, self.hidden_features)
+ self.act = activation()
+ self.fc2 = torch.nn.Linear(self.hidden_features, self.out_features)
+ self.drop = torch.nn.Dropout(drop, inplace=True)
+
+ def forward(self, data: torch.Tensor):
+ data = self.fc1(data)
+ data = self.act(data)
+ data = self.drop(data)
+ data = self.fc2(data)
+ data = self.drop(data)
+ return data
+
+
+class OctreeDWConvBn(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ kernel_size: List[int] = [3],
+ stride: int = 1,
+ nempty: bool = False,
+ ):
+ super().__init__()
+ self.conv = dwconv.OctreeDWConv(
+ in_channels, kernel_size, nempty, use_bias=False
+ )
+ self.bn = torch.nn.BatchNorm1d(in_channels)
+
+ def forward(self, data: torch.Tensor, octree: Octree, depth: int):
+ out = self.conv(data, octree, depth)
+ out = self.bn(out)
+ return out
+
+
+class RPE(torch.nn.Module):
+ def __init__(self, patch_size: int, num_heads: int, dilation: int = 1):
+ super().__init__()
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.dilation = dilation
+ self.pos_bnd = self.get_pos_bnd(patch_size)
+ self.rpe_num = 2 * self.pos_bnd + 1
+ self.rpe_table = torch.nn.Parameter(torch.zeros(3 * self.rpe_num, num_heads))
+ torch.nn.init.trunc_normal_(self.rpe_table, std=0.02)
+
+ def get_pos_bnd(self, patch_size: int):
+ return int(0.8 * patch_size * self.dilation**0.5)
+
+ def xyz2idx(self, xyz: torch.Tensor):
+ mul = torch.arange(3, device=xyz.device) * self.rpe_num
+ xyz = xyz.clamp(-self.pos_bnd, self.pos_bnd)
+ idx = xyz + (self.pos_bnd + mul)
+ return idx
+
+ def forward(self, xyz):
+ idx = self.xyz2idx(xyz)
+ out = self.rpe_table.index_select(0, idx.reshape(-1))
+ out = out.view(idx.shape + (-1,)).sum(3)
+ out = out.permute(0, 3, 1, 2) # (N, K, K, H) -> (N, H, K, K)
+ return out
+
+ def extra_repr(self) -> str:
+ return "num_heads={}, pos_bnd={}, dilation={}".format(
+ self.num_heads, self.pos_bnd, self.dilation
+ ) # noqa
+
+
+class OctreeAttention(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ patch_size: int,
+ num_heads: int,
+ qkv_bias: bool = True,
+ qk_scale: Optional[float] = None,
+ attn_drop: float = 0.0,
+ proj_drop: float = 0.0,
+ dilation: int = 1,
+ use_rpe: bool = True,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.dilation = dilation
+ self.use_rpe = use_rpe
+ self.scale = qk_scale or (dim // num_heads) ** -0.5
+
+ self.qkv = torch.nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = torch.nn.Dropout(attn_drop)
+ self.proj = torch.nn.Linear(dim, dim)
+ self.proj_drop = torch.nn.Dropout(proj_drop)
+ self.softmax = torch.nn.Softmax(dim=-1)
+ self.rpe = RPE(patch_size, num_heads, dilation) if use_rpe else None
+
+ def forward(self, data: torch.Tensor, octree: OctreeT, depth: int):
+ H = self.num_heads
+ K = self.patch_size
+ C = self.dim
+ D = self.dilation
+
+ # patch partition
+ data = octree.patch_partition(data, depth)
+ if D > 1: # dilation
+ rel_pos = octree.dilate_pos[depth]
+ mask = octree.dilate_mask[depth]
+ data = data.view(-1, K, D, C).transpose(1, 2).reshape(-1, C)
+ else:
+ rel_pos = octree.rel_pos[depth]
+ mask = octree.patch_mask[depth]
+ data = data.view(-1, K, C)
+
+ # qkv
+ qkv = self.qkv(data).reshape(-1, K, 3, H, C // H).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # (N, H, K, C')
+ q = q * self.scale
+
+ # attn
+ attn = q @ k.transpose(-2, -1) # (N, H, K, K)
+ attn = self.apply_rpe(attn, rel_pos) # (N, H, K, K)
+ attn = attn + mask.unsqueeze(1)
+ attn = self.softmax(attn)
+ attn = self.attn_drop(attn)
+ data = (attn @ v).transpose(1, 2).reshape(-1, C)
+
+ # patch reverse
+ if D > 1: # dilation
+ data = data.view(-1, D, K, C).transpose(1, 2).reshape(-1, C)
+ data = octree.patch_reverse(data, depth)
+
+ # ffn
+ data = self.proj(data)
+ data = self.proj_drop(data)
+ return data
+
+ def apply_rpe(self, attn, rel_pos):
+ if self.use_rpe:
+ attn = attn + self.rpe(rel_pos)
+ return attn
+
+ def extra_repr(self) -> str:
+ return "dim={}, patch_size={}, num_heads={}, dilation={}".format(
+ self.dim, self.patch_size, self.num_heads, self.dilation
+ ) # noqa
+
+
+class OctFormerBlock(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ num_heads: int,
+ patch_size: int = 32,
+ dilation: int = 0,
+ mlp_ratio: float = 4.0,
+ qkv_bias: bool = True,
+ qk_scale: Optional[float] = None,
+ attn_drop: float = 0.0,
+ proj_drop: float = 0.0,
+ drop_path: float = 0.0,
+ nempty: bool = True,
+ activation: torch.nn.Module = torch.nn.GELU,
+ **kwargs
+ ):
+ super().__init__()
+ self.norm1 = torch.nn.LayerNorm(dim)
+ self.attention = OctreeAttention(
+ dim,
+ patch_size,
+ num_heads,
+ qkv_bias,
+ qk_scale,
+ attn_drop,
+ proj_drop,
+ dilation,
+ )
+ self.norm2 = torch.nn.LayerNorm(dim)
+ self.mlp = MLP(dim, int(dim * mlp_ratio), dim, activation, proj_drop)
+ self.drop_path = ocnn.nn.OctreeDropPath(drop_path, nempty)
+ self.cpe = OctreeDWConvBn(dim, nempty=nempty)
+
+ def forward(self, data: torch.Tensor, octree: OctreeT, depth: int):
+ data = self.cpe(data, octree, depth) + data
+ attn = self.attention(self.norm1(data), octree, depth)
+ data = data + self.drop_path(attn, octree, depth)
+ ffn = self.mlp(self.norm2(data))
+ data = data + self.drop_path(ffn, octree, depth)
+ return data
+
+
+class OctFormerStage(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ num_heads: int,
+ patch_size: int = 32,
+ dilation: int = 0,
+ mlp_ratio: float = 4.0,
+ qkv_bias: bool = True,
+ qk_scale: Optional[float] = None,
+ attn_drop: float = 0.0,
+ proj_drop: float = 0.0,
+ drop_path: float = 0.0,
+ nempty: bool = True,
+ activation: torch.nn.Module = torch.nn.GELU,
+ interval: int = 6,
+ use_checkpoint: bool = True,
+ num_blocks: int = 2,
+ octformer_block=OctFormerBlock,
+ **kwargs
+ ):
+ super().__init__()
+ self.num_blocks = num_blocks
+ self.use_checkpoint = use_checkpoint
+ self.interval = interval # normalization interval
+ self.num_norms = (num_blocks - 1) // self.interval
+
+ self.blocks = torch.nn.ModuleList(
+ [
+ octformer_block(
+ dim=dim,
+ num_heads=num_heads,
+ patch_size=patch_size,
+ dilation=1 if (i % 2 == 0) else dilation,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ drop_path=(
+ drop_path[i] if isinstance(drop_path, list) else drop_path
+ ),
+ nempty=nempty,
+ activation=activation,
+ )
+ for i in range(num_blocks)
+ ]
+ )
+ # self.norms = torch.nn.ModuleList([
+ # torch.nn.BatchNorm1d(dim) for _ in range(self.num_norms)])
+
+ def forward(self, data: torch.Tensor, octree: OctreeT, depth: int):
+ for i in range(self.num_blocks):
+ if self.use_checkpoint and self.training:
+ data = checkpoint(self.blocks[i], data, octree, depth)
+ else:
+ data = self.blocks[i](data, octree, depth)
+ # if i % self.interval == 0 and i != 0:
+ # data = self.norms[(i - 1) // self.interval](data)
+ return data
+
+
+class OctFormerDecoder(torch.nn.Module):
+ def __init__(
+ self, channels: List[int], fpn_channel: int, nempty: bool, head_up: int = 1
+ ):
+ super().__init__()
+ self.head_up = head_up
+ self.num_stages = len(channels)
+ self.conv1x1 = torch.nn.ModuleList(
+ [
+ torch.nn.Linear(channels[i], fpn_channel)
+ for i in range(self.num_stages - 1, -1, -1)
+ ]
+ )
+ self.upsample = ocnn.nn.OctreeUpsample("nearest", nempty)
+ self.conv3x3 = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeConvBnRelu(
+ fpn_channel, fpn_channel, kernel_size=[3], stride=1, nempty=nempty
+ )
+ for _ in range(self.num_stages)
+ ]
+ )
+ self.up_conv = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeDeconvBnRelu(
+ fpn_channel, fpn_channel, kernel_size=[3], stride=2, nempty=nempty
+ )
+ for _ in range(self.head_up)
+ ]
+ )
+
+ def forward(self, features: Dict[int, torch.Tensor], octree: Octree):
+ depth = min(features.keys())
+ depth_max = max(features.keys())
+ assert self.num_stages == len(features)
+
+ feature = self.conv1x1[0](features[depth])
+ conv_out = self.conv3x3[0](feature, octree, depth)
+ out = self.upsample(conv_out, octree, depth, depth_max)
+ for i in range(1, self.num_stages):
+ depth_i = depth + i
+ feature = self.upsample(feature, octree, depth_i - 1)
+ feature = self.conv1x1[i](features[depth_i]) + feature
+ conv_out = self.conv3x3[i](feature, octree, depth_i)
+ out = out + self.upsample(conv_out, octree, depth_i, depth_max)
+ for i in range(self.head_up):
+ out = self.up_conv[i](out, octree, depth_max + i)
+ return out
+
+
+class PatchEmbed(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels: int = 3,
+ dim: int = 96,
+ num_down: int = 2,
+ nempty: bool = True,
+ **kwargs
+ ):
+ super().__init__()
+ self.num_stages = num_down
+ self.delta_depth = -num_down
+ channels = [int(dim * 2**i) for i in range(-self.num_stages, 1)]
+
+ self.convs = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeConvBnRelu(
+ in_channels if i == 0 else channels[i],
+ channels[i],
+ kernel_size=[3],
+ stride=1,
+ nempty=nempty,
+ )
+ for i in range(self.num_stages)
+ ]
+ )
+ self.downsamples = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeConvBnRelu(
+ channels[i],
+ channels[i + 1],
+ kernel_size=[2],
+ stride=2,
+ nempty=nempty,
+ )
+ for i in range(self.num_stages)
+ ]
+ )
+ self.proj = ocnn.modules.OctreeConvBnRelu(
+ channels[-1], dim, kernel_size=[3], stride=1, nempty=nempty
+ )
+
+ def forward(self, data: torch.Tensor, octree: Octree, depth: int):
+ # TODO: reduce to single input
+ for i in range(self.num_stages):
+ depth_i = depth - i
+ data = self.convs[i](data, octree, depth_i)
+ data = self.downsamples[i](data, octree, depth_i)
+ data = self.proj(data, octree, depth_i - 1)
+ return data
+
+
+class Downsample(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: List[int] = (2,),
+ nempty: bool = True,
+ ):
+ super().__init__()
+ self.norm = torch.nn.BatchNorm1d(out_channels)
+ self.conv = ocnn.nn.OctreeConv(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=2,
+ nempty=nempty,
+ use_bias=True,
+ )
+
+ def forward(self, data: torch.Tensor, octree: Octree, depth: int):
+ data = self.conv(data, octree, depth)
+ data = self.norm(data)
+ return data
+
+
+@MODELS.register_module("OctFormer-v1m1")
+class OctFormer(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ fpn_channels=168,
+ channels=(96, 192, 384, 384),
+ num_blocks=(2, 2, 18, 2),
+ num_heads=(6, 12, 24, 24),
+ patch_size=26,
+ stem_down=2,
+ head_up=2,
+ dilation=4,
+ drop_path=0.5,
+ nempty=True,
+ octree_scale_factor=10.24,
+ octree_depth=11,
+ octree_full_depth=2,
+ ):
+ super().__init__()
+ assert ocnn is not None, "Please follow `README.md` to install ocnn.`"
+ assert dwconv is not None, "Please follow `README.md` to install dwconv.`"
+
+ self.patch_size = patch_size
+ self.dilation = dilation
+ self.nempty = nempty
+ self.num_stages = len(num_blocks)
+ self.stem_down = stem_down
+ self.octree_scale_factor = octree_scale_factor
+ self.octree_depth = octree_depth
+ self.octree_full_depth = octree_full_depth
+ drop_ratio = torch.linspace(0, drop_path, sum(num_blocks)).tolist()
+
+ self.patch_embed = PatchEmbed(in_channels, channels[0], stem_down, nempty)
+ self.layers = torch.nn.ModuleList(
+ [
+ OctFormerStage(
+ dim=channels[i],
+ num_heads=num_heads[i],
+ patch_size=patch_size,
+ drop_path=drop_ratio[
+ sum(num_blocks[:i]) : sum(num_blocks[: i + 1])
+ ],
+ dilation=dilation,
+ nempty=nempty,
+ num_blocks=num_blocks[i],
+ )
+ for i in range(self.num_stages)
+ ]
+ )
+ self.downsamples = torch.nn.ModuleList(
+ [
+ Downsample(channels[i], channels[i + 1], kernel_size=[2], nempty=nempty)
+ for i in range(self.num_stages - 1)
+ ]
+ )
+ self.decoder = OctFormerDecoder(
+ channels=channels, fpn_channel=fpn_channels, nempty=nempty, head_up=head_up
+ )
+ self.interp = ocnn.nn.OctreeInterp("nearest", nempty)
+ self.seg_head = (
+ nn.Sequential(
+ nn.Linear(fpn_channels, fpn_channels),
+ torch.nn.BatchNorm1d(fpn_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(fpn_channels, num_classes),
+ )
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def points2octree(self, points):
+ octree = ocnn.octree.Octree(self.octree_depth, self.octree_full_depth)
+ octree.build_octree(points)
+ return octree
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ normal = data_dict["normal"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+
+ point = Points(
+ points=coord / self.octree_scale_factor,
+ normals=normal,
+ features=feat,
+ batch_id=batch.unsqueeze(-1),
+ batch_size=len(offset),
+ )
+ octree = ocnn.octree.Octree(
+ depth=self.octree_depth,
+ full_depth=self.octree_full_depth,
+ batch_size=len(offset),
+ device=coord.device,
+ )
+ octree.build_octree(point)
+ octree.construct_all_neigh()
+
+ feat = self.patch_embed(octree.features[octree.depth], octree, octree.depth)
+ depth = octree.depth - self.stem_down # current octree depth
+ octree = OctreeT(
+ octree,
+ self.patch_size,
+ self.dilation,
+ self.nempty,
+ max_depth=depth,
+ start_depth=depth - self.num_stages + 1,
+ )
+ features = {}
+ for i in range(self.num_stages):
+ depth_i = depth - i
+ feat = self.layers[i](feat, octree, depth_i)
+ features[depth_i] = feat
+ if i < self.num_stages - 1:
+ feat = self.downsamples[i](feat, octree, depth_i)
+ out = self.decoder(features, octree)
+ # interp representation to points before Octreeization
+ query_pts = torch.cat([point.points, point.batch_id], dim=1).contiguous()
+ out = self.interp(out, octree, octree.depth, query_pts)
+ out = self.seg_head(out)
+ return out
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d9f35f2a05f4a88043a45f2da64faf21f38f520
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/__init__.py
@@ -0,0 +1 @@
+from .point_group_v1m1_base import PointGroup
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/point_group_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/point_group_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a01e5abe9b7b3c2dd8205e265033c2d2e76602e
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/point_group_v1m1_base.py
@@ -0,0 +1,184 @@
+"""
+PointGroup for instance segmentation
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Chengyao Wang
+Please cite our work if the code is helpful to you.
+"""
+
+from contextlib import nullcontext
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+ from pointgroup_ops import ballquery_batch_p, bfs_cluster
+except ImportError:
+ ballquery_batch_p, bfs_cluster = None, None
+
+from pointcept.models.builder import MODELS, build_model
+from pointcept.models.utils import batch2offset, offset2batch
+from pointcept.models.utils.structure import Point
+
+
+@MODELS.register_module("PG-v1m1")
+class PointGroup(nn.Module):
+
+ def __init__(
+ self,
+ backbone,
+ backbone_out_channels=64,
+ semantic_num_classes=20,
+ semantic_ignore_index=-1,
+ segment_ignore_index=(-1, 0, 1),
+ instance_ignore_index=-1,
+ cluster_thresh=1.5,
+ cluster_closed_points=300,
+ cluster_propose_points=100,
+ cluster_min_points=50,
+ voxel_size=0.02,
+ freeze_backbone=False,
+ ):
+ super().__init__()
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ self.semantic_num_classes = semantic_num_classes
+ self.segment_ignore_index = segment_ignore_index
+ self.semantic_ignore_index = semantic_ignore_index
+ self.instance_ignore_index = instance_ignore_index
+ self.cluster_thresh = cluster_thresh
+ self.cluster_closed_points = cluster_closed_points
+ self.cluster_propose_points = cluster_propose_points
+ self.cluster_min_points = cluster_min_points
+ self.voxel_size = voxel_size
+ self.backbone = build_model(backbone)
+ self.bias_head = nn.Sequential(
+ nn.Linear(backbone_out_channels, backbone_out_channels),
+ norm_fn(backbone_out_channels),
+ nn.ReLU(),
+ nn.Linear(backbone_out_channels, 3),
+ )
+ self.seg_head = nn.Linear(backbone_out_channels, semantic_num_classes)
+ self.ce_criteria = torch.nn.CrossEntropyLoss(
+ ignore_index=semantic_ignore_index)
+
+ if freeze_backbone:
+ self.optional_freeze = torch.no_grad
+ else:
+ self.optional_freeze = nullcontext
+
+ def forward(self, data_dict):
+
+ coord = data_dict["coord"]
+ segment = data_dict["segment"]
+ instance = data_dict["instance"]
+ instance_centroid = data_dict["instance_centroid"]
+ offset = data_dict["offset"]
+
+ point = Point(data_dict)
+
+ with self.optional_freeze():
+ point = self.backbone(point)
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+
+ bias_pred = self.bias_head(feat)
+ logit_pred = self.seg_head(feat)
+
+ # compute loss
+ seg_loss = self.ce_criteria(logit_pred, segment)
+
+ mask = (instance != self.instance_ignore_index).float()
+ bias_gt = instance_centroid - coord
+ bias_dist = torch.sum(torch.abs(bias_pred - bias_gt), dim=-1)
+ bias_l1_loss = torch.sum(bias_dist * mask) / (torch.sum(mask) + 1e-8)
+
+ bias_pred_norm = bias_pred / (
+ torch.norm(bias_pred, p=2, dim=1, keepdim=True) + 1e-8)
+ bias_gt_norm = bias_gt / (
+ torch.norm(bias_gt, p=2, dim=1, keepdim=True) + 1e-8)
+ cosine_similarity = -(bias_pred_norm * bias_gt_norm).sum(-1)
+ bias_cosine_loss = torch.sum(
+ cosine_similarity * mask) / (torch.sum(mask) + 1e-8)
+
+ loss = seg_loss + bias_l1_loss + bias_cosine_loss
+ return_dict = dict(
+ loss=loss,
+ seg_loss=seg_loss,
+ bias_l1_loss=bias_l1_loss,
+ bias_cosine_loss=bias_cosine_loss,
+ )
+
+ if not self.training:
+ center_pred = coord + bias_pred
+ center_pred /= self.voxel_size
+ logit_pred = F.softmax(logit_pred, dim=-1)
+ segment_pred = torch.max(logit_pred, 1)[1] # [n]
+ # cluster
+ mask = (~torch.concat(
+ [(segment_pred == index).unsqueeze(-1)
+ for index in self.segment_ignore_index],
+ dim=1,
+ ).sum(-1).bool())
+
+ if mask.sum() == 0:
+ proposals_idx = torch.zeros(0).int()
+ proposals_offset = torch.zeros(1).int()
+ else:
+ center_pred_ = center_pred[mask]
+ segment_pred_ = segment_pred[mask]
+
+ batch_ = offset2batch(offset)[mask]
+ offset_ = nn.ConstantPad1d((1, 0), 0)(batch2offset(batch_))
+ idx, start_len = ballquery_batch_p(
+ center_pred_,
+ batch_.int(),
+ offset_.int(),
+ self.cluster_thresh,
+ self.cluster_closed_points,
+ )
+ proposals_idx, proposals_offset = bfs_cluster(
+ segment_pred_.int().cpu(),
+ idx.cpu(),
+ start_len.cpu(),
+ self.cluster_min_points,
+ )
+ proposals_idx[:, 1] = (
+ mask.nonzero().view(-1)[proposals_idx[:, 1].long()].int())
+
+ # get proposal
+ proposals_pred = torch.zeros(
+ (proposals_offset.shape[0] - 1, center_pred.shape[0]),
+ dtype=torch.int)
+ proposals_pred[proposals_idx[:, 0].long(),
+ proposals_idx[:, 1].long()] = 1
+ instance_pred = segment_pred[proposals_idx[:, 1][
+ proposals_offset[:-1].long()].long()]
+ proposals_point_num = proposals_pred.sum(1)
+ proposals_mask = proposals_point_num > self.cluster_propose_points
+ proposals_pred = proposals_pred[proposals_mask]
+ instance_pred = instance_pred[proposals_mask]
+
+ pred_scores = []
+ pred_classes = []
+ pred_masks = proposals_pred.detach().cpu()
+ for proposal_id in range(len(proposals_pred)):
+ segment_ = proposals_pred[proposal_id]
+ confidence_ = logit_pred[segment_.bool(),
+ instance_pred[proposal_id]].mean()
+ object_ = instance_pred[proposal_id]
+ pred_scores.append(confidence_)
+ pred_classes.append(object_)
+ if len(pred_scores) > 0:
+ pred_scores = torch.stack(pred_scores).cpu()
+ pred_classes = torch.stack(pred_classes).cpu()
+ else:
+ pred_scores = torch.tensor([])
+ pred_classes = torch.tensor([])
+
+ return_dict["pred_scores"] = pred_scores
+ return_dict["pred_masks"] = pred_masks
+ return_dict["pred_classes"] = pred_classes
+ return return_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/utils.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d095b5bc89291ec30418c0aaf0eb9f672fe26ccc
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_group/utils.py
@@ -0,0 +1,176 @@
+import torch
+from torch.autograd import Function
+import pointgroup_ops
+
+
+class BallQueryBatchP(Function):
+ @staticmethod
+ def forward(ctx, coords, batch_idxs, batch_offsets, radius, meanActive):
+ """
+ :param ctx:
+ :param coords: (n, 3) float
+ :param batch_idxs: (n) int
+ :param batch_offsets: (B+1) int
+ :param radius: float
+ :param meanActive: int
+ :return: idx (nActive), int
+ :return: start_len (n, 2), int
+ """
+
+ n = coords.size(0)
+
+ assert coords.is_contiguous() and coords.is_cuda
+ assert batch_idxs.is_contiguous() and batch_idxs.is_cuda
+ assert batch_offsets.is_contiguous() and batch_offsets.is_cuda
+
+ while True:
+ idx = torch.cuda.IntTensor(n * meanActive).zero_()
+ start_len = torch.cuda.IntTensor(n, 2).zero_()
+ nActive = pointgroup_ops.ballquery_batch_p(
+ coords, batch_idxs, batch_offsets, idx, start_len, n, meanActive, radius
+ )
+ if nActive <= n * meanActive:
+ break
+ meanActive = int(nActive // n + 1)
+ idx = idx[:nActive]
+
+ return idx, start_len
+
+ @staticmethod
+ def backward(ctx, a=None, b=None):
+ return None, None, None
+
+
+ballquery_batch_p = BallQueryBatchP.apply
+
+
+class Clustering:
+ def __init__(
+ self,
+ ignored_labels,
+ class_mapping,
+ thresh=0.03,
+ closed_points=300,
+ min_points=50,
+ propose_points=100,
+ score_func=torch.max,
+ ) -> None:
+ self.ignored_labels = ignored_labels
+ self.thresh = thresh
+ self.closed_points = closed_points
+ self.min_points = min_points
+ self.class_mapping = class_mapping
+ self.propose_points = propose_points
+ self.score_func = score_func
+
+ def cluster(self, vertices, scores):
+ labels = torch.max(scores, 1)[1] # (N) long, cuda
+ proposals_idx, proposals_offset = self.cluster_(vertices, labels)
+
+ ## debug
+ # import ipdb; ipdb.set_trace()
+ # colors = np.array(create_color_palette())[labels.cpu()]
+ # write_triangle_mesh(vertices, colors, None, 'semantics.ply')
+
+ # scatter
+ proposals_pred = torch.zeros(
+ (proposals_offset.shape[0] - 1, vertices.shape[0]), dtype=torch.int
+ ) # (nProposal, N), int, cuda
+ proposals_pred[proposals_idx[:, 0].long(), proposals_idx[:, 1].long()] = 1
+ labels = labels[proposals_idx[:, 1][proposals_offset[:-1].long()].long()]
+
+ proposals_pointnum = proposals_pred.sum(1)
+ npoint_mask = proposals_pointnum > self.propose_points
+
+ proposals_pred = proposals_pred[npoint_mask]
+ labels = labels[npoint_mask]
+ return proposals_pred, labels
+
+ def cluster_(self, vertices, labels):
+ """
+ :param batch_idxs: (N), int, cuda
+ :labels: 0-19
+ """
+ batch_idxs = torch.zeros_like(labels)
+
+ mask_non_ignored = torch.ones_like(labels).bool()
+ for ignored_label in self.ignored_labels:
+ mask_non_ignored = mask_non_ignored & (
+ self.class_mapping[labels] != ignored_label
+ )
+ object_idxs = mask_non_ignored.nonzero().view(-1)
+
+ vertices_ = vertices[object_idxs].float()
+ labels_ = labels[object_idxs].int()
+
+ if vertices_.numel() == 0:
+ return torch.zeros((0, 2)).int(), torch.zeros(1).int()
+
+ batch_idxs_ = batch_idxs[object_idxs].int()
+ batch_offsets_ = torch.FloatTensor([0, object_idxs.shape[0]]).int().cuda()
+
+ idx, start_len = ballquery_batch_p(
+ vertices_, batch_idxs_, batch_offsets_, self.thresh, self.closed_points
+ )
+ proposals_idx, proposals_offset = bfs_cluster(
+ labels_.cpu(), idx.cpu(), start_len.cpu(), self.min_points
+ )
+ proposals_idx[:, 1] = object_idxs[proposals_idx[:, 1].long()].int()
+
+ return proposals_idx, proposals_offset
+
+ def get_instances(self, vertices, scores):
+ proposals_pred, labels = self.cluster(vertices, scores)
+ instances = {}
+ for proposal_id in range(len(proposals_pred)):
+ clusters_i = proposals_pred[proposal_id]
+ score = scores[clusters_i.bool(), labels[proposal_id]]
+ score = self.score_func(score)
+ instances[proposal_id] = {}
+ instances[proposal_id]["conf"] = score.cpu().numpy()
+ instances[proposal_id]["label_id"] = self.class_mapping.cpu()[
+ labels[proposal_id]
+ ]
+ instances[proposal_id]["pred_mask"] = clusters_i.cpu().numpy()
+ return instances
+
+
+class BFSCluster(Function):
+ @staticmethod
+ def forward(ctx, semantic_label, ball_query_idxs, start_len, threshold):
+ """
+ :param ctx:
+ :param semantic_label: (N), int
+ :param ball_query_idxs: (nActive), int
+ :param start_len: (N, 2), int
+ :return: cluster_idxs: int (sumNPoint, 2), dim 0 for cluster_id, dim 1 for corresponding point idxs in N
+ :return: cluster_offsets: int (nCluster + 1)
+ """
+
+ N = start_len.size(0)
+
+ assert semantic_label.is_contiguous()
+ assert ball_query_idxs.is_contiguous()
+ assert start_len.is_contiguous()
+
+ cluster_idxs = semantic_label.new()
+ cluster_offsets = semantic_label.new()
+
+ pointgroup_ops.bfs_cluster(
+ semantic_label,
+ ball_query_idxs,
+ start_len,
+ cluster_idxs,
+ cluster_offsets,
+ N,
+ threshold,
+ )
+
+ return cluster_idxs, cluster_offsets
+
+ @staticmethod
+ def backward(ctx, a=None):
+ return None
+
+
+bfs_cluster = BFSCluster.apply
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4c980b70b8c49dfc51625623071f21d6405d856
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/__init__.py
@@ -0,0 +1,4 @@
+from .point_prompt_training_v1m1_language_guided import *
+from .point_prompt_training_v1m2_decoupled import *
+
+from .prompt_driven_normalization import PDNorm
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/point_prompt_training_v1m1_language_guided.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/point_prompt_training_v1m1_language_guided.py
new file mode 100644
index 0000000000000000000000000000000000000000..10c09a5be7e437ecdc99b92507d17da0e7d369b4
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/point_prompt_training_v1m1_language_guided.py
@@ -0,0 +1,122 @@
+"""
+Point Prompt Training
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+from pointcept.models.utils.structure import Point
+from pointcept.models.builder import MODELS
+from pointcept.models.losses import build_criteria
+
+
+@MODELS.register_module("PPT-v1m1")
+class PointPromptTraining(nn.Module):
+ """
+ PointPromptTraining provides Data-driven Context and enables multi-dataset training with
+ Language-driven Categorical Alignment. PDNorm is supported by SpUNet-v1m3 to adapt the
+ backbone to a specific dataset with a given dataset condition and context.
+ """
+
+ def __init__(
+ self,
+ backbone=None,
+ criteria=None,
+ backbone_out_channels=96,
+ context_channels=256,
+ conditions=("Structured3D", "ScanNet", "S3DIS"),
+ template="[x]",
+ clip_model="ViT-B/16",
+ # fmt: off
+ class_name=(
+ "wall", "floor", "cabinet", "bed", "chair", "sofa", "table", "door",
+ "window", "bookshelf", "bookcase", "picture", "counter", "desk", "shelves", "curtain",
+ "dresser", "pillow", "mirror", "ceiling", "refrigerator", "television", "shower curtain", "nightstand",
+ "toilet", "sink", "lamp", "bathtub", "garbagebin", "board", "beam", "column",
+ "clutter", "otherstructure", "otherfurniture", "otherprop",
+ ),
+ valid_index=(
+ (0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 33, 34, 35),
+ (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 20, 22, 24, 25, 27, 34),
+ (0, 1, 4, 5, 6, 7, 8, 10, 19, 29, 30, 31, 32),
+ ),
+ # fmt: on
+ backbone_mode=False,
+ ):
+ super().__init__()
+ assert len(conditions) == len(valid_index)
+ assert backbone.type in [
+ "SpUNet-v1m3",
+ "PT-v2m3",
+ "PT-v3m1",
+ ] # SpUNet v1m3: Sparse UNet with PDNorm
+ self.backbone = MODELS.build(backbone)
+ self.criteria = build_criteria(criteria)
+ self.conditions = conditions
+ self.valid_index = valid_index
+ self.embedding_table = nn.Embedding(len(conditions), context_channels)
+ self.backbone_mode = backbone_mode
+ if not self.backbone_mode:
+ import clip
+
+ clip_model, _ = clip.load(
+ clip_model, device="cpu", download_root="./.cache/clip"
+ )
+ clip_model.requires_grad_(False)
+ class_prompt = [template.replace("[x]", name) for name in class_name]
+ class_token = clip.tokenize(class_prompt)
+ class_embedding = clip_model.encode_text(class_token)
+ class_embedding = class_embedding / class_embedding.norm(
+ dim=-1, keepdim=True
+ )
+ self.register_buffer("class_embedding", class_embedding)
+ self.proj_head = nn.Linear(
+ backbone_out_channels, clip_model.text_projection.shape[1]
+ )
+ self.logit_scale = clip_model.logit_scale
+
+ def forward(self, data_dict):
+ condition = data_dict["condition"][0]
+ assert condition in self.conditions
+ context = self.embedding_table(
+ torch.tensor(
+ [self.conditions.index(condition)], device=data_dict["coord"].device
+ )
+ )
+ data_dict["context"] = context
+ point = self.backbone(data_dict)
+ # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ if self.backbone_mode:
+ # PPT serve as a multi-dataset backbone when enable backbone mode
+ return feat
+ feat = self.proj_head(feat)
+ feat = feat / feat.norm(dim=-1, keepdim=True)
+ sim = (
+ feat
+ @ self.class_embedding[
+ self.valid_index[self.conditions.index(condition)], :
+ ].t()
+ )
+ logit_scale = self.logit_scale.exp()
+ seg_logits = logit_scale * sim
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in data_dict.keys():
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/point_prompt_training_v1m2_decoupled.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/point_prompt_training_v1m2_decoupled.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9131fdac70d4d11e0eb87716e2c40cc17f0b5e8
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/point_prompt_training_v1m2_decoupled.py
@@ -0,0 +1,78 @@
+"""
+Point Prompt Training with decoupled segmentation head
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+from pointcept.models.utils.structure import Point
+from pointcept.models.builder import MODELS
+from pointcept.models.losses import build_criteria
+
+
+@MODELS.register_module("PPT-v1m2")
+class PointPromptTraining(nn.Module):
+ """
+ PointPromptTraining v1m2 provides Data-driven Context and enables multi-dataset training with
+ Decoupled Segmentation Head. PDNorm is supported by SpUNet-v1m3 to adapt the
+ backbone to a specific dataset with a given dataset condition and context.
+ """
+
+ def __init__(
+ self,
+ backbone=None,
+ criteria=None,
+ backbone_out_channels=96,
+ context_channels=256,
+ conditions=("Structured3D", "ScanNet", "S3DIS"),
+ num_classes=(25, 20, 13),
+ backbone_mode=False,
+ ):
+ super().__init__()
+ assert len(conditions) == len(num_classes)
+ assert backbone.type in ["SpUNet-v1m3", "PT-v2m3", "PT-v3m1"]
+ self.backbone = MODELS.build(backbone)
+ self.criteria = build_criteria(criteria)
+ self.conditions = conditions
+ self.embedding_table = nn.Embedding(len(conditions), context_channels)
+ self.backbone_mode = backbone_mode
+ self.seg_heads = nn.ModuleList([
+ nn.Linear(backbone_out_channels, num_cls)
+ for num_cls in num_classes
+ ])
+
+ def forward(self, data_dict):
+ condition = data_dict["condition"][0]
+ assert condition in self.conditions
+ context = self.embedding_table(
+ torch.tensor([self.conditions.index(condition)],
+ device=data_dict["coord"].device))
+ data_dict["context"] = context
+ point = self.backbone(data_dict)
+ # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ if self.backbone_mode:
+ # PPT serve as a multi-dataset backbone when enable backbone mode
+ return point
+ seg_head = self.seg_heads[self.conditions.index(condition)]
+ seg_logits = seg_head(feat)
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in data_dict.keys():
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/prompt_driven_normalization.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/prompt_driven_normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d7d0d0c01dd4ccf939afeff870b5d72cab403a3
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_prompt_training/prompt_driven_normalization.py
@@ -0,0 +1,47 @@
+import torch.nn as nn
+
+from pointcept.models.modules import PointModule, PointSequential
+from pointcept.models.builder import MODULES
+
+
+@MODULES.register_module()
+class PDNorm(PointModule):
+ def __init__(
+ self,
+ num_features,
+ norm_layer,
+ context_channels=256,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ decouple=True,
+ adaptive=False,
+ ):
+ super().__init__()
+ self.conditions = conditions
+ self.decouple = decouple
+ self.adaptive = adaptive
+ if self.decouple:
+ self.norm = nn.ModuleList([norm_layer(num_features) for _ in conditions])
+ else:
+ self.norm = norm_layer
+ if self.adaptive:
+ self.modulation = nn.Sequential(
+ nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
+ )
+
+ def forward(self, point):
+ assert {"feat", "condition"}.issubset(point.keys())
+ if isinstance(point.condition, str):
+ condition = point.condition
+ else:
+ condition = point.condition[0]
+ if self.decouple:
+ assert condition in self.conditions
+ norm = self.norm[self.conditions.index(condition)]
+ else:
+ norm = self.norm
+ point.feat = norm(point.feat)
+ if self.adaptive:
+ assert "context" in point.keys()
+ shift, scale = self.modulation(point.context).chunk(2, dim=1)
+ point.feat = point.feat * (1.0 + scale) + shift
+ return point
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6493a312bfcf559642e6a2cc77d96c3770f0dd1
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/__init__.py
@@ -0,0 +1,3 @@
+from .point_transformer_seg import *
+from .point_transformer_partseg import *
+from .point_transformer_cls import *
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_cls.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_cls.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e12746fef73e9b3ee75b72942fbc8dc96e6e1bf
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_cls.py
@@ -0,0 +1,131 @@
+"""
+Point Transformer V1 for Object Classification
+
+Might be a bit different from the original paper
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+
+from .point_transformer_seg import TransitionDown, Bottleneck
+from pointcept.models.builder import MODELS
+
+
+class PointTransformerCls(nn.Module):
+ def __init__(self, block, blocks, in_channels=6, num_classes=40):
+ super().__init__()
+ self.in_channels = in_channels
+ self.in_planes, planes = in_channels, [32, 64, 128, 256, 512]
+ fpn_planes, fpnhead_planes, share_planes = 128, 64, 8
+ stride, nsample = [1, 4, 4, 4, 4], [8, 16, 16, 16, 16]
+ self.enc1 = self._make_enc(
+ block,
+ planes[0],
+ blocks[0],
+ share_planes,
+ stride=stride[0],
+ nsample=nsample[0],
+ ) # N/1
+ self.enc2 = self._make_enc(
+ block,
+ planes[1],
+ blocks[1],
+ share_planes,
+ stride=stride[1],
+ nsample=nsample[1],
+ ) # N/4
+ self.enc3 = self._make_enc(
+ block,
+ planes[2],
+ blocks[2],
+ share_planes,
+ stride=stride[2],
+ nsample=nsample[2],
+ ) # N/16
+ self.enc4 = self._make_enc(
+ block,
+ planes[3],
+ blocks[3],
+ share_planes,
+ stride=stride[3],
+ nsample=nsample[3],
+ ) # N/64
+ self.enc5 = self._make_enc(
+ block,
+ planes[4],
+ blocks[4],
+ share_planes,
+ stride=stride[4],
+ nsample=nsample[4],
+ ) # N/256
+ self.cls = nn.Sequential(
+ nn.Linear(planes[4], 256),
+ nn.BatchNorm1d(256),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(256, 128),
+ nn.BatchNorm1d(128),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(128, num_classes),
+ )
+
+ def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16):
+ layers = [
+ TransitionDown(self.in_planes, planes * block.expansion, stride, nsample)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(1, blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ p0 = data_dict["coord"]
+ x0 = data_dict["feat"]
+ o0 = data_dict["offset"].int()
+ x0 = p0 if self.in_channels == 3 else torch.cat((p0, x0), 1)
+ p1, x1, o1 = self.enc1([p0, x0, o0])
+ p2, x2, o2 = self.enc2([p1, x1, o1])
+ p3, x3, o3 = self.enc3([p2, x2, o2])
+ p4, x4, o4 = self.enc4([p3, x3, o3])
+ p5, x5, o5 = self.enc5([p4, x4, o4])
+ x = []
+ for i in range(o5.shape[0]):
+ if i == 0:
+ s_i, e_i, cnt = 0, o5[0], o5[0]
+ else:
+ s_i, e_i, cnt = o5[i - 1], o5[i], o5[i] - o5[i - 1]
+ x_b = x5[s_i:e_i, :].sum(0, True) / cnt
+ x.append(x_b)
+ x = torch.cat(x, 0)
+ x = self.cls(x)
+ return x
+
+
+@MODELS.register_module("PointTransformer-Cls26")
+class PointTransformerCls26(PointTransformerCls):
+ def __init__(self, **kwargs):
+ super(PointTransformerCls26, self).__init__(
+ Bottleneck, [1, 1, 1, 1, 1], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Cls38")
+class PointTransformerCls38(PointTransformerCls):
+ def __init__(self, **kwargs):
+ super(PointTransformerCls38, self).__init__(
+ Bottleneck, [1, 2, 2, 2, 2], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Cls50")
+class PointTransformerCls50(PointTransformerCls):
+ def __init__(self, **kwargs):
+ super(PointTransformerCls50, self).__init__(
+ Bottleneck, [1, 2, 3, 5, 2], **kwargs
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_partseg.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_partseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..3326a9f7d6fd62a9394e434135615339a3c679f8
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_partseg.py
@@ -0,0 +1,374 @@
+"""
+Point Transformer V1 for Part Segmentation
+
+Might be a bit different from the original paper
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import einops
+import pointops
+
+from pointcept.models.builder import MODELS
+from .utils import LayerNorm1d
+
+
+class PointTransformerLayer(nn.Module):
+ def __init__(self, in_planes, out_planes, share_planes=8, nsample=16):
+ super().__init__()
+ self.mid_planes = mid_planes = out_planes // 1
+ self.out_planes = out_planes
+ self.share_planes = share_planes
+ self.nsample = nsample
+ self.linear_q = nn.Linear(in_planes, mid_planes)
+ self.linear_k = nn.Linear(in_planes, mid_planes)
+ self.linear_v = nn.Linear(in_planes, out_planes)
+ self.linear_p = nn.Sequential(
+ nn.Linear(3, 3),
+ LayerNorm1d(3),
+ nn.ReLU(inplace=True),
+ nn.Linear(3, out_planes),
+ )
+ self.linear_w = nn.Sequential(
+ LayerNorm1d(mid_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(mid_planes, out_planes // share_planes),
+ LayerNorm1d(out_planes // share_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(out_planes // share_planes, out_planes // share_planes),
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ def forward(self, pxo) -> torch.Tensor:
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ x_q, x_k, x_v = self.linear_q(x), self.linear_k(x), self.linear_v(x)
+ x_k, idx = pointops.knn_query_and_group(
+ x_k, p, o, new_xyz=p, new_offset=o, nsample=self.nsample, with_xyz=True
+ )
+ x_v, _ = pointops.knn_query_and_group(
+ x_v,
+ p,
+ o,
+ new_xyz=p,
+ new_offset=o,
+ idx=idx,
+ nsample=self.nsample,
+ with_xyz=False,
+ )
+ p_r, x_k = x_k[:, :, 0:3], x_k[:, :, 3:]
+ p_r = self.linear_p(p_r)
+ r_qk = (
+ x_k
+ - x_q.unsqueeze(1)
+ + einops.reduce(
+ p_r, "n ns (i j) -> n ns j", reduction="sum", j=self.mid_planes
+ )
+ )
+ w = self.linear_w(r_qk) # (n, nsample, c)
+ w = self.softmax(w)
+ x = torch.einsum(
+ "n t s i, n t i -> n s i",
+ einops.rearrange(x_v + p_r, "n ns (s i) -> n ns s i", s=self.share_planes),
+ w,
+ )
+ x = einops.rearrange(x, "n s i -> n (s i)")
+ return x
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_planes, out_planes, stride=1, nsample=16):
+ super().__init__()
+ self.stride, self.nsample = stride, nsample
+ if stride != 1:
+ self.linear = nn.Linear(3 + in_planes, out_planes, bias=False)
+ self.pool = nn.MaxPool1d(nsample)
+ else:
+ self.linear = nn.Linear(in_planes, out_planes, bias=False)
+ self.bn = nn.BatchNorm1d(out_planes)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ if self.stride != 1:
+ n_o, count = [o[0].item() // self.stride], o[0].item() // self.stride
+ for i in range(1, o.shape[0]):
+ count += (o[i].item() - o[i - 1].item()) // self.stride
+ n_o.append(count)
+ n_o = torch.cuda.IntTensor(n_o)
+ idx = pointops.farthest_point_sampling(p, o, n_o) # (m)
+ n_p = p[idx.long(), :] # (m, 3)
+ x, _ = pointops.knn_query_and_group(
+ x,
+ p,
+ offset=o,
+ new_xyz=n_p,
+ new_offset=n_o,
+ nsample=self.nsample,
+ with_xyz=True,
+ )
+ x = self.relu(
+ self.bn(self.linear(x).transpose(1, 2).contiguous())
+ ) # (m, c, nsample)
+ x = self.pool(x).squeeze(-1) # (m, c)
+ p, o = n_p, n_o
+ else:
+ x = self.relu(self.bn(self.linear(x))) # (n, c)
+ return [p, x, o]
+
+
+class TransitionUp(nn.Module):
+ def __init__(self, in_planes, out_planes=None, num_shape_class=None):
+ super().__init__()
+ if out_planes is None:
+ self.num_shape_class = num_shape_class
+ if num_shape_class is not None:
+ self.linear1 = nn.Sequential(
+ nn.Linear(2 * in_planes + 1024, in_planes),
+ nn.BatchNorm1d(in_planes),
+ nn.ReLU(inplace=True),
+ )
+ else:
+ self.linear1 = nn.Sequential(
+ nn.Linear(2 * in_planes, in_planes),
+ nn.BatchNorm1d(in_planes),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, in_planes), nn.ReLU(inplace=True)
+ )
+ if num_shape_class is not None:
+ self.linear3 = nn.Sequential(
+ nn.Linear(num_shape_class, 1024), nn.ReLU(inplace=True)
+ )
+ else:
+ self.linear1 = nn.Sequential(
+ nn.Linear(out_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, pxo1, pxo2=None, y=None):
+ if pxo2 is None:
+ _, x, o = pxo1 # (n, 3), (n, c), (b)
+ x_tmp = []
+ for i in range(o.shape[0]):
+ if i == 0:
+ s_i, e_i, cnt = 0, o[0], o[0]
+ else:
+ s_i, e_i, cnt = o[i - 1], o[i], o[i] - o[i - 1]
+ x_b = x[s_i:e_i, :]
+ y_b = y[i].unsqueeze(-1).unsqueeze(-1).long()
+ y_onehot = torch.zeros(1, self.num_shape_class).cuda() # (1, l)
+ y_onehot.scatter_(1, y_b, 1) # (1, l)
+ x_b = torch.cat(
+ (
+ x_b,
+ self.linear2(x_b.sum(0, True) / cnt).repeat(cnt, 1),
+ self.linear3(y_onehot).repeat(cnt, 1),
+ ),
+ dim=1,
+ )
+ x_tmp.append(x_b)
+ x = torch.cat(x_tmp, 0)
+ x = self.linear1(x)
+ else:
+ p1, x1, o1 = pxo1
+ p2, x2, o2 = pxo2
+ x = self.linear1(x1) + pointops.interpolation(
+ p2, p1, self.linear2(x2), o2, o1
+ )
+ return x
+
+
+class Bottleneck(nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, share_planes=8, nsample=16):
+ super(Bottleneck, self).__init__()
+ self.linear1 = nn.Linear(in_planes, planes, bias=False)
+ self.bn1 = nn.BatchNorm1d(planes)
+ self.transformer = PointTransformerLayer(planes, planes, share_planes, nsample)
+ self.bn2 = nn.BatchNorm1d(planes)
+ self.linear3 = nn.Linear(planes, planes * self.expansion, bias=False)
+ self.bn3 = nn.BatchNorm1d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ identity = x
+ x = self.relu(self.bn1(self.linear1(x)))
+ x = self.relu(self.bn2(self.transformer([p, x, o])))
+ x = self.bn3(self.linear3(x))
+ x += identity
+ x = self.relu(x)
+ return [p, x, o]
+
+
+class PointTransformerSeg(nn.Module):
+ def __init__(
+ self, block, blocks, in_channels=6, num_classes=50, num_shape_classes=None
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_shape_classes = num_shape_classes
+ self.in_planes, planes = in_channels, [32, 64, 128, 256, 512]
+ fpn_planes, fpnhead_planes, share_planes = 128, 64, 8
+ stride, nsample = [1, 4, 4, 4, 4], [8, 16, 16, 16, 16]
+ self.enc1 = self._make_enc(
+ block,
+ planes[0],
+ blocks[0],
+ share_planes,
+ stride=stride[0],
+ nsample=nsample[0],
+ ) # N/1
+ self.enc2 = self._make_enc(
+ block,
+ planes[1],
+ blocks[1],
+ share_planes,
+ stride=stride[1],
+ nsample=nsample[1],
+ ) # N/4
+ self.enc3 = self._make_enc(
+ block,
+ planes[2],
+ blocks[2],
+ share_planes,
+ stride=stride[2],
+ nsample=nsample[2],
+ ) # N/16
+ self.enc4 = self._make_enc(
+ block,
+ planes[3],
+ blocks[3],
+ share_planes,
+ stride=stride[3],
+ nsample=nsample[3],
+ ) # N/64
+ self.enc5 = self._make_enc(
+ block,
+ planes[4],
+ blocks[4],
+ share_planes,
+ stride=stride[4],
+ nsample=nsample[4],
+ ) # N/256
+ self.dec5 = self._make_dec(
+ block,
+ planes[4],
+ 1,
+ share_planes,
+ num_shape_classes=num_shape_classes,
+ nsample=nsample[4],
+ is_head=True,
+ ) # transform p5
+ self.dec4 = self._make_dec(
+ block, planes[3], 1, share_planes, nsample=nsample[3]
+ ) # fusion p5 and p4
+ self.dec3 = self._make_dec(
+ block, planes[2], 1, share_planes, nsample=nsample[2]
+ ) # fusion p4 and p3
+ self.dec2 = self._make_dec(
+ block, planes[1], 1, share_planes, nsample=nsample[1]
+ ) # fusion p3 and p2
+ self.dec1 = self._make_dec(
+ block, planes[0], 1, share_planes, nsample=nsample[0]
+ ) # fusion p2 and p1
+ self.cls = nn.Sequential(
+ nn.Linear(planes[0], planes[0]),
+ nn.BatchNorm1d(planes[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(planes[0], num_classes),
+ )
+
+ def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16):
+ layers = [
+ TransitionDown(self.in_planes, planes * block.expansion, stride, nsample)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def _make_dec(
+ self,
+ block,
+ planes,
+ blocks,
+ share_planes=8,
+ num_shape_classes=None,
+ nsample=16,
+ is_head=False,
+ ):
+ layers = [
+ TransitionUp(
+ self.in_planes,
+ None if is_head else planes * block.expansion,
+ num_shape_classes,
+ )
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ p0 = data_dict["coord"]
+ x0 = data_dict["feat"]
+ o0 = data_dict["offset"].int()
+ if self.num_shape_classes is not None:
+ y = data_dict["cls_token"]
+ p1, x1, o1 = self.enc1([p0, x0, o0])
+ p2, x2, o2 = self.enc2([p1, x1, o1])
+ p3, x3, o3 = self.enc3([p2, x2, o2])
+ p4, x4, o4 = self.enc4([p3, x3, o3])
+ p5, x5, o5 = self.enc5([p4, x4, o4])
+ if self.num_shape_classes is not None:
+ x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5], y=y), o5])[1]
+ else:
+ x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5]), o5])[1]
+ x4 = self.dec4[1:]([p4, self.dec4[0]([p4, x4, o4], [p5, x5, o5]), o4])[1]
+ x3 = self.dec3[1:]([p3, self.dec3[0]([p3, x3, o3], [p4, x4, o4]), o3])[1]
+ x2 = self.dec2[1:]([p2, self.dec2[0]([p2, x2, o2], [p3, x3, o3]), o2])[1]
+ x1 = self.dec1[1:]([p1, self.dec1[0]([p1, x1, o1], [p2, x2, o2]), o1])[1]
+ x = self.cls(x1)
+ return x
+
+
+@MODELS.register_module("PointTransformer-PartSeg26")
+class PointTransformerSeg26(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg26, self).__init__(
+ Bottleneck, [1, 1, 1, 1, 1], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-PartSeg38")
+class PointTransformerSeg38(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg38, self).__init__(
+ Bottleneck, [1, 2, 2, 2, 2], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-PartSeg50")
+class PointTransformerSeg50(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg50, self).__init__(
+ Bottleneck, [1, 2, 3, 5, 2], **kwargs
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_seg.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..248cacad1ade65e48fa4686560fb40617a0ea449
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/point_transformer_seg.py
@@ -0,0 +1,327 @@
+"""
+Point Transformer V1 for Semantic Segmentation
+
+Might be a bit different from the original paper
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import einops
+import pointops
+
+from pointcept.models.builder import MODELS
+from .utils import LayerNorm1d
+
+
+class PointTransformerLayer(nn.Module):
+ def __init__(self, in_planes, out_planes, share_planes=8, nsample=16):
+ super().__init__()
+ self.mid_planes = mid_planes = out_planes // 1
+ self.out_planes = out_planes
+ self.share_planes = share_planes
+ self.nsample = nsample
+ self.linear_q = nn.Linear(in_planes, mid_planes)
+ self.linear_k = nn.Linear(in_planes, mid_planes)
+ self.linear_v = nn.Linear(in_planes, out_planes)
+ self.linear_p = nn.Sequential(
+ nn.Linear(3, 3),
+ LayerNorm1d(3),
+ nn.ReLU(inplace=True),
+ nn.Linear(3, out_planes),
+ )
+ self.linear_w = nn.Sequential(
+ LayerNorm1d(mid_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(mid_planes, out_planes // share_planes),
+ LayerNorm1d(out_planes // share_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(out_planes // share_planes, out_planes // share_planes),
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ def forward(self, pxo) -> torch.Tensor:
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ x_q, x_k, x_v = self.linear_q(x), self.linear_k(x), self.linear_v(x)
+ x_k, idx = pointops.knn_query_and_group(
+ x_k, p, o, new_xyz=p, new_offset=o, nsample=self.nsample, with_xyz=True
+ )
+ x_v, _ = pointops.knn_query_and_group(
+ x_v,
+ p,
+ o,
+ new_xyz=p,
+ new_offset=o,
+ idx=idx,
+ nsample=self.nsample,
+ with_xyz=False,
+ )
+ p_r, x_k = x_k[:, :, 0:3], x_k[:, :, 3:]
+ p_r = self.linear_p(p_r)
+ r_qk = (
+ x_k
+ - x_q.unsqueeze(1)
+ + einops.reduce(
+ p_r, "n ns (i j) -> n ns j", reduction="sum", j=self.mid_planes
+ )
+ )
+ w = self.linear_w(r_qk) # (n, nsample, c)
+ w = self.softmax(w)
+ x = torch.einsum(
+ "n t s i, n t i -> n s i",
+ einops.rearrange(x_v + p_r, "n ns (s i) -> n ns s i", s=self.share_planes),
+ w,
+ )
+ x = einops.rearrange(x, "n s i -> n (s i)")
+ return x
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_planes, out_planes, stride=1, nsample=16):
+ super().__init__()
+ self.stride, self.nsample = stride, nsample
+ if stride != 1:
+ self.linear = nn.Linear(3 + in_planes, out_planes, bias=False)
+ self.pool = nn.MaxPool1d(nsample)
+ else:
+ self.linear = nn.Linear(in_planes, out_planes, bias=False)
+ self.bn = nn.BatchNorm1d(out_planes)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ if self.stride != 1:
+ n_o, count = [o[0].item() // self.stride], o[0].item() // self.stride
+ for i in range(1, o.shape[0]):
+ count += (o[i].item() - o[i - 1].item()) // self.stride
+ n_o.append(count)
+ n_o = torch.cuda.IntTensor(n_o)
+ idx = pointops.farthest_point_sampling(p, o, n_o) # (m)
+ n_p = p[idx.long(), :] # (m, 3)
+ x, _ = pointops.knn_query_and_group(
+ x,
+ p,
+ offset=o,
+ new_xyz=n_p,
+ new_offset=n_o,
+ nsample=self.nsample,
+ with_xyz=True,
+ )
+ x = self.relu(
+ self.bn(self.linear(x).transpose(1, 2).contiguous())
+ ) # (m, c, nsample)
+ x = self.pool(x).squeeze(-1) # (m, c)
+ p, o = n_p, n_o
+ else:
+ x = self.relu(self.bn(self.linear(x))) # (n, c)
+ return [p, x, o]
+
+
+class TransitionUp(nn.Module):
+ def __init__(self, in_planes, out_planes=None):
+ super().__init__()
+ if out_planes is None:
+ self.linear1 = nn.Sequential(
+ nn.Linear(2 * in_planes, in_planes),
+ nn.BatchNorm1d(in_planes),
+ nn.ReLU(inplace=True),
+ )
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, in_planes), nn.ReLU(inplace=True)
+ )
+ else:
+ self.linear1 = nn.Sequential(
+ nn.Linear(out_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, pxo1, pxo2=None):
+ if pxo2 is None:
+ _, x, o = pxo1 # (n, 3), (n, c), (b)
+ x_tmp = []
+ for i in range(o.shape[0]):
+ if i == 0:
+ s_i, e_i, cnt = 0, o[0], o[0]
+ else:
+ s_i, e_i, cnt = o[i - 1], o[i], o[i] - o[i - 1]
+ x_b = x[s_i:e_i, :]
+ x_b = torch.cat(
+ (x_b, self.linear2(x_b.sum(0, True) / cnt).repeat(cnt, 1)), 1
+ )
+ x_tmp.append(x_b)
+ x = torch.cat(x_tmp, 0)
+ x = self.linear1(x)
+ else:
+ p1, x1, o1 = pxo1
+ p2, x2, o2 = pxo2
+ x = self.linear1(x1) + pointops.interpolation(
+ p2, p1, self.linear2(x2), o2, o1
+ )
+ return x
+
+
+class Bottleneck(nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, share_planes=8, nsample=16):
+ super(Bottleneck, self).__init__()
+ self.linear1 = nn.Linear(in_planes, planes, bias=False)
+ self.bn1 = nn.BatchNorm1d(planes)
+ self.transformer = PointTransformerLayer(planes, planes, share_planes, nsample)
+ self.bn2 = nn.BatchNorm1d(planes)
+ self.linear3 = nn.Linear(planes, planes * self.expansion, bias=False)
+ self.bn3 = nn.BatchNorm1d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ identity = x
+ x = self.relu(self.bn1(self.linear1(x)))
+ x = self.relu(self.bn2(self.transformer([p, x, o])))
+ x = self.bn3(self.linear3(x))
+ x += identity
+ x = self.relu(x)
+ return [p, x, o]
+
+
+class PointTransformerSeg(nn.Module):
+ def __init__(self, block, blocks, in_channels=6, num_classes=13):
+ super().__init__()
+ self.in_channels = in_channels
+ self.in_planes, planes = in_channels, [32, 64, 128, 256, 512]
+ fpn_planes, fpnhead_planes, share_planes = 128, 64, 8
+ stride, nsample = [1, 4, 4, 4, 4], [8, 16, 16, 16, 16]
+ self.enc1 = self._make_enc(
+ block,
+ planes[0],
+ blocks[0],
+ share_planes,
+ stride=stride[0],
+ nsample=nsample[0],
+ ) # N/1
+ self.enc2 = self._make_enc(
+ block,
+ planes[1],
+ blocks[1],
+ share_planes,
+ stride=stride[1],
+ nsample=nsample[1],
+ ) # N/4
+ self.enc3 = self._make_enc(
+ block,
+ planes[2],
+ blocks[2],
+ share_planes,
+ stride=stride[2],
+ nsample=nsample[2],
+ ) # N/16
+ self.enc4 = self._make_enc(
+ block,
+ planes[3],
+ blocks[3],
+ share_planes,
+ stride=stride[3],
+ nsample=nsample[3],
+ ) # N/64
+ self.enc5 = self._make_enc(
+ block,
+ planes[4],
+ blocks[4],
+ share_planes,
+ stride=stride[4],
+ nsample=nsample[4],
+ ) # N/256
+ self.dec5 = self._make_dec(
+ block, planes[4], 1, share_planes, nsample=nsample[4], is_head=True
+ ) # transform p5
+ self.dec4 = self._make_dec(
+ block, planes[3], 1, share_planes, nsample=nsample[3]
+ ) # fusion p5 and p4
+ self.dec3 = self._make_dec(
+ block, planes[2], 1, share_planes, nsample=nsample[2]
+ ) # fusion p4 and p3
+ self.dec2 = self._make_dec(
+ block, planes[1], 1, share_planes, nsample=nsample[1]
+ ) # fusion p3 and p2
+ self.dec1 = self._make_dec(
+ block, planes[0], 1, share_planes, nsample=nsample[0]
+ ) # fusion p2 and p1
+ self.cls = nn.Sequential(
+ nn.Linear(planes[0], planes[0]),
+ nn.BatchNorm1d(planes[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(planes[0], num_classes),
+ )
+
+ def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16):
+ layers = [
+ TransitionDown(self.in_planes, planes * block.expansion, stride, nsample)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def _make_dec(
+ self, block, planes, blocks, share_planes=8, nsample=16, is_head=False
+ ):
+ layers = [
+ TransitionUp(self.in_planes, None if is_head else planes * block.expansion)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ p0 = data_dict["coord"]
+ x0 = data_dict["feat"]
+ o0 = data_dict["offset"].int()
+ p1, x1, o1 = self.enc1([p0, x0, o0])
+ p2, x2, o2 = self.enc2([p1, x1, o1])
+ p3, x3, o3 = self.enc3([p2, x2, o2])
+ p4, x4, o4 = self.enc4([p3, x3, o3])
+ p5, x5, o5 = self.enc5([p4, x4, o4])
+ x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5]), o5])[1]
+ x4 = self.dec4[1:]([p4, self.dec4[0]([p4, x4, o4], [p5, x5, o5]), o4])[1]
+ x3 = self.dec3[1:]([p3, self.dec3[0]([p3, x3, o3], [p4, x4, o4]), o3])[1]
+ x2 = self.dec2[1:]([p2, self.dec2[0]([p2, x2, o2], [p3, x3, o3]), o2])[1]
+ x1 = self.dec1[1:]([p1, self.dec1[0]([p1, x1, o1], [p2, x2, o2]), o1])[1]
+ x = self.cls(x1)
+ return x
+
+
+@MODELS.register_module("PointTransformer-Seg26")
+class PointTransformerSeg26(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg26, self).__init__(
+ Bottleneck, [1, 1, 1, 1, 1], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Seg38")
+class PointTransformerSeg38(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg38, self).__init__(
+ Bottleneck, [1, 2, 2, 2, 2], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Seg50")
+class PointTransformerSeg50(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg50, self).__init__(
+ Bottleneck, [1, 2, 3, 5, 2], **kwargs
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/utils.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5687701835bb1f8a8936ea5ae5d52285567dc77
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer/utils.py
@@ -0,0 +1,14 @@
+import torch
+import torch.nn as nn
+
+torch.nn.LayerNorm
+
+
+class LayerNorm1d(nn.BatchNorm1d):
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ return (
+ super()
+ .forward(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9689fa2518b599bc6f94e6f8d0ea461859b8909
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/__init__.py
@@ -0,0 +1,10 @@
+"""
+Point Transformer V2
+
+Copyright (c) Xiaoyang Wu (xiaoyang.wu@connect.hku.hk). All Rights Reserved.
+Please cite our work if you use any part of the code.
+"""
+
+from .point_transformer_v2m1_origin import *
+from .point_transformer_v2m2_base import *
+from .point_transformer_v2m3_pdnorm import *
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m1_origin.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m1_origin.py
new file mode 100644
index 0000000000000000000000000000000000000000..b325d9eb7d5e1507ce62d5cbf60bb000cf83acbc
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m1_origin.py
@@ -0,0 +1,614 @@
+"""
+Point Transformer V2 mode 1
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from copy import deepcopy
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from torch_geometric.nn.pool import voxel_grid
+from torch_scatter import segment_csr
+
+import einops
+from timm.models.layers import DropPath
+import pointops
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class GroupedLinear(nn.Module):
+ __constants__ = ["in_features", "out_features", "groups"]
+ in_features: int
+ out_features: int
+ groups: int
+ weight: torch.Tensor
+
+ def __init__(
+ self, in_features: int, out_features: int, groups: int, device=None, dtype=None
+ ) -> None:
+ factory_kwargs = {"device": device, "dtype": dtype}
+ super(GroupedLinear, self).__init__()
+ self.in_features = in_features
+ self.out_features = out_features
+ self.groups = groups
+ assert in_features & groups == 0
+ assert out_features % groups == 0
+ # for convenient, currently only support out_features == groups, one output
+ assert out_features == groups
+ self.weight = nn.Parameter(torch.empty((1, in_features), **factory_kwargs))
+ self.reset_parameters()
+
+ def reset_parameters(self) -> None:
+ nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ return (
+ (input * self.weight)
+ .reshape(
+ list(input.shape[:-1]) + [self.groups, input.shape[-1] // self.groups]
+ )
+ .sum(-1)
+ )
+
+ def extra_repr(self) -> str:
+ return "in_features={}, out_features={}, bias={}".format(
+ self.in_features, self.out_features, self.bias is not None
+ )
+
+
+class PointBatchNorm(nn.Module):
+ """
+ Batch Normalization for Point Clouds data in shape of [B*N, C], [B*N, L, C]
+ """
+
+ def __init__(self, embed_channels):
+ super().__init__()
+ self.norm = nn.BatchNorm1d(embed_channels)
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ if input.dim() == 3:
+ return (
+ self.norm(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
+ elif input.dim() == 2:
+ return self.norm(input)
+ else:
+ raise NotImplementedError
+
+
+class GroupedVectorAttention(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ attn_drop_rate=0.0,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ ):
+ super(GroupedVectorAttention, self).__init__()
+ self.embed_channels = embed_channels
+ self.groups = groups
+ assert embed_channels % groups == 0
+ self.attn_drop_rate = attn_drop_rate
+ self.qkv_bias = qkv_bias
+ self.pe_multiplier = pe_multiplier
+ self.pe_bias = pe_bias
+
+ self.linear_q = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.linear_k = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear_v = nn.Linear(embed_channels, embed_channels, bias=qkv_bias)
+
+ if self.pe_multiplier:
+ self.linear_p_multiplier = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ if self.pe_bias:
+ self.linear_p_bias = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ self.weight_encoding = nn.Sequential(
+ GroupedLinear(embed_channels, groups, groups),
+ PointBatchNorm(groups),
+ nn.ReLU(inplace=True),
+ nn.Linear(groups, groups),
+ )
+ self.softmax = nn.Softmax(dim=1)
+ self.attn_drop = nn.Dropout(attn_drop_rate)
+
+ def forward(self, feat, coord, reference_index):
+ query, key, value = (
+ self.linear_q(feat),
+ self.linear_k(feat),
+ self.linear_v(feat),
+ )
+ key = pointops.grouping(reference_index, key, coord, with_xyz=True)
+ value = pointops.grouping(reference_index, value, coord, with_xyz=False)
+ pos, key = key[:, :, 0:3], key[:, :, 3:]
+ relation_qk = key - query.unsqueeze(1)
+ if self.pe_multiplier:
+ pem = self.linear_p_multiplier(pos)
+ relation_qk = relation_qk * pem
+ if self.pe_bias:
+ peb = self.linear_p_bias(pos)
+ relation_qk = relation_qk + peb
+ value = value + peb
+
+ weight = self.weight_encoding(relation_qk)
+ weight = self.attn_drop(self.softmax(weight))
+
+ mask = torch.sign(reference_index + 1)
+ weight = torch.einsum("n s g, n s -> n s g", weight, mask)
+ value = einops.rearrange(value, "n ns (g i) -> n ns g i", g=self.groups)
+ feat = torch.einsum("n s g i, n s g -> n g i", value, weight)
+ feat = einops.rearrange(feat, "n g i -> n (g i)")
+ return feat
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(Block, self).__init__()
+ self.attn = GroupedVectorAttention(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ attn_drop_rate=attn_drop_rate,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ )
+ self.fc1 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.fc3 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.norm1 = PointBatchNorm(embed_channels)
+ self.norm2 = PointBatchNorm(embed_channels)
+ self.norm3 = PointBatchNorm(embed_channels)
+ self.act = nn.ReLU(inplace=True)
+ self.enable_checkpoint = enable_checkpoint
+ self.drop_path = (
+ DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ )
+
+ def forward(self, points, reference_index):
+ coord, feat, offset = points
+ identity = feat
+ feat = self.act(self.norm1(self.fc1(feat)))
+ feat = (
+ self.attn(feat, coord, reference_index)
+ if not self.enable_checkpoint
+ else checkpoint(self.attn, feat, coord, reference_index)
+ )
+ feat = self.act(self.norm2(feat))
+ feat = self.norm3(self.fc3(feat))
+ feat = identity + self.drop_path(feat)
+ feat = self.act(feat)
+ return [coord, feat, offset]
+
+
+class BlockSequence(nn.Module):
+ def __init__(
+ self,
+ depth,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(BlockSequence, self).__init__()
+
+ if isinstance(drop_path_rate, list):
+ drop_path_rates = drop_path_rate
+ assert len(drop_path_rates) == depth
+ elif isinstance(drop_path_rate, float):
+ drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+ else:
+ drop_path_rates = [0.0 for _ in range(depth)]
+
+ self.neighbours = neighbours
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rates[i],
+ enable_checkpoint=enable_checkpoint,
+ )
+ self.blocks.append(block)
+
+ def forward(self, points):
+ coord, feat, offset = points
+ # reference index query of neighbourhood attention
+ # for windows attention, modify reference index query method
+ reference_index, _ = pointops.knn_query(self.neighbours, coord, offset)
+ for block in self.blocks:
+ points = block(points, reference_index)
+ return points
+
+
+class GridPool(nn.Module):
+ """
+ Partition-based Pooling (Grid Pooling)
+ """
+
+ def __init__(self, in_channels, out_channels, grid_size, bias=False):
+ super(GridPool, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.grid_size = grid_size
+
+ self.fc = nn.Linear(in_channels, out_channels, bias=bias)
+ self.norm = PointBatchNorm(out_channels)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, points, start=None):
+ coord, feat, offset = points
+ batch = offset2batch(offset)
+ feat = self.act(self.norm(self.fc(feat)))
+ start = (
+ segment_csr(
+ coord,
+ torch.cat([batch.new_zeros(1), torch.cumsum(batch.bincount(), dim=0)]),
+ reduce="min",
+ )
+ if start is None
+ else start
+ )
+ cluster = voxel_grid(
+ pos=coord - start[batch], size=self.grid_size, batch=batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ _, sorted_cluster_indices = torch.sort(cluster)
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ coord = segment_csr(coord[sorted_cluster_indices], idx_ptr, reduce="mean")
+ feat = segment_csr(feat[sorted_cluster_indices], idx_ptr, reduce="max")
+ batch = batch[idx_ptr[:-1]]
+ offset = batch2offset(batch)
+ return [coord, feat, offset], cluster
+
+
+class UnpoolWithSkip(nn.Module):
+ """
+ Map Unpooling with skip connection
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ bias=True,
+ skip=True,
+ backend="map",
+ ):
+ super(UnpoolWithSkip, self).__init__()
+ self.in_channels = in_channels
+ self.skip_channels = skip_channels
+ self.out_channels = out_channels
+ self.skip = skip
+ self.backend = backend
+ assert self.backend in ["map", "interp"]
+
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.proj_skip = nn.Sequential(
+ nn.Linear(skip_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, points, skip_points, cluster=None):
+ coord, feat, offset = points
+ skip_coord, skip_feat, skip_offset = skip_points
+ if self.backend == "map" and cluster is not None:
+ feat = self.proj(feat)[cluster]
+ else:
+ feat = pointops.interpolation(
+ coord, skip_coord, self.proj(feat), offset, skip_offset
+ )
+ if self.skip:
+ feat = feat + self.proj_skip(skip_feat)
+ return [skip_coord, feat, skip_offset]
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ grid_size=None,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ ):
+ super(Encoder, self).__init__()
+
+ self.down = GridPool(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ grid_size=grid_size,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ points, cluster = self.down(points)
+ return self.blocks(points), cluster
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ groups,
+ depth,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(Decoder, self).__init__()
+
+ self.up = UnpoolWithSkip(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ skip_channels=skip_channels,
+ backend=unpool_backend,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points, skip_points, cluster):
+ points = self.up(points, skip_points, cluster)
+ return self.blocks(points)
+
+
+class GVAPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(GVAPatchEmbed, self).__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, embed_channels, bias=False),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ coord, feat, offset = points
+ feat = self.proj(feat)
+ return self.blocks([coord, feat, offset])
+
+
+@MODELS.register_module("PT-v2m1")
+class PointTransformerV2(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ patch_embed_depth=1,
+ patch_embed_channels=48,
+ patch_embed_groups=6,
+ patch_embed_neighbours=8,
+ enc_depths=(2, 2, 6, 2),
+ enc_channels=(96, 192, 384, 512),
+ enc_groups=(12, 24, 48, 64),
+ enc_neighbours=(16, 16, 16, 16),
+ dec_depths=(1, 1, 1, 1),
+ dec_channels=(48, 96, 192, 384),
+ dec_groups=(6, 12, 24, 48),
+ dec_neighbours=(16, 16, 16, 16),
+ grid_sizes=(0.06, 0.12, 0.24, 0.48),
+ attn_qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(PointTransformerV2, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_depths)
+ assert self.num_stages == len(dec_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(dec_channels)
+ assert self.num_stages == len(enc_groups)
+ assert self.num_stages == len(dec_groups)
+ assert self.num_stages == len(enc_neighbours)
+ assert self.num_stages == len(dec_neighbours)
+ assert self.num_stages == len(grid_sizes)
+ self.patch_embed = GVAPatchEmbed(
+ in_channels=in_channels,
+ embed_channels=patch_embed_channels,
+ groups=patch_embed_groups,
+ depth=patch_embed_depth,
+ neighbours=patch_embed_neighbours,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ enc_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(enc_depths))
+ ]
+ dec_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(dec_depths))
+ ]
+ enc_channels = [patch_embed_channels] + list(enc_channels)
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ self.enc_stages = nn.ModuleList()
+ self.dec_stages = nn.ModuleList()
+ for i in range(self.num_stages):
+ enc = Encoder(
+ depth=enc_depths[i],
+ in_channels=enc_channels[i],
+ embed_channels=enc_channels[i + 1],
+ groups=enc_groups[i],
+ grid_size=grid_sizes[i],
+ neighbours=enc_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=enc_dp_rates[
+ sum(enc_depths[:i]) : sum(enc_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ )
+ dec = Decoder(
+ depth=dec_depths[i],
+ in_channels=dec_channels[i + 1],
+ skip_channels=enc_channels[i],
+ embed_channels=dec_channels[i],
+ groups=dec_groups[i],
+ neighbours=dec_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dec_dp_rates[
+ sum(dec_depths[:i]) : sum(dec_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ unpool_backend=unpool_backend,
+ )
+ self.enc_stages.append(enc)
+ self.dec_stages.append(dec)
+ self.seg_head = (
+ nn.Sequential(
+ nn.Linear(dec_channels[0], dec_channels[0]),
+ PointBatchNorm(dec_channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(dec_channels[0], num_classes),
+ )
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"].int()
+
+ # a batch of point cloud is a list of coord, feat and offset
+ points = [coord, feat, offset]
+ points = self.patch_embed(points)
+ skips = [[points]]
+ for i in range(self.num_stages):
+ points, cluster = self.enc_stages[i](points)
+ skips[-1].append(cluster) # record grid cluster of pooling
+ skips.append([points]) # record points info of current stage
+
+ points = skips.pop(-1)[0] # unpooling points info in the last enc stage
+ for i in reversed(range(self.num_stages)):
+ skip_points, cluster = skips.pop(-1)
+ points = self.dec_stages[i](points, skip_points, cluster)
+ coord, feat, offset = points
+ seg_logits = self.seg_head(feat)
+ return seg_logits
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m2_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m2_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..dec45ff92504cf184505d0cad96d30346613df46
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m2_base.py
@@ -0,0 +1,576 @@
+"""
+Point Transformer V2 Mode 2 (recommend)
+
+Disable Grouped Linear
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from copy import deepcopy
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from torch_geometric.nn.pool import voxel_grid
+from torch_scatter import segment_csr
+
+import einops
+from timm.models.layers import DropPath
+import pointops
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class PointBatchNorm(nn.Module):
+ """
+ Batch Normalization for Point Clouds data in shape of [B*N, C], [B*N, L, C]
+ """
+
+ def __init__(self, embed_channels):
+ super().__init__()
+ self.norm = nn.BatchNorm1d(embed_channels)
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ if input.dim() == 3:
+ return (
+ self.norm(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
+ elif input.dim() == 2:
+ return self.norm(input)
+ else:
+ raise NotImplementedError
+
+
+class GroupedVectorAttention(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ attn_drop_rate=0.0,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ ):
+ super(GroupedVectorAttention, self).__init__()
+ self.embed_channels = embed_channels
+ self.groups = groups
+ assert embed_channels % groups == 0
+ self.attn_drop_rate = attn_drop_rate
+ self.qkv_bias = qkv_bias
+ self.pe_multiplier = pe_multiplier
+ self.pe_bias = pe_bias
+
+ self.linear_q = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.linear_k = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear_v = nn.Linear(embed_channels, embed_channels, bias=qkv_bias)
+
+ if self.pe_multiplier:
+ self.linear_p_multiplier = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ if self.pe_bias:
+ self.linear_p_bias = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ self.weight_encoding = nn.Sequential(
+ nn.Linear(embed_channels, groups),
+ PointBatchNorm(groups),
+ nn.ReLU(inplace=True),
+ nn.Linear(groups, groups),
+ )
+ self.softmax = nn.Softmax(dim=1)
+ self.attn_drop = nn.Dropout(attn_drop_rate)
+
+ def forward(self, feat, coord, reference_index):
+ query, key, value = (
+ self.linear_q(feat),
+ self.linear_k(feat),
+ self.linear_v(feat),
+ )
+ key = pointops.grouping(reference_index, key, coord, with_xyz=True)
+ value = pointops.grouping(reference_index, value, coord, with_xyz=False)
+ pos, key = key[:, :, 0:3], key[:, :, 3:]
+ relation_qk = key - query.unsqueeze(1)
+ if self.pe_multiplier:
+ pem = self.linear_p_multiplier(pos)
+ relation_qk = relation_qk * pem
+ if self.pe_bias:
+ peb = self.linear_p_bias(pos)
+ relation_qk = relation_qk + peb
+ value = value + peb
+
+ weight = self.weight_encoding(relation_qk)
+ weight = self.attn_drop(self.softmax(weight))
+
+ mask = torch.sign(reference_index + 1)
+ weight = torch.einsum("n s g, n s -> n s g", weight, mask)
+ value = einops.rearrange(value, "n ns (g i) -> n ns g i", g=self.groups)
+ feat = torch.einsum("n s g i, n s g -> n g i", value, weight)
+ feat = einops.rearrange(feat, "n g i -> n (g i)")
+ return feat
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(Block, self).__init__()
+ self.attn = GroupedVectorAttention(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ attn_drop_rate=attn_drop_rate,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ )
+ self.fc1 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.fc3 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.norm1 = PointBatchNorm(embed_channels)
+ self.norm2 = PointBatchNorm(embed_channels)
+ self.norm3 = PointBatchNorm(embed_channels)
+ self.act = nn.ReLU(inplace=True)
+ self.enable_checkpoint = enable_checkpoint
+ self.drop_path = (
+ DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ )
+
+ def forward(self, points, reference_index):
+ coord, feat, offset = points
+ identity = feat
+ feat = self.act(self.norm1(self.fc1(feat)))
+ feat = (
+ self.attn(feat, coord, reference_index)
+ if not self.enable_checkpoint
+ else checkpoint(self.attn, feat, coord, reference_index)
+ )
+ feat = self.act(self.norm2(feat))
+ feat = self.norm3(self.fc3(feat))
+ feat = identity + self.drop_path(feat)
+ feat = self.act(feat)
+ return [coord, feat, offset]
+
+
+class BlockSequence(nn.Module):
+ def __init__(
+ self,
+ depth,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(BlockSequence, self).__init__()
+
+ if isinstance(drop_path_rate, list):
+ drop_path_rates = drop_path_rate
+ assert len(drop_path_rates) == depth
+ elif isinstance(drop_path_rate, float):
+ drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+ else:
+ drop_path_rates = [0.0 for _ in range(depth)]
+
+ self.neighbours = neighbours
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rates[i],
+ enable_checkpoint=enable_checkpoint,
+ )
+ self.blocks.append(block)
+
+ def forward(self, points):
+ coord, feat, offset = points
+ # reference index query of neighbourhood attention
+ # for windows attention, modify reference index query method
+ reference_index, _ = pointops.knn_query(self.neighbours, coord, offset)
+ for block in self.blocks:
+ points = block(points, reference_index)
+ return points
+
+
+class GridPool(nn.Module):
+ """
+ Partition-based Pooling (Grid Pooling)
+ """
+
+ def __init__(self, in_channels, out_channels, grid_size, bias=False):
+ super(GridPool, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.grid_size = grid_size
+
+ self.fc = nn.Linear(in_channels, out_channels, bias=bias)
+ self.norm = PointBatchNorm(out_channels)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, points, start=None):
+ coord, feat, offset = points
+ batch = offset2batch(offset)
+ feat = self.act(self.norm(self.fc(feat)))
+ start = (
+ segment_csr(
+ coord,
+ torch.cat([batch.new_zeros(1), torch.cumsum(batch.bincount(), dim=0)]),
+ reduce="min",
+ )
+ if start is None
+ else start
+ )
+ cluster = voxel_grid(
+ pos=coord - start[batch], size=self.grid_size, batch=batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ _, sorted_cluster_indices = torch.sort(cluster)
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ coord = segment_csr(coord[sorted_cluster_indices], idx_ptr, reduce="mean")
+ feat = segment_csr(feat[sorted_cluster_indices], idx_ptr, reduce="max")
+ batch = batch[idx_ptr[:-1]]
+ offset = batch2offset(batch)
+ return [coord, feat, offset], cluster
+
+
+class UnpoolWithSkip(nn.Module):
+ """
+ Map Unpooling with skip connection
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ bias=True,
+ skip=True,
+ backend="map",
+ ):
+ super(UnpoolWithSkip, self).__init__()
+ self.in_channels = in_channels
+ self.skip_channels = skip_channels
+ self.out_channels = out_channels
+ self.skip = skip
+ self.backend = backend
+ assert self.backend in ["map", "interp"]
+
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.proj_skip = nn.Sequential(
+ nn.Linear(skip_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, points, skip_points, cluster=None):
+ coord, feat, offset = points
+ skip_coord, skip_feat, skip_offset = skip_points
+ if self.backend == "map" and cluster is not None:
+ feat = self.proj(feat)[cluster]
+ else:
+ feat = pointops.interpolation(
+ coord, skip_coord, self.proj(feat), offset, skip_offset
+ )
+ if self.skip:
+ feat = feat + self.proj_skip(skip_feat)
+ return [skip_coord, feat, skip_offset]
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ grid_size=None,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ ):
+ super(Encoder, self).__init__()
+
+ self.down = GridPool(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ grid_size=grid_size,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ points, cluster = self.down(points)
+ return self.blocks(points), cluster
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ groups,
+ depth,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(Decoder, self).__init__()
+
+ self.up = UnpoolWithSkip(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ skip_channels=skip_channels,
+ backend=unpool_backend,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points, skip_points, cluster):
+ points = self.up(points, skip_points, cluster)
+ return self.blocks(points)
+
+
+class GVAPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(GVAPatchEmbed, self).__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, embed_channels, bias=False),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ coord, feat, offset = points
+ feat = self.proj(feat)
+ return self.blocks([coord, feat, offset])
+
+
+@MODELS.register_module("PT-v2m2")
+class PointTransformerV2(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ patch_embed_depth=1,
+ patch_embed_channels=48,
+ patch_embed_groups=6,
+ patch_embed_neighbours=8,
+ enc_depths=(2, 2, 6, 2),
+ enc_channels=(96, 192, 384, 512),
+ enc_groups=(12, 24, 48, 64),
+ enc_neighbours=(16, 16, 16, 16),
+ dec_depths=(1, 1, 1, 1),
+ dec_channels=(48, 96, 192, 384),
+ dec_groups=(6, 12, 24, 48),
+ dec_neighbours=(16, 16, 16, 16),
+ grid_sizes=(0.06, 0.12, 0.24, 0.48),
+ attn_qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(PointTransformerV2, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_depths)
+ assert self.num_stages == len(dec_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(dec_channels)
+ assert self.num_stages == len(enc_groups)
+ assert self.num_stages == len(dec_groups)
+ assert self.num_stages == len(enc_neighbours)
+ assert self.num_stages == len(dec_neighbours)
+ assert self.num_stages == len(grid_sizes)
+ self.patch_embed = GVAPatchEmbed(
+ in_channels=in_channels,
+ embed_channels=patch_embed_channels,
+ groups=patch_embed_groups,
+ depth=patch_embed_depth,
+ neighbours=patch_embed_neighbours,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ enc_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(enc_depths))
+ ]
+ dec_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(dec_depths))
+ ]
+ enc_channels = [patch_embed_channels] + list(enc_channels)
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ self.enc_stages = nn.ModuleList()
+ self.dec_stages = nn.ModuleList()
+ for i in range(self.num_stages):
+ enc = Encoder(
+ depth=enc_depths[i],
+ in_channels=enc_channels[i],
+ embed_channels=enc_channels[i + 1],
+ groups=enc_groups[i],
+ grid_size=grid_sizes[i],
+ neighbours=enc_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=enc_dp_rates[
+ sum(enc_depths[:i]) : sum(enc_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ )
+ dec = Decoder(
+ depth=dec_depths[i],
+ in_channels=dec_channels[i + 1],
+ skip_channels=enc_channels[i],
+ embed_channels=dec_channels[i],
+ groups=dec_groups[i],
+ neighbours=dec_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dec_dp_rates[
+ sum(dec_depths[:i]) : sum(dec_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ unpool_backend=unpool_backend,
+ )
+ self.enc_stages.append(enc)
+ self.dec_stages.append(dec)
+ self.seg_head = (
+ nn.Sequential(
+ nn.Linear(dec_channels[0], dec_channels[0]),
+ PointBatchNorm(dec_channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(dec_channels[0], num_classes),
+ )
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"].int()
+
+ # a batch of point cloud is a list of coord, feat and offset
+ points = [coord, feat, offset]
+ points = self.patch_embed(points)
+ skips = [[points]]
+ for i in range(self.num_stages):
+ points, cluster = self.enc_stages[i](points)
+ skips[-1].append(cluster) # record grid cluster of pooling
+ skips.append([points]) # record points info of current stage
+
+ points = skips.pop(-1)[0] # unpooling points info in the last enc stage
+ for i in reversed(range(self.num_stages)):
+ skip_points, cluster = skips.pop(-1)
+ points = self.dec_stages[i](points, skip_points, cluster)
+ coord, feat, offset = points
+ seg_logits = self.seg_head(feat)
+ return seg_logits
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m3_pdnorm.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m3_pdnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b944f19f2b13a73ae01bad4c094c51e4213896c4
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v2/point_transformer_v2m3_pdnorm.py
@@ -0,0 +1,659 @@
+"""
+Point Transformer V2M3
+
+Enable Prompt-Driven Normalization for Point Prompt Training
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from copy import deepcopy
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from torch_geometric.nn.pool import voxel_grid
+from torch_scatter import segment_csr
+
+import einops
+from timm.models.layers import DropPath
+import pointops
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class PDBatchNorm(torch.nn.Module):
+ def __init__(
+ self,
+ num_features,
+ context_channels=256,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ decouple=True,
+ adaptive=False,
+ affine=True,
+ ):
+ super().__init__()
+ self.conditions = conditions
+ self.decouple = decouple
+ self.adaptive = adaptive
+ self.affine = affine
+ if self.decouple:
+ self.bns = nn.ModuleList(
+ [
+ nn.BatchNorm1d(
+ num_features=num_features,
+ eps=eps,
+ momentum=momentum,
+ affine=affine,
+ )
+ for _ in conditions
+ ]
+ )
+ else:
+ self.bn = nn.BatchNorm1d(
+ num_features=num_features, eps=eps, momentum=momentum, affine=affine
+ )
+ if self.adaptive:
+ self.modulation = nn.Sequential(
+ nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
+ )
+
+ def forward(self, feat, condition=None, context=None):
+ if self.decouple:
+ assert condition in self.conditions
+ bn = self.bns[self.conditions.index(condition)]
+ else:
+ bn = self.bn
+ feat = bn(feat)
+ if self.adaptive:
+ assert context is not None
+ shift, scale = self.modulation(context).chunk(2, dim=1)
+ feat = feat * (1.0 + scale) + shift
+ return feat
+
+
+class PointBatchNorm(nn.Module):
+ """
+ Batch Normalization for Point Clouds data in shape of [B*N, C], [B*N, L, C]
+ """
+
+ def __init__(self, embed_channels):
+ super().__init__()
+ self.norm = nn.BatchNorm1d(embed_channels)
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ if input.dim() == 3:
+ return (
+ self.norm(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
+ elif input.dim() == 2:
+ return self.norm(input)
+ else:
+ raise NotImplementedError
+
+
+class GroupedVectorAttention(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ attn_drop_rate=0.0,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ ):
+ super(GroupedVectorAttention, self).__init__()
+ self.embed_channels = embed_channels
+ self.groups = groups
+ assert embed_channels % groups == 0
+ self.attn_drop_rate = attn_drop_rate
+ self.qkv_bias = qkv_bias
+ self.pe_multiplier = pe_multiplier
+ self.pe_bias = pe_bias
+
+ self.linear_q = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.linear_k = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear_v = nn.Linear(embed_channels, embed_channels, bias=qkv_bias)
+
+ if self.pe_multiplier:
+ self.linear_p_multiplier = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ if self.pe_bias:
+ self.linear_p_bias = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ self.weight_encoding = nn.Sequential(
+ nn.Linear(embed_channels, groups),
+ PointBatchNorm(groups),
+ nn.ReLU(inplace=True),
+ nn.Linear(groups, groups),
+ )
+ self.softmax = nn.Softmax(dim=1)
+ self.attn_drop = nn.Dropout(attn_drop_rate)
+
+ def forward(self, feat, coord, reference_index):
+ query, key, value = (
+ self.linear_q(feat),
+ self.linear_k(feat),
+ self.linear_v(feat),
+ )
+ key = pointops.grouping(reference_index, key, coord, with_xyz=True)
+ value = pointops.grouping(reference_index, value, coord, with_xyz=False)
+ pos, key = key[:, :, 0:3], key[:, :, 3:]
+ relation_qk = key - query.unsqueeze(1)
+ if self.pe_multiplier:
+ pem = self.linear_p_multiplier(pos)
+ relation_qk = relation_qk * pem
+ if self.pe_bias:
+ peb = self.linear_p_bias(pos)
+ relation_qk = relation_qk + peb
+ value = value + peb
+
+ weight = self.weight_encoding(relation_qk)
+ weight = self.attn_drop(self.softmax(weight))
+
+ mask = torch.sign(reference_index + 1)
+ weight = torch.einsum("n s g, n s -> n s g", weight, mask)
+ value = einops.rearrange(value, "n ns (g i) -> n ns g i", g=self.groups)
+ feat = torch.einsum("n s g i, n s g -> n g i", value, weight)
+ feat = einops.rearrange(feat, "n g i -> n (g i)")
+ return feat
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ norm_fn=None,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(Block, self).__init__()
+ self.attn = GroupedVectorAttention(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ attn_drop_rate=attn_drop_rate,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ )
+
+ assert norm_fn is not None
+
+ self.fc1 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.fc3 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.norm1 = norm_fn(embed_channels)
+ self.norm2 = norm_fn(embed_channels)
+ self.norm3 = norm_fn(embed_channels)
+ self.act = nn.ReLU(inplace=True)
+ self.enable_checkpoint = enable_checkpoint
+ self.drop_path = (
+ DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ )
+
+ def forward(self, points, reference_index):
+ coord, feat, offset, condition, context = points
+ identity = feat
+ feat = self.act(self.norm1(self.fc1(feat), condition, context))
+ feat = (
+ self.attn(feat, coord, reference_index)
+ if not self.enable_checkpoint
+ else checkpoint(self.attn, feat, coord, reference_index)
+ )
+ feat = self.act(self.norm2(feat, condition, context))
+ feat = self.norm3(self.fc3(feat), condition, context)
+ feat = identity + self.drop_path(feat)
+ feat = self.act(feat)
+ return [coord, feat, offset, condition, context]
+
+
+class BlockSequence(nn.Module):
+ def __init__(
+ self,
+ depth,
+ embed_channels,
+ groups,
+ neighbours=16,
+ norm_fn=None,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(BlockSequence, self).__init__()
+
+ if isinstance(drop_path_rate, list):
+ drop_path_rates = drop_path_rate
+ assert len(drop_path_rates) == depth
+ elif isinstance(drop_path_rate, float):
+ drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+ else:
+ drop_path_rates = [0.0 for _ in range(depth)]
+
+ self.neighbours = neighbours
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels=embed_channels,
+ groups=groups,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rates[i],
+ enable_checkpoint=enable_checkpoint,
+ )
+ self.blocks.append(block)
+
+ def forward(self, points):
+ coord, feat, offset, condition, context = points
+ # reference index query of neighbourhood attention
+ # for windows attention, modify reference index query method
+ reference_index, _ = pointops.knn_query(self.neighbours, coord, offset)
+ for block in self.blocks:
+ points = block(points, reference_index)
+ return points
+
+
+class GridPool(nn.Module):
+ """
+ Partition-based Pooling (Grid Pooling)
+ """
+
+ def __init__(self, in_channels, out_channels, grid_size, norm_fn, bias=False):
+ super(GridPool, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.grid_size = grid_size
+
+ self.fc = nn.Linear(in_channels, out_channels, bias=bias)
+ self.norm = norm_fn(out_channels)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, points, start=None):
+ coord, feat, offset, condition, context = points
+ batch = offset2batch(offset)
+ feat = self.act(self.norm(self.fc(feat), condition, context))
+ start = (
+ segment_csr(
+ coord,
+ torch.cat([batch.new_zeros(1), torch.cumsum(batch.bincount(), dim=0)]),
+ reduce="min",
+ )
+ if start is None
+ else start
+ )
+ cluster = voxel_grid(
+ pos=coord - start[batch], size=self.grid_size, batch=batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ _, sorted_cluster_indices = torch.sort(cluster)
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ coord = segment_csr(coord[sorted_cluster_indices], idx_ptr, reduce="mean")
+ feat = segment_csr(feat[sorted_cluster_indices], idx_ptr, reduce="max")
+ batch = batch[idx_ptr[:-1]]
+ offset = batch2offset(batch)
+ return [coord, feat, offset, condition, context], cluster
+
+
+class UnpoolWithSkip(nn.Module):
+ """
+ Map Unpooling with skip connection
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ norm_fn,
+ bias=True,
+ skip=True,
+ backend="map",
+ ):
+ super(UnpoolWithSkip, self).__init__()
+ self.in_channels = in_channels
+ self.skip_channels = skip_channels
+ self.out_channels = out_channels
+ self.skip = skip
+ self.backend = backend
+ assert self.backend in ["map", "interp"]
+
+ self.proj_linear = nn.Linear(in_channels, out_channels, bias=bias)
+ self.proj_norm = norm_fn(out_channels)
+ self.proj_act = nn.ReLU(inplace=True)
+
+ self.proj_skip_linear = nn.Linear(skip_channels, out_channels, bias=bias)
+ self.proj_skip_norm = norm_fn(out_channels)
+ self.proj_skip_act = nn.ReLU(inplace=True)
+
+ def forward(self, points, skip_points, cluster=None):
+ coord, feat, offset, condition, context = points
+ skip_coord, skip_feat, skip_offset, _, _ = skip_points
+ feat = self.proj_act(self.proj_norm(self.proj_linear(feat), condition, context))
+ if self.backend == "map" and cluster is not None:
+ feat = feat[cluster]
+ else:
+ feat = pointops.interpolation(coord, skip_coord, feat, offset, skip_offset)
+ if self.skip:
+ feat = feat + self.proj_skip_act(
+ self.proj_skip_norm(
+ self.proj_skip_linear(skip_feat), condition, context
+ )
+ )
+ return [skip_coord, feat, skip_offset, condition, context]
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ norm_fn,
+ grid_size=None,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ ):
+ super(Encoder, self).__init__()
+
+ self.down = GridPool(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ grid_size=grid_size,
+ norm_fn=norm_fn,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ points, cluster = self.down(points)
+ return self.blocks(points), cluster
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ groups,
+ depth,
+ norm_fn,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(Decoder, self).__init__()
+
+ self.up = UnpoolWithSkip(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ skip_channels=skip_channels,
+ backend=unpool_backend,
+ norm_fn=norm_fn,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points, skip_points, cluster):
+ points = self.up(points, skip_points, cluster)
+ return self.blocks(points)
+
+
+class GVAPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ norm_fn,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(GVAPatchEmbed, self).__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ self.proj_linear = nn.Linear(in_channels, embed_channels, bias=False)
+ self.proj_norm = norm_fn(embed_channels)
+ self.proj_act = nn.ReLU(inplace=True)
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ coord, feat, offset, condition, context = points
+ feat = self.proj_act(self.proj_norm(self.proj_linear(feat), condition, context))
+ return self.blocks([coord, feat, offset, condition, context])
+
+
+@MODELS.register_module("PT-v2m3")
+class PointTransformerV2(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ patch_embed_depth=1,
+ patch_embed_channels=48,
+ patch_embed_groups=6,
+ patch_embed_neighbours=8,
+ enc_depths=(2, 2, 6, 2),
+ enc_channels=(96, 192, 384, 512),
+ enc_groups=(12, 24, 48, 64),
+ enc_neighbours=(16, 16, 16, 16),
+ dec_depths=(1, 1, 1, 1),
+ dec_channels=(48, 96, 192, 384),
+ dec_groups=(6, 12, 24, 48),
+ dec_neighbours=(16, 16, 16, 16),
+ grid_sizes=(0.06, 0.12, 0.24, 0.48),
+ attn_qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ context_channels=256,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ norm_decouple=True,
+ norm_adaptive=True,
+ norm_affine=False,
+ ):
+ super(PointTransformerV2, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_depths)
+ assert self.num_stages == len(dec_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(dec_channels)
+ assert self.num_stages == len(enc_groups)
+ assert self.num_stages == len(dec_groups)
+ assert self.num_stages == len(enc_neighbours)
+ assert self.num_stages == len(dec_neighbours)
+ assert self.num_stages == len(grid_sizes)
+
+ norm_fn = partial(
+ PDBatchNorm,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=conditions,
+ context_channels=context_channels,
+ decouple=norm_decouple,
+ adaptive=norm_adaptive,
+ affine=norm_affine,
+ )
+
+ self.patch_embed = GVAPatchEmbed(
+ in_channels=in_channels,
+ embed_channels=patch_embed_channels,
+ groups=patch_embed_groups,
+ depth=patch_embed_depth,
+ neighbours=patch_embed_neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ enc_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(enc_depths))
+ ]
+ dec_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(dec_depths))
+ ]
+ enc_channels = [patch_embed_channels] + list(enc_channels)
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ self.enc_stages = nn.ModuleList()
+ self.dec_stages = nn.ModuleList()
+ for i in range(self.num_stages):
+ enc = Encoder(
+ depth=enc_depths[i],
+ in_channels=enc_channels[i],
+ embed_channels=enc_channels[i + 1],
+ groups=enc_groups[i],
+ grid_size=grid_sizes[i],
+ neighbours=enc_neighbours[i],
+ norm_fn=norm_fn,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=enc_dp_rates[
+ sum(enc_depths[:i]) : sum(enc_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ )
+ dec = Decoder(
+ depth=dec_depths[i],
+ in_channels=dec_channels[i + 1],
+ skip_channels=enc_channels[i],
+ embed_channels=dec_channels[i],
+ groups=dec_groups[i],
+ neighbours=dec_neighbours[i],
+ norm_fn=norm_fn,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dec_dp_rates[
+ sum(dec_depths[:i]) : sum(dec_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ unpool_backend=unpool_backend,
+ )
+ self.enc_stages.append(enc)
+ self.dec_stages.append(dec)
+ self.seg_head = (
+ nn.Sequential(nn.Linear(dec_channels[0], num_classes))
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"].int()
+ condition = data_dict["condition"][0]
+ context = data_dict["context"] if "context" in data_dict.keys() else None
+
+ # a batch of point cloud is a list of coord, feat and offset
+ points = [coord, feat, offset, condition, context]
+ points = self.patch_embed(points)
+ skips = [[points]]
+ for i in range(self.num_stages):
+ points, cluster = self.enc_stages[i](points)
+ skips[-1].append(cluster) # record grid cluster of pooling
+ skips.append([points]) # record points info of current stage
+
+ points = skips.pop(-1)[0] # unpooling points info in the last enc stage
+ for i in reversed(range(self.num_stages)):
+ skip_points, cluster = skips.pop(-1)
+ points = self.dec_stages[i](points, skip_points, cluster)
+ coord, feat, offset, _, _ = points
+ seg_logits = self.seg_head(feat)
+ return seg_logits
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v3/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fe25f32abaf4d60241dcf21507f85a47e46f070
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v3/__init__.py
@@ -0,0 +1 @@
+from .point_transformer_v3m1_base import *
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v3/point_transformer_v3m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v3/point_transformer_v3m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9f567162dc424324ee5c30a0803fe3ea465f9b1
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/point_transformer_v3/point_transformer_v3m1_base.py
@@ -0,0 +1,714 @@
+"""
+Point Transformer - V3 Mode1
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from addict import Dict
+import math
+import torch
+import torch.nn as nn
+import spconv.pytorch as spconv
+import torch_scatter
+from timm.models.layers import DropPath
+
+try:
+ import flash_attn
+except ImportError:
+ flash_attn = None
+
+from pointcept.models.point_prompt_training import PDNorm
+from pointcept.models.builder import MODELS
+from pointcept.models.utils.misc import offset2bincount
+from pointcept.models.utils.structure import Point
+from pointcept.models.modules import PointModule, PointSequential
+
+
+class RPE(torch.nn.Module):
+ def __init__(self, patch_size, num_heads):
+ super().__init__()
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.pos_bnd = int((4 * patch_size) ** (1 / 3) * 2)
+ self.rpe_num = 2 * self.pos_bnd + 1
+ self.rpe_table = torch.nn.Parameter(torch.zeros(3 * self.rpe_num, num_heads))
+ torch.nn.init.trunc_normal_(self.rpe_table, std=0.02)
+
+ def forward(self, coord):
+ idx = (
+ coord.clamp(-self.pos_bnd, self.pos_bnd) # clamp into bnd
+ + self.pos_bnd # relative position to positive index
+ + torch.arange(3, device=coord.device) * self.rpe_num # x, y, z stride
+ )
+ out = self.rpe_table.index_select(0, idx.reshape(-1))
+ out = out.view(idx.shape + (-1,)).sum(3)
+ out = out.permute(0, 3, 1, 2) # (N, K, K, H) -> (N, H, K, K)
+ return out
+
+
+class SerializedAttention(PointModule):
+ def __init__(
+ self,
+ channels,
+ num_heads,
+ patch_size,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ order_index=0,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=True,
+ upcast_softmax=True,
+ ):
+ super().__init__()
+ assert channels % num_heads == 0
+ self.channels = channels
+ self.num_heads = num_heads
+ self.scale = qk_scale or (channels // num_heads) ** -0.5
+ self.order_index = order_index
+ self.upcast_attention = upcast_attention
+ self.upcast_softmax = upcast_softmax
+ self.enable_rpe = enable_rpe
+ self.enable_flash = enable_flash
+ if enable_flash:
+ assert (
+ enable_rpe is False
+ ), "Set enable_rpe to False when enable Flash Attention"
+ assert (
+ upcast_attention is False
+ ), "Set upcast_attention to False when enable Flash Attention"
+ assert (
+ upcast_softmax is False
+ ), "Set upcast_softmax to False when enable Flash Attention"
+ assert flash_attn is not None, "Make sure flash_attn is installed."
+ self.patch_size = patch_size
+ self.attn_drop = attn_drop
+ else:
+ # when disable flash attention, we still don't want to use mask
+ # consequently, patch size will auto set to the
+ # min number of patch_size_max and number of points
+ self.patch_size_max = patch_size
+ self.patch_size = 0
+ self.attn_drop = torch.nn.Dropout(attn_drop)
+
+ self.qkv = torch.nn.Linear(channels, channels * 3, bias=qkv_bias)
+ self.proj = torch.nn.Linear(channels, channels)
+ self.proj_drop = torch.nn.Dropout(proj_drop)
+ self.softmax = torch.nn.Softmax(dim=-1)
+ self.rpe = RPE(patch_size, num_heads) if self.enable_rpe else None
+
+ @torch.no_grad()
+ def get_rel_pos(self, point, order):
+ K = self.patch_size
+ rel_pos_key = f"rel_pos_{self.order_index}"
+ if rel_pos_key not in point.keys():
+ grid_coord = point.grid_coord[order]
+ grid_coord = grid_coord.reshape(-1, K, 3)
+ point[rel_pos_key] = grid_coord.unsqueeze(2) - grid_coord.unsqueeze(1)
+ return point[rel_pos_key]
+
+ @torch.no_grad()
+ def get_padding_and_inverse(self, point):
+ pad_key = "pad"
+ unpad_key = "unpad"
+ cu_seqlens_key = "cu_seqlens_key"
+ if (
+ pad_key not in point.keys()
+ or unpad_key not in point.keys()
+ or cu_seqlens_key not in point.keys()
+ ):
+ offset = point.offset
+ bincount = offset2bincount(offset)
+ bincount_pad = (
+ torch.div(
+ bincount + self.patch_size - 1,
+ self.patch_size,
+ rounding_mode="trunc",
+ )
+ * self.patch_size
+ )
+ # only pad point when num of points larger than patch_size
+ mask_pad = bincount > self.patch_size
+ bincount_pad = ~mask_pad * bincount + mask_pad * bincount_pad
+ _offset = nn.functional.pad(offset, (1, 0))
+ _offset_pad = nn.functional.pad(torch.cumsum(bincount_pad, dim=0), (1, 0))
+ pad = torch.arange(_offset_pad[-1], device=offset.device)
+ unpad = torch.arange(_offset[-1], device=offset.device)
+ cu_seqlens = []
+ for i in range(len(offset)):
+ unpad[_offset[i] : _offset[i + 1]] += _offset_pad[i] - _offset[i]
+ if bincount[i] != bincount_pad[i]:
+ pad[
+ _offset_pad[i + 1]
+ - self.patch_size
+ + (bincount[i] % self.patch_size) : _offset_pad[i + 1]
+ ] = pad[
+ _offset_pad[i + 1]
+ - 2 * self.patch_size
+ + (bincount[i] % self.patch_size) : _offset_pad[i + 1]
+ - self.patch_size
+ ]
+ pad[_offset_pad[i] : _offset_pad[i + 1]] -= _offset_pad[i] - _offset[i]
+ cu_seqlens.append(
+ torch.arange(
+ _offset_pad[i],
+ _offset_pad[i + 1],
+ step=self.patch_size,
+ dtype=torch.int32,
+ device=offset.device,
+ )
+ )
+ point[pad_key] = pad
+ point[unpad_key] = unpad
+ point[cu_seqlens_key] = nn.functional.pad(
+ torch.concat(cu_seqlens), (0, 1), value=_offset_pad[-1]
+ )
+ return point[pad_key], point[unpad_key], point[cu_seqlens_key]
+
+ def forward(self, point):
+ if not self.enable_flash:
+ self.patch_size = min(
+ offset2bincount(point.offset).min().tolist(), self.patch_size_max
+ )
+
+ H = self.num_heads
+ K = self.patch_size
+ C = self.channels
+
+ pad, unpad, cu_seqlens = self.get_padding_and_inverse(point)
+
+ order = point.serialized_order[self.order_index][pad]
+ inverse = unpad[point.serialized_inverse[self.order_index]]
+
+ # padding and reshape feat and batch for serialized point patch
+ qkv = self.qkv(point.feat)[order]
+
+ if not self.enable_flash:
+ # encode and reshape qkv: (N', K, 3, H, C') => (3, N', H, K, C')
+ q, k, v = (
+ qkv.reshape(-1, K, 3, H, C // H).permute(2, 0, 3, 1, 4).unbind(dim=0)
+ )
+ # attn
+ if self.upcast_attention:
+ q = q.float()
+ k = k.float()
+ attn = (q * self.scale) @ k.transpose(-2, -1) # (N', H, K, K)
+ if self.enable_rpe:
+ attn = attn + self.rpe(self.get_rel_pos(point, order))
+ if self.upcast_softmax:
+ attn = attn.float()
+ attn = self.softmax(attn)
+ attn = self.attn_drop(attn).to(qkv.dtype)
+ feat = (attn @ v).transpose(1, 2).reshape(-1, C)
+ else:
+ feat = flash_attn.flash_attn_varlen_qkvpacked_func(
+ qkv.half().reshape(-1, 3, H, C // H),
+ cu_seqlens,
+ max_seqlen=self.patch_size,
+ dropout_p=self.attn_drop if self.training else 0,
+ softmax_scale=self.scale,
+ ).reshape(-1, C)
+ feat = feat.to(qkv.dtype)
+ feat = feat[inverse]
+
+ # ffn
+ feat = self.proj(feat)
+ feat = self.proj_drop(feat)
+ point.feat = feat
+ return point
+
+
+class MLP(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ hidden_channels=None,
+ out_channels=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_channels = out_channels or in_channels
+ hidden_channels = hidden_channels or in_channels
+ self.fc1 = nn.Linear(in_channels, hidden_channels)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_channels, out_channels)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class Block(PointModule):
+ def __init__(
+ self,
+ channels,
+ num_heads,
+ patch_size=48,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ drop_path=0.0,
+ norm_layer=nn.LayerNorm,
+ act_layer=nn.GELU,
+ pre_norm=True,
+ order_index=0,
+ cpe_indice_key=None,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=True,
+ upcast_softmax=True,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.pre_norm = pre_norm
+
+ self.cpe = PointSequential(
+ spconv.SubMConv3d(
+ channels,
+ channels,
+ kernel_size=3,
+ bias=True,
+ indice_key=cpe_indice_key,
+ ),
+ nn.Linear(channels, channels),
+ norm_layer(channels),
+ )
+
+ self.norm1 = PointSequential(norm_layer(channels))
+ self.attn = SerializedAttention(
+ channels=channels,
+ patch_size=patch_size,
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ order_index=order_index,
+ enable_rpe=enable_rpe,
+ enable_flash=enable_flash,
+ upcast_attention=upcast_attention,
+ upcast_softmax=upcast_softmax,
+ )
+ self.norm2 = PointSequential(norm_layer(channels))
+ self.mlp = PointSequential(
+ MLP(
+ in_channels=channels,
+ hidden_channels=int(channels * mlp_ratio),
+ out_channels=channels,
+ act_layer=act_layer,
+ drop=proj_drop,
+ )
+ )
+ self.drop_path = PointSequential(
+ DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ )
+
+ def forward(self, point: Point):
+ shortcut = point.feat
+ point = self.cpe(point)
+ point.feat = shortcut + point.feat
+ shortcut = point.feat
+ if self.pre_norm:
+ point = self.norm1(point)
+ point = self.drop_path(self.attn(point))
+ point.feat = shortcut + point.feat
+ if not self.pre_norm:
+ point = self.norm1(point)
+
+ shortcut = point.feat
+ if self.pre_norm:
+ point = self.norm2(point)
+ point = self.drop_path(self.mlp(point))
+ point.feat = shortcut + point.feat
+ if not self.pre_norm:
+ point = self.norm2(point)
+ point.sparse_conv_feat = point.sparse_conv_feat.replace_feature(point.feat)
+ return point
+
+
+class SerializedPooling(PointModule):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ stride=2,
+ norm_layer=None,
+ act_layer=None,
+ reduce="max",
+ shuffle_orders=True,
+ traceable=True, # record parent and cluster
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ assert stride == 2 ** (math.ceil(stride) - 1).bit_length() # 2, 4, 8
+ # TODO: add support to grid pool (any stride)
+ self.stride = stride
+ assert reduce in ["sum", "mean", "min", "max"]
+ self.reduce = reduce
+ self.shuffle_orders = shuffle_orders
+ self.traceable = traceable
+
+ self.proj = nn.Linear(in_channels, out_channels)
+ if norm_layer is not None:
+ self.norm = PointSequential(norm_layer(out_channels))
+ if act_layer is not None:
+ self.act = PointSequential(act_layer())
+
+ def forward(self, point: Point):
+ pooling_depth = (math.ceil(self.stride) - 1).bit_length()
+ if pooling_depth > point.serialized_depth:
+ pooling_depth = 0
+ assert {
+ "serialized_code",
+ "serialized_order",
+ "serialized_inverse",
+ "serialized_depth",
+ }.issubset(
+ point.keys()
+ ), "Run point.serialization() point cloud before SerializedPooling"
+
+ code = point.serialized_code >> pooling_depth * 3
+ code_, cluster, counts = torch.unique(
+ code[0],
+ sorted=True,
+ return_inverse=True,
+ return_counts=True,
+ )
+ # indices of point sorted by cluster, for torch_scatter.segment_csr
+ _, indices = torch.sort(cluster)
+ # index pointer for sorted point, for torch_scatter.segment_csr
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ # head_indices of each cluster, for reduce attr e.g. code, batch
+ head_indices = indices[idx_ptr[:-1]]
+ # generate down code, order, inverse
+ code = code[:, head_indices]
+ order = torch.argsort(code)
+ inverse = torch.zeros_like(order).scatter_(
+ dim=1,
+ index=order,
+ src=torch.arange(0, code.shape[1], device=order.device).repeat(
+ code.shape[0], 1
+ ),
+ )
+
+ if self.shuffle_orders:
+ perm = torch.randperm(code.shape[0])
+ code = code[perm]
+ order = order[perm]
+ inverse = inverse[perm]
+
+ # collect information
+ point_dict = Dict(
+ feat=torch_scatter.segment_csr(
+ self.proj(point.feat)[indices], idx_ptr, reduce=self.reduce
+ ),
+ coord=torch_scatter.segment_csr(
+ point.coord[indices], idx_ptr, reduce="mean"
+ ),
+ grid_coord=point.grid_coord[head_indices] >> pooling_depth,
+ serialized_code=code,
+ serialized_order=order,
+ serialized_inverse=inverse,
+ serialized_depth=point.serialized_depth - pooling_depth,
+ batch=point.batch[head_indices],
+ )
+
+ if "condition" in point.keys():
+ point_dict["condition"] = point.condition
+ if "context" in point.keys():
+ point_dict["context"] = point.context
+
+ if self.traceable:
+ point_dict["pooling_inverse"] = cluster
+ point_dict["pooling_parent"] = point
+ point = Point(point_dict)
+ if self.norm is not None:
+ point = self.norm(point)
+ if self.act is not None:
+ point = self.act(point)
+ point.sparsify()
+ return point
+
+
+class SerializedUnpooling(PointModule):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ norm_layer=None,
+ act_layer=None,
+ traceable=False, # record parent and cluster
+ ):
+ super().__init__()
+ self.proj = PointSequential(nn.Linear(in_channels, out_channels))
+ self.proj_skip = PointSequential(nn.Linear(skip_channels, out_channels))
+
+ if norm_layer is not None:
+ self.proj.add(norm_layer(out_channels))
+ self.proj_skip.add(norm_layer(out_channels))
+
+ if act_layer is not None:
+ self.proj.add(act_layer())
+ self.proj_skip.add(act_layer())
+
+ self.traceable = traceable
+
+ def forward(self, point):
+ assert "pooling_parent" in point.keys()
+ assert "pooling_inverse" in point.keys()
+ parent = point.pop("pooling_parent")
+ inverse = point.pop("pooling_inverse")
+ point = self.proj(point)
+ parent = self.proj_skip(parent)
+ parent.feat = parent.feat + point.feat[inverse]
+
+ if self.traceable:
+ parent["unpooling_parent"] = point
+ return parent
+
+
+class Embedding(PointModule):
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ norm_layer=None,
+ act_layer=None,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+
+ # TODO: check remove spconv
+ self.stem = PointSequential(
+ conv=spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ )
+ )
+ if norm_layer is not None:
+ self.stem.add(norm_layer(embed_channels), name="norm")
+ if act_layer is not None:
+ self.stem.add(act_layer(), name="act")
+
+ def forward(self, point: Point):
+ point = self.stem(point)
+ return point
+
+
+@MODELS.register_module("PT-v3m1")
+class PointTransformerV3(PointModule):
+ def __init__(
+ self,
+ in_channels=6,
+ order=("z", "z-trans"),
+ stride=(2, 2, 2, 2),
+ enc_depths=(2, 2, 2, 6, 2),
+ enc_channels=(32, 64, 128, 256, 512),
+ enc_num_head=(2, 4, 8, 16, 32),
+ enc_patch_size=(48, 48, 48, 48, 48),
+ dec_depths=(2, 2, 2, 2),
+ dec_channels=(64, 64, 128, 256),
+ dec_num_head=(4, 4, 8, 16),
+ dec_patch_size=(48, 48, 48, 48),
+ mlp_ratio=4,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ drop_path=0.3,
+ pre_norm=True,
+ shuffle_orders=True,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=False,
+ upcast_softmax=False,
+ cls_mode=False,
+ pdnorm_bn=False,
+ pdnorm_ln=False,
+ pdnorm_decouple=True,
+ pdnorm_adaptive=False,
+ pdnorm_affine=True,
+ pdnorm_conditions=("ScanNet", "S3DIS", "Structured3D"),
+ ):
+ super().__init__()
+ self.num_stages = len(enc_depths)
+ self.order = [order] if isinstance(order, str) else order
+ self.cls_mode = cls_mode
+ self.shuffle_orders = shuffle_orders
+
+ assert self.num_stages == len(stride) + 1
+ assert self.num_stages == len(enc_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(enc_num_head)
+ assert self.num_stages == len(enc_patch_size)
+ assert self.cls_mode or self.num_stages == len(dec_depths) + 1
+ assert self.cls_mode or self.num_stages == len(dec_channels) + 1
+ assert self.cls_mode or self.num_stages == len(dec_num_head) + 1
+ assert self.cls_mode or self.num_stages == len(dec_patch_size) + 1
+
+ # norm layers
+ if pdnorm_bn:
+ bn_layer = partial(
+ PDNorm,
+ norm_layer=partial(
+ nn.BatchNorm1d, eps=1e-3, momentum=0.01, affine=pdnorm_affine
+ ),
+ conditions=pdnorm_conditions,
+ decouple=pdnorm_decouple,
+ adaptive=pdnorm_adaptive,
+ )
+ else:
+ bn_layer = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ if pdnorm_ln:
+ ln_layer = partial(
+ PDNorm,
+ norm_layer=partial(nn.LayerNorm, elementwise_affine=pdnorm_affine),
+ conditions=pdnorm_conditions,
+ decouple=pdnorm_decouple,
+ adaptive=pdnorm_adaptive,
+ )
+ else:
+ ln_layer = nn.LayerNorm
+ # activation layers
+ act_layer = nn.GELU
+
+ self.embedding = Embedding(
+ in_channels=in_channels,
+ embed_channels=enc_channels[0],
+ norm_layer=bn_layer,
+ act_layer=act_layer,
+ )
+
+ # encoder
+ enc_drop_path = [
+ x.item() for x in torch.linspace(0, drop_path, sum(enc_depths))
+ ]
+ self.enc = PointSequential()
+ for s in range(self.num_stages):
+ enc_drop_path_ = enc_drop_path[
+ sum(enc_depths[:s]) : sum(enc_depths[: s + 1])
+ ]
+ enc = PointSequential()
+ if s > 0:
+ enc.add(
+ SerializedPooling(
+ in_channels=enc_channels[s - 1],
+ out_channels=enc_channels[s],
+ stride=stride[s - 1],
+ norm_layer=bn_layer,
+ act_layer=act_layer,
+ ),
+ name="down",
+ )
+ for i in range(enc_depths[s]):
+ enc.add(
+ Block(
+ channels=enc_channels[s],
+ num_heads=enc_num_head[s],
+ patch_size=enc_patch_size[s],
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ drop_path=enc_drop_path_[i],
+ norm_layer=ln_layer,
+ act_layer=act_layer,
+ pre_norm=pre_norm,
+ order_index=i % len(self.order),
+ cpe_indice_key=f"stage{s}",
+ enable_rpe=enable_rpe,
+ enable_flash=enable_flash,
+ upcast_attention=upcast_attention,
+ upcast_softmax=upcast_softmax,
+ ),
+ name=f"block{i}",
+ )
+ if len(enc) != 0:
+ self.enc.add(module=enc, name=f"enc{s}")
+
+ # decoder
+ if not self.cls_mode:
+ dec_drop_path = [
+ x.item() for x in torch.linspace(0, drop_path, sum(dec_depths))
+ ]
+ self.dec = PointSequential()
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ for s in reversed(range(self.num_stages - 1)):
+ dec_drop_path_ = dec_drop_path[
+ sum(dec_depths[:s]) : sum(dec_depths[: s + 1])
+ ]
+ dec_drop_path_.reverse()
+ dec = PointSequential()
+ dec.add(
+ SerializedUnpooling(
+ in_channels=dec_channels[s + 1],
+ skip_channels=enc_channels[s],
+ out_channels=dec_channels[s],
+ norm_layer=bn_layer,
+ act_layer=act_layer,
+ ),
+ name="up",
+ )
+ for i in range(dec_depths[s]):
+ dec.add(
+ Block(
+ channels=dec_channels[s],
+ num_heads=dec_num_head[s],
+ patch_size=dec_patch_size[s],
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ drop_path=dec_drop_path_[i],
+ norm_layer=ln_layer,
+ act_layer=act_layer,
+ pre_norm=pre_norm,
+ order_index=i % len(self.order),
+ cpe_indice_key=f"stage{s}",
+ enable_rpe=enable_rpe,
+ enable_flash=enable_flash,
+ upcast_attention=upcast_attention,
+ upcast_softmax=upcast_softmax,
+ ),
+ name=f"block{i}",
+ )
+ self.dec.add(module=dec, name=f"dec{s}")
+
+ def forward(self, data_dict):
+ point = Point(data_dict)
+ point.serialization(order=self.order, shuffle_orders=self.shuffle_orders)
+ point.sparsify()
+
+ point = self.embedding(point)
+ point = self.enc(point)
+ if not self.cls_mode:
+ point = self.dec(point)
+ # else:
+ # point.feat = torch_scatter.segment_csr(
+ # src=point.feat,
+ # indptr=nn.functional.pad(point.offset, (1, 0)),
+ # reduce="mean",
+ # )
+ return point
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aafc3859f43bd47da8cd966a57d4307cc771525f
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/__init__.py
@@ -0,0 +1,4 @@
+from .mink_unet import *
+from .spconv_unet_v1m1_base import *
+from .spconv_unet_v1m2_bn_momentum import *
+from .spconv_unet_v1m3_pdnorm import *
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/mink_unet.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/mink_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ff8a01d0500abc207b82a8252a8131b5e671469
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/mink_unet.py
@@ -0,0 +1,442 @@
+"""
+SparseUNet Driven by MinkowskiEngine
+
+Modified from chrischoy/SpatioTemporalSegmentation
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+
+try:
+ import MinkowskiEngine as ME
+except ImportError:
+ ME = None
+
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+class BasicBlock(nn.Module):
+ expansion = 1
+
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ bn_momentum=0.1,
+ dimension=-1,
+ ):
+ super(BasicBlock, self).__init__()
+ assert dimension > 0
+
+ self.conv1 = ME.MinkowskiConvolution(
+ inplanes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ dimension=dimension,
+ )
+ self.norm1 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+ self.conv2 = ME.MinkowskiConvolution(
+ planes,
+ planes,
+ kernel_size=3,
+ stride=1,
+ dilation=dilation,
+ dimension=dimension,
+ )
+ self.norm2 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+ self.relu = ME.MinkowskiReLU(inplace=True)
+ self.downsample = downsample
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.norm1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.norm2(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ bn_momentum=0.1,
+ dimension=-1,
+ ):
+ super(Bottleneck, self).__init__()
+ assert dimension > 0
+
+ self.conv1 = ME.MinkowskiConvolution(
+ inplanes, planes, kernel_size=1, dimension=dimension
+ )
+ self.norm1 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+
+ self.conv2 = ME.MinkowskiConvolution(
+ planes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ dimension=dimension,
+ )
+ self.norm2 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+
+ self.conv3 = ME.MinkowskiConvolution(
+ planes, planes * self.expansion, kernel_size=1, dimension=dimension
+ )
+ self.norm3 = ME.MinkowskiBatchNorm(
+ planes * self.expansion, momentum=bn_momentum
+ )
+
+ self.relu = ME.MinkowskiReLU(inplace=True)
+ self.downsample = downsample
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.norm1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.norm2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.norm3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class MinkUNetBase(nn.Module):
+ BLOCK = None
+ PLANES = None
+ DILATIONS = (1, 1, 1, 1, 1, 1, 1, 1)
+ LAYERS = (2, 2, 2, 2, 2, 2, 2, 2)
+ PLANES = (32, 64, 128, 256, 256, 128, 96, 96)
+ INIT_DIM = 32
+ OUT_TENSOR_STRIDE = 1
+
+ def __init__(self, in_channels, out_channels, dimension=3):
+ super().__init__()
+ assert ME is not None, "Please follow `README.md` to install MinkowskiEngine.`"
+ self.D = dimension
+ assert self.BLOCK is not None
+ # Output of the first conv concated to conv6
+ self.inplanes = self.INIT_DIM
+ self.conv0p1s1 = ME.MinkowskiConvolution(
+ in_channels, self.inplanes, kernel_size=5, dimension=self.D
+ )
+
+ self.bn0 = ME.MinkowskiBatchNorm(self.inplanes)
+
+ self.conv1p1s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bn1 = ME.MinkowskiBatchNorm(self.inplanes)
+
+ self.block1 = self._make_layer(self.BLOCK, self.PLANES[0], self.LAYERS[0])
+
+ self.conv2p2s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bn2 = ME.MinkowskiBatchNorm(self.inplanes)
+
+ self.block2 = self._make_layer(self.BLOCK, self.PLANES[1], self.LAYERS[1])
+
+ self.conv3p4s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+
+ self.bn3 = ME.MinkowskiBatchNorm(self.inplanes)
+ self.block3 = self._make_layer(self.BLOCK, self.PLANES[2], self.LAYERS[2])
+
+ self.conv4p8s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bn4 = ME.MinkowskiBatchNorm(self.inplanes)
+ self.block4 = self._make_layer(self.BLOCK, self.PLANES[3], self.LAYERS[3])
+
+ self.convtr4p16s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[4], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr4 = ME.MinkowskiBatchNorm(self.PLANES[4])
+
+ self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion
+ self.block5 = self._make_layer(self.BLOCK, self.PLANES[4], self.LAYERS[4])
+ self.convtr5p8s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[5], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr5 = ME.MinkowskiBatchNorm(self.PLANES[5])
+
+ self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion
+ self.block6 = self._make_layer(self.BLOCK, self.PLANES[5], self.LAYERS[5])
+ self.convtr6p4s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[6], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr6 = ME.MinkowskiBatchNorm(self.PLANES[6])
+
+ self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion
+ self.block7 = self._make_layer(self.BLOCK, self.PLANES[6], self.LAYERS[6])
+ self.convtr7p2s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[7], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr7 = ME.MinkowskiBatchNorm(self.PLANES[7])
+
+ self.inplanes = self.PLANES[7] + self.INIT_DIM
+ self.block8 = self._make_layer(self.BLOCK, self.PLANES[7], self.LAYERS[7])
+
+ self.final = ME.MinkowskiConvolution(
+ self.PLANES[7] * self.BLOCK.expansion,
+ out_channels,
+ kernel_size=1,
+ bias=True,
+ dimension=self.D,
+ )
+ self.relu = ME.MinkowskiReLU(inplace=True)
+
+ self.weight_initialization()
+
+ def weight_initialization(self):
+ for m in self.modules():
+ if isinstance(m, ME.MinkowskiConvolution):
+ ME.utils.kaiming_normal_(m.kernel, mode="fan_out", nonlinearity="relu")
+
+ if isinstance(m, ME.MinkowskiBatchNorm):
+ nn.init.constant_(m.bn.weight, 1)
+ nn.init.constant_(m.bn.bias, 0)
+
+ def _make_layer(self, block, planes, blocks, stride=1, dilation=1, bn_momentum=0.1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ ME.MinkowskiConvolution(
+ self.inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=stride,
+ dimension=self.D,
+ ),
+ ME.MinkowskiBatchNorm(planes * block.expansion),
+ )
+ layers = []
+ layers.append(
+ block(
+ self.inplanes,
+ planes,
+ stride=stride,
+ dilation=dilation,
+ downsample=downsample,
+ dimension=self.D,
+ )
+ )
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(
+ block(
+ self.inplanes, planes, stride=1, dilation=dilation, dimension=self.D
+ )
+ )
+
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+ in_field = ME.TensorField(
+ feat,
+ coordinates=torch.cat([batch.unsqueeze(-1).int(), grid_coord.int()], dim=1),
+ quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
+ minkowski_algorithm=ME.MinkowskiAlgorithm.SPEED_OPTIMIZED,
+ device=feat.device,
+ )
+ x = in_field.sparse()
+
+ out = self.conv0p1s1(x)
+ out = self.bn0(out)
+ out_p1 = self.relu(out)
+
+ out = self.conv1p1s2(out_p1)
+ out = self.bn1(out)
+ out = self.relu(out)
+ out_b1p2 = self.block1(out)
+
+ out = self.conv2p2s2(out_b1p2)
+ out = self.bn2(out)
+ out = self.relu(out)
+ out_b2p4 = self.block2(out)
+
+ out = self.conv3p4s2(out_b2p4)
+ out = self.bn3(out)
+ out = self.relu(out)
+ out_b3p8 = self.block3(out)
+
+ # tensor_stride=16
+ out = self.conv4p8s2(out_b3p8)
+ out = self.bn4(out)
+ out = self.relu(out)
+ out = self.block4(out)
+
+ # tensor_stride=8
+ out = self.convtr4p16s2(out)
+ out = self.bntr4(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_b3p8)
+ out = self.block5(out)
+
+ # tensor_stride=4
+ out = self.convtr5p8s2(out)
+ out = self.bntr5(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_b2p4)
+ out = self.block6(out)
+
+ # tensor_stride=2
+ out = self.convtr6p4s2(out)
+ out = self.bntr6(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_b1p2)
+ out = self.block7(out)
+
+ # tensor_stride=1
+ out = self.convtr7p2s2(out)
+ out = self.bntr7(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_p1)
+ out = self.block8(out)
+
+ return self.final(out).slice(in_field).F
+
+
+@MODELS.register_module()
+class MinkUNet14(MinkUNetBase):
+ BLOCK = BasicBlock
+ LAYERS = (1, 1, 1, 1, 1, 1, 1, 1)
+
+
+@MODELS.register_module()
+class MinkUNet18(MinkUNetBase):
+ BLOCK = BasicBlock
+ LAYERS = (2, 2, 2, 2, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet34(MinkUNetBase):
+ BLOCK = BasicBlock
+ LAYERS = (2, 3, 4, 6, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet50(MinkUNetBase):
+ BLOCK = Bottleneck
+ LAYERS = (2, 3, 4, 6, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet101(MinkUNetBase):
+ BLOCK = Bottleneck
+ LAYERS = (2, 3, 4, 23, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet14A(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 128, 128, 96, 96)
+
+
+@MODELS.register_module()
+class MinkUNet14B(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 128, 128, 128, 128)
+
+
+@MODELS.register_module()
+class MinkUNet14C(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 192, 192, 128, 128)
+
+
+@MODELS.register_module()
+class MinkUNet14D(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 384, 384, 384, 384)
+
+
+@MODELS.register_module()
+class MinkUNet18A(MinkUNet18):
+ PLANES = (32, 64, 128, 256, 128, 128, 96, 96)
+
+
+@MODELS.register_module()
+class MinkUNet18B(MinkUNet18):
+ PLANES = (32, 64, 128, 256, 128, 128, 128, 128)
+
+
+@MODELS.register_module()
+class MinkUNet18D(MinkUNet18):
+ PLANES = (32, 64, 128, 256, 384, 384, 384, 384)
+
+
+@MODELS.register_module()
+class MinkUNet34A(MinkUNet34):
+ PLANES = (32, 64, 128, 256, 256, 128, 96, 96)
+
+
+@MODELS.register_module()
+class MinkUNet34B(MinkUNet34):
+ PLANES = (32, 64, 128, 256, 256, 128, 64, 32)
+
+
+@MODELS.register_module()
+class MinkUNet34C(MinkUNet34):
+ PLANES = (32, 64, 128, 256, 256, 128, 96, 96)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfcacb00b8dfb8a38aa9ab6968b0c9c63a63301c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m1_base.py
@@ -0,0 +1,463 @@
+"""
+SparseUNet Driven by SpConv (recommend)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+import spconv.pytorch as spconv
+from torch_geometric.utils import scatter
+
+from timm.models.layers import trunc_normal_
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch
+
+
+class BasicBlock(spconv.SparseModule):
+ expansion = 1
+
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ stride=1,
+ norm_fn=None,
+ indice_key=None,
+ bias=False,
+ ):
+ super().__init__()
+
+ assert norm_fn is not None
+
+ if in_channels == embed_channels:
+ self.proj = spconv.SparseSequential(nn.Identity())
+ else:
+ self.proj = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels, embed_channels, kernel_size=1, bias=False
+ ),
+ norm_fn(embed_channels),
+ )
+
+ self.conv1 = spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn1 = norm_fn(embed_channels)
+ self.relu = nn.ReLU()
+ self.conv2 = spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn2 = norm_fn(embed_channels)
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = out.replace_feature(self.bn1(out.features))
+ out = out.replace_feature(self.relu(out.features))
+
+ out = self.conv2(out)
+ out = out.replace_feature(self.bn2(out.features))
+
+ out = out.replace_feature(out.features + self.proj(residual).features)
+ out = out.replace_feature(self.relu(out.features))
+
+ return out
+
+
+@MODELS.register_module("SpUNet-v1m1")
+class SpUNetBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ cls_mode=False,
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+ self.cls_mode = cls_mode
+
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ block = BasicBlock
+
+ self.conv_input = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ base_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ ),
+ norm_fn(base_channels),
+ nn.ReLU(),
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList() if not self.cls_mode else None
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ spconv.SparseSequential(
+ spconv.SparseConv3d(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(channels[s]),
+ nn.ReLU(),
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+ if not self.cls_mode:
+ # decode num_stages
+ self.up.append(
+ spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(dec_channels),
+ nn.ReLU(),
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels + enc_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ final_in_channels = (
+ channels[-1] if not self.cls_mode else channels[self.num_stages - 1]
+ )
+ self.final = (
+ spconv.SubMConv3d(
+ final_in_channels, num_classes, kernel_size=1, padding=1, bias=True
+ )
+ if num_classes > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, input_dict):
+ grid_coord = input_dict["grid_coord"]
+ feat = input_dict["feat"]
+ offset = input_dict["offset"]
+
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 96).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input(x)
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s](x)
+ x = self.enc[s](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ if not self.cls_mode:
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s](x)
+ skip = skips.pop(-1)
+ x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x = self.dec[s](x)
+
+ x = self.final(x)
+ if self.cls_mode:
+ x = x.replace_feature(
+ scatter(x.features, x.indices[:, 0].long(), reduce="mean", dim=0)
+ )
+ return x.features
+
+
+@MODELS.register_module()
+class SpUNetNoSkipBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ block = BasicBlock
+
+ self.conv_input = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ base_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ ),
+ norm_fn(base_channels),
+ nn.ReLU(),
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList()
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ spconv.SparseSequential(
+ spconv.SparseConv3d(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(channels[s]),
+ nn.ReLU(),
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+
+ # decode num_stages
+ self.up.append(
+ spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(dec_channels),
+ nn.ReLU(),
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ self.final = (
+ spconv.SubMConv3d(
+ channels[-1], out_channels, kernel_size=1, padding=1, bias=True
+ )
+ if out_channels > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 1).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input(x)
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s](x)
+ x = self.enc[s](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s](x)
+ # skip = skips.pop(-1)
+ # x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x = self.dec[s](x)
+
+ x = self.final(x)
+ return x.features
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m2_bn_momentum.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m2_bn_momentum.py
new file mode 100644
index 0000000000000000000000000000000000000000..979b1b8b5488d6c55bbc20ad0cede5002c8a5c67
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m2_bn_momentum.py
@@ -0,0 +1,290 @@
+"""
+SparseUNet Driven by SpConv (recommend)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+try:
+ import spconv.pytorch as spconv
+except ImportError:
+ import warnings
+
+ warnings.warn("Please follow `README.md` to install spconv2.`")
+
+from timm.models.layers import trunc_normal_
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+class BasicBlock(spconv.SparseModule):
+ expansion = 1
+
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ stride=1,
+ norm_fn=None,
+ indice_key=None,
+ bias=False,
+ ):
+ super().__init__()
+
+ assert norm_fn is not None
+
+ if in_channels == embed_channels:
+ self.proj = spconv.SparseSequential(nn.Identity())
+ else:
+ self.proj = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels, embed_channels, kernel_size=1, bias=False
+ ),
+ norm_fn(embed_channels, momentum=0.02),
+ )
+
+ self.conv1 = spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn1 = norm_fn(embed_channels)
+ self.relu = nn.ReLU()
+ self.conv2 = spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn2 = norm_fn(embed_channels)
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = out.replace_feature(self.bn1(out.features))
+ out = out.replace_feature(self.relu(out.features))
+
+ out = self.conv2(out)
+ out = out.replace_feature(self.bn2(out.features))
+
+ out = out.replace_feature(out.features + self.proj(residual).features)
+ out = out.replace_feature(self.relu(out.features))
+
+ return out
+
+
+@MODELS.register_module("SpUNet-v1m2")
+class SpUNetBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ bn_momentum=0.1,
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-5, momentum=bn_momentum)
+ block = BasicBlock
+
+ self.conv_input = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ base_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ ),
+ norm_fn(base_channels, momentum=0.02),
+ nn.ReLU(),
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList()
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ spconv.SparseSequential(
+ spconv.SparseConv3d(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(channels[s], momentum=0.02),
+ nn.ReLU(),
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+
+ # decode num_stages
+ self.up.append(
+ spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(dec_channels, momentum=0.02),
+ nn.ReLU(),
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels + enc_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ self.final = (
+ spconv.SubMConv3d(
+ channels[-1], num_classes, kernel_size=1, padding=1, bias=True
+ )
+ if num_classes > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 1).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input(x)
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s](x)
+ x = self.enc[s](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s](x)
+ skip = skips.pop(-1)
+ x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x = self.dec[s](x)
+
+ x = self.final(x)
+ return x.features
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m3_pdnorm.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m3_pdnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..968f8f2c5a19cf016f9427812a8071ca83d612d5
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/sparse_unet/spconv_unet_v1m3_pdnorm.py
@@ -0,0 +1,429 @@
+"""
+SparseUNet V1M3
+
+Enable Prompt-Driven Normalization for Point Prompt Training
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+import spconv.pytorch as spconv
+from torch_geometric.utils import scatter
+
+from timm.models.layers import trunc_normal_
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch
+
+
+class PDBatchNorm(torch.nn.Module):
+ def __init__(
+ self,
+ num_features,
+ context_channels=256,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ decouple=True,
+ adaptive=False,
+ affine=True,
+ ):
+ super().__init__()
+ self.conditions = conditions
+ self.decouple = decouple
+ self.adaptive = adaptive
+ self.affine = affine
+ if self.decouple:
+ self.bns = nn.ModuleList(
+ [
+ nn.BatchNorm1d(
+ num_features=num_features,
+ eps=eps,
+ momentum=momentum,
+ affine=affine,
+ )
+ for _ in conditions
+ ]
+ )
+ else:
+ self.bn = nn.BatchNorm1d(
+ num_features=num_features, eps=eps, momentum=momentum, affine=affine
+ )
+ if self.adaptive:
+ self.modulation = nn.Sequential(
+ nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
+ )
+
+ def forward(self, feat, condition=None, context=None):
+ if self.decouple:
+ assert condition in self.conditions
+ bn = self.bns[self.conditions.index(condition)]
+ else:
+ bn = self.bn
+ feat = bn(feat)
+ if self.adaptive:
+ assert context is not None
+ shift, scale = self.modulation(context).chunk(2, dim=1)
+ feat = feat * (1.0 + scale) + shift
+ return feat
+
+
+class BasicBlock(spconv.SparseModule):
+ expansion = 1
+
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ stride=1,
+ norm_fn=None,
+ indice_key=None,
+ bias=False,
+ ):
+ super().__init__()
+
+ assert norm_fn is not None
+
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ if in_channels == embed_channels:
+ self.proj = spconv.SparseSequential(nn.Identity())
+ else:
+ # TODO remove norm after project
+ self.proj_conv = spconv.SubMConv3d(
+ in_channels, embed_channels, kernel_size=1, bias=False
+ )
+ self.proj_norm = norm_fn(embed_channels)
+
+ self.conv1 = spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn1 = norm_fn(embed_channels)
+ self.relu = nn.ReLU()
+ self.conv2 = spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn2 = norm_fn(embed_channels)
+ self.stride = stride
+
+ def forward(self, x):
+ x, condition, context = x
+ residual = x
+
+ out = self.conv1(x)
+ out = out.replace_feature(self.bn1(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+
+ out = self.conv2(out)
+ out = out.replace_feature(self.bn2(out.features, condition, context))
+
+ if self.in_channels == self.embed_channels:
+ residual = self.proj(residual)
+ else:
+ residual = residual.replace_feature(
+ self.proj_norm(self.proj_conv(residual).features, condition, context)
+ )
+ out = out.replace_feature(out.features + residual.features)
+ out = out.replace_feature(self.relu(out.features))
+ return out, condition, context
+
+
+class SPConvDown(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ indice_key,
+ kernel_size=2,
+ bias=False,
+ norm_fn=None,
+ ):
+ super().__init__()
+ self.conv = spconv.SparseConv3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=kernel_size,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn = norm_fn(out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x, condition, context = x
+ out = self.conv(x)
+ out = out.replace_feature(self.bn(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+ return out
+
+
+class SPConvUp(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ indice_key,
+ kernel_size=2,
+ bias=False,
+ norm_fn=None,
+ ):
+ super().__init__()
+ self.conv = spconv.SparseInverseConv3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn = norm_fn(out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x, condition, context = x
+ out = self.conv(x)
+ out = out.replace_feature(self.bn(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+ return out
+
+
+class SPConvPatchEmbedding(nn.Module):
+ def __init__(self, in_channels, out_channels, kernel_size=5, norm_fn=None):
+ super().__init__()
+ self.conv = spconv.SubMConv3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ )
+ self.bn = norm_fn(out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x, condition, context = x
+ out = self.conv(x)
+ out = out.replace_feature(self.bn(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+ return out
+
+
+@MODELS.register_module("SpUNet-v1m3")
+class SpUNetBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes=0,
+ base_channels=32,
+ context_channels=256,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ cls_mode=False,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ zero_init=True,
+ norm_decouple=True,
+ norm_adaptive=True,
+ norm_affine=False,
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+ self.cls_mode = cls_mode
+ self.conditions = conditions
+ self.zero_init = zero_init
+
+ norm_fn = partial(
+ PDBatchNorm,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=conditions,
+ context_channels=context_channels,
+ decouple=norm_decouple,
+ adaptive=norm_adaptive,
+ affine=norm_affine,
+ )
+ block = BasicBlock
+
+ self.conv_input = SPConvPatchEmbedding(
+ in_channels, base_channels, kernel_size=5, norm_fn=norm_fn
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList() if not self.cls_mode else None
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ SPConvDown(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ norm_fn=norm_fn,
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+ if not self.cls_mode:
+ # decode num_stages
+ self.up.append(
+ SPConvUp(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ norm_fn=norm_fn,
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels + enc_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ final_in_channels = (
+ channels[-1] if not self.cls_mode else channels[self.num_stages - 1]
+ )
+ self.final = (
+ spconv.SubMConv3d(
+ final_in_channels, num_classes, kernel_size=1, padding=1, bias=True
+ )
+ if num_classes > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ if m.affine:
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+ elif isinstance(m, PDBatchNorm):
+ if self.zero_init:
+ nn.init.constant_(m.modulation[-1].weight, 0)
+ nn.init.constant_(m.modulation[-1].bias, 0)
+
+ def forward(self, input_dict):
+ grid_coord = input_dict["grid_coord"]
+ feat = input_dict["feat"]
+ offset = input_dict["offset"]
+ condition = input_dict["condition"][0]
+ context = input_dict["context"] if "context" in input_dict.keys() else None
+
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 96).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input([x, condition, context])
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s]([x, condition, context])
+ x, _, _ = self.enc[s]([x, condition, context])
+ skips.append(x)
+ x = skips.pop(-1)
+ if not self.cls_mode:
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s]([x, condition, context])
+ skip = skips.pop(-1)
+ x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x, _, _ = self.dec[s]([x, condition, context])
+
+ x = self.final(x)
+ if self.cls_mode:
+ x = x.replace_feature(
+ scatter(x.features, x.indices[:, 0].long(), reduce="mean", dim=0)
+ )
+ return x.features
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/spvcnn/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/spvcnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecdc75a6878026437124c187323ca9676bf35c76
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/spvcnn/__init__.py
@@ -0,0 +1 @@
+from .ts_spvcnn import *
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/spvcnn/ts_spvcnn.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/spvcnn/ts_spvcnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..c26f1ea8d41a8edfbd1542b0f9e607e660441b38
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/spvcnn/ts_spvcnn.py
@@ -0,0 +1,438 @@
+"""
+SPVCNN
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+
+try:
+ import torchsparse
+ import torchsparse.nn as spnn
+ import torchsparse.nn.functional as F
+ from torchsparse.nn.utils import get_kernel_offsets
+ from torchsparse import PointTensor, SparseTensor
+except ImportError:
+ torchsparse = None
+
+
+from pointcept.models.utils import offset2batch
+from pointcept.models.builder import MODELS
+
+
+def initial_voxelize(z):
+ pc_hash = F.sphash(torch.floor(z.C).int())
+ sparse_hash = torch.unique(pc_hash)
+ idx_query = F.sphashquery(pc_hash, sparse_hash)
+ counts = F.spcount(idx_query.int(), len(sparse_hash))
+
+ inserted_coords = F.spvoxelize(torch.floor(z.C), idx_query, counts)
+ inserted_coords = torch.round(inserted_coords).int()
+ inserted_feat = F.spvoxelize(z.F, idx_query, counts)
+
+ new_tensor = SparseTensor(inserted_feat, inserted_coords, 1)
+ new_tensor.cmaps.setdefault(new_tensor.stride, new_tensor.coords)
+ z.additional_features["idx_query"][1] = idx_query
+ z.additional_features["counts"][1] = counts
+ return new_tensor
+
+
+# x: SparseTensor, z: PointTensor
+# return: SparseTensor
+def point_to_voxel(x, z):
+ if (
+ z.additional_features is None
+ or z.additional_features.get("idx_query") is None
+ or z.additional_features["idx_query"].get(x.s) is None
+ ):
+ pc_hash = F.sphash(
+ torch.cat(
+ [
+ torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],
+ z.C[:, -1].int().view(-1, 1),
+ ],
+ 1,
+ )
+ )
+ sparse_hash = F.sphash(x.C)
+ idx_query = F.sphashquery(pc_hash, sparse_hash)
+ counts = F.spcount(idx_query.int(), x.C.shape[0])
+ z.additional_features["idx_query"][x.s] = idx_query
+ z.additional_features["counts"][x.s] = counts
+ else:
+ idx_query = z.additional_features["idx_query"][x.s]
+ counts = z.additional_features["counts"][x.s]
+
+ inserted_feat = F.spvoxelize(z.F, idx_query, counts)
+ new_tensor = SparseTensor(inserted_feat, x.C, x.s)
+ new_tensor.cmaps = x.cmaps
+ new_tensor.kmaps = x.kmaps
+
+ return new_tensor
+
+
+# x: SparseTensor, z: PointTensor
+# return: PointTensor
+def voxel_to_point(x, z, nearest=False):
+ if (
+ z.idx_query is None
+ or z.weights is None
+ or z.idx_query.get(x.s) is None
+ or z.weights.get(x.s) is None
+ ):
+ off = spnn.utils.get_kernel_offsets(2, x.s, 1, device=z.F.device)
+ old_hash = F.sphash(
+ torch.cat(
+ [
+ torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],
+ z.C[:, -1].int().view(-1, 1),
+ ],
+ 1,
+ ),
+ off,
+ )
+ pc_hash = F.sphash(x.C.to(z.F.device))
+ idx_query = F.sphashquery(old_hash, pc_hash)
+ weights = (
+ F.calc_ti_weights(z.C, idx_query, scale=x.s[0]).transpose(0, 1).contiguous()
+ )
+ idx_query = idx_query.transpose(0, 1).contiguous()
+ if nearest:
+ weights[:, 1:] = 0.0
+ idx_query[:, 1:] = -1
+ new_feat = F.spdevoxelize(x.F, idx_query, weights)
+ new_tensor = PointTensor(
+ new_feat, z.C, idx_query=z.idx_query, weights=z.weights
+ )
+ new_tensor.additional_features = z.additional_features
+ new_tensor.idx_query[x.s] = idx_query
+ new_tensor.weights[x.s] = weights
+ z.idx_query[x.s] = idx_query
+ z.weights[x.s] = weights
+
+ else:
+ new_feat = F.spdevoxelize(x.F, z.idx_query.get(x.s), z.weights.get(x.s))
+ new_tensor = PointTensor(
+ new_feat, z.C, idx_query=z.idx_query, weights=z.weights
+ )
+ new_tensor.additional_features = z.additional_features
+
+ return new_tensor
+
+
+class BasicConvolutionBlock(nn.Module):
+ def __init__(self, inc, outc, ks=3, stride=1, dilation=1):
+ super().__init__()
+ self.net = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=ks, dilation=dilation, stride=stride),
+ spnn.BatchNorm(outc),
+ spnn.ReLU(True),
+ )
+
+ def forward(self, x):
+ out = self.net(x)
+ return out
+
+
+class BasicDeconvolutionBlock(nn.Module):
+ def __init__(self, inc, outc, ks=3, stride=1):
+ super().__init__()
+ self.net = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=ks, stride=stride, transposed=True),
+ spnn.BatchNorm(outc),
+ spnn.ReLU(True),
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+class ResidualBlock(nn.Module):
+ def __init__(self, inc, outc, ks=3, stride=1, dilation=1):
+ super().__init__()
+ self.net = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=ks, dilation=dilation, stride=stride),
+ spnn.BatchNorm(outc),
+ spnn.ReLU(True),
+ spnn.Conv3d(outc, outc, kernel_size=ks, dilation=dilation, stride=1),
+ spnn.BatchNorm(outc),
+ )
+
+ if inc == outc and stride == 1:
+ self.downsample = nn.Identity()
+ else:
+ self.downsample = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=1, dilation=1, stride=stride),
+ spnn.BatchNorm(outc),
+ )
+
+ self.relu = spnn.ReLU(True)
+
+ def forward(self, x):
+ out = self.relu(self.net(x) + self.downsample(x))
+ return out
+
+
+@MODELS.register_module()
+class SPVCNN(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 2, 2, 2, 2, 2, 2, 2),
+ ): # not implement
+ super().__init__()
+
+ assert (
+ torchsparse is not None
+ ), "Please follow `README.md` to install torchsparse.`"
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+
+ self.stem = nn.Sequential(
+ spnn.Conv3d(in_channels, base_channels, kernel_size=3, stride=1),
+ spnn.BatchNorm(base_channels),
+ spnn.ReLU(True),
+ spnn.Conv3d(base_channels, base_channels, kernel_size=3, stride=1),
+ spnn.BatchNorm(base_channels),
+ spnn.ReLU(True),
+ )
+
+ self.stage1 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ base_channels, base_channels, ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(base_channels, channels[0], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[0], channels[0], ks=3, stride=1, dilation=1)
+ for _ in range(layers[0] - 1)
+ ]
+ )
+
+ self.stage2 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ channels[0], channels[0], ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(channels[0], channels[1], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[1], channels[1], ks=3, stride=1, dilation=1)
+ for _ in range(layers[1] - 1)
+ ]
+ )
+
+ self.stage3 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ channels[1], channels[1], ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(channels[1], channels[2], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[2], channels[2], ks=3, stride=1, dilation=1)
+ for _ in range(layers[2] - 1)
+ ]
+ )
+
+ self.stage4 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ channels[2], channels[2], ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(channels[2], channels[3], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[3], channels[3], ks=3, stride=1, dilation=1)
+ for _ in range(layers[3] - 1)
+ ]
+ )
+
+ self.up1 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[3], channels[4], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[4] + channels[2],
+ channels[4],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[4], channels[4], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[4] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.up2 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[4], channels[5], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[5] + channels[1],
+ channels[5],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[5], channels[5], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[5] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.up3 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[5], channels[6], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[6] + channels[0],
+ channels[6],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[6], channels[6], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[6] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.up4 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[6], channels[7], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[7] + base_channels,
+ channels[7],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[7], channels[7], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[7] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.classifier = nn.Sequential(nn.Linear(channels[7], out_channels))
+
+ self.point_transforms = nn.ModuleList(
+ [
+ nn.Sequential(
+ nn.Linear(base_channels, channels[3]),
+ nn.BatchNorm1d(channels[3]),
+ nn.ReLU(True),
+ ),
+ nn.Sequential(
+ nn.Linear(channels[3], channels[5]),
+ nn.BatchNorm1d(channels[5]),
+ nn.ReLU(True),
+ ),
+ nn.Sequential(
+ nn.Linear(channels[5], channels[7]),
+ nn.BatchNorm1d(channels[7]),
+ nn.ReLU(True),
+ ),
+ ]
+ )
+
+ self.weight_initialization()
+ self.dropout = nn.Dropout(0.3, True)
+
+ def weight_initialization(self):
+ for m in self.modules():
+ if isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+
+ # x: SparseTensor z: PointTensor
+ z = PointTensor(
+ feat,
+ torch.cat(
+ [grid_coord.float(), batch.unsqueeze(-1).float()], dim=1
+ ).contiguous(),
+ )
+ x0 = initial_voxelize(z)
+
+ x0 = self.stem(x0)
+ z0 = voxel_to_point(x0, z, nearest=False)
+ z0.F = z0.F
+
+ x1 = point_to_voxel(x0, z0)
+ x1 = self.stage1(x1)
+ x2 = self.stage2(x1)
+ x3 = self.stage3(x2)
+ x4 = self.stage4(x3)
+ z1 = voxel_to_point(x4, z0)
+ z1.F = z1.F + self.point_transforms[0](z0.F)
+
+ y1 = point_to_voxel(x4, z1)
+ y1.F = self.dropout(y1.F)
+ y1 = self.up1[0](y1)
+ y1 = torchsparse.cat([y1, x3])
+ y1 = self.up1[1](y1)
+
+ y2 = self.up2[0](y1)
+ y2 = torchsparse.cat([y2, x2])
+ y2 = self.up2[1](y2)
+ z2 = voxel_to_point(y2, z1)
+ z2.F = z2.F + self.point_transforms[1](z1.F)
+
+ y3 = point_to_voxel(y2, z2)
+ y3.F = self.dropout(y3.F)
+ y3 = self.up3[0](y3)
+ y3 = torchsparse.cat([y3, x1])
+ y3 = self.up3[1](y3)
+
+ y4 = self.up4[0](y3)
+ y4 = torchsparse.cat([y4, x0])
+ y4 = self.up4[1](y4)
+ z3 = voxel_to_point(y4, z2)
+ z3.F = z3.F + self.point_transforms[2](z2.F)
+
+ out = self.classifier(z3.F)
+ return out
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..24712d2bc0fe76a52a273172fe9c4f37c807a6c3
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/__init__.py
@@ -0,0 +1,2 @@
+from .stratified_transformer_v1m1_origin import StratifiedTransformer
+from .stratified_transformer_v1m2_refine import StratifiedTransformer
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/stratified_transformer_v1m1_origin.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/stratified_transformer_v1m1_origin.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bf18f71298efa1e96f0697c96e26d2127140c36
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/stratified_transformer_v1m1_origin.py
@@ -0,0 +1,830 @@
+import torch
+import torch.nn as nn
+
+try:
+ import torch_points_kernels as tp
+except ImportError:
+ tp = None
+
+try:
+ from torch_points3d.modules.KPConv.kernels import KPConvLayer
+ from torch_points3d.core.common_modules import FastBatchNorm1d
+except ImportError:
+ KPConvLayer = None
+ FastBatchNorm1d = None
+
+from torch_scatter import scatter_softmax
+from timm.models.layers import DropPath, trunc_normal_
+from torch_geometric.nn.pool import voxel_grid
+
+try:
+ import pointops2.pointops as pointops
+except ImportError:
+ pointops = None
+
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+def get_indice_pairs(
+ p2v_map, counts, new_p2v_map, new_counts, downsample_idx, batch, xyz, window_size, i
+):
+ # p2v_map: [n, k]
+ # counts: [n, ]
+
+ n, k = p2v_map.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < counts.unsqueeze(-1) # [n, k]
+ mask_mat = mask.unsqueeze(-1) & mask.unsqueeze(-2) # [n, k, k]
+ index_0 = p2v_map.unsqueeze(-1).expand(-1, -1, k)[mask_mat] # [M, ]
+ index_1 = p2v_map.unsqueeze(1).expand(-1, k, -1)[mask_mat] # [M, ]
+
+ downsample_mask = torch.zeros_like(batch).bool() # [N, ]
+ downsample_mask[downsample_idx.long()] = True
+
+ downsample_mask = downsample_mask[new_p2v_map] # [n, k]
+ n, k = new_p2v_map.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < new_counts.unsqueeze(-1) # [n, k]
+ downsample_mask = downsample_mask & mask
+ mask_mat = mask.unsqueeze(-1) & downsample_mask.unsqueeze(-2) # [n, k, k]
+ xyz_min = xyz.min(0)[0]
+ if i % 2 == 0:
+ window_coord = (xyz[new_p2v_map] - xyz_min) // window_size # [n, k, 3]
+ else:
+ window_coord = (
+ xyz[new_p2v_map] + 1 / 2 * window_size - xyz_min
+ ) // window_size # [n, k, 3]
+
+ mask_mat_prev = (window_coord.unsqueeze(2) != window_coord.unsqueeze(1)).any(
+ -1
+ ) # [n, k, k]
+ mask_mat = mask_mat & mask_mat_prev # [n, k, k]
+
+ new_index_0 = new_p2v_map.unsqueeze(-1).expand(-1, -1, k)[mask_mat] # [M, ]
+ new_index_1 = new_p2v_map.unsqueeze(1).expand(-1, k, -1)[mask_mat] # [M, ]
+
+ index_0 = torch.cat([index_0, new_index_0], 0)
+ index_1 = torch.cat([index_1, new_index_1], 0)
+ return index_0, index_1
+
+
+def grid_sample(pos, batch, size, start, return_p2v=True):
+ # pos: float [N, 3]
+ # batch: long [N]
+ # size: float [3, ]
+ # start: float [3, ] / None
+
+ cluster = voxel_grid(pos, batch, size, start=start) # [N, ]
+
+ if return_p2v == False:
+ unique, cluster = torch.unique(cluster, sorted=True, return_inverse=True)
+ return cluster
+
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+
+ # obtain p2v_map
+ n = unique.shape[0]
+ k = counts.max().item()
+ p2v_map = cluster.new_zeros(n, k) # [n, k]
+ mask = torch.arange(k).cuda().unsqueeze(0) < counts.unsqueeze(-1) # [n, k]
+ p2v_map[mask] = torch.argsort(cluster)
+
+ return cluster, p2v_map, counts
+
+
+class Mlp(nn.Module):
+ """Multilayer perceptron."""
+
+ def __init__(
+ self,
+ in_features,
+ hidden_features=None,
+ out_features=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop, inplace=True)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_channels, out_channels, ratio, k, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.ratio = ratio
+ self.k = k
+ self.norm = norm_layer(in_channels) if norm_layer else None
+ self.linear = nn.Linear(in_channels, out_channels, bias=False)
+ self.pool = nn.MaxPool1d(k)
+
+ def forward(self, feats, xyz, offset):
+ n_offset, count = [int(offset[0].item() * self.ratio) + 1], int(
+ offset[0].item() * self.ratio
+ ) + 1
+ for i in range(1, offset.shape[0]):
+ count += ((offset[i].item() - offset[i - 1].item()) * self.ratio) + 1
+ n_offset.append(count)
+ n_offset = torch.cuda.IntTensor(n_offset)
+ idx = pointops.furthestsampling(xyz, offset, n_offset) # (m)
+ n_xyz = xyz[idx.long(), :] # (m, 3)
+
+ feats = pointops.queryandgroup(
+ self.k, xyz, n_xyz, feats, None, offset, n_offset, use_xyz=False
+ ) # (m, nsample, 3+c)
+ m, k, c = feats.shape
+ feats = (
+ self.linear(self.norm(feats.view(m * k, c)).view(m, k, c))
+ .transpose(1, 2)
+ .contiguous()
+ )
+ feats = self.pool(feats).squeeze(-1) # (m, c)
+
+ return feats, n_xyz, n_offset
+
+
+class WindowAttention(nn.Module):
+ """Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(
+ self,
+ dim,
+ window_size,
+ num_heads,
+ quant_size,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim**-0.5
+ self.window_size = window_size
+
+ self.quant_size = quant_size
+ self.rel_query = rel_query
+ self.rel_key = rel_key
+ self.rel_value = rel_value
+
+ quant_grid_length = int((2 * window_size + 1e-4) // quant_size)
+
+ if rel_query:
+ self.relative_pos_query_table = nn.Parameter(
+ torch.zeros(2 * quant_grid_length, num_heads, head_dim, 3)
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+ if rel_key:
+ self.relative_pos_key_table = nn.Parameter(
+ torch.zeros(2 * quant_grid_length, num_heads, head_dim, 3)
+ )
+ trunc_normal_(self.relative_pos_key_table, std=0.02)
+ if rel_value:
+ self.relative_pos_value_table = nn.Parameter(
+ torch.zeros(2 * quant_grid_length, num_heads, head_dim, 3)
+ )
+ trunc_normal_(self.relative_pos_value_table, std=0.02)
+
+ self.quant_grid_length = quant_grid_length
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop, inplace=True)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop, inplace=True)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ # def forward(self, feats, xyz, index_0, index_1):
+ def forward(self, feats, xyz, index_0, index_1, index_0_offsets, n_max):
+ """Forward function.
+
+ Args:
+ feats: N, C
+ xyz: N, 3
+ index_0: M,
+ index_1: M,
+ """
+
+ N, C = feats.shape
+ M = index_0.shape[0]
+
+ assert index_0.shape[0] == index_1.shape[0]
+
+ # Query, Key, Value
+ qkv = (
+ self.qkv(feats)
+ .reshape(N, 3, self.num_heads, C // self.num_heads)
+ .permute(1, 0, 2, 3)
+ .contiguous()
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2] # [N, num_heads, C//num_heads]
+ query = query * self.scale
+ attn_flat = pointops.attention_step1_v2(
+ query.float(), key.float(), index_1.int(), index_0_offsets.int(), n_max
+ )
+
+ # # Position embedding
+ relative_position = xyz[index_0] - xyz[index_1]
+ relative_position = torch.round(relative_position * 100000) / 100000
+ relative_position_index = (
+ relative_position + 2 * self.window_size - 0.0001
+ ) // self.quant_size
+ assert (relative_position_index >= 0).all()
+ assert (relative_position_index <= 2 * self.quant_grid_length - 1).all()
+
+ assert self.rel_query and self.rel_key
+ if self.rel_query and self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx_v3(
+ query.float(),
+ index_0_offsets.int(),
+ n_max,
+ key.float(),
+ index_1.int(),
+ self.relative_pos_query_table.float(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ )
+ elif self.rel_query:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ query.float(),
+ index_0.int(),
+ self.relative_pos_query_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ elif self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ key.float(),
+ index_1.int(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ else:
+ relative_position_bias = 0.0
+
+ attn_flat = attn_flat + relative_position_bias # [M, num_heads]
+
+ softmax_attn_flat = scatter_softmax(
+ src=attn_flat, index=index_0, dim=0
+ ) # [M, num_heads]
+
+ if self.rel_value:
+ x = pointops.attention_step2_with_rel_pos_value_v2(
+ softmax_attn_flat.float(),
+ value.float(),
+ index_0_offsets.int(),
+ n_max,
+ index_1.int(),
+ self.relative_pos_value_table.float(),
+ relative_position_index.int(),
+ )
+ else:
+ x = pointops.attention_step2(
+ softmax_attn_flat.float(), value.float(), index_0.int(), index_1.int()
+ )
+
+ x = x.view(N, C)
+
+ x = self.proj(x)
+ x = self.proj_drop(x) # [N, C]
+
+ return x
+
+
+class SwinTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_heads,
+ window_size,
+ quant_size,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ drop_path=0.0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ act_layer=nn.GELU,
+ norm_layer=nn.LayerNorm,
+ mode=4,
+ ): # mode=4:mean
+ super().__init__()
+ self.mode = mode
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim,
+ window_size,
+ num_heads=num_heads,
+ quant_size=quant_size,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ )
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(
+ in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer
+ )
+
+ def forward(self, feats, xyz, index_0, index_1, index_0_offsets, n_max):
+ # feats: [N, c]
+ # pos: [N, 3]
+
+ short_cut = feats
+
+ feats = self.norm1(feats)
+
+ feats = self.attn(
+ feats, xyz, index_0, index_1, index_0_offsets, n_max
+ ) # index_0 MUST be in ascending order
+
+ feats = short_cut + self.drop_path(feats)
+ feats = feats + self.drop_path(self.mlp(self.norm2(feats)))
+
+ return feats
+
+
+class BasicLayer(nn.Module):
+ def __init__(
+ self,
+ downsample_scale,
+ depth,
+ channel,
+ num_heads,
+ window_size,
+ grid_size,
+ quant_size,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ drop_path=0.0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ ratio=0.25,
+ k=16,
+ out_channels=None,
+ ):
+ super().__init__()
+ self.depth = depth
+ self.grid_size = grid_size
+ self.max_window_counts = 64
+ self.window_size = window_size
+ self.downsample_scale = downsample_scale
+
+ self.blocks = nn.ModuleList(
+ [
+ SwinTransformerBlock(
+ channel,
+ num_heads,
+ window_size,
+ quant_size,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ drop_path=(
+ drop_path[i] if isinstance(drop_path, list) else drop_path
+ ),
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ norm_layer=norm_layer,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ self.downsample = (
+ downsample(channel, out_channels, ratio, k) if downsample else None
+ )
+
+ def forward(self, feats, xyz, offset):
+ # feats: N, C
+ # xyz: N, 3
+
+ window_size = torch.tensor([self.window_size] * 3).type_as(xyz).to(xyz.device)
+
+ offset_ = offset.clone()
+ offset_[1:] = offset_[1:] - offset_[:-1]
+ batch = (
+ torch.cat([torch.tensor([ii] * o) for ii, o in enumerate(offset_)], 0)
+ .long()
+ .cuda()
+ )
+
+ v2p_map, p2v_map, counts = grid_sample(xyz, batch, window_size, start=None)
+
+ shift_size = 1 / 2 * window_size
+ shift_v2p_map, shift_p2v_map, shift_counts = grid_sample(
+ xyz + shift_size, batch, window_size, start=xyz.min(0)[0]
+ )
+
+ downsample_scale = self.downsample_scale
+ new_offset, count = [offset[0].item() // downsample_scale + 1], offset[
+ 0
+ ].item() // downsample_scale + 1
+ for i in range(1, offset.shape[0]):
+ count += (offset[i].item() - offset[i - 1].item()) // downsample_scale + 1
+ new_offset.append(count)
+
+ new_offset = torch.cuda.IntTensor(new_offset)
+ downsample_idx = pointops.furthestsampling(
+ xyz, offset.int(), new_offset.int()
+ ) # [N/16,]
+
+ new_window_size = 2 * torch.tensor([self.window_size] * 3).type_as(xyz).to(
+ xyz.device
+ )
+
+ # offset_ = new_offset.clone()
+ # offset_[1:] = offset_[1:] - offset_[:-1]
+ # new_batch = torch.cat([torch.tensor([ii]*o) for ii,o in enumerate(offset_)], 0).long().cuda()
+
+ new_v2p_map, new_p2v_map, new_counts = grid_sample(
+ xyz, batch, new_window_size, start=None
+ )
+
+ shift_size = 1 / 2 * new_window_size
+ shift_new_v2p_map, shift_new_p2v_map, shift_new_counts = grid_sample(
+ xyz + shift_size, batch, new_window_size, start=xyz.min(0)[0]
+ )
+
+ for i, blk in enumerate(self.blocks):
+ p2v_map_blk = p2v_map if i % 2 == 0 else shift_p2v_map
+ counts_blk = counts if i % 2 == 0 else shift_counts
+
+ new_p2v_map_blk = new_p2v_map if i % 2 == 0 else shift_new_p2v_map
+ new_counts_blk = new_counts if i % 2 == 0 else shift_new_counts
+
+ index_0, index_1 = get_indice_pairs(
+ p2v_map_blk,
+ counts_blk,
+ new_p2v_map_blk,
+ new_counts_blk,
+ downsample_idx,
+ batch,
+ xyz,
+ window_size,
+ i,
+ )
+
+ # rearrange index for acceleration
+ index_0, indices = torch.sort(index_0) # [M,]
+ index_1 = index_1[indices] # [M,]
+ index_0_counts = index_0.bincount()
+ n_max = index_0_counts.max()
+ index_0_offsets = index_0_counts.cumsum(dim=-1) # [N]
+ index_0_offsets = torch.cat(
+ [torch.zeros(1, dtype=torch.long).cuda(), index_0_offsets], 0
+ ) # [N+1]
+
+ feats = blk(feats, xyz, index_0, index_1, index_0_offsets, n_max)
+
+ if self.downsample:
+ feats_down, xyz_down, offset_down = self.downsample(feats, xyz, offset)
+ else:
+ feats_down, xyz_down, offset_down = None, None, None
+
+ return feats, xyz, offset, feats_down, xyz_down, offset_down
+
+
+class Upsample(nn.Module):
+ def __init__(self, k, in_channels, out_channels, bn_momentum=0.02):
+ super().__init__()
+ self.k = k
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.linear1 = nn.Sequential(
+ nn.LayerNorm(out_channels), nn.Linear(out_channels, out_channels)
+ )
+ self.linear2 = nn.Sequential(
+ nn.LayerNorm(in_channels), nn.Linear(in_channels, out_channels)
+ )
+
+ def forward(
+ self, feats, xyz, support_xyz, offset, support_offset, support_feats=None
+ ):
+ feats = self.linear1(support_feats) + pointops.interpolation(
+ xyz, support_xyz, self.linear2(feats), offset, support_offset
+ )
+ return feats, support_xyz, support_offset
+
+
+class KPConvSimpleBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ self.kpconv = KPConvLayer(
+ in_channels,
+ out_channels,
+ point_influence=prev_grid_size * sigma,
+ add_one=False,
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = nn.LeakyReLU(negative_slope=negative_slope)
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # xyz: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.activation(self.bn(feats))
+ return feats
+
+
+class KPConvResBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ d_2 = out_channels // 4
+ activation = nn.LeakyReLU(negative_slope=negative_slope)
+ self.unary_1 = torch.nn.Sequential(
+ nn.Linear(in_channels, d_2, bias=False),
+ FastBatchNorm1d(d_2, momentum=bn_momentum),
+ activation,
+ )
+ self.unary_2 = torch.nn.Sequential(
+ nn.Linear(d_2, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ activation,
+ )
+ self.kpconv = KPConvLayer(
+ d_2, d_2, point_influence=prev_grid_size * sigma, add_one=False
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = activation
+
+ if in_channels != out_channels:
+ self.shortcut_op = torch.nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ )
+ else:
+ self.shortcut_op = nn.Identity()
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # xyz: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ shortcut = feats
+ feats = self.unary_1(feats)
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.unary_2(feats)
+ shortcut = self.shortcut_op(shortcut)
+ feats += shortcut
+ return feats
+
+
+@MODELS.register_module("ST-v1m1")
+class StratifiedTransformer(nn.Module):
+ def __init__(
+ self,
+ downsample_scale,
+ depths,
+ channels,
+ num_heads,
+ window_size,
+ up_k,
+ grid_sizes,
+ quant_sizes,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ drop_path_rate=0.2,
+ num_layers=4,
+ concat_xyz=False,
+ num_classes=13,
+ ratio=0.25,
+ k=16,
+ prev_grid_size=0.04,
+ sigma=1.0,
+ stem_transformer=False,
+ kp_ball_radius=0.02 * 2.5,
+ kp_max_neighbor=34,
+ ):
+ super().__init__()
+ assert (
+ KPConvLayer is not None and FastBatchNorm1d is not None
+ ), "Please make sure torch_points3d is installed"
+ assert tp is not None, "Please make sure torch_points_kernels is installed"
+ assert pointops is not None, "Please make sure pointops2 is installed"
+
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+ ] # stochastic depth decay rule
+
+ self.kp_ball_radius = kp_ball_radius
+ self.kp_max_neighbor = kp_max_neighbor
+ if stem_transformer:
+ self.stem_layer = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ 3 if not concat_xyz else 6,
+ channels[0],
+ prev_grid_size,
+ sigma=sigma,
+ )
+ ]
+ )
+ self.layer_start = 0
+ else:
+ self.stem_layer = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ 3 if not concat_xyz else 6,
+ channels[0],
+ prev_grid_size,
+ sigma=sigma,
+ ),
+ KPConvResBlock(
+ channels[0], channels[0], prev_grid_size, sigma=sigma
+ ),
+ ]
+ )
+ self.downsample = TransitionDown(channels[0], channels[1], ratio, k)
+ self.layer_start = 1
+
+ self.layers = nn.ModuleList(
+ [
+ BasicLayer(
+ downsample_scale,
+ depths[i],
+ channels[i],
+ num_heads[i],
+ window_size[i],
+ grid_sizes[i],
+ quant_sizes[i],
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+ downsample=TransitionDown if i < num_layers - 1 else None,
+ ratio=ratio,
+ k=k,
+ out_channels=channels[i + 1] if i < num_layers - 1 else None,
+ )
+ for i in range(self.layer_start, num_layers)
+ ]
+ )
+
+ self.upsamples = nn.ModuleList(
+ [
+ Upsample(up_k, channels[i], channels[i - 1])
+ for i in range(num_layers - 1, 0, -1)
+ ]
+ )
+
+ self.classifier = nn.Sequential(
+ nn.Linear(channels[0], channels[0]),
+ nn.BatchNorm1d(channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(channels[0], num_classes),
+ )
+
+ self.init_weights()
+
+ def forward(self, data_dict):
+ feats = data_dict["feat"]
+ xyz = data_dict["coord"]
+ offset = data_dict["offset"].int()
+ batch = offset2batch(offset)
+ neighbor_idx = tp.ball_query(
+ self.kp_ball_radius,
+ self.kp_max_neighbor,
+ xyz,
+ xyz,
+ mode="partial_dense",
+ batch_x=batch,
+ batch_y=batch,
+ )[0]
+
+ feats_stack = []
+ xyz_stack = []
+ offset_stack = []
+
+ for i, layer in enumerate(self.stem_layer):
+ feats = layer(feats, xyz, batch, neighbor_idx)
+
+ feats = feats.contiguous()
+
+ if self.layer_start == 1:
+ feats_stack.append(feats)
+ xyz_stack.append(xyz)
+ offset_stack.append(offset)
+ feats, xyz, offset = self.downsample(feats, xyz, offset)
+
+ for i, layer in enumerate(self.layers):
+ feats, xyz, offset, feats_down, xyz_down, offset_down = layer(
+ feats, xyz, offset
+ )
+
+ feats_stack.append(feats)
+ xyz_stack.append(xyz)
+ offset_stack.append(offset)
+
+ feats = feats_down
+ xyz = xyz_down
+ offset = offset_down
+
+ feats = feats_stack.pop()
+ xyz = xyz_stack.pop()
+ offset = offset_stack.pop()
+
+ for i, upsample in enumerate(self.upsamples):
+ feats, xyz, offset = upsample(
+ feats,
+ xyz,
+ xyz_stack.pop(),
+ offset,
+ offset_stack.pop(),
+ support_feats=feats_stack.pop(),
+ )
+
+ out = self.classifier(feats)
+
+ return out
+
+ def init_weights(self):
+ """Initialize the weights in backbone."""
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ self.apply(_init_weights)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/stratified_transformer_v1m2_refine.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/stratified_transformer_v1m2_refine.py
new file mode 100644
index 0000000000000000000000000000000000000000..234afc12a7be6ea1feb87259c8c77e1bf0a8b3d3
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/stratified_transformer/stratified_transformer_v1m2_refine.py
@@ -0,0 +1,763 @@
+"""
+Stratified Transformer
+
+Modified from https://github.com/dvlab-research/Stratified-Transformer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from copy import deepcopy
+import torch
+import torch.nn as nn
+
+try:
+ import torch_points_kernels as tp
+except ImportError:
+ tp = None
+
+try:
+ from torch_points3d.modules.KPConv.kernels import KPConvLayer
+ from torch_points3d.core.common_modules import FastBatchNorm1d
+except ImportError:
+ KPConvLayer = None
+ FastBatchNorm1d = None
+
+from torch_scatter import scatter_softmax
+from timm.models.layers import DropPath, trunc_normal_
+from torch_geometric.nn.pool import voxel_grid
+
+try:
+ import pointops2.pointops as pointops
+except ImportError:
+ pointops = None
+
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+def grid_sample(coords, batch, size, start, return_p2v=True):
+ cluster = voxel_grid(coords, batch, size, start=start)
+
+ if not return_p2v:
+ unique, cluster = torch.unique(cluster, sorted=True, return_inverse=True)
+ return cluster
+ else:
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+
+ # obtain p2v_map
+ n = unique.shape[0]
+ k = counts.max().item()
+ p2v_map = cluster.new_zeros(n, k)
+ mask = torch.arange(k).cuda().unsqueeze(0) < counts.unsqueeze(-1)
+ p2v_map[mask] = torch.argsort(cluster)
+ return cluster, p2v_map, counts
+
+
+class WindowAttention(nn.Module):
+ """Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ """
+
+ def __init__(
+ self,
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ scale=None,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ ):
+ super().__init__()
+ self.embed_channels = embed_channels
+ self.head_channels = embed_channels // num_heads
+ self.num_heads = num_heads
+ self.scale = scale or self.head_channels**-0.5
+
+ self.window_size = window_size
+ self.quant_size = quant_size
+
+ self.rel_query = rel_query
+ self.rel_key = rel_key
+ self.rel_value = rel_value
+
+ self.quant_grid_length = int((2 * window_size + 1e-4) // quant_size)
+
+ assert self.rel_query and self.rel_key
+ if rel_query:
+ self.relative_pos_query_table = nn.Parameter(
+ torch.zeros(
+ 2 * self.quant_grid_length, self.num_heads, self.head_channels, 3
+ )
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+
+ if rel_key:
+ self.relative_pos_key_table = nn.Parameter(
+ torch.zeros(
+ 2 * self.quant_grid_length, self.num_heads, self.head_channels, 3
+ )
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+
+ if rel_value:
+ self.relative_pos_value_table = nn.Parameter(
+ torch.zeros(
+ 2 * self.quant_grid_length, self.num_heads, self.head_channels, 3
+ )
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+
+ self.qkv = nn.Linear(embed_channels, embed_channels * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop, inplace=True)
+ self.proj = nn.Linear(embed_channels, embed_channels)
+ self.proj_drop = nn.Dropout(proj_drop, inplace=True)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, feats, coords, index_0, index_1, index_0_offsets, n_max):
+ n, c = feats.shape
+ m = index_0.shape[0]
+
+ assert index_0.shape[0] == index_1.shape[0]
+
+ qkv = (
+ self.qkv(feats)
+ .reshape(n, 3, self.num_heads, c // self.num_heads)
+ .permute(1, 0, 2, 3)
+ .contiguous()
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2]
+ query = query * self.scale
+ attn_flat = pointops.attention_step1_v2(
+ query.float(), key.float(), index_1.int(), index_0_offsets.int(), n_max
+ )
+
+ # Position embedding
+ relative_position = coords[index_0] - coords[index_1]
+ relative_position = torch.round(relative_position * 100000) / 100000
+ relative_position_index = torch.div(
+ relative_position + 2 * self.window_size - 1e-4,
+ self.quant_size,
+ rounding_mode="trunc",
+ )
+ # relative_position_index = (relative_position + 2 * self.window_size - 1e-4) // self.quant_size
+ assert (relative_position_index >= 0).all()
+ assert (relative_position_index <= 2 * self.quant_grid_length - 1).all()
+
+ if self.rel_query and self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx_v3(
+ query.float(),
+ index_0_offsets.int(),
+ n_max,
+ key.float(),
+ index_1.int(),
+ self.relative_pos_query_table.float(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ )
+ elif self.rel_query:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ query.float(),
+ index_0.int(),
+ self.relative_pos_query_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ elif self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ key.float(),
+ index_1.int(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ else:
+ relative_position_bias = 0.0
+
+ attn_flat += relative_position_bias
+ softmax_attn_flat = scatter_softmax(src=attn_flat, index=index_0, dim=0)
+
+ if self.rel_value:
+ x = pointops.attention_step2_with_rel_pos_value_v2(
+ softmax_attn_flat.float(),
+ value.float(),
+ index_0_offsets.int(),
+ n_max,
+ index_1.int(),
+ self.relative_pos_value_table.float(),
+ relative_position_index.int(),
+ )
+ else:
+ x = pointops.attention_step2(
+ softmax_attn_flat.float(), value.float(), index_0.int(), index_1.int()
+ )
+
+ x = x.view(n, c)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+
+class MLP(nn.Module):
+ def __init__(self, in_channels, hidden_channels=None, out_channels=None, drop=0.0):
+ super().__init__()
+ out_channels = out_channels or in_channels
+ hidden_channels = hidden_channels or in_channels
+ self.fc1 = nn.Linear(in_channels, hidden_channels)
+ self.act = nn.GELU()
+ self.fc2 = nn.Linear(hidden_channels, out_channels)
+ self.drop = nn.Dropout(drop, inplace=True)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ mlp_expend_ratio=4.0,
+ drop_path=0.0,
+ qk_scale=None,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ ):
+ super().__init__()
+ self.norm1 = nn.LayerNorm(embed_channels)
+ self.attn = WindowAttention(
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ scale=qk_scale,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ )
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = nn.LayerNorm(embed_channels)
+ self.mlp = MLP(
+ in_channels=embed_channels,
+ hidden_channels=int(embed_channels * mlp_expend_ratio),
+ )
+
+ def forward(self, feats, coords, index_0, index_1, index_0_offsets, n_max):
+ short_cut = feats
+ feats = self.norm1(feats)
+ feats = self.attn(feats, coords, index_0, index_1, index_0_offsets, n_max)
+
+ feats = short_cut + self.drop_path(feats)
+ feats += self.drop_path(self.mlp(self.norm2(feats)))
+ return feats
+
+
+class BasicLayer(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ out_channels,
+ depth,
+ num_heads,
+ window_size,
+ quant_size,
+ mlp_expend_ratio=4.0,
+ down_ratio=0.25,
+ down_num_sample=16,
+ drop_path=None,
+ qk_scale=None,
+ down=True,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ ):
+ super().__init__()
+ self.depth = depth
+ self.window_size = window_size
+ self.quant_size = quant_size
+ self.down_ratio = down_ratio
+
+ if isinstance(drop_path, list):
+ drop_path = drop_path
+ assert len(drop_path) == depth
+ elif isinstance(drop_path, float):
+ drop_path = [deepcopy(drop_path) for _ in range(depth)]
+ else:
+ drop_path = [0.0 for _ in range(depth)]
+
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ mlp_expend_ratio=mlp_expend_ratio,
+ drop_path=drop_path[i],
+ qk_scale=qk_scale,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ )
+ self.blocks.append(block)
+
+ self.down = (
+ TransitionDown(embed_channels, out_channels, down_ratio, down_num_sample)
+ if down
+ else None
+ )
+
+ def forward(self, feats, coords, offset):
+ # window_size -> [window_size, window_size, window_size]
+ window_size = torch.tensor(
+ [self.window_size] * 3, dtype=coords.dtype, device=coords.device
+ )
+ new_window_size = 2 * torch.tensor(
+ [self.window_size] * 3, dtype=coords.dtype, device=coords.device
+ )
+ batch = offset2batch(offset)
+
+ # compute new offset
+ new_offset = [int(offset[0].item() * self.down_ratio) + 1]
+ count = int(offset[0].item() * self.down_ratio) + 1
+ for i in range(1, offset.shape[0]):
+ count += (
+ int((offset[i].item() - offset[i - 1].item()) * self.down_ratio) + 1
+ )
+ new_offset.append(count)
+ new_offset = torch.cuda.IntTensor(new_offset)
+ down_idx = pointops.furthestsampling(coords, offset.int(), new_offset.int())
+
+ # compute window mapping
+ coords_min = coords.min(0).values
+ v2p_map, p2v_map, counts = grid_sample(coords, batch, window_size, start=None)
+ shift_size = window_size * 1 / 2
+ shift_v2p_map, shift_p2v_map, shift_counts = grid_sample(
+ coords + shift_size, batch, window_size, start=coords_min
+ )
+
+ new_v2p_map, new_p2v_map, new_counts = grid_sample(
+ coords, batch, new_window_size, start=None
+ )
+ shift_size = new_window_size * 1 / 2
+ shift_new_v2p_map, shift_new_p2v_map, shift_new_counts = grid_sample(
+ coords + shift_size, batch, new_window_size, start=coords_min
+ )
+
+ # stratified attention
+ for i, blk in enumerate(self.blocks):
+ p2v_map_blk = p2v_map if i % 2 == 0 else shift_p2v_map
+ counts_blk = counts if i % 2 == 0 else shift_counts
+
+ new_p2v_map_blk = new_p2v_map if i % 2 == 0 else shift_new_p2v_map
+ new_counts_blk = new_counts if i % 2 == 0 else shift_new_counts
+
+ n, k = p2v_map_blk.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < counts_blk.unsqueeze(-1)
+ mask_mat = mask.unsqueeze(-1) & mask.unsqueeze(-2)
+ index_0 = p2v_map_blk.unsqueeze(-1).expand(-1, -1, k)[mask_mat]
+ index_1 = p2v_map_blk.unsqueeze(1).expand(-1, k, -1)[mask_mat]
+
+ down_mask = torch.zeros_like(batch).bool()
+ down_mask[down_idx.long()] = True
+ down_mask = down_mask[new_p2v_map_blk] # [n, k], down sample mask
+ n, k = new_p2v_map_blk.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < new_counts_blk.unsqueeze(
+ -1
+ ) # [n, k]
+ down_mask = down_mask & mask # down sample and window mask
+ # [n, k, k] query: dense point in large windows; key: sparse point in large windows
+ mask_mat = mask.unsqueeze(-1) & down_mask.unsqueeze(-2)
+
+ if i % 2 == 0:
+ # [n, k, 3]
+ # window_coord = (coords[new_p2v_map_blk] - coords_min) // window_size
+ window_coord = torch.div(
+ coords[new_p2v_map_blk] - coords_min,
+ window_size,
+ rounding_mode="trunc",
+ )
+ else:
+ # [n, k, 3]
+ # window_coord = (coords[new_p2v_map_blk] - coords_min + 1/2 * window_size) // window_size
+ window_coord = torch.div(
+ coords[new_p2v_map_blk] - coords_min + 1 / 2 * window_size,
+ window_size,
+ rounding_mode="trunc",
+ )
+ # [n, k, k], whether pair points are in same small windows
+ mask_mat_prev = (
+ window_coord.unsqueeze(2) != window_coord.unsqueeze(1)
+ ).any(-1)
+ mask_mat = mask_mat & mask_mat_prev
+
+ new_index_0 = new_p2v_map_blk.unsqueeze(-1).expand(-1, -1, k)[mask_mat]
+ new_index_1 = new_p2v_map_blk.unsqueeze(1).expand(-1, k, -1)[mask_mat]
+
+ index_0 = torch.cat([index_0, new_index_0], 0)
+ index_1 = torch.cat([index_1, new_index_1], 0)
+
+ # rearrange index for acceleration
+ index_0, indices = torch.sort(index_0)
+ index_1 = index_1[indices]
+ index_0_counts = index_0.bincount()
+ n_max = index_0_counts.max()
+ index_0_offsets = index_0_counts.cumsum(dim=-1)
+ index_0_offsets = torch.cat(
+ [torch.zeros(1, dtype=torch.long).cuda(), index_0_offsets], 0
+ )
+
+ feats = blk(feats, coords, index_0, index_1, index_0_offsets, n_max)
+
+ if self.down:
+ feats_down, coords_down, offset_down = self.down(feats, coords, offset)
+ else:
+ feats_down, coords_down, offset_down = None, None, None
+
+ return feats, coords, offset, feats_down, coords_down, offset_down
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_channels, out_channels, ratio, k, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.ratio = ratio
+ self.k = k
+ self.norm = norm_layer(in_channels) if norm_layer else None
+ self.linear = nn.Linear(in_channels, out_channels, bias=False)
+ self.pool = nn.MaxPool1d(k)
+
+ def forward(self, feats, coords, offset):
+ new_offset, count = [int(offset[0].item() * self.ratio) + 1], int(
+ offset[0].item() * self.ratio
+ ) + 1
+ for i in range(1, offset.shape[0]):
+ count += ((offset[i].item() - offset[i - 1].item()) * self.ratio) + 1
+ new_offset.append(count)
+ new_offset = torch.cuda.IntTensor(new_offset)
+ idx = pointops.furthestsampling(coords, offset, new_offset) # (m)
+ new_coords = coords[idx.long(), :] # (m, 3)
+
+ feats = pointops.queryandgroup(
+ self.k, coords, new_coords, feats, None, offset, new_offset, use_xyz=False
+ ) # (m, nsample, 3+c)
+ m, k, c = feats.shape
+ feats = (
+ self.linear(self.norm(feats.view(m * k, c)).view(m, k, c))
+ .transpose(1, 2)
+ .contiguous()
+ )
+ feats = self.pool(feats).squeeze(-1) # (m, c)
+ return feats, new_coords, new_offset
+
+
+class TransitionUp(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.linear1 = nn.Sequential(
+ nn.LayerNorm(out_channels), nn.Linear(out_channels, out_channels)
+ )
+
+ self.linear2 = nn.Sequential(
+ nn.LayerNorm(in_channels), nn.Linear(in_channels, out_channels)
+ )
+
+ def forward(self, feats, coords, offset, skip_feats, skip_coords, skip_offset):
+ feats = self.linear1(skip_feats) + pointops.interpolation(
+ coords, skip_coords, self.linear2(feats), offset, skip_offset
+ )
+ return feats, skip_coords, skip_offset
+
+
+class KPConvSimpleBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ self.kpconv = KPConvLayer(
+ in_channels,
+ out_channels,
+ point_influence=prev_grid_size * sigma,
+ add_one=False,
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = nn.LeakyReLU(negative_slope=negative_slope)
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # coords: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.activation(self.bn(feats))
+ return feats
+
+
+class KPConvResBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ d_2 = out_channels // 4
+ activation = nn.LeakyReLU(negative_slope=negative_slope)
+ self.unary_1 = torch.nn.Sequential(
+ nn.Linear(in_channels, d_2, bias=False),
+ FastBatchNorm1d(d_2, momentum=bn_momentum),
+ activation,
+ )
+ self.unary_2 = torch.nn.Sequential(
+ nn.Linear(d_2, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ activation,
+ )
+ self.kpconv = KPConvLayer(
+ d_2, d_2, point_influence=prev_grid_size * sigma, add_one=False
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = activation
+
+ if in_channels != out_channels:
+ self.shortcut_op = torch.nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ )
+ else:
+ self.shortcut_op = nn.Identity()
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # coords: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ shortcut = feats
+ feats = self.unary_1(feats)
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.unary_2(feats)
+ shortcut = self.shortcut_op(shortcut)
+ feats += shortcut
+ return feats
+
+
+@MODELS.register_module("ST-v1m2")
+class StratifiedTransformer(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ channels=(48, 96, 192, 384, 384),
+ num_heads=(6, 12, 24, 24),
+ depths=(3, 9, 3, 3),
+ window_size=(0.2, 0.4, 0.8, 1.6),
+ quant_size=(0.01, 0.02, 0.04, 0.08),
+ mlp_expend_ratio=4.0,
+ down_ratio=0.25,
+ down_num_sample=16,
+ kp_ball_radius=2.5 * 0.02,
+ kp_max_neighbor=34,
+ kp_grid_size=0.02,
+ kp_sigma=1.0,
+ drop_path_rate=0.2,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ stem=True,
+ ):
+ super().__init__()
+ assert (
+ KPConvLayer is not None and FastBatchNorm1d is not None
+ ), "Please make sure torch_points3d is installed"
+ assert tp is not None, "Please make sure torch_points_kernels is installed"
+ assert pointops is not None, "Please make sure pointops2 is installed"
+ # stochastic depth decay rule
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+ self.kp_ball_radius = kp_ball_radius
+ self.kp_max_neighbor = kp_max_neighbor
+ self.stem = stem
+ if stem:
+ self.point_embed = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ in_channels, channels[0], kp_grid_size, sigma=kp_sigma
+ ),
+ KPConvResBlock(
+ channels[0], channels[0], kp_grid_size, sigma=kp_sigma
+ ),
+ ]
+ )
+ self.down = TransitionDown(
+ channels[0], channels[1], down_ratio, down_num_sample
+ )
+ else:
+ assert channels[0] == channels[1]
+ self.point_embed = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ in_channels, channels[1], kp_grid_size, sigma=kp_sigma
+ ),
+ ]
+ )
+
+ num_layers = len(depths)
+ self.layers = nn.ModuleList()
+ for i in range(num_layers):
+ layer = BasicLayer(
+ embed_channels=channels[i + 1],
+ out_channels=channels[i + 2] if i < num_layers - 1 else channels[i + 1],
+ depth=depths[i],
+ num_heads=num_heads[i],
+ window_size=window_size[i],
+ quant_size=quant_size[i],
+ mlp_expend_ratio=mlp_expend_ratio,
+ down_ratio=down_ratio,
+ down_num_sample=down_num_sample,
+ drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ down=True if i < num_layers - 1 else False,
+ )
+ self.layers.append(layer)
+
+ self.up = nn.ModuleList(
+ [
+ TransitionUp(channels[i + 1], channels[i])
+ for i in reversed(range(1, num_layers))
+ ]
+ )
+ if self.stem:
+ self.up.append(TransitionUp(channels[1], channels[0]))
+
+ self.classifier = nn.Sequential(
+ nn.Linear(channels[0], channels[0]),
+ nn.BatchNorm1d(channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(channels[0], num_classes),
+ )
+
+ self.init_weights()
+
+ def forward(self, data_dict):
+ feats = data_dict["feat"]
+ coords = data_dict["coord"]
+ offset = data_dict["offset"].int()
+ batch = offset2batch(offset)
+ neighbor_idx = tp.ball_query(
+ self.kp_ball_radius,
+ self.kp_max_neighbor,
+ coords,
+ coords,
+ mode="partial_dense",
+ batch_x=batch,
+ batch_y=batch,
+ )[0]
+
+ feats_stack = []
+ coords_stack = []
+ offset_stack = []
+
+ for i, layer in enumerate(self.point_embed):
+ feats = layer(feats, coords, batch, neighbor_idx)
+
+ feats = feats.contiguous()
+ if self.stem:
+ feats_stack.append(feats)
+ coords_stack.append(coords)
+ offset_stack.append(offset)
+ feats, coords, offset = self.down(feats, coords, offset)
+
+ for i, layer in enumerate(self.layers):
+ feats, coords, offset, feats_down, coords_down, offset_down = layer(
+ feats, coords, offset
+ )
+
+ feats_stack.append(feats)
+ coords_stack.append(coords)
+ offset_stack.append(offset)
+
+ feats = feats_down
+ coords = coords_down
+ offset = offset_down
+
+ feats = feats_stack.pop()
+ coords = coords_stack.pop()
+ offset = offset_stack.pop()
+
+ for i, up in enumerate(self.up):
+ feats, coords, offset = up(
+ feats,
+ coords,
+ offset,
+ feats_stack.pop(),
+ coords_stack.pop(),
+ offset_stack.pop(),
+ )
+
+ out = self.classifier(feats)
+ return out
+
+ def init_weights(self):
+ """Initialize the weights in backbone."""
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ self.apply(_init_weights)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36050969d9abb027778008e4d6d8f77710f52392
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/__init__.py
@@ -0,0 +1 @@
+from .swin3d_v1m1_base import Swin3DUNet
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/mink_layers.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/mink_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee3e8cfc002e8311ac196335592c337644659612
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/mink_layers.py
@@ -0,0 +1,249 @@
+"""
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import MinkowskiEngine as ME
+import numpy as np
+
+
+def assign_feats(sp, x):
+ return ME.SparseTensor(
+ features=x.float(),
+ coordinate_map_key=sp.coordinate_map_key,
+ coordinate_manager=sp.coordinate_manager,
+ )
+
+
+class MinkConvBN(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ ):
+ super().__init__()
+ self.conv_layers = nn.Sequential(
+ ME.MinkowskiConvolution(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ dimension=dimension,
+ ),
+ ME.MinkowskiBatchNorm(out_channels),
+ )
+
+ def forward(self, x):
+ x = self.conv_layers(x)
+ return x
+
+
+class MinkConvBNRelu(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ ):
+ super().__init__()
+ self.conv_layers = nn.Sequential(
+ ME.MinkowskiConvolution(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ dimension=dimension,
+ ),
+ ME.MinkowskiBatchNorm(out_channels),
+ ME.MinkowskiReLU(inplace=True),
+ )
+
+ def forward(self, x):
+ x = self.conv_layers(x)
+ if x.F.dtype == torch.float16:
+ x = assign_feats(x, x.F.float())
+ return x
+
+
+class MinkDeConvBNRelu(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ ):
+ super().__init__()
+ self.conv_layers = nn.Sequential(
+ ME.MinkowskiConvolutionTranspose(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ dimension=dimension,
+ ),
+ ME.MinkowskiBatchNorm(out_channels),
+ ME.MinkowskiReLU(),
+ )
+
+ def forward(self, x):
+ x = self.conv_layers(x)
+ return x
+
+
+class MinkResBlock(nn.Module):
+ def __init__(self, in_channels, out_channels, stride=1, dilation=1):
+ super(MinkResBlock, self).__init__()
+
+ self.conv1 = ME.MinkowskiConvolution(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ bias=False,
+ dimension=3,
+ )
+ self.norm1 = ME.MinkowskiBatchNorm(out_channels)
+ self.conv2 = ME.MinkowskiConvolution(
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ stride=1,
+ dilation=dilation,
+ bias=False,
+ dimension=3,
+ )
+
+ self.norm2 = ME.MinkowskiBatchNorm(out_channels)
+ self.relu = ME.MinkowskiReLU(inplace=True)
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.norm1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.norm2(out)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class SparseTensorLinear(nn.Module):
+ def __init__(self, in_channels, out_channels, bias=False):
+ super().__init__()
+ self.linear = nn.Linear(in_channels, out_channels, bias=bias)
+
+ def forward(self, sp):
+ x = self.linear(sp.F)
+ return assign_feats(sp, x.float())
+
+
+class SparseTensorLayerNorm(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.norm = nn.LayerNorm(dim)
+
+ def forward(self, sp):
+ x = self.norm(sp.F)
+ return assign_feats(sp, x.float())
+
+
+class MinkResBlock_v2(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ d_2 = out_channels // 4
+ self.conv1 = torch.nn.Sequential(
+ SparseTensorLinear(in_channels, d_2, bias=False),
+ ME.MinkowskiBatchNorm(d_2),
+ ME.MinkowskiReLU(),
+ )
+ self.unary_2 = torch.nn.Sequential(
+ SparseTensorLinear(d_2, out_channels, bias=False),
+ ME.MinkowskiBatchNorm(out_channels),
+ ME.MinkowskiReLU(),
+ )
+ self.spconv = ME.MinkowskiConvolution(
+ in_channels=d_2,
+ out_channels=d_2,
+ kernel_size=5,
+ stride=1,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ )
+ if in_channels != out_channels:
+ self.shortcut_op = torch.nn.Sequential(
+ SparseTensorLinear(in_channels, out_channels, bias=False),
+ ME.MinkowskiBatchNorm(out_channels),
+ )
+ else:
+ self.shortcut_op = nn.Identity()
+
+ def forward(self, x):
+ # feats: [N, C]
+ # xyz: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+ shortcut = x
+ x = self.unary_1(x)
+ x = self.spconv(x)
+ x = self.unary_2(x)
+ shortcut = self.shortcut_op(shortcut)
+ x += shortcut
+ return x
+
+
+class MinkResBlock_BottleNeck(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(MinkResBlock_BottleNeck, self).__init__()
+ bottle_neck = out_channels // 4
+ self.conv1x1a = MinkConvBNRelu(
+ in_channels, bottle_neck, kernel_size=1, stride=1
+ )
+ self.conv3x3 = MinkConvBNRelu(bottle_neck, bottle_neck, kernel_size=3, stride=1)
+ self.conv1x1b = MinkConvBN(bottle_neck, out_channels, kernel_size=1, stride=1)
+ if in_channels != out_channels:
+ self.conv1x1c = MinkConvBN(
+ in_channels, out_channels, kernel_size=1, stride=1
+ )
+ else:
+ self.conv1x1c = None
+ self.relu = ME.MinkowskiReLU(inplace=True)
+
+ def forward(self, x):
+ residual = x
+ out = self.conv1x1a(x)
+ out = self.conv3x3(out)
+ out = self.conv1x1b(out)
+ if self.conv1x1c is not None:
+ residual = self.conv1x1c(residual)
+ out = self.relu(out + residual)
+
+ return out
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/swin3d_layers.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/swin3d_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e737e9677ae93f8f5f9188ba774fcd1d0fa42443
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/swin3d_layers.py
@@ -0,0 +1,876 @@
+"""
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""
+
+import numpy as np
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath, trunc_normal_
+import MinkowskiEngine as ME
+from MinkowskiEngine import SparseTensor
+from Swin3D.sparse_dl.attn.attn_coff import (
+ SelfAttnAIOFunction,
+ PosEmb,
+ TableDims,
+ IndexMode,
+ PrecisionMode,
+)
+import Swin3D.sparse_dl.knn
+from Swin3D.sparse_dl.knn import KNN
+
+from .mink_layers import (
+ assign_feats,
+ SparseTensorLayerNorm,
+ SparseTensorLinear,
+)
+
+
+def query_knn_feature(
+ K, src_xyz, query_xyz, src_feat, src_offset, query_offset, return_idx=False
+):
+ """
+ gather feature in the KNN neighborhood
+ """
+ assert (
+ src_xyz.is_contiguous()
+ and query_xyz.is_contiguous()
+ and src_feat.is_contiguous()
+ )
+ if query_xyz is None:
+ query_xyz = src_xyz
+ query_offset = src_offset
+
+ idx, _ = KNN.apply(K, src_xyz, query_xyz, src_offset, query_offset)
+
+ n, m, c = src_xyz.shape[0], query_xyz.shape[0], src_feat.shape[1]
+ grouped_feat = src_feat[idx.view(-1).long(), :].view(m, K, c)
+
+ if return_idx:
+ return grouped_feat, idx
+ else:
+ return grouped_feat
+
+
+def knn_linear_interpolation(
+ src_xyz, query_xyz, src_feat, src_offset, query_offset, K=3
+):
+ """
+ interpolation feature using distance in KNN neighborhood
+ """
+ N, C = query_xyz.shape[0], src_feat.shape[1]
+ assert (
+ src_xyz.is_contiguous()
+ and query_xyz.is_contiguous()
+ and src_feat.is_contiguous()
+ )
+ # (N, K)
+ idx, dist = KNN.apply(K, src_xyz, query_xyz, src_offset, query_offset)
+ weight = 1.0 / (dist + 1e-8)
+ norm = torch.sum(weight, dim=1, keepdim=True)
+ weight = weight / norm
+ query_feat = torch.zeros((N, C), dtype=src_feat.dtype, device=src_feat.device)
+ for i in range(K):
+ query_feat += src_feat[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)
+ return query_feat
+
+
+def sparse_self_attention(
+ w_w_id: torch.Tensor, w_sizes: torch.Tensor, protocol: str = "v1"
+):
+ """
+ Args:
+ indices [torch.Tensor]: sparse window index with shape [N, 2], N is the total
+ number of non-empty voxels with indices (window_id, within_window_id). window_id
+ is ordered and starts from 0; within_window_id is a sparse index to indicate the
+ offset of kernel_size ** 3.
+ feats [torch.Tensor]: sprase features of each non-empty voxel with shape [N, C]
+ Outputs:
+ [M, 3]: sparse indices of cofficient matrix (window_id, att_a_id, att_b_id). att_a_id
+ and att_b_id are the within_window_id
+ [M, 1]: the sparse coffient matrix
+
+ Spaces:
+ W: total number of windows
+ N: total number of input voxels
+ M: total number of output cofficients
+ """
+ w_sizes_2 = w_sizes**2
+
+ # w2n_indices - [W], mapping window index to window global offset in input
+ # space
+ w_cumsum = torch.cumsum(w_sizes, dim=-1)
+ w2n_indices = torch.cat(
+ [torch.zeros(1, dtype=w_cumsum.dtype, device=w_cumsum.device), w_cumsum[:-1]]
+ )
+
+ # w2m indices - [W], mapping window index to window global offset in output
+ # space
+ w2_cumsum = torch.cumsum(w_sizes_2, dim=-1)
+ w2m_indices = torch.cat(
+ [torch.zeros(1, dtype=w2_cumsum.dtype, device=w2_cumsum.device), w2_cumsum[:-1]]
+ )
+
+ # m2w indices - [M], mapping element global offset to the window index
+ m2w_indices = torch.zeros(
+ [w2_cumsum[-1]], dtype=w_sizes.dtype, device=w_sizes.device
+ )
+ m2w_offset = torch.zeros(
+ [w2_cumsum[-1]], dtype=w_sizes.dtype, device=w_sizes.device
+ )
+ m2w_indices[w2m_indices[1:]] = 1
+ m2w_offset[w2m_indices[1:]] = w_sizes_2[:-1]
+ m2w_indices = torch.cumsum(m2w_indices, dim=-1)
+ m2w_offset = torch.cumsum(m2w_offset, dim=-1)
+
+ # m_indices = [M], element global offset in output space
+ m_indices = torch.arange(
+ 0, w2_cumsum[-1], dtype=w_sizes.dtype, device=w_sizes.device
+ )
+
+ # m2n_indices - [M], mapping element global offset to the window global offset
+ # in input space
+ m2n_indices = w2n_indices[m2w_indices]
+
+ m_offset = m_indices - m2w_offset
+ m2w_sizes = w_sizes[m2w_indices]
+
+ # print_log_main("m_offset:", m_offset, m_offset.shape)
+ # print_log_main("m2n_indices:", m2n_indices, m2n_indices.shape)
+
+ y_offset = m2n_indices + m_offset % m2w_sizes
+ x_offset = m2n_indices + torch.div(m_offset, m2w_sizes, rounding_mode="floor")
+
+ # print_log_main("=================================")
+ # print_log_main(w_sizes[:5])
+ # print_log_main(x_offset[:50])
+ # print_log_main(y_offset[:50])
+ # coord = torch.stack([m2w_indices, w_w_id[x_offset], w_w_id[y_offset]], axis=-1)
+ if protocol == "v1":
+ return x_offset, y_offset
+ elif protocol == "v2":
+ return x_offset, y_offset, m2w_indices, w_sizes, w2n_indices, w2m_indices
+
+
+class Mlp(nn.Module):
+ def __init__(
+ self,
+ in_features,
+ hidden_features=None,
+ out_features=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class GridCoordsDown(nn.Module):
+ """
+ downsample the grid coordinates
+ keep the nearest point to the average point of the downsampled grid
+ """
+
+ def __init__(self, stride):
+ super().__init__()
+ self.stride = stride
+ self.avg_pool = ME.MinkowskiAvgPooling(
+ kernel_size=self.stride, stride=self.stride, dimension=3
+ )
+ self.unpool = ME.MinkowskiPoolingTranspose(
+ kernel_size=stride, stride=stride, dimension=3
+ )
+ self.max_pool = ME.MinkowskiMaxPooling(
+ kernel_size=self.stride, stride=self.stride, dimension=3
+ )
+
+ def forward(self, coords_sp, sp, return_map=False):
+ device = sp.C.device
+ # is_pool = True means pooling map
+ # is_pool = False means conv map (query as center)
+
+ N = sp.shape[0]
+ avg_coords_sp = self.avg_pool(coords_sp)
+ dist_sp = self.unpool(avg_coords_sp) - coords_sp
+ dist = dist_sp.F
+ dist = -torch.sqrt((dist**2).sum(dim=1)).unsqueeze(1)
+ dist_sp = assign_feats(dist_sp, dist)
+ min_dist_sp = self.max_pool(dist_sp)
+ map_pair = sp.coordinate_manager.kernel_map(
+ dist_sp.coordinate_map_key,
+ min_dist_sp.coordinate_map_key,
+ stride=self.stride,
+ kernel_size=self.stride,
+ is_pool=True,
+ )[0]
+ in_map, out_map = map_pair
+ broad_min_dist_sp = self.unpool(min_dist_sp)
+ mask = (broad_min_dist_sp.F == dist_sp.F).squeeze(1)
+ in_map = in_map[mask].long()
+ out_map = out_map[mask].long()
+ downsample_map = torch.zeros(N, dtype=torch.long, device=device) - 1
+ downsample_map[out_map] = in_map
+ assert (downsample_map >= 0).all()
+ assert (dist_sp.F[downsample_map] == min_dist_sp.F).all()
+ new_coords = coords_sp.F[downsample_map]
+ new_coords_sp = assign_feats(sp, new_coords)
+ if return_map:
+ return new_coords_sp, downsample_map
+ else:
+ return new_coords_sp
+
+
+def get_offset(batch):
+ offset = []
+ bs = batch.max() + 1
+ for i in range(bs):
+ offset.append(torch.sum(batch == i))
+ offset = torch.cuda.IntTensor(offset)
+ offset = offset.cumsum(dim=0).int()
+ return offset
+
+
+class GridDownsample(nn.Module):
+ """
+ use stride to downsample voxel
+ use grid maxpooling with kernel_size
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
+ super().__init__()
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.sp_pool = ME.MinkowskiMaxPooling(
+ kernel_size=kernel_size, stride=stride, dimension=3
+ )
+ self.coords_pool = GridCoordsDown(stride=stride)
+ self.norm = SparseTensorLayerNorm(in_channels)
+ self.linear = SparseTensorLinear(in_channels, out_channels)
+
+ def forward(self, sp, coords_sp):
+ sp_down = self.sp_pool(self.linear(self.norm(sp)))
+ coords_sp_down = self.coords_pool(coords_sp, sp_down)
+ return sp_down, coords_sp_down
+
+ def extra_repr(self) -> str:
+ return f"kernel_size={self.kernel_size}, stride={self.stride}, in_channels={self.in_channels}, out_channels={self.out_channels}"
+
+
+class GridKNNDownsample(nn.Module):
+ """
+ use stride to downsample voxel
+ use KNN to do maxpooling
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
+ super().__init__()
+ self.stride = stride
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.k = 16
+ self.sp_pool = ME.MinkowskiMaxPooling(
+ kernel_size=stride, stride=stride, dimension=3
+ )
+ self.coords_pool = GridCoordsDown(stride=stride)
+ self.norm = nn.LayerNorm(in_channels)
+ self.linear = nn.Linear(in_channels, out_channels, bias=False)
+ self.pool = nn.MaxPool1d(self.k)
+
+ def forward(self, sp, coords_sp):
+ # calculate the voxel
+ sp_down = self.sp_pool(sp)
+ # for downsampled cRSE
+ coords_sp_down = self.coords_pool(coords_sp, sp_down)
+ offset = get_offset(sp.C[:, 0])
+ n_offset = get_offset(sp_down.C[:, 0])
+
+ xyz = coords_sp.F[:, 1:4].detach().contiguous()
+ n_xyz = coords_sp_down.F[:, 1:4].detach().contiguous()
+ feats = query_knn_feature(self.k, xyz, n_xyz, sp.F, offset, n_offset)
+ m, k, c = feats.shape
+ feats = (
+ self.linear(self.norm(feats.view(m * k, c)).view(m, k, c))
+ .transpose(1, 2)
+ .contiguous()
+ )
+ feats = self.pool(feats).squeeze(-1)
+ sp = assign_feats(sp_down, feats.float())
+ coords_sp = coords_sp_down
+ return sp, coords_sp
+
+ def extra_repr(self) -> str:
+ return f"kernel_size={self.k}, stride={self.stride}, in_channels={self.in_channels}, out_channels={self.out_channels}"
+
+
+class Upsample(nn.Module):
+ """
+ upsample using trilinear interpolation
+ follower by attn block according to self.attn
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ attn=True,
+ up_k=3,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.linear1 = nn.Sequential(
+ nn.LayerNorm(out_channels), nn.Linear(out_channels, out_channels)
+ )
+ self.linear2 = nn.Sequential(
+ nn.LayerNorm(in_channels), nn.Linear(in_channels, out_channels)
+ )
+ self.up_k = up_k
+ self.attn = attn and window_size > 0
+ if self.attn:
+ self.block = BasicLayer(
+ dim=out_channels,
+ depth=1,
+ num_heads=num_heads,
+ window_size=window_size,
+ quant_size=quant_size,
+ drop_path=0.1,
+ downsample=None,
+ out_channels=None,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+
+ def forward(self, sp, coords_sp, sp_up, coords_sp_up):
+ feats = sp.F
+ support_feats = sp_up.F
+ xyz = coords_sp.F[:, 1:4].detach().contiguous()
+ support_xyz = coords_sp_up.F[:, 1:4].detach().contiguous()
+ offset = get_offset(sp.C[:, 0])
+ support_offset = get_offset(sp_up.C[:, 0])
+
+ feats = self.linear1(support_feats) + knn_linear_interpolation(
+ xyz, support_xyz, self.linear2(feats), offset, support_offset, K=self.up_k
+ )
+ sp_up = assign_feats(sp_up, feats)
+ if self.attn:
+ sp_up, _, _ = self.block(sp_up, coords_sp_up)
+ return sp_up
+
+ def extra_repr(self) -> str:
+ return f"up_k={self.up_k}, in_channels={self.in_channels}, out_channels={self.out_channels}, attn={self.attn}"
+
+
+class WindowAttention(nn.Module):
+ """
+ Window based multi-head self attention (W-MSA) module with cRSE.
+ Designed for sparse structure
+ It supports both of shifted and non-shifted window.
+
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ quant_size (int): quant_size for for finer cRSE table
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ cRSE (str | 'XYZ', 'XYZ_RGB', 'XYZ_RGB_NORM'): cRSE mode. Default: 'XYZ_RGB'
+ fp16_mode (int | 0, 1, 2): fp16 mode for attention module, Default: 0
+ 0: fp32 forward and fp32 backward
+ 1: fp16 forward and fp32 backward
+ 2: fp16 forward and fp16 backward
+ """
+
+ def __init__(
+ self,
+ dim,
+ window_size,
+ quant_size,
+ num_heads,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim**-0.5
+
+ # color in [-1, 1], color_windowsize = 2
+ # normal in [-1, 1], normal_windowsize = 2
+ self.color_windowsize = 2
+ self.normal_windowsize = 2
+
+ self.fp16_mode = fp16_mode
+
+ table_offsets = []
+ self.cRSE = cRSE
+ if "XYZ" in cRSE:
+ self.xyz_quant_size = quant_size
+ quant_grid_length_xyz = window_size * self.xyz_quant_size
+ table_shape_xyz = (3, 2 * quant_grid_length_xyz, num_heads, head_dim)
+ self.query_xyz_table = nn.Parameter(torch.zeros(table_shape_xyz))
+ trunc_normal_(self.query_xyz_table, std=0.02)
+ self.key_xyz_table = nn.Parameter(torch.zeros(table_shape_xyz))
+ trunc_normal_(self.key_xyz_table, std=0.02)
+ self.value_xyz_table = nn.Parameter(torch.zeros(table_shape_xyz))
+ trunc_normal_(self.value_xyz_table, std=0.02)
+ table_offsets += [np.prod(table_shape_xyz[1:])] * 3
+
+ if "RGB" in cRSE:
+ self.color_quant_size = quant_size * 2
+ quant_grid_length_rgb = self.color_windowsize * self.color_quant_size
+ table_shape_rgb = (3, 2 * quant_grid_length_rgb, num_heads, head_dim)
+ self.query_rgb_table = nn.Parameter(torch.zeros(table_shape_rgb))
+ trunc_normal_(self.query_rgb_table, std=0.02)
+ self.key_rgb_table = nn.Parameter(torch.zeros(table_shape_rgb))
+ trunc_normal_(self.key_rgb_table, std=0.02)
+ self.value_rgb_table = nn.Parameter(torch.zeros(table_shape_rgb))
+ trunc_normal_(self.value_rgb_table, std=0.02)
+ table_offsets += [np.prod(table_shape_rgb[1:])] * 3
+
+ if "NORM" in cRSE:
+ self.normal_quant_size = quant_size * 2
+ quant_grid_length_norm = self.normal_windowsize * self.normal_quant_size
+ table_shape_norm = (3, 2 * quant_grid_length_norm, num_heads, head_dim)
+ self.query_norm_table = nn.Parameter(torch.zeros(table_shape_norm))
+ trunc_normal_(self.query_norm_table, std=0.02)
+ self.key_norm_table = nn.Parameter(torch.zeros(table_shape_norm))
+ trunc_normal_(self.key_norm_table, std=0.02)
+ self.value_norm_table = nn.Parameter(torch.zeros(table_shape_norm))
+ trunc_normal_(self.value_norm_table, std=0.02)
+ table_offsets += [np.prod(table_shape_norm[1:])] * 3
+
+ self.table_offsets = table_offsets
+
+ self.quant_size = quant_size
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop, inplace=True)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop, inplace=True)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, feats: torch.Tensor, attn_args):
+ """Forward function.
+
+ Args:
+ feats: N, C
+ attn_args: arguments for computing attention
+ """
+ num_v, _ = feats.shape
+ num_sc = self.dim // self.num_heads
+
+ (
+ x_offset,
+ y_offset,
+ m2w_indices,
+ w_sizes,
+ w2n_indices,
+ n2n_indices,
+ w2m_indices,
+ n_coords,
+ ) = attn_args
+
+ # Query, Key, Value
+ qkv = self.qkv(feats)
+ qkv = (
+ qkv.reshape(num_v, 3, self.num_heads, num_sc)
+ .permute(1, 0, 2, 3)
+ .contiguous()
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2] # [N, num_heads, C//num_heads]
+ query = query * self.scale
+
+ table_offsets = torch.IntTensor(self.table_offsets).cuda()
+ query_table, key_table, value_table = [], [], []
+ n_cRSE = []
+ if "XYZ" in self.cRSE:
+ n_xyz = n_coords[:, 0:3]
+ n_xyz = n_xyz * self.quant_size
+ n_cRSE.append(n_xyz)
+ query_table.append(self.query_xyz_table.view(-1))
+ key_table.append(self.key_xyz_table.view(-1))
+ value_table.append(self.value_xyz_table.view(-1))
+ if "RGB" in self.cRSE:
+ n_rgb = n_coords[:, 3:6]
+ n_rgb = n_rgb * self.color_quant_size
+ n_cRSE.append(n_rgb)
+ query_table.append(self.query_rgb_table.view(-1))
+ key_table.append(self.key_rgb_table.view(-1))
+ value_table.append(self.value_rgb_table.view(-1))
+ if "NORM" in self.cRSE:
+ n_norm = n_coords[:, 6:9]
+ n_norm = n_norm * self.normal_quant_size
+ n_cRSE.append(n_norm)
+ query_table.append(self.query_norm_table.view(-1))
+ key_table.append(self.key_norm_table.view(-1))
+ value_table.append(self.value_norm_table.view(-1))
+
+ n_cRSE = torch.cat(n_cRSE, dim=1)
+
+ indices = [m2w_indices, w_sizes, w2m_indices, w2n_indices, n2n_indices, n_cRSE]
+ query_table = torch.cat(query_table)
+ key_table = torch.cat(key_table)
+ value_table = torch.cat(value_table)
+
+ if self.fp16_mode == 0:
+ # do not use fp16
+ # cast q,k,v to fp32 in forward and backward
+ fp16_mode = PrecisionMode.HALF_NONE
+ elif self.fp16_mode == 1:
+ # use fp16 only in forward
+ fp16_mode = PrecisionMode.HALF_FORWARD
+ elif self.fp16_mode == 2:
+ # use fp16 both in forward and backward
+ fp16_mode = PrecisionMode.HALF_ALL
+
+ updated_values = SelfAttnAIOFunction.apply(
+ query,
+ key,
+ value,
+ query_table,
+ key_table,
+ value_table,
+ table_offsets,
+ indices,
+ PosEmb.SEPARATE,
+ TableDims.D0,
+ IndexMode.INDIRECT,
+ fp16_mode,
+ )
+
+ updated_values = updated_values.flatten(1)
+ updated_feats = updated_values.view(num_v, self.dim)
+
+ updated_feats = self.proj(updated_feats)
+ updated_feats = self.proj_drop(updated_feats) # [N, C]
+
+ return updated_feats
+
+
+class SwinTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_heads,
+ window_size,
+ quant_size,
+ drop_path=0.0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ act_layer=nn.GELU,
+ norm_layer=nn.LayerNorm,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.window_size = window_size
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim,
+ window_size=self.window_size,
+ quant_size=quant_size,
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(
+ in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer
+ )
+
+ def forward(self, feats, attn_args):
+ # feats: [N, c]
+ short_cut = feats
+ feats = self.norm1(feats)
+ feats = self.attn(feats, attn_args) # [N, c]
+
+ feats = short_cut + self.drop_path(feats)
+ feats = feats + self.drop_path(self.mlp(self.norm2(feats)))
+
+ return feats
+
+
+class BasicLayer(nn.Module):
+ """A basic Swin3D layer for one stage.
+
+ Args:
+ dim (int): Number of input channels.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ quant_size (int): quant_size for for finer cRSE table
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ cRSE (str | 'XYZ', 'XYZ_RGB', 'XYZ_RGB_NORM'): cRSE mode. Default: 'XYZ_RGB'
+ fp16_mode (int | 0, 1, 2): fp16 mode for attention module, Default: 0
+ 0: fp32 forward and fp32 backward
+ 1: fp16 forward and fp32 backward
+ 2: fp16 forward and fp16 backward
+ """
+
+ def __init__(
+ self,
+ dim,
+ depth,
+ num_heads,
+ window_size,
+ quant_size,
+ out_channels=None,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_path=0.0,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ down_stride=2,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.window_size = window_size
+ self.depth = depth
+ self.dim = dim
+ self.num_heads = num_heads
+ self.quant_size = quant_size
+ self.cRSE = cRSE
+ self.fp16_mode = fp16_mode
+
+ self.shift_size = window_size // 2
+ # build blocks
+ self.blocks = nn.ModuleList(
+ [
+ SwinTransformerBlock(
+ dim,
+ num_heads,
+ window_size,
+ quant_size,
+ drop_path=(
+ drop_path[i] if isinstance(drop_path, list) else drop_path
+ ),
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ norm_layer=norm_layer,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ self.pool = ME.MinkowskiMaxPooling(
+ kernel_size=self.window_size, stride=self.window_size, dimension=3
+ )
+
+ if downsample is not None:
+ if out_channels is None:
+ out_channels = dim * 2
+ self.downsample = downsample(
+ dim, out_channels, kernel_size=down_stride, stride=down_stride
+ )
+ else:
+ self.downsample = None
+
+ def get_map_pair(self, sp):
+ """
+ use minkowski pool to calculate windows
+ get the mapping from voxel to window
+ """
+ window_size = [self.window_size] * 3
+ pool_sp = self.pool(sp)
+ windows = pool_sp.C
+ window_N = windows.shape[0]
+
+ stride_in = sp.coordinate_map_key.get_tensor_stride()
+ x, y, z = [
+ torch.arange(window_size[i], device=self.device) * stride_in[i]
+ for i in range(3)
+ ]
+ x, y, z = torch.meshgrid(x, y, z)
+ i = torch.zeros_like(x, device=self.device)
+ local_window = torch.stack([i, x, y, z], dim=-1).flatten(0, -2)
+ all_windows = windows.unsqueeze(1) + local_window.unsqueeze(0)
+ all_windows = all_windows.flatten(0, -2).int()
+ cm = sp.coordinate_manager
+ query_key, (map, inverse_map) = cm.insert_and_map(
+ all_windows, tensor_stride=stride_in
+ )
+ map_pair = cm.kernel_map(query_key, sp.coordinate_map_key, kernel_size=1)[0]
+ return map_pair, window_N
+
+ def get_window_mapping(self, sp):
+ """
+ calculate the relationshape in the window:
+ w_w_id: non-empty idx inside the window(sorted by window)
+ w_w_xyz: xyz inside the window(sorted by window)
+ nempty_num: non-empty voxel number in each window
+ sort_idx: sort voxel according to window_id, to gather the point inside the same window
+ inv_sort_idx: inverse sort index
+ """
+ map_pair, window_N = self.get_map_pair(sp)
+ window_size = self.window_size
+ nW = window_size**3
+ in_map, out_map = map_pair
+ in_map, sort_idx = torch.sort(in_map)
+ # assert out_map == arange(out_map.shape[0])
+ out_map = out_map[sort_idx]
+ sort_idx = out_map.long()
+ inv_sort_idx = torch.zeros_like(sort_idx)
+ inv_sort_idx[sort_idx] = torch.arange(
+ sort_idx.shape[0], dtype=sort_idx.dtype, device=self.device
+ )
+ N = window_N * nW
+ v2w_mask = torch.zeros(N, dtype=torch.bool, device=self.device)
+ w_id = (
+ torch.arange(window_N, dtype=torch.long, device=self.device)
+ .unsqueeze(1)
+ .repeat(1, nW)
+ .view(-1)
+ )
+ w_w_id = (
+ torch.arange(nW, dtype=torch.long, device=self.device)
+ .unsqueeze(0)
+ .repeat(window_N, 1)
+ .view(-1)
+ )
+ v2w_mask[in_map.long()] = True
+ nempty_num = v2w_mask.view(-1, nW).sum(dim=-1)
+ w_id = w_id[in_map.long()]
+ w_w_id = w_w_id[in_map.long()]
+ w_w_xyz = torch.stack(
+ [
+ w_w_id // window_size // window_size,
+ w_w_id // window_size % window_size,
+ w_w_id % window_size,
+ ],
+ dim=-1,
+ )
+ return w_w_id, w_w_xyz, nempty_num, sort_idx, inv_sort_idx
+
+ def get_index01(self, sp, local_xyz, colors):
+ """
+ calculate the arguments for sparse attention
+ """
+ (
+ w_w_id,
+ w_w_xyz,
+ nempty_num,
+ n2n_indices,
+ inv_sort_idx,
+ ) = self.get_window_mapping(sp)
+ local_xyz = local_xyz[n2n_indices]
+ colors = colors[n2n_indices]
+ # recover the relative pos in the voxel
+ n_coords = w_w_xyz + local_xyz
+ n_coords = torch.cat([n_coords, colors], dim=1)
+ (
+ x_offset,
+ y_offset,
+ m2w_indices,
+ w_sizes,
+ w2n_indices,
+ w2m_indices,
+ ) = sparse_self_attention(w_w_id, nempty_num, protocol="v2")
+ return (
+ x_offset,
+ y_offset,
+ m2w_indices,
+ w_sizes,
+ w2n_indices,
+ n2n_indices,
+ w2m_indices,
+ n_coords,
+ )
+
+ def get_shifted_sp(self, sp):
+ """
+ get the shifted sparse tensor for shift-window
+ """
+ stride_in = sp.coordinate_map_key.get_tensor_stride()
+ shift_size = self.shift_size * stride_in[0]
+ shifted_C = sp.C.clone()
+ shifted_C[:, 1:] += shift_size
+ shifted_sp = SparseTensor(
+ features=sp.F,
+ coordinates=shifted_C,
+ device=self.device,
+ tensor_stride=stride_in,
+ )
+ return shifted_sp
+
+ def get_window_pos(self, sp):
+ stride_in = sp.coordinate_map_key.get_tensor_stride()
+ return (sp.C[:, 1:] / stride_in[0]) % self.window_size
+
+ def forward(self, sp, coords_sp):
+ """
+ xyz: position of point inside voxel
+ colors: other signal for cRSE, include colors and normals
+ local_xyz: relative position of point indide voxel(using for finer cRSE table)
+ """
+ colors = coords_sp.F[:, 4:]
+ xyz = coords_sp.F[:, :4]
+ local_xyz = (xyz - coords_sp.C)[
+ :, 1:
+ ] / coords_sp.coordinate_map_key.get_tensor_stride()[0]
+ self.device = sp.device
+ sp_shift = self.get_shifted_sp(sp)
+
+ attn_args = self.get_index01(sp, local_xyz, colors)
+ attn_args_shift = self.get_index01(sp_shift, local_xyz, colors)
+
+ feats = sp.F
+ for i, blk in enumerate(self.blocks):
+ attn_args_blk = attn_args if i % 2 == 0 else attn_args_shift
+ feats = blk(feats, attn_args_blk) # [N, C]
+
+ sp = assign_feats(sp, feats)
+ if self.downsample is not None:
+ sp_down, coords_sp = self.downsample(sp, coords_sp)
+ return sp, sp_down, coords_sp
+ else:
+ return sp, sp, coords_sp
+
+ def extra_repr(self) -> str:
+ return f"window_size={self.window_size}, depth={self.depth}, channel={self.dim}, num_heads={self.num_heads}, quant_size={self.quant_size}, cRSE={self.cRSE}, fp16_mode={self.fp16_mode}"
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/swin3d_v1m1_base.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/swin3d_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..1295e5d791e8ac33d3d4c43be03d4f08ade1345f
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/swin3d/swin3d_v1m1_base.py
@@ -0,0 +1,190 @@
+import torch
+import torch.nn as nn
+import MinkowskiEngine as ME
+from MinkowskiEngine import SparseTensor
+from timm.models.layers import trunc_normal_
+
+from .mink_layers import MinkConvBNRelu, MinkResBlock
+from .swin3d_layers import GridDownsample, GridKNNDownsample, BasicLayer, Upsample
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+@MODELS.register_module("Swin3D-v1m1")
+class Swin3DUNet(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ base_grid_size,
+ depths,
+ channels,
+ num_heads,
+ window_sizes,
+ quant_size,
+ drop_path_rate=0.2,
+ up_k=3,
+ num_layers=5,
+ stem_transformer=True,
+ down_stride=2,
+ upsample="linear",
+ knn_down=True,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+ ] # stochastic depth decay rule
+ if knn_down:
+ downsample = GridKNNDownsample
+ else:
+ downsample = GridDownsample
+
+ self.cRSE = cRSE
+ if stem_transformer:
+ self.stem_layer = MinkConvBNRelu(
+ in_channels=in_channels,
+ out_channels=channels[0],
+ kernel_size=3,
+ stride=1,
+ )
+ self.layer_start = 0
+ else:
+ self.stem_layer = nn.Sequential(
+ MinkConvBNRelu(
+ in_channels=in_channels,
+ out_channels=channels[0],
+ kernel_size=3,
+ stride=1,
+ ),
+ MinkResBlock(in_channels=channels[0], out_channels=channels[0]),
+ )
+ self.downsample = downsample(
+ channels[0], channels[1], kernel_size=down_stride, stride=down_stride
+ )
+ self.layer_start = 1
+ self.layers = nn.ModuleList(
+ [
+ BasicLayer(
+ dim=channels[i],
+ depth=depths[i],
+ num_heads=num_heads[i],
+ window_size=window_sizes[i],
+ quant_size=quant_size,
+ drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+ downsample=downsample if i < num_layers - 1 else None,
+ down_stride=down_stride if i == 0 else 2,
+ out_channels=channels[i + 1] if i < num_layers - 1 else None,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+ for i in range(self.layer_start, num_layers)
+ ]
+ )
+
+ if "attn" in upsample:
+ up_attn = True
+ else:
+ up_attn = False
+
+ self.upsamples = nn.ModuleList(
+ [
+ Upsample(
+ channels[i],
+ channels[i - 1],
+ num_heads[i - 1],
+ window_sizes[i - 1],
+ quant_size,
+ attn=up_attn,
+ up_k=up_k,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+ for i in range(num_layers - 1, 0, -1)
+ ]
+ )
+
+ self.classifier = nn.Sequential(
+ nn.Linear(channels[0], channels[0]),
+ nn.BatchNorm1d(channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(channels[0], num_classes),
+ )
+ self.num_classes = num_classes
+ self.base_grid_size = base_grid_size
+ self.init_weights()
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ coord_feat = data_dict["coord_feat"]
+ coord = data_dict["coord"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+ in_field = ME.TensorField(
+ features=torch.cat(
+ [
+ batch.unsqueeze(-1),
+ coord / self.base_grid_size,
+ coord_feat / 1.001,
+ feat,
+ ],
+ dim=1,
+ ),
+ coordinates=torch.cat([batch.unsqueeze(-1).int(), grid_coord.int()], dim=1),
+ quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
+ minkowski_algorithm=ME.MinkowskiAlgorithm.SPEED_OPTIMIZED,
+ device=feat.device,
+ )
+
+ sp = in_field.sparse()
+ coords_sp = SparseTensor(
+ features=sp.F[:, : coord_feat.shape[-1] + 4],
+ coordinate_map_key=sp.coordinate_map_key,
+ coordinate_manager=sp.coordinate_manager,
+ )
+ sp = SparseTensor(
+ features=sp.F[:, coord_feat.shape[-1] + 4 :],
+ coordinate_map_key=sp.coordinate_map_key,
+ coordinate_manager=sp.coordinate_manager,
+ )
+ sp_stack = []
+ coords_sp_stack = []
+ sp = self.stem_layer(sp)
+ if self.layer_start > 0:
+ sp_stack.append(sp)
+ coords_sp_stack.append(coords_sp)
+ sp, coords_sp = self.downsample(sp, coords_sp)
+
+ for i, layer in enumerate(self.layers):
+ coords_sp_stack.append(coords_sp)
+ sp, sp_down, coords_sp = layer(sp, coords_sp)
+ sp_stack.append(sp)
+ assert (coords_sp.C == sp_down.C).all()
+ sp = sp_down
+
+ sp = sp_stack.pop()
+ coords_sp = coords_sp_stack.pop()
+ for i, upsample in enumerate(self.upsamples):
+ sp_i = sp_stack.pop()
+ coords_sp_i = coords_sp_stack.pop()
+ sp = upsample(sp, coords_sp, sp_i, coords_sp_i)
+ coords_sp = coords_sp_i
+
+ output = self.classifier(sp.slice(in_field).F)
+ return output
+
+ def init_weights(self):
+ """Initialize the weights in backbone."""
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ self.apply(_init_weights)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..66e6bc0f62993abb3625a9598f54e7775aeb0008
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/__init__.py
@@ -0,0 +1,4 @@
+from .misc import offset2batch, offset2bincount, batch2offset, off_diagonal
+from .checkpoint import checkpoint
+from .serialization import encode, decode
+from .structure import Point
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/checkpoint.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..58820352bd5d1b37b3905b038816323253ffd3de
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/checkpoint.py
@@ -0,0 +1,57 @@
+"""
+Checkpoint Utils for Models
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+
+
+class CheckpointFunction(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+
+ with torch.no_grad():
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ with torch.enable_grad():
+ # Fixes a bug where the first op in run_function modifies the
+ # Tensor storage in place, which is not allowed for detach()'d
+ # Tensors.
+ shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+ input_grads = torch.autograd.grad(
+ output_tensors,
+ ctx.input_tensors + ctx.input_params,
+ output_grads,
+ allow_unused=True,
+ )
+ del ctx.input_tensors
+ del ctx.input_params
+ del output_tensors
+ return (None, None) + input_grads
+
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ args = tuple(inputs) + tuple(params)
+ return CheckpointFunction.apply(func, len(inputs), *args)
+ else:
+ return func(*inputs)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/misc.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..61dfdfb44a82fc0ef585ca5732518fe85e466889
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/misc.py
@@ -0,0 +1,35 @@
+"""
+General Utils for Models
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+
+
+@torch.inference_mode()
+def offset2bincount(offset):
+ return torch.diff(
+ offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
+ )
+
+
+@torch.inference_mode()
+def offset2batch(offset):
+ bincount = offset2bincount(offset)
+ return torch.arange(
+ len(bincount), device=offset.device, dtype=torch.long
+ ).repeat_interleave(bincount)
+
+
+@torch.inference_mode()
+def batch2offset(batch):
+ return torch.cumsum(batch.bincount(), dim=0).long()
+
+
+def off_diagonal(x):
+ # return a flattened view of the off-diagonal elements of a square matrix
+ n, m = x.shape
+ assert n == m
+ return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..058c5e1001c76d9c7014bf0bbb824eec4f54f476
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/__init__.py
@@ -0,0 +1,8 @@
+from .default import (
+ encode,
+ decode,
+ z_order_encode,
+ z_order_decode,
+ hilbert_encode,
+ hilbert_decode,
+)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/default.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..15898b55625fc0e1125db9b713e900892f04176c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/default.py
@@ -0,0 +1,59 @@
+import torch
+from .z_order import xyz2key as z_order_encode_
+from .z_order import key2xyz as z_order_decode_
+from .hilbert import encode as hilbert_encode_
+from .hilbert import decode as hilbert_decode_
+
+
+@torch.inference_mode()
+def encode(grid_coord, batch=None, depth=16, order="z"):
+ assert order in {"z", "z-trans", "hilbert", "hilbert-trans"}
+ if order == "z":
+ code = z_order_encode(grid_coord, depth=depth)
+ elif order == "z-trans":
+ code = z_order_encode(grid_coord[:, [1, 0, 2]], depth=depth)
+ elif order == "hilbert":
+ code = hilbert_encode(grid_coord, depth=depth)
+ elif order == "hilbert-trans":
+ code = hilbert_encode(grid_coord[:, [1, 0, 2]], depth=depth)
+ else:
+ raise NotImplementedError
+ if batch is not None:
+ batch = batch.long()
+ code = batch << depth * 3 | code
+ return code
+
+
+@torch.inference_mode()
+def decode(code, depth=16, order="z"):
+ assert order in {"z", "hilbert"}
+ batch = code >> depth * 3
+ code = code & ((1 << depth * 3) - 1)
+ if order == "z":
+ grid_coord = z_order_decode(code, depth=depth)
+ elif order == "hilbert":
+ grid_coord = hilbert_decode(code, depth=depth)
+ else:
+ raise NotImplementedError
+ return grid_coord, batch
+
+
+def z_order_encode(grid_coord: torch.Tensor, depth: int = 16):
+ x, y, z = grid_coord[:, 0].long(), grid_coord[:, 1].long(), grid_coord[:, 2].long()
+ # we block the support to batch, maintain batched code in Point class
+ code = z_order_encode_(x, y, z, b=None, depth=depth)
+ return code
+
+
+def z_order_decode(code: torch.Tensor, depth):
+ x, y, z = z_order_decode_(code, depth=depth)
+ grid_coord = torch.stack([x, y, z], dim=-1) # (N, 3)
+ return grid_coord
+
+
+def hilbert_encode(grid_coord: torch.Tensor, depth: int = 16):
+ return hilbert_encode_(grid_coord, num_dims=3, num_bits=depth)
+
+
+def hilbert_decode(code: torch.Tensor, depth: int = 16):
+ return hilbert_decode_(code, num_dims=3, num_bits=depth)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/hilbert.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/hilbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..c96a3a9e15be64059811eb86139f28c6016ad0fe
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/hilbert.py
@@ -0,0 +1,303 @@
+"""
+Hilbert Order
+Modified from https://github.com/PrincetonLIPS/numpy-hilbert-curve
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Kaixin Xu
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+
+
+def right_shift(binary, k=1, axis=-1):
+ """Right shift an array of binary values.
+
+ Parameters:
+ -----------
+ binary: An ndarray of binary values.
+
+ k: The number of bits to shift. Default 1.
+
+ axis: The axis along which to shift. Default -1.
+
+ Returns:
+ --------
+ Returns an ndarray with zero prepended and the ends truncated, along
+ whatever axis was specified."""
+
+ # If we're shifting the whole thing, just return zeros.
+ if binary.shape[axis] <= k:
+ return torch.zeros_like(binary)
+
+ # Determine the padding pattern.
+ # padding = [(0,0)] * len(binary.shape)
+ # padding[axis] = (k,0)
+
+ # Determine the slicing pattern to eliminate just the last one.
+ slicing = [slice(None)] * len(binary.shape)
+ slicing[axis] = slice(None, -k)
+ shifted = torch.nn.functional.pad(
+ binary[tuple(slicing)], (k, 0), mode="constant", value=0
+ )
+
+ return shifted
+
+
+def binary2gray(binary, axis=-1):
+ """Convert an array of binary values into Gray codes.
+
+ This uses the classic X ^ (X >> 1) trick to compute the Gray code.
+
+ Parameters:
+ -----------
+ binary: An ndarray of binary values.
+
+ axis: The axis along which to compute the gray code. Default=-1.
+
+ Returns:
+ --------
+ Returns an ndarray of Gray codes.
+ """
+ shifted = right_shift(binary, axis=axis)
+
+ # Do the X ^ (X >> 1) trick.
+ gray = torch.logical_xor(binary, shifted)
+
+ return gray
+
+
+def gray2binary(gray, axis=-1):
+ """Convert an array of Gray codes back into binary values.
+
+ Parameters:
+ -----------
+ gray: An ndarray of gray codes.
+
+ axis: The axis along which to perform Gray decoding. Default=-1.
+
+ Returns:
+ --------
+ Returns an ndarray of binary values.
+ """
+
+ # Loop the log2(bits) number of times necessary, with shift and xor.
+ shift = 2 ** (torch.Tensor([gray.shape[axis]]).log2().ceil().int() - 1)
+ while shift > 0:
+ gray = torch.logical_xor(gray, right_shift(gray, shift))
+ shift = torch.div(shift, 2, rounding_mode="floor")
+ return gray
+
+
+def encode(locs, num_dims, num_bits):
+ """Decode an array of locations in a hypercube into a Hilbert integer.
+
+ This is a vectorized-ish version of the Hilbert curve implementation by John
+ Skilling as described in:
+
+ Skilling, J. (2004, April). Programming the Hilbert curve. In AIP Conference
+ Proceedings (Vol. 707, No. 1, pp. 381-387). American Institute of Physics.
+
+ Params:
+ -------
+ locs - An ndarray of locations in a hypercube of num_dims dimensions, in
+ which each dimension runs from 0 to 2**num_bits-1. The shape can
+ be arbitrary, as long as the last dimension of the same has size
+ num_dims.
+
+ num_dims - The dimensionality of the hypercube. Integer.
+
+ num_bits - The number of bits for each dimension. Integer.
+
+ Returns:
+ --------
+ The output is an ndarray of uint64 integers with the same shape as the
+ input, excluding the last dimension, which needs to be num_dims.
+ """
+
+ # Keep around the original shape for later.
+ orig_shape = locs.shape
+ bitpack_mask = 1 << torch.arange(0, 8).to(locs.device)
+ bitpack_mask_rev = bitpack_mask.flip(-1)
+
+ if orig_shape[-1] != num_dims:
+ raise ValueError(
+ """
+ The shape of locs was surprising in that the last dimension was of size
+ %d, but num_dims=%d. These need to be equal.
+ """
+ % (orig_shape[-1], num_dims)
+ )
+
+ if num_dims * num_bits > 63:
+ raise ValueError(
+ """
+ num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
+ into a int64. Are you sure you need that many points on your Hilbert
+ curve?
+ """
+ % (num_dims, num_bits, num_dims * num_bits)
+ )
+
+ # Treat the location integers as 64-bit unsigned and then split them up into
+ # a sequence of uint8s. Preserve the association by dimension.
+ locs_uint8 = locs.long().view(torch.uint8).reshape((-1, num_dims, 8)).flip(-1)
+
+ # Now turn these into bits and truncate to num_bits.
+ gray = (
+ locs_uint8.unsqueeze(-1)
+ .bitwise_and(bitpack_mask_rev)
+ .ne(0)
+ .byte()
+ .flatten(-2, -1)[..., -num_bits:]
+ )
+
+ # Run the decoding process the other way.
+ # Iterate forwards through the bits.
+ for bit in range(0, num_bits):
+ # Iterate forwards through the dimensions.
+ for dim in range(0, num_dims):
+ # Identify which ones have this bit active.
+ mask = gray[:, dim, bit]
+
+ # Where this bit is on, invert the 0 dimension for lower bits.
+ gray[:, 0, bit + 1 :] = torch.logical_xor(
+ gray[:, 0, bit + 1 :], mask[:, None]
+ )
+
+ # Where the bit is off, exchange the lower bits with the 0 dimension.
+ to_flip = torch.logical_and(
+ torch.logical_not(mask[:, None]).repeat(1, gray.shape[2] - bit - 1),
+ torch.logical_xor(gray[:, 0, bit + 1 :], gray[:, dim, bit + 1 :]),
+ )
+ gray[:, dim, bit + 1 :] = torch.logical_xor(
+ gray[:, dim, bit + 1 :], to_flip
+ )
+ gray[:, 0, bit + 1 :] = torch.logical_xor(gray[:, 0, bit + 1 :], to_flip)
+
+ # Now flatten out.
+ gray = gray.swapaxes(1, 2).reshape((-1, num_bits * num_dims))
+
+ # Convert Gray back to binary.
+ hh_bin = gray2binary(gray)
+
+ # Pad back out to 64 bits.
+ extra_dims = 64 - num_bits * num_dims
+ padded = torch.nn.functional.pad(hh_bin, (extra_dims, 0), "constant", 0)
+
+ # Convert binary values into uint8s.
+ hh_uint8 = (
+ (padded.flip(-1).reshape((-1, 8, 8)) * bitpack_mask)
+ .sum(2)
+ .squeeze()
+ .type(torch.uint8)
+ )
+
+ # Convert uint8s into uint64s.
+ hh_uint64 = hh_uint8.view(torch.int64).squeeze()
+
+ return hh_uint64
+
+
+def decode(hilberts, num_dims, num_bits):
+ """Decode an array of Hilbert integers into locations in a hypercube.
+
+ This is a vectorized-ish version of the Hilbert curve implementation by John
+ Skilling as described in:
+
+ Skilling, J. (2004, April). Programming the Hilbert curve. In AIP Conference
+ Proceedings (Vol. 707, No. 1, pp. 381-387). American Institute of Physics.
+
+ Params:
+ -------
+ hilberts - An ndarray of Hilbert integers. Must be an integer dtype and
+ cannot have fewer bits than num_dims * num_bits.
+
+ num_dims - The dimensionality of the hypercube. Integer.
+
+ num_bits - The number of bits for each dimension. Integer.
+
+ Returns:
+ --------
+ The output is an ndarray of unsigned integers with the same shape as hilberts
+ but with an additional dimension of size num_dims.
+ """
+
+ if num_dims * num_bits > 64:
+ raise ValueError(
+ """
+ num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
+ into a uint64. Are you sure you need that many points on your Hilbert
+ curve?
+ """
+ % (num_dims, num_bits)
+ )
+
+ # Handle the case where we got handed a naked integer.
+ hilberts = torch.atleast_1d(hilberts)
+
+ # Keep around the shape for later.
+ orig_shape = hilberts.shape
+ bitpack_mask = 2 ** torch.arange(0, 8).to(hilberts.device)
+ bitpack_mask_rev = bitpack_mask.flip(-1)
+
+ # Treat each of the hilberts as a s equence of eight uint8.
+ # This treats all of the inputs as uint64 and makes things uniform.
+ hh_uint8 = (
+ hilberts.ravel().type(torch.int64).view(torch.uint8).reshape((-1, 8)).flip(-1)
+ )
+
+ # Turn these lists of uints into lists of bits and then truncate to the size
+ # we actually need for using Skilling's procedure.
+ hh_bits = (
+ hh_uint8.unsqueeze(-1)
+ .bitwise_and(bitpack_mask_rev)
+ .ne(0)
+ .byte()
+ .flatten(-2, -1)[:, -num_dims * num_bits :]
+ )
+
+ # Take the sequence of bits and Gray-code it.
+ gray = binary2gray(hh_bits)
+
+ # There has got to be a better way to do this.
+ # I could index them differently, but the eventual packbits likes it this way.
+ gray = gray.reshape((-1, num_bits, num_dims)).swapaxes(1, 2)
+
+ # Iterate backwards through the bits.
+ for bit in range(num_bits - 1, -1, -1):
+ # Iterate backwards through the dimensions.
+ for dim in range(num_dims - 1, -1, -1):
+ # Identify which ones have this bit active.
+ mask = gray[:, dim, bit]
+
+ # Where this bit is on, invert the 0 dimension for lower bits.
+ gray[:, 0, bit + 1 :] = torch.logical_xor(
+ gray[:, 0, bit + 1 :], mask[:, None]
+ )
+
+ # Where the bit is off, exchange the lower bits with the 0 dimension.
+ to_flip = torch.logical_and(
+ torch.logical_not(mask[:, None]),
+ torch.logical_xor(gray[:, 0, bit + 1 :], gray[:, dim, bit + 1 :]),
+ )
+ gray[:, dim, bit + 1 :] = torch.logical_xor(
+ gray[:, dim, bit + 1 :], to_flip
+ )
+ gray[:, 0, bit + 1 :] = torch.logical_xor(gray[:, 0, bit + 1 :], to_flip)
+
+ # Pad back out to 64 bits.
+ extra_dims = 64 - num_bits
+ padded = torch.nn.functional.pad(gray, (extra_dims, 0), "constant", 0)
+
+ # Now chop these up into blocks of 8.
+ locs_chopped = padded.flip(-1).reshape((-1, num_dims, 8, 8))
+
+ # Take those blocks and turn them unto uint8s.
+ # from IPython import embed; embed()
+ locs_uint8 = (locs_chopped * bitpack_mask).sum(3).squeeze().type(torch.uint8)
+
+ # Finally, treat these as uint64s.
+ flat_locs = locs_uint8.view(torch.int64)
+
+ # Return them in the expected shape.
+ return flat_locs.reshape((*orig_shape, num_dims))
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/z_order.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/z_order.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fd01a5bcf4b6c76c5d75db4999326e174409ee3
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/serialization/z_order.py
@@ -0,0 +1,126 @@
+# --------------------------------------------------------
+# Octree-based Sparse Convolutional Neural Networks
+# Copyright (c) 2022 Peng-Shuai Wang
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Peng-Shuai Wang
+# --------------------------------------------------------
+
+import torch
+from typing import Optional, Union
+
+
+class KeyLUT:
+ def __init__(self):
+ r256 = torch.arange(256, dtype=torch.int64)
+ r512 = torch.arange(512, dtype=torch.int64)
+ zero = torch.zeros(256, dtype=torch.int64)
+ device = torch.device("cpu")
+
+ self._encode = {
+ device: (
+ self.xyz2key(r256, zero, zero, 8),
+ self.xyz2key(zero, r256, zero, 8),
+ self.xyz2key(zero, zero, r256, 8),
+ )
+ }
+ self._decode = {device: self.key2xyz(r512, 9)}
+
+ def encode_lut(self, device=torch.device("cpu")):
+ if device not in self._encode:
+ cpu = torch.device("cpu")
+ self._encode[device] = tuple(e.to(device) for e in self._encode[cpu])
+ return self._encode[device]
+
+ def decode_lut(self, device=torch.device("cpu")):
+ if device not in self._decode:
+ cpu = torch.device("cpu")
+ self._decode[device] = tuple(e.to(device) for e in self._decode[cpu])
+ return self._decode[device]
+
+ def xyz2key(self, x, y, z, depth):
+ key = torch.zeros_like(x)
+ for i in range(depth):
+ mask = 1 << i
+ key = (
+ key
+ | ((x & mask) << (2 * i + 2))
+ | ((y & mask) << (2 * i + 1))
+ | ((z & mask) << (2 * i + 0))
+ )
+ return key
+
+ def key2xyz(self, key, depth):
+ x = torch.zeros_like(key)
+ y = torch.zeros_like(key)
+ z = torch.zeros_like(key)
+ for i in range(depth):
+ x = x | ((key & (1 << (3 * i + 2))) >> (2 * i + 2))
+ y = y | ((key & (1 << (3 * i + 1))) >> (2 * i + 1))
+ z = z | ((key & (1 << (3 * i + 0))) >> (2 * i + 0))
+ return x, y, z
+
+
+_key_lut = KeyLUT()
+
+
+def xyz2key(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ z: torch.Tensor,
+ b: Optional[Union[torch.Tensor, int]] = None,
+ depth: int = 16,
+):
+ r"""Encodes :attr:`x`, :attr:`y`, :attr:`z` coordinates to the shuffled keys
+ based on pre-computed look up tables. The speed of this function is much
+ faster than the method based on for-loop.
+
+ Args:
+ x (torch.Tensor): The x coordinate.
+ y (torch.Tensor): The y coordinate.
+ z (torch.Tensor): The z coordinate.
+ b (torch.Tensor or int): The batch index of the coordinates, and should be
+ smaller than 32768. If :attr:`b` is :obj:`torch.Tensor`, the size of
+ :attr:`b` must be the same as :attr:`x`, :attr:`y`, and :attr:`z`.
+ depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
+ """
+
+ EX, EY, EZ = _key_lut.encode_lut(x.device)
+ x, y, z = x.long(), y.long(), z.long()
+
+ mask = 255 if depth > 8 else (1 << depth) - 1
+ key = EX[x & mask] | EY[y & mask] | EZ[z & mask]
+ if depth > 8:
+ mask = (1 << (depth - 8)) - 1
+ key16 = EX[(x >> 8) & mask] | EY[(y >> 8) & mask] | EZ[(z >> 8) & mask]
+ key = key16 << 24 | key
+
+ if b is not None:
+ b = b.long()
+ key = b << 48 | key
+
+ return key
+
+
+def key2xyz(key: torch.Tensor, depth: int = 16):
+ r"""Decodes the shuffled key to :attr:`x`, :attr:`y`, :attr:`z` coordinates
+ and the batch index based on pre-computed look up tables.
+
+ Args:
+ key (torch.Tensor): The shuffled key.
+ depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
+ """
+
+ DX, DY, DZ = _key_lut.decode_lut(key.device)
+ x, y, z = torch.zeros_like(key), torch.zeros_like(key), torch.zeros_like(key)
+
+ b = key >> 48
+ key = key & ((1 << 48) - 1)
+
+ n = (depth + 2) // 3
+ for i in range(n):
+ k = key >> (i * 9) & 511
+ x = x | (DX[k] << (i * 3))
+ y = y | (DY[k] << (i * 3))
+ z = z | (DZ[k] << (i * 3))
+
+ return x, y, z, b
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/structure.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/structure.py
new file mode 100644
index 0000000000000000000000000000000000000000..47fcd054067967f1ce5953d32df288ecc41c7aae
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/models/utils/structure.py
@@ -0,0 +1,180 @@
+import torch
+import spconv.pytorch as spconv
+
+try:
+ import ocnn
+except ImportError:
+ ocnn = None
+from addict import Dict
+
+from pointcept.models.utils.serialization import encode, decode
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class Point(Dict):
+ """
+ Point Structure of Pointcept
+
+ A Point (point cloud) in Pointcept is a dictionary that contains various properties of
+ a batched point cloud. The property with the following names have a specific definition
+ as follows:
+
+ - "coord": original coordinate of point cloud;
+ - "grid_coord": grid coordinate for specific grid size (related to GridSampling);
+ Point also support the following optional attributes:
+ - "offset": if not exist, initialized as batch size is 1;
+ - "batch": if not exist, initialized as batch size is 1;
+ - "feat": feature of point cloud, default input of model;
+ - "grid_size": Grid size of point cloud (related to GridSampling);
+ (related to Serialization)
+ - "serialized_depth": depth of serialization, 2 ** depth * grid_size describe the maximum of point cloud range;
+ - "serialized_code": a list of serialization codes;
+ - "serialized_order": a list of serialization order determined by code;
+ - "serialized_inverse": a list of inverse mapping determined by code;
+ (related to Sparsify: SpConv)
+ - "sparse_shape": Sparse shape for Sparse Conv Tensor;
+ - "sparse_conv_feat": SparseConvTensor init with information provide by Point;
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ # If one of "offset" or "batch" do not exist, generate by the existing one
+ if "batch" not in self.keys() and "offset" in self.keys():
+ self["batch"] = offset2batch(self.offset)
+ elif "offset" not in self.keys() and "batch" in self.keys():
+ self["offset"] = batch2offset(self.batch)
+
+ def serialization(self, order="z", depth=None, shuffle_orders=False):
+ """
+ Point Cloud Serialization
+
+ relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
+ """
+ assert "batch" in self.keys()
+ if "grid_coord" not in self.keys():
+ # if you don't want to operate GridSampling in data augmentation,
+ # please add the following augmentation into your pipline:
+ # dict(type="Copy", keys_dict={"grid_size": 0.01}),
+ # (adjust `grid_size` to what your want)
+ assert {"grid_size", "coord"}.issubset(self.keys())
+ self["grid_coord"] = torch.div(
+ self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
+ ).int()
+
+ if depth is None:
+ # Adaptive measure the depth of serialization cube (length = 2 ^ depth)
+ depth = int(self.grid_coord.max()).bit_length()
+ self["serialized_depth"] = depth
+ # Maximum bit length for serialization code is 63 (int64)
+ assert depth * 3 + len(self.offset).bit_length() <= 63
+ # Here we follow OCNN and set the depth limitation to 16 (48bit) for the point position.
+ # Although depth is limited to less than 16, we can encode a 655.36^3 (2^16 * 0.01) meter^3
+ # cube with a grid size of 0.01 meter. We consider it is enough for the current stage.
+ # We can unlock the limitation by optimizing the z-order encoding function if necessary.
+ assert depth <= 16
+
+ # The serialization codes are arranged as following structures:
+ # [Order1 ([n]),
+ # Order2 ([n]),
+ # ...
+ # OrderN ([n])] (k, n)
+ code = [
+ encode(self.grid_coord, self.batch, depth, order=order_) for order_ in order
+ ]
+ code = torch.stack(code)
+ order = torch.argsort(code)
+ inverse = torch.zeros_like(order).scatter_(
+ dim=1,
+ index=order,
+ src=torch.arange(0, code.shape[1], device=order.device).repeat(
+ code.shape[0], 1
+ ),
+ )
+
+ if shuffle_orders:
+ perm = torch.randperm(code.shape[0])
+ code = code[perm]
+ order = order[perm]
+ inverse = inverse[perm]
+
+ self["serialized_code"] = code
+ self["serialized_order"] = order
+ self["serialized_inverse"] = inverse
+
+ def sparsify(self, pad=96):
+ """
+ Point Cloud Serialization
+
+ Point cloud is sparse, here we use "sparsify" to specifically refer to
+ preparing "spconv.SparseConvTensor" for SpConv.
+
+ relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
+
+ pad: padding sparse for sparse shape.
+ """
+ assert {"feat", "batch"}.issubset(self.keys())
+ if "grid_coord" not in self.keys():
+ # if you don't want to operate GridSampling in data augmentation,
+ # please add the following augmentation into your pipline:
+ # dict(type="Copy", keys_dict={"grid_size": 0.01}),
+ # (adjust `grid_size` to what your want)
+ assert {"grid_size", "coord"}.issubset(self.keys())
+ self["grid_coord"] = torch.div(
+ self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
+ ).int()
+ if "sparse_shape" in self.keys():
+ sparse_shape = self.sparse_shape
+ else:
+ sparse_shape = torch.add(
+ torch.max(self.grid_coord, dim=0).values, pad
+ ).tolist()
+ sparse_conv_feat = spconv.SparseConvTensor(
+ features=self.feat,
+ indices=torch.cat(
+ [self.batch.unsqueeze(-1).int(), self.grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=self.batch[-1].tolist() + 1,
+ )
+ self["sparse_shape"] = sparse_shape
+ self["sparse_conv_feat"] = sparse_conv_feat
+
+ def octreetization(self, depth=None, full_depth=None):
+ """
+ Point Cloud Octreelization
+
+ Generate octree with OCNN
+ relay on ["grid_coord", "batch", "feat"]
+ """
+ assert (
+ ocnn is not None
+ ), "Please follow https://github.com/octree-nn/ocnn-pytorch install ocnn."
+ assert {"grid_coord", "feat", "batch"}.issubset(self.keys())
+ # add 1 to make grid space support shift order
+ if depth is None:
+ if "depth" in self.keys():
+ depth = self.depth
+ else:
+ depth = int(self.grid_coord.max() + 1).bit_length()
+ if full_depth is None:
+ full_depth = 2
+ self["depth"] = depth
+ assert depth <= 16 # maximum in ocnn
+
+ # [0, 2**depth] -> [0, 2] -> [-1, 1]
+ coord = self.grid_coord / 2 ** (self.depth - 1) - 1.0
+ point = ocnn.octree.Points(
+ points=coord,
+ features=self.feat,
+ batch_id=self.batch.unsqueeze(-1),
+ batch_size=self.batch[-1] + 1,
+ )
+ octree = ocnn.octree.Octree(
+ depth=depth,
+ full_depth=full_depth,
+ batch_size=self.batch[-1] + 1,
+ device=coord.device,
+ )
+ octree.build_octree(point)
+ octree.construct_all_neigh()
+ self["octree"] = octree
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/__init__.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/cache.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..623897e42a7a4256a65a1a0e9a7b5c0c46ce5a3e
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/cache.py
@@ -0,0 +1,56 @@
+"""
+Data Cache Utils
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import SharedArray
+
+try:
+ from multiprocessing.shared_memory import ShareableList
+except ImportError:
+ import warnings
+
+ warnings.warn("Please update python version >= 3.8 to enable shared_memory")
+import numpy as np
+
+
+def shared_array(name, var=None):
+ if var is not None:
+ # check exist
+ if os.path.exists(f"/dev/shm/{name}"):
+ return SharedArray.attach(f"shm://{name}")
+ # create shared_array
+ data = SharedArray.create(f"shm://{name}", var.shape, dtype=var.dtype)
+ data[...] = var[...]
+ data.flags.writeable = False
+ else:
+ data = SharedArray.attach(f"shm://{name}").copy()
+ return data
+
+
+def shared_dict(name, var=None):
+ name = str(name)
+ assert "." not in name # '.' is used as sep flag
+ data = {}
+ if var is not None:
+ assert isinstance(var, dict)
+ keys = var.keys()
+ # current version only cache np.array
+ keys_valid = []
+ for key in keys:
+ if isinstance(var[key], np.ndarray):
+ keys_valid.append(key)
+ keys = keys_valid
+
+ ShareableList(sequence=keys, name=name + ".keys")
+ for key in keys:
+ if isinstance(var[key], np.ndarray):
+ data[key] = shared_array(name=f"{name}.{key}", var=var[key])
+ else:
+ keys = list(ShareableList(name=name + ".keys"))
+ for key in keys:
+ data[key] = shared_array(name=f"{name}.{key}")
+ return data
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/comm.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/comm.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e29e7c690fe0500d3d9a84b6a8749e2f4f4655
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/comm.py
@@ -0,0 +1,198 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+This file contains primitives for multi-gpu communication.
+This is useful when doing distributed training.
+Modified from detectron2(https://github.com/facebookresearch/detectron2)
+
+Copyright (c) Xiaoyang Wu (xiaoyang.wu@connect.hku.hk). All Rights Reserved.
+Please cite our work if you use any part of the code.
+"""
+
+import functools
+import numpy as np
+import torch
+import torch.distributed as dist
+
+_LOCAL_PROCESS_GROUP = None
+"""
+A torch process group which only includes processes that on the same machine as the current process.
+This variable is set when processes are spawned by `launch()` in "engine/launch.py".
+"""
+
+
+def get_world_size() -> int:
+ if not dist.is_available():
+ return 1
+ if not dist.is_initialized():
+ return 1
+ return dist.get_world_size()
+
+
+def get_rank() -> int:
+ if not dist.is_available():
+ return 0
+ if not dist.is_initialized():
+ return 0
+ return dist.get_rank()
+
+
+def get_local_rank() -> int:
+ """
+ Returns:
+ The rank of the current process within the local (per-machine) process group.
+ """
+ if not dist.is_available():
+ return 0
+ if not dist.is_initialized():
+ return 0
+ assert (
+ _LOCAL_PROCESS_GROUP is not None
+ ), "Local process group is not created! Please use launch() to spawn processes!"
+ return dist.get_rank(group=_LOCAL_PROCESS_GROUP)
+
+
+def get_local_size() -> int:
+ """
+ Returns:
+ The size of the per-machine process group,
+ i.e. the number of processes per machine.
+ """
+ if not dist.is_available():
+ return 1
+ if not dist.is_initialized():
+ return 1
+ return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)
+
+
+def is_main_process() -> bool:
+ return get_rank() == 0
+
+
+def synchronize():
+ """
+ Helper function to synchronize (barrier) among all processes when
+ using distributed training
+ """
+ if not dist.is_available():
+ return
+ if not dist.is_initialized():
+ return
+ world_size = dist.get_world_size()
+ if world_size == 1:
+ return
+ if dist.get_backend() == dist.Backend.NCCL:
+ # This argument is needed to avoid warnings.
+ # It's valid only for NCCL backend.
+ dist.barrier(device_ids=[torch.cuda.current_device()])
+ else:
+ dist.barrier()
+
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+ """
+ Return a process group based on gloo backend, containing all the ranks
+ The result is cached.
+ """
+ if dist.get_backend() == "nccl":
+ return dist.new_group(backend="gloo")
+ else:
+ return dist.group.WORLD
+
+
+def all_gather(data, group=None):
+ """
+ Run all_gather on arbitrary picklable data (not necessarily tensors).
+ Args:
+ data: any picklable object
+ group: a torch process group. By default, will use a group which
+ contains all ranks on gloo backend.
+ Returns:
+ list[data]: list of data gathered from each rank
+ """
+ if get_world_size() == 1:
+ return [data]
+ if group is None:
+ group = (
+ _get_global_gloo_group()
+ ) # use CPU group by default, to reduce GPU RAM usage.
+ world_size = dist.get_world_size(group)
+ if world_size == 1:
+ return [data]
+
+ output = [None for _ in range(world_size)]
+ dist.all_gather_object(output, data, group=group)
+ return output
+
+
+def gather(data, dst=0, group=None):
+ """
+ Run gather on arbitrary picklable data (not necessarily tensors).
+ Args:
+ data: any picklable object
+ dst (int): destination rank
+ group: a torch process group. By default, will use a group which
+ contains all ranks on gloo backend.
+ Returns:
+ list[data]: on dst, a list of data gathered from each rank. Otherwise,
+ an empty list.
+ """
+ if get_world_size() == 1:
+ return [data]
+ if group is None:
+ group = _get_global_gloo_group()
+ world_size = dist.get_world_size(group=group)
+ if world_size == 1:
+ return [data]
+ rank = dist.get_rank(group=group)
+
+ if rank == dst:
+ output = [None for _ in range(world_size)]
+ dist.gather_object(data, output, dst=dst, group=group)
+ return output
+ else:
+ dist.gather_object(data, None, dst=dst, group=group)
+ return []
+
+
+def shared_random_seed():
+ """
+ Returns:
+ int: a random number that is the same across all workers.
+ If workers need a shared RNG, they can use this shared seed to
+ create one.
+ All workers must call this function, otherwise it will deadlock.
+ """
+ ints = np.random.randint(2**31)
+ all_ints = all_gather(ints)
+ return all_ints[0]
+
+
+def reduce_dict(input_dict, average=True):
+ """
+ Reduce the values in the dictionary from all processes so that process with rank
+ 0 has the reduced results.
+ Args:
+ input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor.
+ average (bool): whether to do average or sum
+ Returns:
+ a dict with the same keys as input_dict, after reduction.
+ """
+ world_size = get_world_size()
+ if world_size < 2:
+ return input_dict
+ with torch.no_grad():
+ names = []
+ values = []
+ # sort the keys so that they are consistent across processes
+ for k in sorted(input_dict.keys()):
+ names.append(k)
+ values.append(input_dict[k])
+ values = torch.stack(values, dim=0)
+ dist.reduce(values, dst=0)
+ if dist.get_rank() == 0 and average:
+ # only main process gets accumulated, so only divide by
+ # world_size in this case
+ values /= world_size
+ reduced_dict = {k: v for k, v in zip(names, values)}
+ return reduced_dict
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/config.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..316dd458b3760b38feeb33d941ad9ad060364a61
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/config.py
@@ -0,0 +1,694 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import ast
+import copy
+import os
+import os.path as osp
+import platform
+import shutil
+import sys
+import tempfile
+import uuid
+import warnings
+from argparse import Action, ArgumentParser
+from collections import abc
+from importlib import import_module
+
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+from .misc import import_modules_from_strings
+from .path import check_file_exist
+
+if platform.system() == "Windows":
+ import regex as re
+else:
+ import re
+
+BASE_KEY = "_base_"
+DELETE_KEY = "_delete_"
+DEPRECATION_KEY = "_deprecation_"
+RESERVED_KEYS = ["filename", "text", "pretty_text"]
+
+
+class ConfigDict(Dict):
+ def __missing__(self, name):
+ raise KeyError(name)
+
+ def __getattr__(self, name):
+ try:
+ value = super(ConfigDict, self).__getattr__(name)
+ except KeyError:
+ ex = AttributeError(
+ f"'{self.__class__.__name__}' object has no " f"attribute '{name}'"
+ )
+ except Exception as e:
+ ex = e
+ else:
+ return value
+ raise ex
+
+
+def add_args(parser, cfg, prefix=""):
+ for k, v in cfg.items():
+ if isinstance(v, str):
+ parser.add_argument("--" + prefix + k)
+ elif isinstance(v, int):
+ parser.add_argument("--" + prefix + k, type=int)
+ elif isinstance(v, float):
+ parser.add_argument("--" + prefix + k, type=float)
+ elif isinstance(v, bool):
+ parser.add_argument("--" + prefix + k, action="store_true")
+ elif isinstance(v, dict):
+ add_args(parser, v, prefix + k + ".")
+ elif isinstance(v, abc.Iterable):
+ parser.add_argument("--" + prefix + k, type=type(v[0]), nargs="+")
+ else:
+ print(f"cannot parse key {prefix + k} of type {type(v)}")
+ return parser
+
+
+class Config:
+ """A facility for config and config files.
+
+ It supports common file formats as configs: python/json/yaml. The interface
+ is the same as a dict object and also allows access config values as
+ attributes.
+
+ Example:
+ >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+ >>> cfg.a
+ 1
+ >>> cfg.b
+ {'b1': [0, 1]}
+ >>> cfg.b.b1
+ [0, 1]
+ >>> cfg = Config.fromfile('tests/data/config/a.py')
+ >>> cfg.filename
+ "/home/kchen/projects/mmcv/tests/data/config/a.py"
+ >>> cfg.item4
+ 'test'
+ >>> cfg
+ "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
+ "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
+ """
+
+ @staticmethod
+ def _validate_py_syntax(filename):
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ content = f.read()
+ try:
+ ast.parse(content)
+ except SyntaxError as e:
+ raise SyntaxError(
+ "There are syntax errors in config " f"file {filename}: {e}"
+ )
+
+ @staticmethod
+ def _substitute_predefined_vars(filename, temp_config_name):
+ file_dirname = osp.dirname(filename)
+ file_basename = osp.basename(filename)
+ file_basename_no_extension = osp.splitext(file_basename)[0]
+ file_extname = osp.splitext(filename)[1]
+ support_templates = dict(
+ fileDirname=file_dirname,
+ fileBasename=file_basename,
+ fileBasenameNoExtension=file_basename_no_extension,
+ fileExtname=file_extname,
+ )
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ config_file = f.read()
+ for key, value in support_templates.items():
+ regexp = r"\{\{\s*" + str(key) + r"\s*\}\}"
+ value = value.replace("\\", "/")
+ config_file = re.sub(regexp, value, config_file)
+ with open(temp_config_name, "w", encoding="utf-8") as tmp_config_file:
+ tmp_config_file.write(config_file)
+
+ @staticmethod
+ def _pre_substitute_base_vars(filename, temp_config_name):
+ """Substitute base variable placehoders to string, so that parsing
+ would work."""
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ config_file = f.read()
+ base_var_dict = {}
+ regexp = r"\{\{\s*" + BASE_KEY + r"\.([\w\.]+)\s*\}\}"
+ base_vars = set(re.findall(regexp, config_file))
+ for base_var in base_vars:
+ randstr = f"_{base_var}_{uuid.uuid4().hex.lower()[:6]}"
+ base_var_dict[randstr] = base_var
+ regexp = r"\{\{\s*" + BASE_KEY + r"\." + base_var + r"\s*\}\}"
+ config_file = re.sub(regexp, f'"{randstr}"', config_file)
+ with open(temp_config_name, "w", encoding="utf-8") as tmp_config_file:
+ tmp_config_file.write(config_file)
+ return base_var_dict
+
+ @staticmethod
+ def _substitute_base_vars(cfg, base_var_dict, base_cfg):
+ """Substitute variable strings to their actual values."""
+ cfg = copy.deepcopy(cfg)
+
+ if isinstance(cfg, dict):
+ for k, v in cfg.items():
+ if isinstance(v, str) and v in base_var_dict:
+ new_v = base_cfg
+ for new_k in base_var_dict[v].split("."):
+ new_v = new_v[new_k]
+ cfg[k] = new_v
+ elif isinstance(v, (list, tuple, dict)):
+ cfg[k] = Config._substitute_base_vars(v, base_var_dict, base_cfg)
+ elif isinstance(cfg, tuple):
+ cfg = tuple(
+ Config._substitute_base_vars(c, base_var_dict, base_cfg) for c in cfg
+ )
+ elif isinstance(cfg, list):
+ cfg = [
+ Config._substitute_base_vars(c, base_var_dict, base_cfg) for c in cfg
+ ]
+ elif isinstance(cfg, str) and cfg in base_var_dict:
+ new_v = base_cfg
+ for new_k in base_var_dict[cfg].split("."):
+ new_v = new_v[new_k]
+ cfg = new_v
+
+ return cfg
+
+ @staticmethod
+ def _file2dict(filename, use_predefined_variables=True):
+ filename = osp.abspath(osp.expanduser(filename))
+ check_file_exist(filename)
+ fileExtname = osp.splitext(filename)[1]
+ if fileExtname not in [".py", ".json", ".yaml", ".yml"]:
+ raise IOError("Only py/yml/yaml/json type are supported now!")
+
+ with tempfile.TemporaryDirectory() as temp_config_dir:
+ temp_config_file = tempfile.NamedTemporaryFile(
+ dir=temp_config_dir, suffix=fileExtname
+ )
+ if platform.system() == "Windows":
+ temp_config_file.close()
+ temp_config_name = osp.basename(temp_config_file.name)
+ # Substitute predefined variables
+ if use_predefined_variables:
+ Config._substitute_predefined_vars(filename, temp_config_file.name)
+ else:
+ shutil.copyfile(filename, temp_config_file.name)
+ # Substitute base variables from placeholders to strings
+ base_var_dict = Config._pre_substitute_base_vars(
+ temp_config_file.name, temp_config_file.name
+ )
+
+ if filename.endswith(".py"):
+ temp_module_name = osp.splitext(temp_config_name)[0]
+ sys.path.insert(0, temp_config_dir)
+ Config._validate_py_syntax(filename)
+ mod = import_module(temp_module_name)
+ sys.path.pop(0)
+ cfg_dict = {
+ name: value
+ for name, value in mod.__dict__.items()
+ if not name.startswith("__")
+ }
+ # delete imported module
+ del sys.modules[temp_module_name]
+ elif filename.endswith((".yml", ".yaml", ".json")):
+ raise NotImplementedError
+ # close temp file
+ temp_config_file.close()
+
+ # check deprecation information
+ if DEPRECATION_KEY in cfg_dict:
+ deprecation_info = cfg_dict.pop(DEPRECATION_KEY)
+ warning_msg = (
+ f"The config file {filename} will be deprecated " "in the future."
+ )
+ if "expected" in deprecation_info:
+ warning_msg += f' Please use {deprecation_info["expected"]} ' "instead."
+ if "reference" in deprecation_info:
+ warning_msg += (
+ " More information can be found at "
+ f'{deprecation_info["reference"]}'
+ )
+ warnings.warn(warning_msg)
+
+ cfg_text = filename + "\n"
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ cfg_text += f.read()
+
+ if BASE_KEY in cfg_dict:
+ cfg_dir = osp.dirname(filename)
+ base_filename = cfg_dict.pop(BASE_KEY)
+ base_filename = (
+ base_filename if isinstance(base_filename, list) else [base_filename]
+ )
+
+ cfg_dict_list = list()
+ cfg_text_list = list()
+ for f in base_filename:
+ _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f))
+ cfg_dict_list.append(_cfg_dict)
+ cfg_text_list.append(_cfg_text)
+
+ base_cfg_dict = dict()
+ for c in cfg_dict_list:
+ duplicate_keys = base_cfg_dict.keys() & c.keys()
+ if len(duplicate_keys) > 0:
+ raise KeyError(
+ "Duplicate key is not allowed among bases. "
+ f"Duplicate keys: {duplicate_keys}"
+ )
+ base_cfg_dict.update(c)
+
+ # Substitute base variables from strings to their actual values
+ cfg_dict = Config._substitute_base_vars(
+ cfg_dict, base_var_dict, base_cfg_dict
+ )
+
+ base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
+ cfg_dict = base_cfg_dict
+
+ # merge cfg_text
+ cfg_text_list.append(cfg_text)
+ cfg_text = "\n".join(cfg_text_list)
+
+ return cfg_dict, cfg_text
+
+ @staticmethod
+ def _merge_a_into_b(a, b, allow_list_keys=False):
+ """merge dict ``a`` into dict ``b`` (non-inplace).
+
+ Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
+ in-place modifications.
+
+ Args:
+ a (dict): The source dict to be merged into ``b``.
+ b (dict): The origin dict to be fetch keys from ``a``.
+ allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
+ are allowed in source ``a`` and will replace the element of the
+ corresponding index in b if b is a list. Default: False.
+
+ Returns:
+ dict: The modified dict of ``b`` using ``a``.
+
+ Examples:
+ # Normally merge a into b.
+ >>> Config._merge_a_into_b(
+ ... dict(obj=dict(a=2)), dict(obj=dict(a=1)))
+ {'obj': {'a': 2}}
+
+ # Delete b first and merge a into b.
+ >>> Config._merge_a_into_b(
+ ... dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1)))
+ {'obj': {'a': 2}}
+
+ # b is a list
+ >>> Config._merge_a_into_b(
+ ... {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True)
+ [{'a': 2}, {'b': 2}]
+ """
+ b = b.copy()
+ for k, v in a.items():
+ if allow_list_keys and k.isdigit() and isinstance(b, list):
+ k = int(k)
+ if len(b) <= k:
+ raise KeyError(f"Index {k} exceeds the length of list {b}")
+ b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
+ elif isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
+ allowed_types = (dict, list) if allow_list_keys else dict
+ if not isinstance(b[k], allowed_types):
+ raise TypeError(
+ f"{k}={v} in child config cannot inherit from base "
+ f"because {k} is a dict in the child config but is of "
+ f"type {type(b[k])} in base config. You may set "
+ f"`{DELETE_KEY}=True` to ignore the base config"
+ )
+ b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
+ else:
+ b[k] = v
+ return b
+
+ @staticmethod
+ def fromfile(filename, use_predefined_variables=True, import_custom_modules=True):
+ cfg_dict, cfg_text = Config._file2dict(filename, use_predefined_variables)
+ if import_custom_modules and cfg_dict.get("custom_imports", None):
+ import_modules_from_strings(**cfg_dict["custom_imports"])
+ return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
+
+ @staticmethod
+ def fromstring(cfg_str, file_format):
+ """Generate config from config str.
+
+ Args:
+ cfg_str (str): Config str.
+ file_format (str): Config file format corresponding to the
+ config str. Only py/yml/yaml/json type are supported now!
+
+ Returns:
+ obj:`Config`: Config obj.
+ """
+ if file_format not in [".py", ".json", ".yaml", ".yml"]:
+ raise IOError("Only py/yml/yaml/json type are supported now!")
+ if file_format != ".py" and "dict(" in cfg_str:
+ # check if users specify a wrong suffix for python
+ warnings.warn('Please check "file_format", the file format may be .py')
+ with tempfile.NamedTemporaryFile(
+ "w", encoding="utf-8", suffix=file_format, delete=False
+ ) as temp_file:
+ temp_file.write(cfg_str)
+ # on windows, previous implementation cause error
+ # see PR 1077 for details
+ cfg = Config.fromfile(temp_file.name)
+ os.remove(temp_file.name)
+ return cfg
+
+ @staticmethod
+ def auto_argparser(description=None):
+ """Generate argparser from config file automatically (experimental)"""
+ partial_parser = ArgumentParser(description=description)
+ partial_parser.add_argument("config", help="config file path")
+ cfg_file = partial_parser.parse_known_args()[0].config
+ cfg = Config.fromfile(cfg_file)
+ parser = ArgumentParser(description=description)
+ parser.add_argument("config", help="config file path")
+ add_args(parser, cfg)
+ return parser, cfg
+
+ def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
+ if cfg_dict is None:
+ cfg_dict = dict()
+ elif not isinstance(cfg_dict, dict):
+ raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
+ for key in cfg_dict:
+ if key in RESERVED_KEYS:
+ raise KeyError(f"{key} is reserved for config file")
+
+ super(Config, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
+ super(Config, self).__setattr__("_filename", filename)
+ if cfg_text:
+ text = cfg_text
+ elif filename:
+ with open(filename, "r") as f:
+ text = f.read()
+ else:
+ text = ""
+ super(Config, self).__setattr__("_text", text)
+
+ @property
+ def filename(self):
+ return self._filename
+
+ @property
+ def text(self):
+ return self._text
+
+ @property
+ def pretty_text(self):
+ indent = 4
+
+ def _indent(s_, num_spaces):
+ s = s_.split("\n")
+ if len(s) == 1:
+ return s_
+ first = s.pop(0)
+ s = [(num_spaces * " ") + line for line in s]
+ s = "\n".join(s)
+ s = first + "\n" + s
+ return s
+
+ def _format_basic_types(k, v, use_mapping=False):
+ if isinstance(v, str):
+ v_str = f"'{v}'"
+ else:
+ v_str = str(v)
+
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: {v_str}"
+ else:
+ attr_str = f"{str(k)}={v_str}"
+ attr_str = _indent(attr_str, indent)
+
+ return attr_str
+
+ def _format_list(k, v, use_mapping=False):
+ # check if all items in the list are dict
+ if all(isinstance(_, dict) for _ in v):
+ v_str = "[\n"
+ v_str += "\n".join(
+ f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
+ ).rstrip(",")
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: {v_str}"
+ else:
+ attr_str = f"{str(k)}={v_str}"
+ attr_str = _indent(attr_str, indent) + "]"
+ else:
+ attr_str = _format_basic_types(k, v, use_mapping)
+ return attr_str
+
+ def _contain_invalid_identifier(dict_str):
+ contain_invalid_identifier = False
+ for key_name in dict_str:
+ contain_invalid_identifier |= not str(key_name).isidentifier()
+ return contain_invalid_identifier
+
+ def _format_dict(input_dict, outest_level=False):
+ r = ""
+ s = []
+
+ use_mapping = _contain_invalid_identifier(input_dict)
+ if use_mapping:
+ r += "{"
+ for idx, (k, v) in enumerate(input_dict.items()):
+ is_last = idx >= len(input_dict) - 1
+ end = "" if outest_level or is_last else ","
+ if isinstance(v, dict):
+ v_str = "\n" + _format_dict(v)
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: dict({v_str}"
+ else:
+ attr_str = f"{str(k)}=dict({v_str}"
+ attr_str = _indent(attr_str, indent) + ")" + end
+ elif isinstance(v, list):
+ attr_str = _format_list(k, v, use_mapping) + end
+ else:
+ attr_str = _format_basic_types(k, v, use_mapping) + end
+
+ s.append(attr_str)
+ r += "\n".join(s)
+ if use_mapping:
+ r += "}"
+ return r
+
+ cfg_dict = self._cfg_dict.to_dict()
+ text = _format_dict(cfg_dict, outest_level=True)
+ # copied from setup.cfg
+ yapf_style = dict(
+ based_on_style="pep8",
+ blank_line_before_nested_class_or_def=True,
+ split_before_expression_after_opening_paren=True,
+ )
+ text, _ = FormatCode(text, style_config=yapf_style, verify=True)
+
+ return text
+
+ def __repr__(self):
+ return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
+
+ def __len__(self):
+ return len(self._cfg_dict)
+
+ def __getattr__(self, name):
+ return getattr(self._cfg_dict, name)
+
+ def __getitem__(self, name):
+ return self._cfg_dict.__getitem__(name)
+
+ def __setattr__(self, name, value):
+ if isinstance(value, dict):
+ value = ConfigDict(value)
+ self._cfg_dict.__setattr__(name, value)
+
+ def __setitem__(self, name, value):
+ if isinstance(value, dict):
+ value = ConfigDict(value)
+ self._cfg_dict.__setitem__(name, value)
+
+ def __iter__(self):
+ return iter(self._cfg_dict)
+
+ def __getstate__(self):
+ return (self._cfg_dict, self._filename, self._text)
+
+ def __setstate__(self, state):
+ _cfg_dict, _filename, _text = state
+ super(Config, self).__setattr__("_cfg_dict", _cfg_dict)
+ super(Config, self).__setattr__("_filename", _filename)
+ super(Config, self).__setattr__("_text", _text)
+
+ def dump(self, file=None):
+ cfg_dict = super(Config, self).__getattribute__("_cfg_dict").to_dict()
+ if self.filename.endswith(".py"):
+ if file is None:
+ return self.pretty_text
+ else:
+ with open(file, "w", encoding="utf-8") as f:
+ f.write(self.pretty_text)
+ else:
+ import mmcv
+
+ if file is None:
+ file_format = self.filename.split(".")[-1]
+ return mmcv.dump(cfg_dict, file_format=file_format)
+ else:
+ mmcv.dump(cfg_dict, file)
+
+ def merge_from_dict(self, options, allow_list_keys=True):
+ """Merge list into cfg_dict.
+
+ Merge the dict parsed by MultipleKVAction into this cfg.
+
+ Examples:
+ >>> options = {'models.backbone.depth': 50,
+ ... 'models.backbone.with_cp':True}
+ >>> cfg = Config(dict(models=dict(backbone=dict(type='ResNet'))))
+ >>> cfg.merge_from_dict(options)
+ >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+ >>> assert cfg_dict == dict(
+ ... models=dict(backbone=dict(depth=50, with_cp=True)))
+
+ # Merge list element
+ >>> cfg = Config(dict(pipeline=[
+ ... dict(type='LoadImage'), dict(type='LoadAnnotations')]))
+ >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})
+ >>> cfg.merge_from_dict(options, allow_list_keys=True)
+ >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+ >>> assert cfg_dict == dict(pipeline=[
+ ... dict(type='SelfLoadImage'), dict(type='LoadAnnotations')])
+
+ Args:
+ options (dict): dict of configs to merge from.
+ allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
+ are allowed in ``options`` and will replace the element of the
+ corresponding index in the config if the config is a list.
+ Default: True.
+ """
+ option_cfg_dict = {}
+ for full_key, v in options.items():
+ d = option_cfg_dict
+ key_list = full_key.split(".")
+ for subkey in key_list[:-1]:
+ d.setdefault(subkey, ConfigDict())
+ d = d[subkey]
+ subkey = key_list[-1]
+ d[subkey] = v
+
+ cfg_dict = super(Config, self).__getattribute__("_cfg_dict")
+ super(Config, self).__setattr__(
+ "_cfg_dict",
+ Config._merge_a_into_b(
+ option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys
+ ),
+ )
+
+
+class DictAction(Action):
+ """
+ argparse action to split an argument into KEY=VALUE form
+ on the first = and append to a dictionary. List options can
+ be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
+ brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
+ list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
+ """
+
+ @staticmethod
+ def _parse_int_float_bool(val):
+ try:
+ return int(val)
+ except ValueError:
+ pass
+ try:
+ return float(val)
+ except ValueError:
+ pass
+ if val.lower() in ["true", "false"]:
+ return True if val.lower() == "true" else False
+ return val
+
+ @staticmethod
+ def _parse_iterable(val):
+ """Parse iterable values in the string.
+
+ All elements inside '()' or '[]' are treated as iterable values.
+
+ Args:
+ val (str): Value string.
+
+ Returns:
+ list | tuple: The expanded list or tuple from the string.
+
+ Examples:
+ >>> DictAction._parse_iterable('1,2,3')
+ [1, 2, 3]
+ >>> DictAction._parse_iterable('[a, b, c]')
+ ['a', 'b', 'c']
+ >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]')
+ [(1, 2, 3), ['a', 'b'], 'c']
+ """
+
+ def find_next_comma(string):
+ """Find the position of next comma in the string.
+
+ If no ',' is found in the string, return the string length. All
+ chars inside '()' and '[]' are treated as one element and thus ','
+ inside these brackets are ignored.
+ """
+ assert (string.count("(") == string.count(")")) and (
+ string.count("[") == string.count("]")
+ ), f"Imbalanced brackets exist in {string}"
+ end = len(string)
+ for idx, char in enumerate(string):
+ pre = string[:idx]
+ # The string before this ',' is balanced
+ if (
+ (char == ",")
+ and (pre.count("(") == pre.count(")"))
+ and (pre.count("[") == pre.count("]"))
+ ):
+ end = idx
+ break
+ return end
+
+ # Strip ' and " characters and replace whitespace.
+ val = val.strip("'\"").replace(" ", "")
+ is_tuple = False
+ if val.startswith("(") and val.endswith(")"):
+ is_tuple = True
+ val = val[1:-1]
+ elif val.startswith("[") and val.endswith("]"):
+ val = val[1:-1]
+ elif "," not in val:
+ # val is a single value
+ return DictAction._parse_int_float_bool(val)
+
+ values = []
+ while len(val) > 0:
+ comma_idx = find_next_comma(val)
+ element = DictAction._parse_iterable(val[:comma_idx])
+ values.append(element)
+ val = val[comma_idx + 1 :]
+ if is_tuple:
+ values = tuple(values)
+ return values
+
+ def __call__(self, parser, namespace, values, option_string=None):
+ options = {}
+ for kv in values:
+ key, val = kv.split("=", maxsplit=1)
+ options[key] = self._parse_iterable(val)
+ setattr(namespace, self.dest, options)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/env.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/env.py
new file mode 100644
index 0000000000000000000000000000000000000000..653f007dde5c4a7564e732da88dd47e7d37adf97
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/env.py
@@ -0,0 +1,36 @@
+"""
+Environment Utils
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import random
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+
+from datetime import datetime
+
+
+def get_random_seed():
+ seed = (
+ os.getpid()
+ + int(datetime.now().strftime("%S%f"))
+ + int.from_bytes(os.urandom(2), "big")
+ )
+ return seed
+
+
+def set_seed(seed=None):
+ if seed is None:
+ seed = get_random_seed()
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+ cudnn.benchmark = False
+ cudnn.deterministic = True
+ os.environ["PYTHONHASHSEED"] = str(seed)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/events.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/events.py
new file mode 100644
index 0000000000000000000000000000000000000000..831638a2111f425113925cca5cd2d2bbb91c1c52
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/events.py
@@ -0,0 +1,593 @@
+"""
+Events Utils
+
+Modified from Detectron2
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import datetime
+import json
+import logging
+import os
+import time
+import torch
+import numpy as np
+
+from typing import List, Optional, Tuple
+from collections import defaultdict
+from contextlib import contextmanager
+
+__all__ = [
+ "get_event_storage",
+ "JSONWriter",
+ "TensorboardXWriter",
+ "CommonMetricPrinter",
+ "EventStorage",
+]
+
+_CURRENT_STORAGE_STACK = []
+
+
+def get_event_storage():
+ """
+ Returns:
+ The :class:`EventStorage` object that's currently being used.
+ Throws an error if no :class:`EventStorage` is currently enabled.
+ """
+ assert len(
+ _CURRENT_STORAGE_STACK
+ ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!"
+ return _CURRENT_STORAGE_STACK[-1]
+
+
+class EventWriter:
+ """
+ Base class for writers that obtain events from :class:`EventStorage` and process them.
+ """
+
+ def write(self):
+ raise NotImplementedError
+
+ def close(self):
+ pass
+
+
+class JSONWriter(EventWriter):
+ """
+ Write scalars to a json file.
+ It saves scalars as one json per line (instead of a big json) for easy parsing.
+ Examples parsing such a json file:
+ ::
+ $ cat metrics.json | jq -s '.[0:2]'
+ [
+ {
+ "data_time": 0.008433341979980469,
+ "iteration": 19,
+ "loss": 1.9228371381759644,
+ "loss_box_reg": 0.050025828182697296,
+ "loss_classifier": 0.5316952466964722,
+ "loss_mask": 0.7236229181289673,
+ "loss_rpn_box": 0.0856662318110466,
+ "loss_rpn_cls": 0.48198649287223816,
+ "lr": 0.007173333333333333,
+ "time": 0.25401854515075684
+ },
+ {
+ "data_time": 0.007216215133666992,
+ "iteration": 39,
+ "loss": 1.282649278640747,
+ "loss_box_reg": 0.06222952902317047,
+ "loss_classifier": 0.30682939291000366,
+ "loss_mask": 0.6970193982124329,
+ "loss_rpn_box": 0.038663312792778015,
+ "loss_rpn_cls": 0.1471673548221588,
+ "lr": 0.007706666666666667,
+ "time": 0.2490077018737793
+ }
+ ]
+ $ cat metrics.json | jq '.loss_mask'
+ 0.7126231789588928
+ 0.689423680305481
+ 0.6776131987571716
+ ...
+ """
+
+ def __init__(self, json_file, window_size=20):
+ """
+ Args:
+ json_file (str): path to the json file. New data will be appended if the file exists.
+ window_size (int): the window size of median smoothing for the scalars whose
+ `smoothing_hint` are True.
+ """
+ self._file_handle = open(json_file, "a")
+ self._window_size = window_size
+ self._last_write = -1
+
+ def write(self):
+ storage = get_event_storage()
+ to_save = defaultdict(dict)
+
+ for k, (v, iter) in storage.latest_with_smoothing_hint(
+ self._window_size
+ ).items():
+ # keep scalars that have not been written
+ if iter <= self._last_write:
+ continue
+ to_save[iter][k] = v
+ if len(to_save):
+ all_iters = sorted(to_save.keys())
+ self._last_write = max(all_iters)
+
+ for itr, scalars_per_iter in to_save.items():
+ scalars_per_iter["iteration"] = itr
+ self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n")
+ self._file_handle.flush()
+ try:
+ os.fsync(self._file_handle.fileno())
+ except AttributeError:
+ pass
+
+ def close(self):
+ self._file_handle.close()
+
+
+class TensorboardXWriter(EventWriter):
+ """
+ Write all scalars to a tensorboard file.
+ """
+
+ def __init__(self, log_dir: str, window_size: int = 20, **kwargs):
+ """
+ Args:
+ log_dir (str): the directory to save the output events
+ window_size (int): the scalars will be median-smoothed by this window size
+ kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`
+ """
+ self._window_size = window_size
+ from torch.utils.tensorboard import SummaryWriter
+
+ self._writer = SummaryWriter(log_dir, **kwargs)
+ self._last_write = -1
+
+ def write(self):
+ storage = get_event_storage()
+ new_last_write = self._last_write
+ for k, (v, iter) in storage.latest_with_smoothing_hint(
+ self._window_size
+ ).items():
+ if iter > self._last_write:
+ self._writer.add_scalar(k, v, iter)
+ new_last_write = max(new_last_write, iter)
+ self._last_write = new_last_write
+
+ # storage.put_{image,histogram} is only meant to be used by
+ # tensorboard writer. So we access its internal fields directly from here.
+ if len(storage._vis_data) >= 1:
+ for img_name, img, step_num in storage._vis_data:
+ self._writer.add_image(img_name, img, step_num)
+ # Storage stores all image data and rely on this writer to clear them.
+ # As a result it assumes only one writer will use its image data.
+ # An alternative design is to let storage store limited recent
+ # data (e.g. only the most recent image) that all writers can access.
+ # In that case a writer may not see all image data if its period is long.
+ storage.clear_images()
+
+ if len(storage._histograms) >= 1:
+ for params in storage._histograms:
+ self._writer.add_histogram_raw(**params)
+ storage.clear_histograms()
+
+ def close(self):
+ if hasattr(self, "_writer"): # doesn't exist when the code fails at import
+ self._writer.close()
+
+
+class CommonMetricPrinter(EventWriter):
+ """
+ Print **common** metrics to the terminal, including
+ iteration time, ETA, memory, all losses, and the learning rate.
+ It also applies smoothing using a window of 20 elements.
+ It's meant to print common metrics in common ways.
+ To print something in more customized ways, please implement a similar printer by yourself.
+ """
+
+ def __init__(self, max_iter: Optional[int] = None, window_size: int = 20):
+ """
+ Args:
+ max_iter: the maximum number of iterations to train.
+ Used to compute ETA. If not given, ETA will not be printed.
+ window_size (int): the losses will be median-smoothed by this window size
+ """
+ self.logger = logging.getLogger(__name__)
+ self._max_iter = max_iter
+ self._window_size = window_size
+ self._last_write = (
+ None # (step, time) of last call to write(). Used to compute ETA
+ )
+
+ def _get_eta(self, storage) -> Optional[str]:
+ if self._max_iter is None:
+ return ""
+ iteration = storage.iter
+ try:
+ eta_seconds = storage.history("time").median(1000) * (
+ self._max_iter - iteration - 1
+ )
+ storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False)
+ return str(datetime.timedelta(seconds=int(eta_seconds)))
+ except KeyError:
+ # estimate eta on our own - more noisy
+ eta_string = None
+ if self._last_write is not None:
+ estimate_iter_time = (time.perf_counter() - self._last_write[1]) / (
+ iteration - self._last_write[0]
+ )
+ eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1)
+ eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+ self._last_write = (iteration, time.perf_counter())
+ return eta_string
+
+ def write(self):
+ storage = get_event_storage()
+ iteration = storage.iter
+ if iteration == self._max_iter:
+ # This hook only reports training progress (loss, ETA, etc) but not other data,
+ # therefore do not write anything after training succeeds, even if this method
+ # is called.
+ return
+
+ try:
+ data_time = storage.history("data_time").avg(20)
+ except KeyError:
+ # they may not exist in the first few iterations (due to warmup)
+ # or when SimpleTrainer is not used
+ data_time = None
+ try:
+ iter_time = storage.history("time").global_avg()
+ except KeyError:
+ iter_time = None
+ try:
+ lr = "{:.5g}".format(storage.history("lr").latest())
+ except KeyError:
+ lr = "N/A"
+
+ eta_string = self._get_eta(storage)
+
+ if torch.cuda.is_available():
+ max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
+ else:
+ max_mem_mb = None
+
+ # NOTE: max_mem is parsed by grep in "dev/parse_results.sh"
+ self.logger.info(
+ " {eta}iter: {iter} {losses} {time}{data_time}lr: {lr} {memory}".format(
+ eta=f"eta: {eta_string} " if eta_string else "",
+ iter=iteration,
+ losses=" ".join(
+ [
+ "{}: {:.4g}".format(k, v.median(self._window_size))
+ for k, v in storage.histories().items()
+ if "loss" in k
+ ]
+ ),
+ time=(
+ "time: {:.4f} ".format(iter_time) if iter_time is not None else ""
+ ),
+ data_time=(
+ "data_time: {:.4f} ".format(data_time)
+ if data_time is not None
+ else ""
+ ),
+ lr=lr,
+ memory=(
+ "max_mem: {:.0f}M".format(max_mem_mb)
+ if max_mem_mb is not None
+ else ""
+ ),
+ )
+ )
+
+
+class EventStorage:
+ """
+ The user-facing class that provides metric storage functionalities.
+ In the future we may add support for storing / logging other types of data if needed.
+ """
+
+ def __init__(self, start_iter=0):
+ """
+ Args:
+ start_iter (int): the iteration number to start with
+ """
+ self._history = defaultdict(AverageMeter)
+ self._smoothing_hints = {}
+ self._latest_scalars = {}
+ self._iter = start_iter
+ self._current_prefix = ""
+ self._vis_data = []
+ self._histograms = []
+
+ # def put_image(self, img_name, img_tensor):
+ # """
+ # Add an `img_tensor` associated with `img_name`, to be shown on
+ # tensorboard.
+ # Args:
+ # img_name (str): The name of the image to put into tensorboard.
+ # img_tensor (torch.Tensor or numpy.array): An `uint8` or `float`
+ # Tensor of shape `[channel, height, width]` where `channel` is
+ # 3. The image format should be RGB. The elements in img_tensor
+ # can either have values in [0, 1] (float32) or [0, 255] (uint8).
+ # The `img_tensor` will be visualized in tensorboard.
+ # """
+ # self._vis_data.append((img_name, img_tensor, self._iter))
+
+ def put_scalar(self, name, value, n=1, smoothing_hint=False):
+ """
+ Add a scalar `value` to the `HistoryBuffer` associated with `name`.
+ Args:
+ smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be
+ smoothed when logged. The hint will be accessible through
+ :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint
+ and apply custom smoothing rule.
+ It defaults to True because most scalars we save need to be smoothed to
+ provide any useful signal.
+ """
+ name = self._current_prefix + name
+ history = self._history[name]
+ history.update(value, n)
+ self._latest_scalars[name] = (value, self._iter)
+
+ existing_hint = self._smoothing_hints.get(name)
+ if existing_hint is not None:
+ assert (
+ existing_hint == smoothing_hint
+ ), "Scalar {} was put with a different smoothing_hint!".format(name)
+ else:
+ self._smoothing_hints[name] = smoothing_hint
+
+ # def put_scalars(self, *, smoothing_hint=True, **kwargs):
+ # """
+ # Put multiple scalars from keyword arguments.
+ # Examples:
+ # storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True)
+ # """
+ # for k, v in kwargs.items():
+ # self.put_scalar(k, v, smoothing_hint=smoothing_hint)
+ #
+ # def put_histogram(self, hist_name, hist_tensor, bins=1000):
+ # """
+ # Create a histogram from a tensor.
+ # Args:
+ # hist_name (str): The name of the histogram to put into tensorboard.
+ # hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted
+ # into a histogram.
+ # bins (int): Number of histogram bins.
+ # """
+ # ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item()
+ #
+ # # Create a histogram with PyTorch
+ # hist_counts = torch.histc(hist_tensor, bins=bins)
+ # hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32)
+ #
+ # # Parameter for the add_histogram_raw function of SummaryWriter
+ # hist_params = dict(
+ # tag=hist_name,
+ # min=ht_min,
+ # max=ht_max,
+ # num=len(hist_tensor),
+ # sum=float(hist_tensor.sum()),
+ # sum_squares=float(torch.sum(hist_tensor**2)),
+ # bucket_limits=hist_edges[1:].tolist(),
+ # bucket_counts=hist_counts.tolist(),
+ # global_step=self._iter,
+ # )
+ # self._histograms.append(hist_params)
+
+ def history(self, name):
+ """
+ Returns:
+ AverageMeter: the history for name
+ """
+ ret = self._history.get(name, None)
+ if ret is None:
+ raise KeyError("No history metric available for {}!".format(name))
+ return ret
+
+ def histories(self):
+ """
+ Returns:
+ dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars
+ """
+ return self._history
+
+ def latest(self):
+ """
+ Returns:
+ dict[str -> (float, int)]: mapping from the name of each scalar to the most
+ recent value and the iteration number its added.
+ """
+ return self._latest_scalars
+
+ def latest_with_smoothing_hint(self, window_size=20):
+ """
+ Similar to :meth:`latest`, but the returned values
+ are either the un-smoothed original latest value,
+ or a median of the given window_size,
+ depend on whether the smoothing_hint is True.
+ This provides a default behavior that other writers can use.
+ """
+ result = {}
+ for k, (v, itr) in self._latest_scalars.items():
+ result[k] = (
+ self._history[k].median(window_size) if self._smoothing_hints[k] else v,
+ itr,
+ )
+ return result
+
+ def smoothing_hints(self):
+ """
+ Returns:
+ dict[name -> bool]: the user-provided hint on whether the scalar
+ is noisy and needs smoothing.
+ """
+ return self._smoothing_hints
+
+ def step(self):
+ """
+ User should either: (1) Call this function to increment storage.iter when needed. Or
+ (2) Set `storage.iter` to the correct iteration number before each iteration.
+ The storage will then be able to associate the new data with an iteration number.
+ """
+ self._iter += 1
+
+ @property
+ def iter(self):
+ """
+ Returns:
+ int: The current iteration number. When used together with a trainer,
+ this is ensured to be the same as trainer.iter.
+ """
+ return self._iter
+
+ @iter.setter
+ def iter(self, val):
+ self._iter = int(val)
+
+ @property
+ def iteration(self):
+ # for backward compatibility
+ return self._iter
+
+ def __enter__(self):
+ _CURRENT_STORAGE_STACK.append(self)
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ assert _CURRENT_STORAGE_STACK[-1] == self
+ _CURRENT_STORAGE_STACK.pop()
+
+ @contextmanager
+ def name_scope(self, name):
+ """
+ Yields:
+ A context within which all the events added to this storage
+ will be prefixed by the name scope.
+ """
+ old_prefix = self._current_prefix
+ self._current_prefix = name.rstrip("/") + "/"
+ yield
+ self._current_prefix = old_prefix
+
+ def clear_images(self):
+ """
+ Delete all the stored images for visualization. This should be called
+ after images are written to tensorboard.
+ """
+ self._vis_data = []
+
+ def clear_histograms(self):
+ """
+ Delete all the stored histograms for visualization.
+ This should be called after histograms are written to tensorboard.
+ """
+ self._histograms = []
+
+ def reset_history(self, name):
+ ret = self._history.get(name, None)
+ if ret is None:
+ raise KeyError("No history metric available for {}!".format(name))
+ ret.reset()
+
+ def reset_histories(self):
+ for name in self._history.keys():
+ self._history[name].reset()
+
+
+class AverageMeter:
+ """Computes and stores the average and current value"""
+
+ def __init__(self):
+ self.val = 0
+ self.avg = 0
+ self.total = 0
+ self.count = 0
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.total = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ self.val = val
+ self.total += val * n
+ self.count += n
+ self.avg = self.total / self.count
+
+
+class HistoryBuffer:
+ """
+ Track a series of scalar values and provide access to smoothed values over a
+ window or the global average of the series.
+ """
+
+ def __init__(self, max_length: int = 1000000) -> None:
+ """
+ Args:
+ max_length: maximal number of values that can be stored in the
+ buffer. When the capacity of the buffer is exhausted, old
+ values will be removed.
+ """
+ self._max_length: int = max_length
+ self._data: List[Tuple[float, float]] = [] # (value, iteration) pairs
+ self._count: int = 0
+ self._global_avg: float = 0
+
+ def update(self, value: float, iteration: Optional[float] = None) -> None:
+ """
+ Add a new scalar value produced at certain iteration. If the length
+ of the buffer exceeds self._max_length, the oldest element will be
+ removed from the buffer.
+ """
+ if iteration is None:
+ iteration = self._count
+ if len(self._data) == self._max_length:
+ self._data.pop(0)
+ self._data.append((value, iteration))
+
+ self._count += 1
+ self._global_avg += (value - self._global_avg) / self._count
+
+ def latest(self) -> float:
+ """
+ Return the latest scalar value added to the buffer.
+ """
+ return self._data[-1][0]
+
+ def median(self, window_size: int) -> float:
+ """
+ Return the median of the latest `window_size` values in the buffer.
+ """
+ return np.median([x[0] for x in self._data[-window_size:]])
+
+ def avg(self, window_size: int) -> float:
+ """
+ Return the mean of the latest `window_size` values in the buffer.
+ """
+ return np.mean([x[0] for x in self._data[-window_size:]])
+
+ def global_avg(self) -> float:
+ """
+ Return the mean of all the elements in the buffer. Note that this
+ includes those getting removed due to limited buffer storage.
+ """
+ return self._global_avg
+
+ def values(self) -> List[Tuple[float, float]]:
+ """
+ Returns:
+ list[(number, iteration)]: content of the current buffer.
+ """
+ return self._data
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/logger.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddaf2c5a765c9f1325737c3cbc73e1169f13cdd4
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/logger.py
@@ -0,0 +1,172 @@
+"""
+Logger Utils
+
+Modified from mmcv
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import logging
+import torch
+import torch.distributed as dist
+
+from termcolor import colored
+
+logger_initialized = {}
+root_status = 0
+
+
+class _ColorfulFormatter(logging.Formatter):
+ def __init__(self, *args, **kwargs):
+ self._root_name = kwargs.pop("root_name") + "."
+ super(_ColorfulFormatter, self).__init__(*args, **kwargs)
+
+ def formatMessage(self, record):
+ log = super(_ColorfulFormatter, self).formatMessage(record)
+ if record.levelno == logging.WARNING:
+ prefix = colored("WARNING", "red", attrs=["blink"])
+ elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
+ prefix = colored("ERROR", "red", attrs=["blink", "underline"])
+ else:
+ return log
+ return prefix + " " + log
+
+
+def get_logger(name, log_file=None, log_level=logging.INFO, file_mode="a", color=False):
+ """Initialize and get a logger by name.
+
+ If the logger has not been initialized, this method will initialize the
+ logger by adding one or two handlers, otherwise the initialized logger will
+ be directly returned. During initialization, a StreamHandler will always be
+ added. If `log_file` is specified and the process rank is 0, a FileHandler
+ will also be added.
+
+ Args:
+ name (str): Logger name.
+ log_file (str | None): The log filename. If specified, a FileHandler
+ will be added to the logger.
+ log_level (int): The logger level. Note that only the process of
+ rank 0 is affected, and other processes will set the level to
+ "Error" thus be silent most of the time.
+ file_mode (str): The file mode used in opening log file.
+ Defaults to 'a'.
+ color (bool): Colorful log output. Defaults to True
+
+ Returns:
+ logging.Logger: The expected logger.
+ """
+ logger = logging.getLogger(name)
+
+ if name in logger_initialized:
+ return logger
+ # handle hierarchical names
+ # e.g., logger "a" is initialized, then logger "a.b" will skip the
+ # initialization since it is a child of "a".
+ for logger_name in logger_initialized:
+ if name.startswith(logger_name):
+ return logger
+
+ logger.propagate = False
+
+ stream_handler = logging.StreamHandler()
+ handlers = [stream_handler]
+
+ if dist.is_available() and dist.is_initialized():
+ rank = dist.get_rank()
+ else:
+ rank = 0
+
+ # only rank 0 will add a FileHandler
+ if rank == 0 and log_file is not None:
+ # Here, the default behaviour of the official logger is 'a'. Thus, we
+ # provide an interface to change the file mode to the default
+ # behaviour.
+ file_handler = logging.FileHandler(log_file, file_mode)
+ handlers.append(file_handler)
+
+ plain_formatter = logging.Formatter(
+ "[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s"
+ )
+ if color:
+ formatter = _ColorfulFormatter(
+ colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s",
+ datefmt="%m/%d %H:%M:%S",
+ root_name=name,
+ )
+ else:
+ formatter = plain_formatter
+ for handler in handlers:
+ handler.setFormatter(formatter)
+ handler.setLevel(log_level)
+ logger.addHandler(handler)
+
+ if rank == 0:
+ logger.setLevel(log_level)
+ else:
+ logger.setLevel(logging.ERROR)
+
+ logger_initialized[name] = True
+
+ return logger
+
+
+def print_log(msg, logger=None, level=logging.INFO):
+ """Print a log message.
+
+ Args:
+ msg (str): The message to be logged.
+ logger (logging.Logger | str | None): The logger to be used.
+ Some special loggers are:
+ - "silent": no message will be printed.
+ - other str: the logger obtained with `get_root_logger(logger)`.
+ - None: The `print()` method will be used to print log messages.
+ level (int): Logging level. Only available when `logger` is a Logger
+ object or "root".
+ """
+ if logger is None:
+ print(msg)
+ elif isinstance(logger, logging.Logger):
+ logger.log(level, msg)
+ elif logger == "silent":
+ pass
+ elif isinstance(logger, str):
+ _logger = get_logger(logger)
+ _logger.log(level, msg)
+ else:
+ raise TypeError(
+ "logger should be either a logging.Logger object, str, "
+ f'"silent" or None, but got {type(logger)}'
+ )
+
+
+def get_root_logger(log_file=None, log_level=logging.INFO, file_mode="a"):
+ """Get the root logger.
+
+ The logger will be initialized if it has not been initialized. By default a
+ StreamHandler will be added. If `log_file` is specified, a FileHandler will
+ also be added. The name of the root logger is the top-level package name.
+
+ Args:
+ log_file (str | None): The log filename. If specified, a FileHandler
+ will be added to the root logger.
+ log_level (int): The root logger level. Note that only the process of
+ rank 0 is affected, while other processes will set the level to
+ "Error" and be silent most of the time.
+ file_mode (str): File Mode of logger. (w or a)
+
+ Returns:
+ logging.Logger: The root logger.
+ """
+ logger = get_logger(
+ name="pointcept", log_file=log_file, log_level=log_level, file_mode=file_mode
+ )
+ return logger
+
+
+def _log_api_usage(identifier: str):
+ """
+ Internal function used to log the usage of different detectron2 components
+ inside facebook's infra.
+ """
+ torch._C._log_api_usage_once("pointcept." + identifier)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/misc.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..3177bae3882ccad347002165d2b34d5dc2540359
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/misc.py
@@ -0,0 +1,164 @@
+"""
+Misc
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import warnings
+from collections import abc
+import numpy as np
+import torch
+from importlib import import_module
+
+
+class AverageMeter(object):
+ """Computes and stores the average and current value"""
+
+ def __init__(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ self.val = val
+ self.sum += val * n
+ self.count += n
+ self.avg = self.sum / self.count
+
+
+def intersection_and_union(output, target, K, ignore_index=-1):
+ # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.
+ assert output.ndim in [1, 2, 3]
+ assert output.shape == target.shape
+ output = output.reshape(output.size).copy()
+ target = target.reshape(target.size)
+ output[np.where(target == ignore_index)[0]] = ignore_index
+ intersection = output[np.where(output == target)[0]]
+ area_intersection, _ = np.histogram(intersection, bins=np.arange(K + 1))
+ area_output, _ = np.histogram(output, bins=np.arange(K + 1))
+ area_target, _ = np.histogram(target, bins=np.arange(K + 1))
+ area_union = area_output + area_target - area_intersection
+ return area_intersection, area_union, area_target
+
+
+def intersection_and_union_gpu(output, target, k, ignore_index=-1):
+ # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.
+ assert output.dim() in [1, 2, 3]
+ assert output.shape == target.shape
+ output = output.view(-1)
+ target = target.view(-1)
+ output[target == ignore_index] = ignore_index
+ intersection = output[output == target]
+ area_intersection = torch.histc(intersection, bins=k, min=0, max=k - 1)
+ area_output = torch.histc(output, bins=k, min=0, max=k - 1)
+ area_target = torch.histc(target, bins=k, min=0, max=k - 1)
+ area_union = area_output + area_target - area_intersection
+ return area_intersection, area_union, area_target
+
+
+def make_dirs(dir_name):
+ if not os.path.exists(dir_name):
+ os.makedirs(dir_name, exist_ok=True)
+
+
+def find_free_port():
+ import socket
+
+ sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ # Binding to port 0 will cause the OS to find an available port for us
+ sock.bind(("", 0))
+ port = sock.getsockname()[1]
+ sock.close()
+ # NOTE: there is still a chance the port could be taken by other processes.
+ return port
+
+
+def is_seq_of(seq, expected_type, seq_type=None):
+ """Check whether it is a sequence of some type.
+
+ Args:
+ seq (Sequence): The sequence to be checked.
+ expected_type (type): Expected type of sequence items.
+ seq_type (type, optional): Expected sequence type.
+
+ Returns:
+ bool: Whether the sequence is valid.
+ """
+ if seq_type is None:
+ exp_seq_type = abc.Sequence
+ else:
+ assert isinstance(seq_type, type)
+ exp_seq_type = seq_type
+ if not isinstance(seq, exp_seq_type):
+ return False
+ for item in seq:
+ if not isinstance(item, expected_type):
+ return False
+ return True
+
+
+def is_str(x):
+ """Whether the input is an string instance.
+
+ Note: This method is deprecated since python 2 is no longer supported.
+ """
+ return isinstance(x, str)
+
+
+def import_modules_from_strings(imports, allow_failed_imports=False):
+ """Import modules from the given list of strings.
+
+ Args:
+ imports (list | str | None): The given module names to be imported.
+ allow_failed_imports (bool): If True, the failed imports will return
+ None. Otherwise, an ImportError is raise. Default: False.
+
+ Returns:
+ list[module] | module | None: The imported modules.
+
+ Examples:
+ >>> osp, sys = import_modules_from_strings(
+ ... ['os.path', 'sys'])
+ >>> import os.path as osp_
+ >>> import sys as sys_
+ >>> assert osp == osp_
+ >>> assert sys == sys_
+ """
+ if not imports:
+ return
+ single_import = False
+ if isinstance(imports, str):
+ single_import = True
+ imports = [imports]
+ if not isinstance(imports, list):
+ raise TypeError(f"custom_imports must be a list but got type {type(imports)}")
+ imported = []
+ for imp in imports:
+ if not isinstance(imp, str):
+ raise TypeError(f"{imp} is of type {type(imp)} and cannot be imported.")
+ try:
+ imported_tmp = import_module(imp)
+ except ImportError:
+ if allow_failed_imports:
+ warnings.warn(f"{imp} failed to import and is ignored.", UserWarning)
+ imported_tmp = None
+ else:
+ raise ImportError
+ imported.append(imported_tmp)
+ if single_import:
+ imported = imported[0]
+ return imported
+
+
+class DummyClass:
+ def __init__(self):
+ pass
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/optimizer.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..355ec8916ad041ca02b404029983b0f59933fb8c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/optimizer.py
@@ -0,0 +1,55 @@
+"""
+Optimizer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.registry import Registry
+
+OPTIMIZERS = Registry("optimizers")
+
+
+OPTIMIZERS.register_module(module=torch.optim.SGD, name="SGD")
+OPTIMIZERS.register_module(module=torch.optim.Adam, name="Adam")
+OPTIMIZERS.register_module(module=torch.optim.AdamW, name="AdamW")
+
+
+def build_optimizer(cfg, model, param_dicts=None):
+ if param_dicts is None:
+ cfg.params = model.parameters()
+ else:
+ cfg.params = [dict(names=[], params=[], lr=cfg.lr)]
+ for i in range(len(param_dicts)):
+ param_group = dict(names=[], params=[])
+ if "lr" in param_dicts[i].keys():
+ param_group["lr"] = param_dicts[i].lr
+ if "momentum" in param_dicts[i].keys():
+ param_group["momentum"] = param_dicts[i].momentum
+ if "weight_decay" in param_dicts[i].keys():
+ param_group["weight_decay"] = param_dicts[i].weight_decay
+ cfg.params.append(param_group)
+
+ for n, p in model.named_parameters():
+ flag = False
+ for i in range(len(param_dicts)):
+ if param_dicts[i].keyword in n:
+ cfg.params[i + 1]["names"].append(n)
+ cfg.params[i + 1]["params"].append(p)
+ flag = True
+ break
+ if not flag:
+ cfg.params[0]["names"].append(n)
+ cfg.params[0]["params"].append(p)
+
+ logger = get_root_logger()
+ for i in range(len(cfg.params)):
+ param_names = cfg.params[i].pop("names")
+ message = ""
+ for key in cfg.params[i].keys():
+ if key != "params":
+ message += f" {key}: {cfg.params[i][key]};"
+ logger.info(f"Params Group {i+1} -{message} Params: {param_names}.")
+ return OPTIMIZERS.build(cfg=cfg)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/path.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/path.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce98fa5fd0dfbf6e1d61e833ecc35fea4ab2782b
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/path.py
@@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+from pathlib import Path
+
+from .misc import is_str
+
+
+def is_filepath(x):
+ return is_str(x) or isinstance(x, Path)
+
+
+def fopen(filepath, *args, **kwargs):
+ if is_str(filepath):
+ return open(filepath, *args, **kwargs)
+ elif isinstance(filepath, Path):
+ return filepath.open(*args, **kwargs)
+ raise ValueError("`filepath` should be a string or a Path")
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+ if not osp.isfile(filename):
+ raise FileNotFoundError(msg_tmpl.format(filename))
+
+
+def mkdir_or_exist(dir_name, mode=0o777):
+ if dir_name == "":
+ return
+ dir_name = osp.expanduser(dir_name)
+ os.makedirs(dir_name, mode=mode, exist_ok=True)
+
+
+def symlink(src, dst, overwrite=True, **kwargs):
+ if os.path.lexists(dst) and overwrite:
+ os.remove(dst)
+ os.symlink(src, dst, **kwargs)
+
+
+def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True):
+ """Scan a directory to find the interested files.
+
+ Args:
+ dir_path (str | obj:`Path`): Path of the directory.
+ suffix (str | tuple(str), optional): File suffix that we are
+ interested in. Default: None.
+ recursive (bool, optional): If set to True, recursively scan the
+ directory. Default: False.
+ case_sensitive (bool, optional) : If set to False, ignore the case of
+ suffix. Default: True.
+
+ Returns:
+ A generator for all the interested files with relative paths.
+ """
+ if isinstance(dir_path, (str, Path)):
+ dir_path = str(dir_path)
+ else:
+ raise TypeError('"dir_path" must be a string or Path object')
+
+ if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+ raise TypeError('"suffix" must be a string or tuple of strings')
+
+ if suffix is not None and not case_sensitive:
+ suffix = (
+ suffix.lower()
+ if isinstance(suffix, str)
+ else tuple(item.lower() for item in suffix)
+ )
+
+ root = dir_path
+
+ def _scandir(dir_path, suffix, recursive, case_sensitive):
+ for entry in os.scandir(dir_path):
+ if not entry.name.startswith(".") and entry.is_file():
+ rel_path = osp.relpath(entry.path, root)
+ _rel_path = rel_path if case_sensitive else rel_path.lower()
+ if suffix is None or _rel_path.endswith(suffix):
+ yield rel_path
+ elif recursive and os.path.isdir(entry.path):
+ # scan recursively if entry.path is a directory
+ yield from _scandir(entry.path, suffix, recursive, case_sensitive)
+
+ return _scandir(dir_path, suffix, recursive, case_sensitive)
+
+
+def find_vcs_root(path, markers=(".git",)):
+ """Finds the root directory (including itself) of specified markers.
+
+ Args:
+ path (str): Path of directory or file.
+ markers (list[str], optional): List of file or directory names.
+
+ Returns:
+ The directory contained one of the markers or None if not found.
+ """
+ if osp.isfile(path):
+ path = osp.dirname(path)
+
+ prev, cur = None, osp.abspath(osp.expanduser(path))
+ while cur != prev:
+ if any(osp.exists(osp.join(cur, marker)) for marker in markers):
+ return cur
+ prev, cur = cur, osp.split(cur)[0]
+ return None
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/registry.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ac308a87d38ff61da14d6b4d5c73b4c68c15a58
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/registry.py
@@ -0,0 +1,316 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import inspect
+import warnings
+from functools import partial
+
+from .misc import is_seq_of
+
+
+def build_from_cfg(cfg, registry, default_args=None):
+ """Build a module from configs dict.
+
+ Args:
+ cfg (dict): Config dict. It should at least contain the key "type".
+ registry (:obj:`Registry`): The registry to search the type from.
+ default_args (dict, optional): Default initialization arguments.
+
+ Returns:
+ object: The constructed object.
+ """
+ if not isinstance(cfg, dict):
+ raise TypeError(f"cfg must be a dict, but got {type(cfg)}")
+ if "type" not in cfg:
+ if default_args is None or "type" not in default_args:
+ raise KeyError(
+ '`cfg` or `default_args` must contain the key "type", '
+ f"but got {cfg}\n{default_args}"
+ )
+ if not isinstance(registry, Registry):
+ raise TypeError(
+ "registry must be an mmcv.Registry object, " f"but got {type(registry)}"
+ )
+ if not (isinstance(default_args, dict) or default_args is None):
+ raise TypeError(
+ "default_args must be a dict or None, " f"but got {type(default_args)}"
+ )
+
+ args = cfg.copy()
+
+ if default_args is not None:
+ for name, value in default_args.items():
+ args.setdefault(name, value)
+
+ obj_type = args.pop("type")
+ if isinstance(obj_type, str):
+ obj_cls = registry.get(obj_type)
+ if obj_cls is None:
+ raise KeyError(f"{obj_type} is not in the {registry.name} registry")
+ elif inspect.isclass(obj_type):
+ obj_cls = obj_type
+ else:
+ raise TypeError(f"type must be a str or valid type, but got {type(obj_type)}")
+ try:
+ return obj_cls(**args)
+ except Exception as e:
+ # Normal TypeError does not print class name.
+ raise type(e)(f"{obj_cls.__name__}: {e}")
+
+
+class Registry:
+ """A registry to map strings to classes.
+
+ Registered object could be built from registry.
+ Example:
+ >>> MODELS = Registry('models')
+ >>> @MODELS.register_module()
+ >>> class ResNet:
+ >>> pass
+ >>> resnet = MODELS.build(dict(type='ResNet'))
+
+ Please refer to
+ https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
+ advanced usage.
+
+ Args:
+ name (str): Registry name.
+ build_func(func, optional): Build function to construct instance from
+ Registry, func:`build_from_cfg` is used if neither ``parent`` or
+ ``build_func`` is specified. If ``parent`` is specified and
+ ``build_func`` is not given, ``build_func`` will be inherited
+ from ``parent``. Default: None.
+ parent (Registry, optional): Parent registry. The class registered in
+ children registry could be built from parent. Default: None.
+ scope (str, optional): The scope of registry. It is the key to search
+ for children registry. If not specified, scope will be the name of
+ the package where class is defined, e.g. mmdet, mmcls, mmseg.
+ Default: None.
+ """
+
+ def __init__(self, name, build_func=None, parent=None, scope=None):
+ self._name = name
+ self._module_dict = dict()
+ self._children = dict()
+ self._scope = self.infer_scope() if scope is None else scope
+
+ # self.build_func will be set with the following priority:
+ # 1. build_func
+ # 2. parent.build_func
+ # 3. build_from_cfg
+ if build_func is None:
+ if parent is not None:
+ self.build_func = parent.build_func
+ else:
+ self.build_func = build_from_cfg
+ else:
+ self.build_func = build_func
+ if parent is not None:
+ assert isinstance(parent, Registry)
+ parent._add_children(self)
+ self.parent = parent
+ else:
+ self.parent = None
+
+ def __len__(self):
+ return len(self._module_dict)
+
+ def __contains__(self, key):
+ return self.get(key) is not None
+
+ def __repr__(self):
+ format_str = (
+ self.__class__.__name__ + f"(name={self._name}, "
+ f"items={self._module_dict})"
+ )
+ return format_str
+
+ @staticmethod
+ def infer_scope():
+ """Infer the scope of registry.
+
+ The name of the package where registry is defined will be returned.
+
+ Example:
+ # in mmdet/models/backbone/resnet.py
+ >>> MODELS = Registry('models')
+ >>> @MODELS.register_module()
+ >>> class ResNet:
+ >>> pass
+ The scope of ``ResNet`` will be ``mmdet``.
+
+
+ Returns:
+ scope (str): The inferred scope name.
+ """
+ # inspect.stack() trace where this function is called, the index-2
+ # indicates the frame where `infer_scope()` is called
+ filename = inspect.getmodule(inspect.stack()[2][0]).__name__
+ split_filename = filename.split(".")
+ return split_filename[0]
+
+ @staticmethod
+ def split_scope_key(key):
+ """Split scope and key.
+
+ The first scope will be split from key.
+
+ Examples:
+ >>> Registry.split_scope_key('mmdet.ResNet')
+ 'mmdet', 'ResNet'
+ >>> Registry.split_scope_key('ResNet')
+ None, 'ResNet'
+
+ Return:
+ scope (str, None): The first scope.
+ key (str): The remaining key.
+ """
+ split_index = key.find(".")
+ if split_index != -1:
+ return key[:split_index], key[split_index + 1 :]
+ else:
+ return None, key
+
+ @property
+ def name(self):
+ return self._name
+
+ @property
+ def scope(self):
+ return self._scope
+
+ @property
+ def module_dict(self):
+ return self._module_dict
+
+ @property
+ def children(self):
+ return self._children
+
+ def get(self, key):
+ """Get the registry record.
+
+ Args:
+ key (str): The class name in string format.
+
+ Returns:
+ class: The corresponding class.
+ """
+ scope, real_key = self.split_scope_key(key)
+ if scope is None or scope == self._scope:
+ # get from self
+ if real_key in self._module_dict:
+ return self._module_dict[real_key]
+ else:
+ # get from self._children
+ if scope in self._children:
+ return self._children[scope].get(real_key)
+ else:
+ # goto root
+ parent = self.parent
+ while parent.parent is not None:
+ parent = parent.parent
+ return parent.get(key)
+
+ def build(self, *args, **kwargs):
+ return self.build_func(*args, **kwargs, registry=self)
+
+ def _add_children(self, registry):
+ """Add children for a registry.
+
+ The ``registry`` will be added as children based on its scope.
+ The parent registry could build objects from children registry.
+
+ Example:
+ >>> models = Registry('models')
+ >>> mmdet_models = Registry('models', parent=models)
+ >>> @mmdet_models.register_module()
+ >>> class ResNet:
+ >>> pass
+ >>> resnet = models.build(dict(type='mmdet.ResNet'))
+ """
+
+ assert isinstance(registry, Registry)
+ assert registry.scope is not None
+ assert (
+ registry.scope not in self.children
+ ), f"scope {registry.scope} exists in {self.name} registry"
+ self.children[registry.scope] = registry
+
+ def _register_module(self, module_class, module_name=None, force=False):
+ if not inspect.isclass(module_class):
+ raise TypeError("module must be a class, " f"but got {type(module_class)}")
+
+ if module_name is None:
+ module_name = module_class.__name__
+ if isinstance(module_name, str):
+ module_name = [module_name]
+ for name in module_name:
+ if not force and name in self._module_dict:
+ raise KeyError(f"{name} is already registered " f"in {self.name}")
+ self._module_dict[name] = module_class
+
+ def deprecated_register_module(self, cls=None, force=False):
+ warnings.warn(
+ "The old API of register_module(module, force=False) "
+ "is deprecated and will be removed, please use the new API "
+ "register_module(name=None, force=False, module=None) instead."
+ )
+ if cls is None:
+ return partial(self.deprecated_register_module, force=force)
+ self._register_module(cls, force=force)
+ return cls
+
+ def register_module(self, name=None, force=False, module=None):
+ """Register a module.
+
+ A record will be added to `self._module_dict`, whose key is the class
+ name or the specified name, and value is the class itself.
+ It can be used as a decorator or a normal function.
+
+ Example:
+ >>> backbones = Registry('backbone')
+ >>> @backbones.register_module()
+ >>> class ResNet:
+ >>> pass
+
+ >>> backbones = Registry('backbone')
+ >>> @backbones.register_module(name='mnet')
+ >>> class MobileNet:
+ >>> pass
+
+ >>> backbones = Registry('backbone')
+ >>> class ResNet:
+ >>> pass
+ >>> backbones.register_module(ResNet)
+
+ Args:
+ name (str | None): The module name to be registered. If not
+ specified, the class name will be used.
+ force (bool, optional): Whether to override an existing class with
+ the same name. Default: False.
+ module (type): Module class to be registered.
+ """
+ if not isinstance(force, bool):
+ raise TypeError(f"force must be a boolean, but got {type(force)}")
+ # NOTE: This is a walkaround to be compatible with the old api,
+ # while it may introduce unexpected bugs.
+ if isinstance(name, type):
+ return self.deprecated_register_module(name, force=force)
+
+ # raise the error ahead of time
+ if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
+ raise TypeError(
+ "name must be either of None, an instance of str or a sequence"
+ f" of str, but got {type(name)}"
+ )
+
+ # use it as a normal method: x.register_module(module=SomeClass)
+ if module is not None:
+ self._register_module(module_class=module, module_name=name, force=force)
+ return module
+
+ # use it as a decorator: @x.register_module()
+ def _register(cls):
+ self._register_module(module_class=cls, module_name=name, force=force)
+ return cls
+
+ return _register
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/scheduler.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e2e29fdde2e2668c023af36afdb89e73fb9ce53
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/scheduler.py
@@ -0,0 +1,147 @@
+"""
+Scheduler
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch.optim.lr_scheduler as lr_scheduler
+from .registry import Registry
+
+SCHEDULERS = Registry("schedulers")
+
+
+@SCHEDULERS.register_module()
+class MultiStepLR(lr_scheduler.MultiStepLR):
+ def __init__(
+ self,
+ optimizer,
+ milestones,
+ total_steps,
+ gamma=0.1,
+ last_epoch=-1,
+ verbose=False,
+ ):
+ super().__init__(
+ optimizer=optimizer,
+ milestones=[rate * total_steps for rate in milestones],
+ gamma=gamma,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class MultiStepWithWarmupLR(lr_scheduler.LambdaLR):
+ def __init__(
+ self,
+ optimizer,
+ milestones,
+ total_steps,
+ gamma=0.1,
+ warmup_rate=0.05,
+ warmup_scale=1e-6,
+ last_epoch=-1,
+ verbose=False,
+ ):
+ milestones = [rate * total_steps for rate in milestones]
+
+ def multi_step_with_warmup(s):
+ factor = 1.0
+ for i in range(len(milestones)):
+ if s < milestones[i]:
+ break
+ factor *= gamma
+
+ if s <= warmup_rate * total_steps:
+ warmup_coefficient = 1 - (1 - s / warmup_rate / total_steps) * (
+ 1 - warmup_scale
+ )
+ else:
+ warmup_coefficient = 1.0
+ return warmup_coefficient * factor
+
+ super().__init__(
+ optimizer=optimizer,
+ lr_lambda=multi_step_with_warmup,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class PolyLR(lr_scheduler.LambdaLR):
+ def __init__(self, optimizer, total_steps, power=0.9, last_epoch=-1, verbose=False):
+ super().__init__(
+ optimizer=optimizer,
+ lr_lambda=lambda s: (1 - s / (total_steps + 1)) ** power,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class ExpLR(lr_scheduler.LambdaLR):
+ def __init__(self, optimizer, total_steps, gamma=0.9, last_epoch=-1, verbose=False):
+ super().__init__(
+ optimizer=optimizer,
+ lr_lambda=lambda s: gamma ** (s / total_steps),
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class CosineAnnealingLR(lr_scheduler.CosineAnnealingLR):
+ def __init__(self, optimizer, total_steps, eta_min=0, last_epoch=-1, verbose=False):
+ super().__init__(
+ optimizer=optimizer,
+ T_max=total_steps,
+ eta_min=eta_min,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class OneCycleLR(lr_scheduler.OneCycleLR):
+ r"""
+ torch.optim.lr_scheduler.OneCycleLR, Block total_steps
+ """
+
+ def __init__(
+ self,
+ optimizer,
+ max_lr,
+ total_steps=None,
+ pct_start=0.3,
+ anneal_strategy="cos",
+ cycle_momentum=True,
+ base_momentum=0.85,
+ max_momentum=0.95,
+ div_factor=25.0,
+ final_div_factor=1e4,
+ three_phase=False,
+ last_epoch=-1,
+ verbose=False,
+ ):
+ super().__init__(
+ optimizer=optimizer,
+ max_lr=max_lr,
+ total_steps=total_steps,
+ pct_start=pct_start,
+ anneal_strategy=anneal_strategy,
+ cycle_momentum=cycle_momentum,
+ base_momentum=base_momentum,
+ max_momentum=max_momentum,
+ div_factor=div_factor,
+ final_div_factor=final_div_factor,
+ three_phase=three_phase,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+def build_scheduler(cfg, optimizer):
+ cfg.optimizer = optimizer
+ return SCHEDULERS.build(cfg=cfg)
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/timer.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3de4a16e33c43fe61ea3088f82216fd62eb6e959
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/timer.py
@@ -0,0 +1,70 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# -*- coding: utf-8 -*-
+
+from time import perf_counter
+from typing import Optional
+
+
+class Timer:
+ """
+ A timer which computes the time elapsed since the start/reset of the timer.
+ """
+
+ def __init__(self) -> None:
+ self.reset()
+
+ def reset(self) -> None:
+ """
+ Reset the timer.
+ """
+ self._start = perf_counter()
+ self._paused: Optional[float] = None
+ self._total_paused = 0
+ self._count_start = 1
+
+ def pause(self) -> None:
+ """
+ Pause the timer.
+ """
+ if self._paused is not None:
+ raise ValueError("Trying to pause a Timer that is already paused!")
+ self._paused = perf_counter()
+
+ def is_paused(self) -> bool:
+ """
+ Returns:
+ bool: whether the timer is currently paused
+ """
+ return self._paused is not None
+
+ def resume(self) -> None:
+ """
+ Resume the timer.
+ """
+ if self._paused is None:
+ raise ValueError("Trying to resume a Timer that is not paused!")
+ # pyre-fixme[58]: `-` is not supported for operand types `float` and
+ # `Optional[float]`.
+ self._total_paused += perf_counter() - self._paused
+ self._paused = None
+ self._count_start += 1
+
+ def seconds(self) -> float:
+ """
+ Returns:
+ (float): the total number of seconds since the start/reset of the
+ timer, excluding the time when the timer is paused.
+ """
+ if self._paused is not None:
+ end_time: float = self._paused # type: ignore
+ else:
+ end_time = perf_counter()
+ return end_time - self._start - self._total_paused
+
+ def avg_seconds(self) -> float:
+ """
+ Returns:
+ (float): the average number of seconds between every start/reset and
+ pause.
+ """
+ return self.seconds() / self._count_start
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/visualization.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a010dd8289f60119d1bfbccdff65edb908e24f6
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/utils/visualization.py
@@ -0,0 +1,89 @@
+"""
+Visualization Utils
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import open3d as o3d
+import numpy as np
+import torch
+
+
+def to_numpy(x):
+ if isinstance(x, torch.Tensor):
+ x = x.clone().detach().cpu().numpy()
+ assert isinstance(x, np.ndarray)
+ return x
+
+
+def save_point_cloud(coord, color=None, file_path="pc.ply", logger=None):
+ os.makedirs(os.path.dirname(file_path), exist_ok=True)
+ coord = to_numpy(coord)
+ if color is not None:
+ color = to_numpy(color)
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(coord)
+ pcd.colors = o3d.utility.Vector3dVector(
+ np.ones_like(coord) if color is None else color
+ )
+ o3d.io.write_point_cloud(file_path, pcd)
+ if logger is not None:
+ logger.info(f"Save Point Cloud to: {file_path}")
+
+
+def save_bounding_boxes(
+ bboxes_corners, color=(1.0, 0.0, 0.0), file_path="bbox.ply", logger=None
+):
+ bboxes_corners = to_numpy(bboxes_corners)
+ # point list
+ points = bboxes_corners.reshape(-1, 3)
+ # line list
+ box_lines = np.array(
+ [
+ [0, 1],
+ [1, 2],
+ [2, 3],
+ [3, 0],
+ [4, 5],
+ [5, 6],
+ [6, 7],
+ [7, 0],
+ [0, 4],
+ [1, 5],
+ [2, 6],
+ [3, 7],
+ ]
+ )
+ lines = []
+ for i, _ in enumerate(bboxes_corners):
+ lines.append(box_lines + i * 8)
+ lines = np.concatenate(lines)
+ # color list
+ color = np.array([color for _ in range(len(lines))])
+ # generate line set
+ line_set = o3d.geometry.LineSet()
+ line_set.points = o3d.utility.Vector3dVector(points)
+ line_set.lines = o3d.utility.Vector2iVector(lines)
+ line_set.colors = o3d.utility.Vector3dVector(color)
+ o3d.io.write_line_set(file_path, line_set)
+
+ if logger is not None:
+ logger.info(f"Save Boxes to: {file_path}")
+
+
+def save_lines(
+ points, lines, color=(1.0, 0.0, 0.0), file_path="lines.ply", logger=None
+):
+ points = to_numpy(points)
+ lines = to_numpy(lines)
+ colors = np.array([color for _ in range(len(lines))])
+ line_set = o3d.geometry.LineSet()
+ line_set.points = o3d.utility.Vector3dVector(points)
+ line_set.lines = o3d.utility.Vector2iVector(lines)
+ line_set.colors = o3d.utility.Vector3dVector(colors)
+ o3d.io.write_line_set(file_path, line_set)
+
+ if logger is not None:
+ logger.info(f"Save Lines to: {file_path}")
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/build_image.sh b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/build_image.sh
new file mode 100644
index 0000000000000000000000000000000000000000..31a6a7fc23e57b3b738450d5c42fed4cc45b9b65
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/build_image.sh
@@ -0,0 +1,83 @@
+TORCH_VERSION=2.0.1
+CUDA_VERSION=11.7
+CUDNN_VERSION=8
+
+ARGS=`getopt -o t:c: -l torch:,cuda:,cudnn: -n "$0" -- "$@"`
+[ $? != 0 ] && exit 1
+eval set -- "${ARGS}"
+while true ; do
+ case "$1" in
+ -t | --torch)
+ TORCH_VERSION=$2
+ shift 2
+ ;;
+ -c | --cuda)
+ CUDA_VERSION=$2
+ shift 2
+ ;;
+ --cudnn)
+ CUDNN_VERSION=$2
+ shift 2
+ ;;
+ --)
+ break
+ ;;
+ *)
+ echo "Invalid option: $1"
+ exit 1
+ ;;
+ esac
+done
+
+CUDA_VERSION_NO_DOT=`echo ${CUDA_VERSION} | tr -d "."`
+BASE_TORCH_TAG=${TORCH_VERSION}-cuda${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel
+IMG_TAG=pointcept/pointcept:pytorch${BASE_TORCH_TAG}
+
+echo "TORCH VERSION: ${TORCH_VERSION}"
+echo "CUDA VERSION: ${CUDA_VERSION}"
+echo "CUDNN VERSION: ${CUDNN_VERSION}"
+
+
+cat > ./Dockerfile <<- EOM
+FROM pytorch/pytorch:${BASE_TORCH_TAG}
+
+# Fix nvidia-key error issue (NO_PUBKEY A4B469963BF863CC)
+RUN rm /etc/apt/sources.list.d/*.list
+
+# Installing apt packages
+RUN export DEBIAN_FRONTEND=noninteractive \
+ && apt -y update --no-install-recommends \
+ && apt -y install --no-install-recommends \
+ git wget tmux vim zsh build-essential cmake ninja-build libopenblas-dev libsparsehash-dev \
+ && apt autoremove -y \
+ && apt clean -y \
+ && export DEBIAN_FRONTEND=dialog
+
+# Install Pointcept environment
+RUN conda install h5py pyyaml -c anaconda -y
+RUN conda install sharedarray tensorboard tensorboardx yapf addict einops scipy plyfile termcolor timm -c conda-forge -y
+RUN conda install pytorch-cluster pytorch-scatter pytorch-sparse -c pyg -y
+
+RUN pip install --upgrade pip
+RUN pip install torch-geometric
+RUN pip install spconv-cu${CUDA_VERSION_NO_DOT}
+RUN pip install open3d
+
+# Build MinkowskiEngine
+RUN git clone https://github.com/NVIDIA/MinkowskiEngine.git
+WORKDIR /workspace/MinkowskiEngine
+RUN TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0+PTX 8.0" python setup.py install --blas=openblas --force_cuda
+WORKDIR /workspace
+
+# Build pointops
+RUN git clone https://github.com/Pointcept/Pointcept.git
+RUN TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0+PTX 8.0" pip install Pointcept/libs/pointops -v
+
+# Build pointgroup_ops
+RUN TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0+PTX 8.0" pip install Pointcept/libs/pointgroup_ops -v
+
+# Build swin3d
+RUN TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX 8.0" pip install -U git+https://github.com/microsoft/Swin3D.git -v
+EOM
+
+docker build . -f ./Dockerfile -t $IMG_TAG
\ No newline at end of file
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/create_tars.sh b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/create_tars.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8bd990b2fc6d3448202a04db63c2adb707c2652b
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/create_tars.sh
@@ -0,0 +1,67 @@
+#!/bin/sh
+
+# Variables
+SOURCE_DIR=$1
+DEST_DIR=$2
+MAX_SIZE=$(awk "BEGIN {printf \"%d\", $3 * 1024 * 1024}") # Convert GB to KB as an integer
+
+# Get the base name of the source directory to use as TAR_NAME
+TAR_NAME=$(basename "$SOURCE_DIR")
+
+# Create destination directory if it doesn't exist
+mkdir -p "$DEST_DIR"
+
+# Function to create a new tar file
+create_tar() {
+ tar_number=$1
+ file_list=$2
+ tar_name=$(printf "%s/${TAR_NAME}_%0${width}d.tar.gz" "$DEST_DIR" "$tar_number")
+ tar -zcvf "$tar_name" -C "$SOURCE_DIR" -T "$file_list"
+}
+
+# Initialize
+tar_number=1
+current_size=0
+temp_dir=$(mktemp -d)
+file_list="$temp_dir/file_list_$tar_number"
+echo Start indexing "file_list_$tar_number"
+
+cd "$SOURCE_DIR" || exit 1
+
+# Iterate over all files in the source directory
+find . -type f | while IFS= read -r file; do
+ file_size=$(du -k "$file" | cut -f1)
+
+ if [ $(( current_size + file_size )) -gt $MAX_SIZE ]; then
+ tar_number=$((tar_number + 1))
+ file_list="$temp_dir/file_list_$tar_number"
+ echo Start indexing "file_list_$tar_number"
+ current_size=0
+ fi
+
+ echo "$file" >> "$file_list"
+ current_size=$((current_size + file_size))
+done
+
+# Determine the width for the tar file numbers
+total_files=$(find "$temp_dir" -name 'file_list_*' | wc -l)
+width=${#total_files}
+
+# Set PARALLEL_PROCESSES to the number of file lists if not provided
+PARALLEL_PROCESSES=${4:-$total_files}
+
+# Debug information
+echo "Total files: $total_files"
+echo "Width: $width"
+echo "Parallel processes: $PARALLEL_PROCESSES"
+
+# Run tar creation in parallel
+find "$temp_dir" -name 'file_list_*' | xargs -P "$PARALLEL_PROCESSES" -I {} sh -c '
+ file_list={}
+ tar_number=$(basename "$file_list" | cut -d_ -f3)
+ tar_name=$(printf "%s/'"$TAR_NAME"'_%0'"$width"'d.tar.gz" "'"$DEST_DIR"'" "$tar_number")
+ tar -zcvf "$tar_name" -C "'"$SOURCE_DIR"'" -T "$file_list"
+'
+
+# Clean up
+rm -rf "$temp_dir"
\ No newline at end of file
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/test.sh b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a104f98e67873c7741711b63da6cdbd8c88b73f4
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/test.sh
@@ -0,0 +1,74 @@
+#!/bin/sh
+
+cd $(dirname $(dirname "$0")) || exit
+PYTHON=python
+
+TEST_CODE=test.py
+
+DATASET=scannet
+CONFIG="None"
+EXP_NAME=debug
+WEIGHT=model_best
+GPU=None
+
+while getopts "p:d:c:n:w:g:" opt; do
+ case $opt in
+ p)
+ PYTHON=$OPTARG
+ ;;
+ d)
+ DATASET=$OPTARG
+ ;;
+ c)
+ CONFIG=$OPTARG
+ ;;
+ n)
+ EXP_NAME=$OPTARG
+ ;;
+ w)
+ WEIGHT=$OPTARG
+ ;;
+ g)
+ GPU=$OPTARG
+ ;;
+ \?)
+ echo "Invalid option: -$OPTARG"
+ ;;
+ esac
+done
+
+if [ "${NUM_GPU}" = 'None' ]
+then
+ NUM_GPU=`$PYTHON -c 'import torch; print(torch.cuda.device_count())'`
+fi
+
+echo "Experiment name: $EXP_NAME"
+echo "Python interpreter dir: $PYTHON"
+echo "Dataset: $DATASET"
+echo "GPU Num: $GPU"
+
+EXP_DIR=exp/${DATASET}/${EXP_NAME}
+MODEL_DIR=${EXP_DIR}/model
+CODE_DIR=${EXP_DIR}/code
+CONFIG_DIR=${EXP_DIR}/config.py
+
+if [ "${CONFIG}" = "None" ]
+then
+ CONFIG_DIR=${EXP_DIR}/config.py
+else
+ CONFIG_DIR=configs/${DATASET}/${CONFIG}.py
+fi
+
+echo "Loading config in:" $CONFIG_DIR
+#export PYTHONPATH=./$CODE_DIR
+export PYTHONPATH=./
+echo "Running code in: $CODE_DIR"
+
+
+echo " =========> RUN TASK <========="
+
+#$PYTHON -u "$CODE_DIR"/tools/$TEST_CODE \
+$PYTHON -u tools/$TEST_CODE \
+ --config-file "$CONFIG_DIR" \
+ --num-gpus "$GPU" \
+ --options save_path="$EXP_DIR" weight="${MODEL_DIR}"/"${WEIGHT}".pth
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/train.sh b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2910ba1e92423ce8decf40eeeb4d5115da60b8b9
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/scripts/train.sh
@@ -0,0 +1,92 @@
+#!/bin/sh
+
+cd $(dirname $(dirname "$0")) || exit
+ROOT_DIR=$(pwd)
+PYTHON=python
+
+TRAIN_CODE=train.py
+
+DATASET=scannet
+CONFIG="None"
+EXP_NAME=debug
+WEIGHT="None"
+RESUME=false
+GPU=None
+
+
+while getopts "p:d:c:n:w:g:r:" opt; do
+ case $opt in
+ p)
+ PYTHON=$OPTARG
+ ;;
+ d)
+ DATASET=$OPTARG
+ ;;
+ c)
+ CONFIG=$OPTARG
+ ;;
+ n)
+ EXP_NAME=$OPTARG
+ ;;
+ w)
+ WEIGHT=$OPTARG
+ ;;
+ r)
+ RESUME=$OPTARG
+ ;;
+ g)
+ GPU=$OPTARG
+ ;;
+ \?)
+ echo "Invalid option: -$OPTARG"
+ ;;
+ esac
+done
+
+if [ "${NUM_GPU}" = 'None' ]
+then
+ NUM_GPU=`$PYTHON -c 'import torch; print(torch.cuda.device_count())'`
+fi
+
+echo "Experiment name: $EXP_NAME"
+echo "Python interpreter dir: $PYTHON"
+echo "Dataset: $DATASET"
+echo "Config: $CONFIG"
+echo "GPU Num: $GPU"
+
+EXP_DIR=exp/${DATASET}/${EXP_NAME}
+MODEL_DIR=${EXP_DIR}/model
+CODE_DIR=${EXP_DIR}/code
+CONFIG_DIR=configs/${DATASET}/${CONFIG}.py
+
+
+echo " =========> CREATE EXP DIR <========="
+echo "Experiment dir: $ROOT_DIR/$EXP_DIR"
+if ${RESUME}
+then
+ CONFIG_DIR=${EXP_DIR}/config.py
+ WEIGHT=$MODEL_DIR/model_last.pth
+else
+ mkdir -p "$MODEL_DIR" "$CODE_DIR"
+ cp -r scripts tools pointcept "$CODE_DIR"
+fi
+
+echo "Loading config in:" $CONFIG_DIR
+export PYTHONPATH=./$CODE_DIR
+echo "Running code in: $CODE_DIR"
+
+
+echo " =========> RUN TASK <========="
+
+if [ "${WEIGHT}" = "None" ]
+then
+ $PYTHON "$CODE_DIR"/tools/$TRAIN_CODE \
+ --config-file "$CONFIG_DIR" \
+ --num-gpus "$GPU" \
+ --options save_path="$EXP_DIR"
+else
+ $PYTHON "$CODE_DIR"/tools/$TRAIN_CODE \
+ --config-file "$CONFIG_DIR" \
+ --num-gpus "$GPU" \
+ --options save_path="$EXP_DIR" resume="$RESUME" weight="$WEIGHT"
+fi
\ No newline at end of file
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/create_waymo_semseg_submission.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/create_waymo_semseg_submission.py
new file mode 100644
index 0000000000000000000000000000000000000000..ded9f68bde40015a1bc7d1b7197ae909ff5831fe
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/create_waymo_semseg_submission.py
@@ -0,0 +1,131 @@
+"""
+Script for Creating Waymo Semantic Segmentation Submission
+
+The Waymo dataset toolkit relies on an old version of Tensorflow
+which share a conflicting dependency with the Pointcept environment,
+therefore we detach the submission generation from the test process
+and the script require the following environment:
+
+```bash
+conda create -n waymo python=3.8 -y
+conda activate waymo
+pip3 install waymo-open-dataset-tf-2-11-0
+```
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import tqdm
+import argparse
+import numpy as np
+import zlib
+import waymo_open_dataset.dataset_pb2 as open_dataset
+from waymo_open_dataset.protos import segmentation_metrics_pb2
+from waymo_open_dataset.protos import segmentation_submission_pb2
+
+
+def compress_array(array: np.ndarray, is_int32: bool = False):
+ """Compress a numpy array to ZLIP compressed serialized MatrixFloat/Int32.
+
+ Args:
+ array: A numpy array.
+ is_int32: If true, use MatrixInt32, otherwise use MatrixFloat.
+
+ Returns:
+ The compressed bytes.
+ """
+ if is_int32:
+ m = open_dataset.MatrixInt32()
+ else:
+ m = open_dataset.MatrixFloat()
+ m.shape.dims.extend(list(array.shape))
+ m.data.extend(array.reshape([-1]).tolist())
+ return zlib.compress(m.SerializeToString())
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--record_path",
+ required=True,
+ help="Path to the prediction result folder of Waymo dataset",
+ )
+ parser.add_argument(
+ "--dataset_path",
+ required=True,
+ help="Path to the processed Waymo dataset",
+ )
+ parser.add_argument(
+ "--split",
+ required=True,
+ choices=["validation", "testing"],
+ help="Split of the prediction ([training, validation, testing]).",
+ )
+ args = parser.parse_args()
+ file_list = [file for file in os.listdir(args.record_path) if file.endswith(".npy")]
+ submission = segmentation_submission_pb2.SemanticSegmentationSubmission()
+ frames = segmentation_metrics_pb2.SegmentationFrameList()
+ bar = tqdm.tqdm(file_list)
+ for file in bar:
+ bar.set_postfix(file=file)
+ context_name, frame_timestamp_micros = file.strip("segment-*_pred.npy").split(
+ "_with_camera_labels_"
+ )
+ # Load prediction.
+ # In Pointcept waymo dataset, we minus 1 to label to ignore UNLABELLED class (0 -> -1)
+ pred = np.load(os.path.join(args.record_path, file)) + 1
+ masks = np.load(
+ os.path.join(
+ args.dataset_path,
+ args.split,
+ f"segment-{context_name}_with_camera_labels",
+ frame_timestamp_micros,
+ "mask.npy",
+ ),
+ allow_pickle=True,
+ )
+ offset = np.cumsum([mask.sum() for mask in masks.reshape(-1)])
+ pred = np.split(pred[: offset[-1]], offset[:-1])
+ pred_ri1 = pred[0]
+ pred_ri2 = pred[5]
+ mask_ri1 = np.expand_dims(masks[0, 0], -1)
+ mask_ri2 = np.expand_dims(masks[1, 0], -1)
+ range_dummy = np.zeros_like(mask_ri1, dtype=np.int32)
+ range_pred_ri1 = np.zeros_like(mask_ri1, dtype=np.int32)
+ range_pred_ri1[mask_ri1] = pred_ri1
+ range_pred_ri1 = np.concatenate([range_dummy, range_pred_ri1], axis=-1)
+ range_pred_ri2 = np.zeros_like(mask_ri2, dtype=np.int32)
+ range_pred_ri2[mask_ri2] = pred_ri2
+ range_pred_ri2 = np.concatenate([range_dummy, range_pred_ri2], axis=-1)
+
+ # generate frame submission
+ segmentation_label = open_dataset.Laser()
+ segmentation_label.name = open_dataset.LaserName.TOP
+ segmentation_label.ri_return1.segmentation_label_compressed = compress_array(
+ range_pred_ri1, is_int32=True
+ )
+ segmentation_label.ri_return2.segmentation_label_compressed = compress_array(
+ range_pred_ri2, is_int32=True
+ )
+ frame = segmentation_metrics_pb2.SegmentationFrame()
+ frame.segmentation_labels.append(segmentation_label)
+ frame.context_name = context_name
+ frame.frame_timestamp_micros = int(frame_timestamp_micros)
+ frames.frames.append(frame)
+ submission.account_name = "***"
+ submission.unique_method_name = "***"
+ submission.authors.append("***")
+ submission.affiliation = "***"
+ submission.method_link = "***"
+ submission.sensor_type = (
+ segmentation_submission_pb2.SemanticSegmentationSubmission.LIDAR_ALL
+ )
+ submission.number_past_frames_exclude_current = 0
+ submission.number_future_frames_exclude_current = 0
+ submission.inference_results.CopyFrom(frames)
+ output_filename = os.path.join(args.record_path, "submission.bin")
+ f = open(output_filename, "wb")
+ f.write(submission.SerializeToString())
+ f.close()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/test.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c66708d417082451f23cb635bf4dd1c59082f625
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/test.py
@@ -0,0 +1,38 @@
+"""
+Main Testing Script
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.engines.defaults import (
+ default_argument_parser,
+ default_config_parser,
+ default_setup,
+)
+from pointcept.engines.test import TESTERS
+from pointcept.engines.launch import launch
+
+
+def main_worker(cfg):
+ cfg = default_setup(cfg)
+ tester = TESTERS.build(dict(type=cfg.test.type, cfg=cfg))
+ tester.test()
+
+
+def main():
+ args = default_argument_parser().parse_args()
+ cfg = default_config_parser(args.config_file, args.options)
+
+ launch(
+ main_worker,
+ num_gpus_per_machine=args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ cfg=(cfg,),
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/test_s3dis_6fold.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/test_s3dis_6fold.py
new file mode 100644
index 0000000000000000000000000000000000000000..711ad42c956412cb9cb68adf596b679e25f48d19
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/test_s3dis_6fold.py
@@ -0,0 +1,102 @@
+"""
+Test script for S3DIS 6-fold cross validation
+
+Gathering Area_X.pth from result folder of experiment record of each area as follows:
+|- RECORDS_PATH
+ |- Area_1.pth
+ |- Area_2.pth
+ |- Area_3.pth
+ |- Area_4.pth
+ |- Area_5.pth
+ |- Area_6.pth
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import os
+
+import torch
+import numpy as np
+import glob
+from pointcept.utils.logger import get_root_logger
+
+CLASS_NAMES = [
+ "ceiling",
+ "floor",
+ "wall",
+ "beam",
+ "column",
+ "window",
+ "door",
+ "table",
+ "chair",
+ "sofa",
+ "bookcase",
+ "board",
+ "clutter",
+]
+
+
+def evaluation(intersection, union, target, logger=None):
+ iou_class = intersection / (union + 1e-10)
+ accuracy_class = intersection / (target + 1e-10)
+ mIoU = np.mean(iou_class)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection) / (sum(target) + 1e-10)
+
+ if logger is not None:
+ logger.info(
+ "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}".format(
+ mIoU, mAcc, allAcc
+ )
+ )
+ for i in range(len(CLASS_NAMES)):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=CLASS_NAMES[i],
+ iou=iou_class[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--record_root",
+ required=True,
+ help="Path to the S3DIS record of each split",
+ )
+ config = parser.parse_args()
+ logger = get_root_logger(
+ log_file=os.path.join(config.record_root, "6-fold.log"),
+ file_mode="w",
+ )
+
+ records = sorted(glob.glob(os.path.join(config.record_root, "Area_*.pth")))
+ assert len(records) == 6
+ intersection_ = np.zeros(len(CLASS_NAMES), dtype=int)
+ union_ = np.zeros(len(CLASS_NAMES), dtype=int)
+ target_ = np.zeros(len(CLASS_NAMES), dtype=int)
+
+ for record in records:
+ area = os.path.basename(record).split(".")[0]
+ info = torch.load(record)
+ logger.info(f"<<<<<<<<<<<<<<<<< Parsing {area} <<<<<<<<<<<<<<<<<")
+ intersection = info["intersection"]
+ union = info["union"]
+ target = info["target"]
+ evaluation(intersection, union, target, logger=logger)
+ intersection_ += intersection
+ union_ += union
+ target_ += target
+
+ logger.info(f"<<<<<<<<<<<<<<<<< Parsing 6-fold <<<<<<<<<<<<<<<<<")
+ evaluation(intersection_, union_, target_, logger=logger)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/train.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3ed749c4d0bae2c3ad26487d92c46c5695341a2
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/tools/train.py
@@ -0,0 +1,38 @@
+"""
+Main Training Script
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.engines.defaults import (
+ default_argument_parser,
+ default_config_parser,
+ default_setup,
+)
+from pointcept.engines.train import TRAINERS
+from pointcept.engines.launch import launch
+
+
+def main_worker(cfg):
+ cfg = default_setup(cfg)
+ trainer = TRAINERS.build(dict(type=cfg.train.type, cfg=cfg))
+ trainer.train()
+
+
+def main():
+ args = default_argument_parser().parse_args()
+ cfg = default_config_parser(args.config_file, args.options)
+
+ launch(
+ main_worker,
+ num_gpus_per_machine=args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ cfg=(cfg,),
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/config.py b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b96fc8a0ccccb8e078e6dd3e30ea876f3f22bdb
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/config.py
@@ -0,0 +1,215 @@
+weight = 'exp/scannet/semseg-pt-v3m1-1-ppt-extreme-alc-20240823-massive-no-val/model/model_mod_insseg.pth'
+resume = False
+evaluate = True
+test_only = False
+seed = 32743774
+save_path = 'exp/scannet/instance_segmentation_ppt_pretrain_ft_full'
+num_worker = 24
+batch_size = 12
+batch_size_val = None
+batch_size_test = None
+epoch = 800
+eval_epoch = 100
+sync_bn = False
+enable_amp = True
+empty_cache = False
+empty_cache_per_epoch = False
+find_unused_parameters = True
+mix_prob = 0
+param_dicts = [dict(keyword='block', lr=0.0006)]
+hooks = [
+ dict(type='CheckpointLoader', keywords='module.', replacement='module.'),
+ dict(type='IterationTimer', warmup_iter=2),
+ dict(type='InformationWriter'),
+ dict(
+ type='InsSegEvaluator',
+ segment_ignore_index=(-1, 0, 1),
+ instance_ignore_index=-1),
+ dict(type='CheckpointSaver', save_freq=None)
+]
+train = dict(type='DefaultTrainer')
+test = dict(type='SemSegTester', verbose=True)
+class_names = [
+ 'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
+ 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain',
+ 'refridgerator', 'shower curtain', 'toilet', 'sink', 'bathtub',
+ 'otherfurniture'
+]
+num_classes = 20
+segment_ignore_index = (-1, 0, 1)
+model = dict(
+ type='PG-v1m1',
+ backbone=dict(
+ type='PPT-v1m2',
+ backbone=dict(
+ type='PT-v3m1',
+ in_channels=6,
+ order=('z', 'z-trans', 'hilbert', 'hilbert-trans'),
+ stride=(2, 2, 2, 2),
+ enc_depths=(3, 3, 3, 6, 3),
+ enc_channels=(48, 96, 192, 384, 512),
+ enc_num_head=(3, 6, 12, 24, 32),
+ enc_patch_size=(1024, 1024, 1024, 1024, 1024),
+ dec_depths=(3, 3, 3, 3),
+ dec_channels=(64, 96, 192, 384),
+ dec_num_head=(4, 6, 12, 24),
+ dec_patch_size=(1024, 1024, 1024, 1024),
+ mlp_ratio=4,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ drop_path=0.3,
+ shuffle_orders=True,
+ pre_norm=True,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=False,
+ upcast_softmax=False,
+ cls_mode=False,
+ pdnorm_bn=True,
+ pdnorm_ln=True,
+ pdnorm_decouple=True,
+ pdnorm_adaptive=False,
+ pdnorm_affine=True,
+ pdnorm_conditions=('ScanNet', 'ScanNet200', 'ScanNet++',
+ 'Structured3D', 'ALC')),
+ criteria=[
+ dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1),
+ dict(
+ type='LovaszLoss',
+ mode='multiclass',
+ loss_weight=1.0,
+ ignore_index=-1)
+ ],
+ backbone_out_channels=64,
+ backbone_mode=True,
+ context_channels=256,
+ conditions=('ScanNet', 'ScanNet200', 'ScanNet++', 'Structured3D',
+ 'ALC'),
+ num_classes=(20, 200, 100, 25, 185)),
+ backbone_out_channels=64,
+ semantic_num_classes=20,
+ semantic_ignore_index=-1,
+ segment_ignore_index=(-1, 0, 1),
+ instance_ignore_index=-1,
+ cluster_thresh=1.5,
+ cluster_closed_points=300,
+ cluster_propose_points=100,
+ cluster_min_points=50,
+ freeze_backbone=False)
+optimizer = dict(type='AdamW', lr=0.006, weight_decay=0.05)
+scheduler = dict(
+ type='OneCycleLR',
+ max_lr=[0.006, 0.0006],
+ pct_start=0.05,
+ anneal_strategy='cos',
+ div_factor=10.0,
+ final_div_factor=1000.0)
+dataset_type = 'ScanNetDataset'
+data_root = 'data/scannet'
+data = dict(
+ num_classes=20,
+ ignore_index=-1,
+ names=[
+ 'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
+ 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain',
+ 'refridgerator', 'shower curtain', 'toilet', 'sink', 'bathtub',
+ 'otherfurniture'
+ ],
+ train=dict(
+ type='ScanNetDataset',
+ split='train',
+ data_root='data/scannet',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='RandomDropout',
+ dropout_ratio=0.2,
+ dropout_application_ratio=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-1, 1],
+ axis='z',
+ center=[0, 0, 0],
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='x',
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='y',
+ p=0.5),
+ dict(type='RandomScale', scale=[0.9, 1.1]),
+ dict(type='RandomFlip', p=0.5),
+ dict(type='RandomJitter', sigma=0.005, clip=0.02),
+ dict(
+ type='ElasticDistortion',
+ distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
+ dict(type='ChromaticAutoContrast', p=0.2, blend_factor=None),
+ dict(type='ChromaticTranslation', p=0.95, ratio=0.1),
+ dict(type='ChromaticJitter', p=0.95, std=0.05),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True,
+ keys=('coord', 'color', 'normal', 'segment', 'instance')),
+ dict(type='SphereCrop', sample_rate=0.8, mode='random'),
+ dict(type='NormalizeColor'),
+ dict(
+ type='InstanceParser',
+ segment_ignore_index=(-1, 0, 1),
+ instance_ignore_index=-1),
+ dict(type='Add', keys_dict=dict(condition='ScanNet')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'instance',
+ 'instance_centroid', 'bbox', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False,
+ loop=8),
+ val=dict(
+ type='ScanNetDataset',
+ split='val',
+ data_root='data/scannet',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='Copy',
+ keys_dict=dict(
+ coord='origin_coord',
+ segment='origin_segment',
+ instance='origin_instance')),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True,
+ keys=('coord', 'color', 'normal', 'segment', 'instance')),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(
+ type='InstanceParser',
+ segment_ignore_index=(-1, 0, 1),
+ instance_ignore_index=-1),
+ dict(type='Add', keys_dict=dict(condition='ScanNet')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'instance',
+ 'origin_coord', 'origin_segment', 'origin_instance',
+ 'instance_centroid', 'bbox', 'condition'),
+ feat_keys=('color', 'normal'),
+ offset_keys_dict=dict(
+ offset='coord', origin_offset='origin_coord'))
+ ],
+ test_mode=False),
+ test=dict())
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729271802.eu-g4-017 b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729271802.eu-g4-017
new file mode 100644
index 0000000000000000000000000000000000000000..21340078cdde918953343454e1acc3a4ac870ed5
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729271802.eu-g4-017
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cf973d19091ee5ea4d782bcfb5915ee0568475f585c43bcbdc8ed12408dab3b
+size 50705
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729286373.eu-g4-018 b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729286373.eu-g4-018
new file mode 100644
index 0000000000000000000000000000000000000000..35c4c4de2e13d46a5e1f958babdfba4c783307ed
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729286373.eu-g4-018
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d300319259836ff6d44aa4ed4ccb1ffb59db8ee0d4548bc5f4675d2b5c6bab69
+size 206030
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729482048.eu-a65-02 b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729482048.eu-a65-02
new file mode 100644
index 0000000000000000000000000000000000000000..d87cbda4d228213e7d4edc4f3536290a7cca030c
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/events.out.tfevents.1729482048.eu-a65-02
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a8a5041d33f8613c4b409bf43dd39c18185d545c41dfa0520bcb7357212297c
+size 23157390
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/model/model_best.pth b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/model/model_best.pth
new file mode 100644
index 0000000000000000000000000000000000000000..fe721962d0380e9a66bd4d3887238dc9388b6a9f
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/model/model_best.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03915f939c63f7fdaa9a809e5e75b43d49e05bffd80faf02a0bea1a37319a57a
+size 1170098052
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/model/model_last.pth b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/model/model_last.pth
new file mode 100644
index 0000000000000000000000000000000000000000..9da778258c5cf1dd4741698a0029e537046748e2
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/model/model_last.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa726e9d9607089d1489521bde202bea6b5f73ce3ad0db007dab3f3da4a3e10e
+size 1170098052
diff --git a/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..0c64f5964a6f2fac5b281a3e64cd5aedcd696505
--- /dev/null
+++ b/scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:541248de863baf1b2576d8010567a7acd8ef5a453cb64cc48463c52535bde91d
+size 19139977
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/.DS_Store b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..a7c68306bd608b835f9ffd341a7029b2b953ad06
Binary files /dev/null and b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/.DS_Store differ
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/.DS_Store b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..4ce3154bb71794bede3ab1688cc7e97755921ee7
Binary files /dev/null and b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/.DS_Store differ
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9cd6499201588526e04eec39619503d568936ff
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/__init__.py
@@ -0,0 +1,25 @@
+from .defaults import DefaultDataset, ConcatDataset
+from .builder import build_dataset
+from .utils import point_collate_fn, collate_fn
+
+# indoor scene
+from .s3dis import S3DISDataset
+from .scannet import ScanNetDataset, ScanNet200Dataset
+from .scannetpp import ScanNetPPDataset
+from .scannet_pair import ScanNetPairDataset
+from .arkitscenes import ArkitScenesDataset
+from .structure3d import Structured3DDataset
+from .alc import ARKitScenesLabelMakerConsensusDataset, ARKitScenesLabelMakerScanNet200Dataset
+from .scannetpp import ScanNetPPDataset
+
+# outdoor scene
+from .semantic_kitti import SemanticKITTIDataset
+from .nuscenes import NuScenesDataset
+from .waymo import WaymoDataset
+
+# object
+from .modelnet import ModelNetDataset
+from .shapenet_part import ShapeNetPartDataset
+
+# dataloader
+from .dataloader import MultiDatasetDataloader
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/alc.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/alc.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b8834c538601c7864c5e4c5c0ea09fb6244eb7b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/alc.py
@@ -0,0 +1,156 @@
+import glob
+import os
+from collections.abc import Sequence
+from copy import deepcopy
+
+import numpy as np
+import torch
+from labelmaker.label_data import get_wordnet
+from torch.utils.data import Dataset
+
+from pointcept.utils.cache import shared_dict
+from pointcept.utils.logger import get_root_logger
+
+from .builder import DATASETS
+from .preprocessing.alc.preprocess_arkitscenes_labelmaker_consensus import get_wordnet_compact_mapping
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_20, VALID_CLASS_IDS_200
+from .transform import TRANSFORMS, Compose
+
+
+@DATASETS.register_module()
+class ARKitScenesLabelMakerConsensusDataset(Dataset):
+
+ label_key = "semantic_pseudo_gt_wn199"
+
+ def __init__(
+ self,
+ split="train",
+ data_root="data/alc",
+ transform=None,
+ ignore_index=-1,
+ test_mode=False,
+ test_cfg=None,
+ cache=False,
+ loop=1,
+ ):
+ super(ARKitScenesLabelMakerConsensusDataset, self).__init__()
+ self.get_class_to_id()
+
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.cache = cache
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+
+ self.ignore_index = ignore_index
+
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_list),
+ self.loop,
+ split,
+ )
+ )
+
+ def get_class_to_id(self):
+ self.class2id = get_wordnet_compact_mapping()[0]
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+
+ if not self.cache:
+ data = torch.load(data_path)
+ else:
+ data_name = data_path.replace(os.path.dirname(self.data_root), "").split(".")[0]
+ cache_name = "pointcept" + data_name.replace(os.path.sep, "-")
+ data = shared_dict(cache_name)
+
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ scene_id = data["scene_id"]
+ segment = data[self.label_key].reshape(-1)
+ instance = np.ones(coord.shape[0]) * -1
+
+ data_dict = dict(
+ coord=coord,
+ normal=normal,
+ color=color,
+ segment=segment,
+ instance=instance,
+ scene_id=scene_id,
+ )
+
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ segment = data_dict.pop("segment")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ input_dict_list = []
+ for data in data_dict_list:
+ data_part_list = self.test_voxelize(data)
+ for data_part in data_part_list:
+ if self.test_crop:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ input_dict_list += data_part
+
+ for i in range(len(input_dict_list)):
+ input_dict_list[i] = self.post_transform(input_dict_list[i])
+ data_dict = dict(fragment_list=input_dict_list, segment=segment, name=self.get_data_name(idx))
+ return data_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ARKitScenesLabelMakerScanNet200Dataset(ARKitScenesLabelMakerConsensusDataset):
+ label_key = "semantic_pseudo_gt_scannet200"
+
+ def get_class_to_id(self):
+ self.class2id = np.array(VALID_CLASS_IDS_200)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/arkitscenes.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/arkitscenes.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5481bf553351b09c5f3081b95bcafc77c37f979
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/arkitscenes.py
@@ -0,0 +1,114 @@
+"""
+ArkitScenes Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_200
+
+
+@DATASETS.register_module()
+class ArkitScenesDataset(Dataset):
+ def __init__(
+ self,
+ split="Training",
+ data_root="data/ARKitScenesMesh",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super(ArkitScenesDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.loop = (
+ loop if not test_mode else 1
+ ) # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+ self.class2id = np.array(VALID_CLASS_IDS_200)
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop)
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_list), self.loop, split
+ )
+ )
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, list):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data = torch.load(self.data_list[idx % len(self.data_list)])
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ segment = np.zeros(coord.shape[0])
+ data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ data_idx = self.data_idx[idx % len(self.data_idx)]
+ return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ segment = data_dict.pop("segment")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ input_dict_list = []
+ for data in data_dict_list:
+ data_part_list = self.test_voxelize(data)
+ for data_part in data_part_list:
+ data_part_list = self.test_crop(data_part)
+ input_dict_list += data_part_list
+
+ for i in range(len(input_dict_list)):
+ input_dict_list[i] = self.post_transform(input_dict_list[i])
+ return input_dict_list, segment
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/builder.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fa5f0ee71bf934d5c1bfe5c71446bfecba49f11
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/builder.py
@@ -0,0 +1,15 @@
+"""
+Dataset Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+DATASETS = Registry("datasets")
+
+
+def build_dataset(cfg):
+ """Build datasets."""
+ return DATASETS.build(cfg)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/dataloader.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3c8e1da41179896eb3443e91b2c49d94b62762a
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/dataloader.py
@@ -0,0 +1,112 @@
+from functools import partial
+import weakref
+import torch
+import torch.utils.data
+
+import pointcept.utils.comm as comm
+from pointcept.datasets.utils import point_collate_fn
+from pointcept.datasets import ConcatDataset
+from pointcept.utils.env import set_seed
+
+
+class MultiDatasetDummySampler:
+ def __init__(self):
+ self.dataloader = None
+
+ def set_epoch(self, epoch):
+ if comm.get_world_size() > 1:
+ for dataloader in self.dataloader.dataloaders:
+ dataloader.sampler.set_epoch(epoch)
+ return
+
+
+class MultiDatasetDataloader:
+ """
+ Multiple Datasets Dataloader, batch data from a same dataset and mix up ratio determined by loop of each sub dataset.
+ The overall length is determined by the main dataset (first) and loop of concat dataset.
+ """
+
+ def __init__(
+ self,
+ concat_dataset: ConcatDataset,
+ batch_size_per_gpu: int,
+ num_worker_per_gpu: int,
+ mix_prob=0,
+ seed=None,
+ ):
+ self.datasets = concat_dataset.datasets
+ self.ratios = [dataset.loop for dataset in self.datasets]
+ # reset data loop, original loop serve as ratios
+ for dataset in self.datasets:
+ dataset.loop = 1
+ # determine union training epoch by main dataset
+ self.datasets[0].loop = concat_dataset.loop
+ # build sub-dataloaders
+ num_workers = num_worker_per_gpu // len(self.datasets)
+ self.dataloaders = []
+ for dataset_id, dataset in enumerate(self.datasets):
+ if comm.get_world_size() > 1:
+ sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+ else:
+ sampler = None
+
+ init_fn = (
+ partial(
+ self._worker_init_fn,
+ dataset_id=dataset_id,
+ num_workers=num_workers,
+ num_datasets=len(self.datasets),
+ rank=comm.get_rank(),
+ seed=seed,
+ )
+ if seed is not None
+ else None
+ )
+ self.dataloaders.append(
+ torch.utils.data.DataLoader(
+ dataset,
+ batch_size=batch_size_per_gpu,
+ shuffle=(sampler is None),
+ num_workers=num_worker_per_gpu,
+ sampler=sampler,
+ collate_fn=partial(point_collate_fn, mix_prob=mix_prob),
+ pin_memory=True,
+ worker_init_fn=init_fn,
+ drop_last=True,
+ persistent_workers=True,
+ )
+ )
+ self.sampler = MultiDatasetDummySampler()
+ self.sampler.dataloader = weakref.proxy(self)
+
+ def __iter__(self):
+ iterator = [iter(dataloader) for dataloader in self.dataloaders]
+ while True:
+ for i in range(len(self.ratios)):
+ for _ in range(self.ratios[i]):
+ try:
+ batch = next(iterator[i])
+ except StopIteration:
+ if i == 0:
+ return
+ else:
+ iterator[i] = iter(self.dataloaders[i])
+ batch = next(iterator[i])
+ yield batch
+
+ def __len__(self):
+ main_data_loader_length = len(self.dataloaders[0])
+ return (
+ main_data_loader_length // self.ratios[0] * sum(self.ratios)
+ + main_data_loader_length % self.ratios[0]
+ )
+
+ @staticmethod
+ def _worker_init_fn(worker_id, num_workers, dataset_id, num_datasets, rank, seed):
+ worker_seed = (
+ num_workers * num_datasets * rank
+ + num_workers * dataset_id
+ + worker_id
+ + seed
+ )
+ set_seed(worker_seed)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/defaults.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9c94941ae5630a852846317614f0b00e39fdf68
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/defaults.py
@@ -0,0 +1,297 @@
+"""
+Default Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+from collections.abc import Sequence
+
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.cache import shared_dict
+from .builder import DATASETS, build_dataset
+from .transform import Compose, TRANSFORMS
+
+
+@DATASETS.register_module()
+class DefaultDataset(Dataset):
+ def __init__(
+ self,
+ split="train",
+ data_root="data/dataset",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super(DefaultDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize) if self.test_cfg.voxelize is not None else None
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop is not None else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data = torch.load(self.data_list[idx % len(self.data_list)])
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ if "semantic_gt" in data.keys():
+ segment = data["semantic_gt"].reshape([-1])
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ result_dict = dict(segment=data_dict.pop("segment"), name=self.get_data_name(idx))
+ if "origin_segment" in data_dict:
+ assert "inverse" in data_dict
+ result_dict["origin_segment"] = data_dict.pop("origin_segment")
+ result_dict["inverse"] = data_dict.pop("inverse")
+
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ fragment_list = []
+ for data in data_dict_list:
+ if self.test_voxelize is not None:
+ data_part_list = self.test_voxelize(data)
+ else:
+ data["index"] = np.arange(data["coord"].shape[0])
+ data_part_list = [data]
+ for data_part in data_part_list:
+ if self.test_crop is not None:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ fragment_list += data_part
+
+ for i in range(len(fragment_list)):
+ fragment_list[i] = self.post_transform(fragment_list[i])
+ result_dict["fragment_list"] = fragment_list
+ return result_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class DefaultDatasetV2(Dataset):
+ VALID_ASSETS = [
+ "coord",
+ "color",
+ "normal",
+ "strength",
+ "segment",
+ "instance",
+ "pose",
+ ]
+
+ def __init__(
+ self,
+ split="train",
+ data_root="data/dataset",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ cache=False,
+ ignore_index=-1,
+ loop=1,
+ ):
+ super(DefaultDatasetV2, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.cache = cache
+ self.ignore_index = ignore_index
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ name = self.get_data_name(idx)
+ if self.cache:
+ cache_name = f"pointcept-{name}"
+ return shared_dict(cache_name)
+
+ data_dict = {}
+ assets = os.listdir(data_path)
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ if asset[:-4] not in self.VALID_ASSETS:
+ continue
+ data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+ data_dict["name"] = name
+
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"].astype(np.float32)
+
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"].astype(np.float32)
+
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"].reshape([-1]).astype(np.int32)
+ else:
+ data_dict["segment"] = np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"].reshape([-1]).astype(np.int32)
+ else:
+ data_dict["instance"] = np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)])
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ result_dict = dict(segment=data_dict.pop("segment"), name=data_dict.pop("name"))
+ if "origin_segment" in data_dict:
+ assert "inverse" in data_dict
+ result_dict["origin_segment"] = data_dict.pop("origin_segment")
+ result_dict["inverse"] = data_dict.pop("inverse")
+
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ fragment_list = []
+ for data in data_dict_list:
+ if self.test_voxelize is not None:
+ data_part_list = self.test_voxelize(data)
+ else:
+ data["index"] = np.arange(data["coord"].shape[0])
+ data_part_list = [data]
+ for data_part in data_part_list:
+ if self.test_crop is not None:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ fragment_list += data_part
+
+ for i in range(len(fragment_list)):
+ fragment_list[i] = self.post_transform(fragment_list[i])
+ result_dict["fragment_list"] = fragment_list
+ return result_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ConcatDataset(Dataset):
+ def __init__(self, datasets, loop=1):
+ super(ConcatDataset, self).__init__()
+ self.datasets = [build_dataset(dataset) for dataset in datasets]
+ self.loop = loop
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in the concat set.".format(len(self.data_list), self.loop))
+
+ def get_data_list(self):
+ data_list = []
+ for i in range(len(self.datasets)):
+ data_list.extend(zip(np.ones(len(self.datasets[i])) * i, np.arange(len(self.datasets[i]))))
+ return data_list
+
+ def get_data(self, idx):
+ dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+ return self.datasets[dataset_idx][data_idx]
+
+ def get_data_name(self, idx):
+ dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+ return self.datasets[dataset_idx].get_data_name(data_idx)
+
+ def __getitem__(self, idx):
+ return self.get_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/modelnet.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/modelnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..213f3ed2dfe4d788380824b87355e6fcae1ee531
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/modelnet.py
@@ -0,0 +1,150 @@
+"""
+ModelNet40 Dataset
+
+get sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape)
+at "https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip"
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import pointops
+import torch
+from torch.utils.data import Dataset
+from copy import deepcopy
+
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ModelNetDataset(Dataset):
+ def __init__(
+ self,
+ split="train",
+ data_root="data/modelnet40",
+ class_names=None,
+ transform=None,
+ num_points=8192,
+ uniform_sampling=True,
+ save_record=True,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super().__init__()
+ self.data_root = data_root
+ self.class_names = dict(zip(class_names, range(len(class_names))))
+ self.split = split
+ self.num_point = num_points
+ self.uniform_sampling = uniform_sampling
+ self.transform = Compose(transform)
+ self.loop = (
+ loop if not test_mode else 1
+ ) # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+ if test_mode:
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_list), self.loop, split
+ )
+ )
+
+ # check, prepare record
+ record_name = f"modelnet40_{self.split}"
+ if num_points is not None:
+ record_name += f"_{num_points}points"
+ if uniform_sampling:
+ record_name += "_uniform"
+ record_path = os.path.join(self.data_root, f"{record_name}.pth")
+ if os.path.isfile(record_path):
+ logger.info(f"Loading record: {record_name} ...")
+ self.data = torch.load(record_path)
+ else:
+ logger.info(f"Preparing record: {record_name} ...")
+ self.data = {}
+ for idx in range(len(self.data_list)):
+ data_name = self.data_list[idx]
+ logger.info(f"Parsing data [{idx}/{len(self.data_list)}]: {data_name}")
+ self.data[data_name] = self.get_data(idx)
+ if save_record:
+ torch.save(self.data, record_path)
+
+ def get_data(self, idx):
+ data_idx = idx % len(self.data_list)
+ data_name = self.data_list[data_idx]
+ if data_name in self.data.keys():
+ return self.data[data_name]
+ else:
+ data_shape = "_".join(data_name.split("_")[0:-1])
+ data_path = os.path.join(
+ self.data_root, data_shape, self.data_list[data_idx] + ".txt"
+ )
+ data = np.loadtxt(data_path, delimiter=",").astype(np.float32)
+ if self.num_point is not None:
+ if self.uniform_sampling:
+ with torch.no_grad():
+ mask = pointops.farthest_point_sampling(
+ torch.tensor(data).float().cuda(),
+ torch.tensor([len(data)]).long().cuda(),
+ torch.tensor([self.num_point]).long().cuda(),
+ )
+ data = data[mask.cpu()]
+ else:
+ data = data[: self.num_point]
+ coord, normal = data[:, 0:3], data[:, 3:6]
+ category = np.array([self.class_names[data_shape]])
+ return dict(coord=coord, normal=normal, category=category)
+
+ def get_data_list(self):
+ assert isinstance(self.split, str)
+ split_path = os.path.join(
+ self.data_root, "modelnet40_{}.txt".format(self.split)
+ )
+ data_list = np.loadtxt(split_path, dtype="str")
+ return data_list
+
+ def get_data_name(self, idx):
+ data_idx = idx % len(self.data_list)
+ return self.data_list[data_idx]
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+ def prepare_train_data(self, idx):
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ assert idx < len(self.data_list)
+ data_dict = self.get_data(idx)
+ category = data_dict.pop("category")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+ for i in range(len(data_dict_list)):
+ data_dict_list[i] = self.post_transform(data_dict_list[i])
+ data_dict = dict(
+ voting_list=data_dict_list,
+ category=category,
+ name=self.get_data_name(idx),
+ )
+ return data_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/nuscenes.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/nuscenes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b126c4bbeaef5d73f6f0eb10decbee71d36f515a
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/nuscenes.py
@@ -0,0 +1,120 @@
+"""
+nuScenes Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Zheng Zhang
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+from collections.abc import Sequence
+import pickle
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class NuScenesDataset(DefaultDataset):
+ def __init__(self, sweeps=10, ignore_index=-1, **kwargs):
+ self.sweeps = sweeps
+ self.ignore_index = ignore_index
+ self.learning_map = self.get_learning_map(ignore_index)
+ super().__init__(ignore_index=ignore_index, **kwargs)
+
+ def get_info_path(self, split):
+ assert split in ["train", "val", "test"]
+ if split == "train":
+ return os.path.join(
+ self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_train.pkl"
+ )
+ elif split == "val":
+ return os.path.join(
+ self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_val.pkl"
+ )
+ elif split == "test":
+ return os.path.join(
+ self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_test.pkl"
+ )
+ else:
+ raise NotImplementedError
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ info_paths = [self.get_info_path(self.split)]
+ elif isinstance(self.split, Sequence):
+ info_paths = [self.get_info_path(s) for s in self.split]
+ else:
+ raise NotImplementedError
+ data_list = []
+ for info_path in info_paths:
+ with open(info_path, "rb") as f:
+ info = pickle.load(f)
+ data_list.extend(info)
+ return data_list
+
+ def get_data(self, idx):
+ data = self.data_list[idx % len(self.data_list)]
+ lidar_path = os.path.join(self.data_root, "raw", data["lidar_path"])
+ points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1).reshape(
+ [-1, 5]
+ )
+ coord = points[:, :3]
+ strength = points[:, 3].reshape([-1, 1]) / 255 # scale strength to [0, 1]
+
+ if "gt_segment_path" in data.keys():
+ gt_segment_path = os.path.join(
+ self.data_root, "raw", data["gt_segment_path"]
+ )
+ segment = np.fromfile(
+ str(gt_segment_path), dtype=np.uint8, count=-1
+ ).reshape([-1])
+ segment = np.vectorize(self.learning_map.__getitem__)(segment).astype(
+ np.int64
+ )
+ else:
+ segment = np.ones((points.shape[0],), dtype=np.int64) * self.ignore_index
+ data_dict = dict(coord=coord, strength=strength, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ # return data name for lidar seg, optimize the code when need to support detection
+ return self.data_list[idx % len(self.data_list)]["lidar_token"]
+
+ @staticmethod
+ def get_learning_map(ignore_index):
+ learning_map = {
+ 0: ignore_index,
+ 1: ignore_index,
+ 2: 6,
+ 3: 6,
+ 4: 6,
+ 5: ignore_index,
+ 6: 6,
+ 7: ignore_index,
+ 8: ignore_index,
+ 9: 0,
+ 10: ignore_index,
+ 11: ignore_index,
+ 12: 7,
+ 13: ignore_index,
+ 14: 1,
+ 15: 2,
+ 16: 2,
+ 17: 3,
+ 18: 4,
+ 19: ignore_index,
+ 20: ignore_index,
+ 21: 5,
+ 22: 8,
+ 23: 9,
+ 24: 10,
+ 25: 11,
+ 26: 12,
+ 27: 13,
+ 28: 14,
+ 29: ignore_index,
+ 30: 15,
+ 31: ignore_index,
+ }
+ return learning_map
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d8fac61417d1409379f2ad1dbdecda8a64791ad
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py
@@ -0,0 +1,375 @@
+import warnings
+
+import torch
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import argparse
+import glob
+import json
+import multiprocessing as mp
+import os
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plyfile
+from labelmaker import label_mappings
+from labelmaker.label_data import get_wordnet
+from labelmaker.scannet_200_labels import VALID_CLASS_IDS_200
+from tqdm import tqdm
+
+IGNORE_INDEX = -1
+
+
+def get_wordnet_to_scannet200_mapping():
+ table = pd.read_csv(Path(os.path.dirname(os.path.realpath(label_mappings.__file__))) / "mappings" / "label_mapping.csv")
+ wordnet = get_wordnet()
+ wordnet_keys = [x["name"] for x in wordnet]
+ mapping = {}
+ for row in table.index:
+ if table["wnsynsetkey"][row] not in wordnet_keys:
+ continue
+ scannet_id = table.loc[row, "id"]
+ wordnet199_id = next(x for x in wordnet if x["name"] == table["wnsynsetkey"][row])["id"]
+
+ if scannet_id in VALID_CLASS_IDS_200:
+ mapping.setdefault(wordnet199_id, set()).add(scannet_id)
+
+ wn199_size = np.array([x["id"] for x in wordnet]).max() + 1
+ mapping_array = np.zeros(shape=(wn199_size,), dtype=np.uint16)
+ for wordnet199_id in mapping.keys():
+ mapping_array[wordnet199_id] = min(mapping[wordnet199_id])
+
+ return mapping_array
+
+
+def get_wordnet_compact_mapping():
+ wordnet_info = get_wordnet()[1:]
+ wordnet_info = sorted(wordnet_info, key=lambda x: x["id"])
+
+ class2id = np.array([item["id"] for item in wordnet_info])
+ id2class = np.array([IGNORE_INDEX] * (class2id.max() + 1))
+ for class_, id_ in enumerate(class2id):
+ id2class[id_] = class_
+
+ return class2id, id2class
+
+
+def get_scannet200_compact_mapping():
+ class2id = np.array(VALID_CLASS_IDS_200)
+ id2class = np.array([IGNORE_INDEX] * (class2id.max() + 1))
+ for class_, id_ in enumerate(VALID_CLASS_IDS_200):
+ id2class[id_] = class_
+
+ return class2id, id2class
+
+
+def get_wordnet_names():
+ wordnet_info = get_wordnet()[1:]
+ wordnet_info = sorted(wordnet_info, key=lambda x: x["id"])
+
+ names = [item["name"].split(".")[0].replace("_", " ") for item in wordnet_info]
+
+ return names
+
+
+def read_plypcd(filepath):
+ """Read ply file and return it as numpy array. Returns None if emtpy."""
+
+ with open(filepath, "rb") as f:
+ plydata = plyfile.PlyData.read(f)
+ if plydata.elements:
+ data = plydata.elements[0].data
+ coords = np.array([data["x"], data["y"], data["z"]], dtype=np.float32).T
+
+ colors = None
+ if ({"red", "green", "blue"} - set(data.dtype.names)) == set():
+ colors = np.array([data["red"], data["green"], data["blue"]], dtype=np.uint8).T
+
+ normals = None
+ if ({"nx", "ny", "nz"} - set(data.dtype.names)) == set():
+ normals = np.array([data["nx"], data["ny"], data["nz"]], dtype=np.float32).T
+
+ return coords, colors, normals
+
+
+def handle_process(
+ scene_dir: str,
+ output_path: str,
+ label_mapping,
+ wn199_id2class,
+ scannet200_id2class,
+):
+ scene_dir = Path(scene_dir)
+
+ print(f"Processing: {scene_dir.name} in {scene_dir.parent.name}")
+
+ coords, colors, normals = read_plypcd(str(scene_dir / "pcd_downsampled.ply"))
+ save_dict = dict(
+ coord=coords,
+ color=colors,
+ scene_id=scene_dir.name,
+ normal=normals,
+ )
+
+ label_file = scene_dir / "labels_downsampled.txt"
+ wordnet_label = np.loadtxt(str(label_file), dtype=np.uint8).reshape(-1, 1)
+ scannet200_label = label_mapping[wordnet_label]
+ save_dict["semantic_pseudo_gt_wn199"] = wn199_id2class[wordnet_label]
+ save_dict["semantic_pseudo_gt_scannet200"] = scannet200_id2class[scannet200_label]
+
+ torch.save(save_dict, output_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ config = parser.parse_args()
+
+ # Create output directories
+ train_output_dir = os.path.join(config.output_root, "train")
+ os.makedirs(train_output_dir, exist_ok=True)
+ val_output_dir = os.path.join(config.output_root, "val")
+ os.makedirs(val_output_dir, exist_ok=True)
+
+ # Load label map
+ wn_scannet200_label_mapping = get_wordnet_to_scannet200_mapping()
+ _, wn199_id2class = get_wordnet_compact_mapping()
+ _, scannet200_id2class = get_scannet200_compact_mapping()
+
+ scene_dirs = []
+ output_paths = []
+
+ # Load train/val splits
+ train_folder = Path(config.dataset_root) / "Training"
+ train_scene_names = os.listdir(str(train_folder))
+ for scene in tqdm(train_scene_names):
+ file_path = train_folder / scene / "pcd_downsampled.ply"
+ if file_path.exists() and os.path.getsize(str(file_path)) <= 50 * 1024 * 1024:
+ scene_dirs.append(str(train_folder / scene))
+ output_paths.append(str(Path(config.output_root) / "train" / f"{scene}.pth"))
+
+ val_folder = Path(config.dataset_root) / "Validation"
+ val_scene_names = os.listdir(str(val_folder))
+ for scene in tqdm(val_scene_names):
+ file_path = val_folder / scene / "pcd_downsampled.ply"
+ if file_path.exists() and os.path.getsize(str(file_path)) <= 50 * 1024 * 1024:
+ scene_dirs.append(str(val_folder / scene))
+ output_paths.append(str(Path(config.output_root) / "val" / f"{scene}.pth"))
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+ print(f"Using {mp.cpu_count()} cores")
+ # pool = ProcessPoolExecutor(max_workers=1)
+ _ = list(
+ pool.map(
+ handle_process,
+ scene_dirs,
+ output_paths,
+ repeat(wn_scannet200_label_mapping),
+ repeat(wn199_id2class),
+ repeat(scannet200_id2class),
+ )
+ )
+
+
+WORDNET_NAMES = (
+ "wall",
+ "chair",
+ "book",
+ "cabinet",
+ "door",
+ "floor",
+ "ashcan",
+ "table",
+ "window",
+ "bookshelf",
+ "display",
+ "cushion",
+ "box",
+ "picture",
+ "ceiling",
+ "doorframe",
+ "desk",
+ "swivel chair",
+ "towel",
+ "sofa",
+ "sink",
+ "backpack",
+ "lamp",
+ "chest of drawers",
+ "apparel",
+ "armchair",
+ "bed",
+ "curtain",
+ "mirror",
+ "plant",
+ "radiator",
+ "toilet tissue",
+ "shoe",
+ "bag",
+ "bottle",
+ "countertop",
+ "coffee table",
+ "toilet",
+ "computer keyboard",
+ "fridge",
+ "stool",
+ "computer",
+ "mug",
+ "telephone",
+ "light",
+ "jacket",
+ "bathtub",
+ "shower curtain",
+ "microwave",
+ "footstool",
+ "baggage",
+ "laptop",
+ "printer",
+ "shower stall",
+ "soap dispenser",
+ "stove",
+ "fan",
+ "paper",
+ "stand",
+ "bench",
+ "wardrobe",
+ "blanket",
+ "booth",
+ "duplicator",
+ "bar",
+ "soap dish",
+ "switch",
+ "coffee maker",
+ "decoration",
+ "range hood",
+ "blackboard",
+ "clock",
+ "railing",
+ "mat",
+ "seat",
+ "bannister",
+ "container",
+ "mouse",
+ "person",
+ "stairway",
+ "basket",
+ "dumbbell",
+ "column",
+ "bucket",
+ "windowsill",
+ "signboard",
+ "dishwasher",
+ "loudspeaker",
+ "washer",
+ "paper towel",
+ "clothes hamper",
+ "piano",
+ "sack",
+ "handcart",
+ "blind",
+ "dish rack",
+ "mailbox",
+ "bag",
+ "bicycle",
+ "ladder",
+ "rack",
+ "tray",
+ "toaster",
+ "paper cutter",
+ "plunger",
+ "dryer",
+ "guitar",
+ "fire extinguisher",
+ "pitcher",
+ "pipe",
+ "plate",
+ "vacuum",
+ "bowl",
+ "hat",
+ "rod",
+ "water cooler",
+ "kettle",
+ "oven",
+ "scale",
+ "broom",
+ "hand blower",
+ "coatrack",
+ "teddy",
+ "alarm clock",
+ "ironing board",
+ "fire alarm",
+ "machine",
+ "music stand",
+ "fireplace",
+ "furniture",
+ "vase",
+ "vent",
+ "candle",
+ "crate",
+ "dustpan",
+ "earphone",
+ "jar",
+ "projector",
+ "gat",
+ "step",
+ "step stool",
+ "vending machine",
+ "coat",
+ "coat hanger",
+ "drinking fountain",
+ "hamper",
+ "thermostat",
+ "banner",
+ "iron",
+ "soap",
+ "chopping board",
+ "kitchen island",
+ "shirt",
+ "sleeping bag",
+ "tire",
+ "toothbrush",
+ "bathrobe",
+ "faucet",
+ "slipper",
+ "thermos",
+ "tripod",
+ "dispenser",
+ "heater",
+ "pool table",
+ "remote control",
+ "stapler",
+ "treadmill",
+ "beanbag",
+ "dartboard",
+ "metronome",
+ "rope",
+ "sewing machine",
+ "shredder",
+ "toolbox",
+ "water heater",
+ "brush",
+ "control",
+ "dais",
+ "dollhouse",
+ "envelope",
+ "food",
+ "frying pan",
+ "helmet",
+ "tennis racket",
+ "umbrella",
+)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bc9b3e47a35f5baa00bcf0f526d5d986b28494e
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py
@@ -0,0 +1,87 @@
+"""
+Preprocessing ArkitScenes
+"""
+
+import os
+import argparse
+import glob
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+import torch
+
+
+def read_plymesh(filepath):
+ """Read ply file and return it as numpy array. Returns None if emtpy."""
+ with open(filepath, "rb") as f:
+ plydata = plyfile.PlyData.read(f)
+ if plydata.elements:
+ vertices = pd.DataFrame(plydata["vertex"].data).values
+ faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+ return vertices, faces
+
+
+def face_normal(vertex, face):
+ v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+ v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+ vec = np.cross(v01, v02)
+ length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+ nf = vec / length
+ area = length * 0.5
+ return nf, area
+
+
+def vertex_normal(vertex, face):
+ nf, area = face_normal(vertex, face)
+ nf = nf * area
+
+ nv = np.zeros_like(vertex)
+ for i in range(face.shape[0]):
+ nv[face[i]] += nf[i]
+
+ length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+ nv = nv / length
+ return nv
+
+
+def parse_scene(scene_path, output_dir):
+ print(f"Parsing scene {scene_path}")
+ split = os.path.basename(os.path.dirname(os.path.dirname(scene_path)))
+ scene_id = os.path.basename(os.path.dirname(scene_path))
+ vertices, faces = read_plymesh(scene_path)
+ coords = vertices[:, :3]
+ colors = vertices[:, 3:6]
+ data_dict = dict(coord=coords, color=colors, scene_id=scene_id)
+ data_dict["normal"] = vertex_normal(coords, faces)
+ torch.save(data_dict, os.path.join(output_dir, split, f"{scene_id}.pth"))
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ opt = parser.parse_args()
+ # Create output directories
+ train_output_dir = os.path.join(opt.output_root, "Training")
+ os.makedirs(train_output_dir, exist_ok=True)
+ val_output_dir = os.path.join(opt.output_root, "Validation")
+ os.makedirs(val_output_dir, exist_ok=True)
+ # Load scene paths
+ scene_paths = sorted(glob.glob(opt.dataset_root + "/3dod/*/*/*_mesh.ply"))
+ # Preprocess data.
+ pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+ # pool = ProcessPoolExecutor(max_workers=1)
+ print("Processing scenes...")
+ _ = list(pool.map(parse_scene, scene_paths, repeat(opt.output_root)))
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ed106f193a488aa76385157aa33fb65e5944a6f
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py
@@ -0,0 +1,607 @@
+"""
+Preprocessing Script for nuScenes Informantion
+modified from OpenPCDet (https://github.com/open-mmlab/OpenPCDet)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from pathlib import Path
+import numpy as np
+import argparse
+import tqdm
+import pickle
+from functools import reduce
+from pyquaternion import Quaternion
+from nuscenes.nuscenes import NuScenes
+from nuscenes.utils import splits
+from nuscenes.utils.geometry_utils import transform_matrix
+
+
+map_name_from_general_to_detection = {
+ "human.pedestrian.adult": "pedestrian",
+ "human.pedestrian.child": "pedestrian",
+ "human.pedestrian.wheelchair": "ignore",
+ "human.pedestrian.stroller": "ignore",
+ "human.pedestrian.personal_mobility": "ignore",
+ "human.pedestrian.police_officer": "pedestrian",
+ "human.pedestrian.construction_worker": "pedestrian",
+ "animal": "ignore",
+ "vehicle.car": "car",
+ "vehicle.motorcycle": "motorcycle",
+ "vehicle.bicycle": "bicycle",
+ "vehicle.bus.bendy": "bus",
+ "vehicle.bus.rigid": "bus",
+ "vehicle.truck": "truck",
+ "vehicle.construction": "construction_vehicle",
+ "vehicle.emergency.ambulance": "ignore",
+ "vehicle.emergency.police": "ignore",
+ "vehicle.trailer": "trailer",
+ "movable_object.barrier": "barrier",
+ "movable_object.trafficcone": "traffic_cone",
+ "movable_object.pushable_pullable": "ignore",
+ "movable_object.debris": "ignore",
+ "static_object.bicycle_rack": "ignore",
+}
+
+
+cls_attr_dist = {
+ "barrier": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "bicycle": {
+ "cycle.with_rider": 2791,
+ "cycle.without_rider": 8946,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "bus": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 9092,
+ "vehicle.parked": 3294,
+ "vehicle.stopped": 3881,
+ },
+ "car": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 114304,
+ "vehicle.parked": 330133,
+ "vehicle.stopped": 46898,
+ },
+ "construction_vehicle": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 882,
+ "vehicle.parked": 11549,
+ "vehicle.stopped": 2102,
+ },
+ "ignore": {
+ "cycle.with_rider": 307,
+ "cycle.without_rider": 73,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 165,
+ "vehicle.parked": 400,
+ "vehicle.stopped": 102,
+ },
+ "motorcycle": {
+ "cycle.with_rider": 4233,
+ "cycle.without_rider": 8326,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "pedestrian": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 157444,
+ "pedestrian.sitting_lying_down": 13939,
+ "pedestrian.standing": 46530,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "traffic_cone": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 0,
+ "vehicle.parked": 0,
+ "vehicle.stopped": 0,
+ },
+ "trailer": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 3421,
+ "vehicle.parked": 19224,
+ "vehicle.stopped": 1895,
+ },
+ "truck": {
+ "cycle.with_rider": 0,
+ "cycle.without_rider": 0,
+ "pedestrian.moving": 0,
+ "pedestrian.sitting_lying_down": 0,
+ "pedestrian.standing": 0,
+ "vehicle.moving": 21339,
+ "vehicle.parked": 55626,
+ "vehicle.stopped": 11097,
+ },
+}
+
+
+def get_available_scenes(nusc):
+ available_scenes = []
+ for scene in nusc.scene:
+ scene_token = scene["token"]
+ scene_rec = nusc.get("scene", scene_token)
+ sample_rec = nusc.get("sample", scene_rec["first_sample_token"])
+ sd_rec = nusc.get("sample_data", sample_rec["data"]["LIDAR_TOP"])
+ has_more_frames = True
+ scene_not_exist = False
+ while has_more_frames:
+ lidar_path, boxes, _ = nusc.get_sample_data(sd_rec["token"])
+ if not Path(lidar_path).exists():
+ scene_not_exist = True
+ break
+ else:
+ break
+ if scene_not_exist:
+ continue
+ available_scenes.append(scene)
+ return available_scenes
+
+
+def get_sample_data(nusc, sample_data_token, selected_anntokens=None):
+ """
+ Returns the data path as well as all annotations related to that sample_data.
+ Note that the boxes are transformed into the current sensor"s coordinate frame.
+ Args:
+ nusc:
+ sample_data_token: Sample_data token.
+ selected_anntokens: If provided only return the selected annotation.
+
+ Returns:
+
+ """
+ # Retrieve sensor & pose records
+ sd_record = nusc.get("sample_data", sample_data_token)
+ cs_record = nusc.get("calibrated_sensor", sd_record["calibrated_sensor_token"])
+ sensor_record = nusc.get("sensor", cs_record["sensor_token"])
+ pose_record = nusc.get("ego_pose", sd_record["ego_pose_token"])
+
+ data_path = nusc.get_sample_data_path(sample_data_token)
+
+ if sensor_record["modality"] == "camera":
+ cam_intrinsic = np.array(cs_record["camera_intrinsic"])
+ else:
+ cam_intrinsic = None
+
+ # Retrieve all sample annotations and map to sensor coordinate system.
+ if selected_anntokens is not None:
+ boxes = list(map(nusc.get_box, selected_anntokens))
+ else:
+ boxes = nusc.get_boxes(sample_data_token)
+
+ # Make list of Box objects including coord system transforms.
+ box_list = []
+ for box in boxes:
+ box.velocity = nusc.box_velocity(box.token)
+ # Move box to ego vehicle coord system
+ box.translate(-np.array(pose_record["translation"]))
+ box.rotate(Quaternion(pose_record["rotation"]).inverse)
+
+ # Move box to sensor coord system
+ box.translate(-np.array(cs_record["translation"]))
+ box.rotate(Quaternion(cs_record["rotation"]).inverse)
+
+ box_list.append(box)
+
+ return data_path, box_list, cam_intrinsic
+
+
+def quaternion_yaw(q: Quaternion) -> float:
+ """
+ Calculate the yaw angle from a quaternion.
+ Note that this only works for a quaternion that represents a box in lidar or global coordinate frame.
+ It does not work for a box in the camera frame.
+ :param q: Quaternion of interest.
+ :return: Yaw angle in radians.
+ """
+
+ # Project into xy plane.
+ v = np.dot(q.rotation_matrix, np.array([1, 0, 0]))
+
+ # Measure yaw using arctan.
+ yaw = np.arctan2(v[1], v[0])
+
+ return yaw
+
+
+def obtain_sensor2top(
+ nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type="lidar"
+):
+ """Obtain the info with RT matric from general sensor to Top LiDAR.
+
+ Args:
+ nusc (class): Dataset class in the nuScenes dataset.
+ sensor_token (str): Sample data token corresponding to the
+ specific sensor type.
+ l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3).
+ l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego
+ in shape (3, 3).
+ e2g_t (np.ndarray): Translation from ego to global in shape (1, 3).
+ e2g_r_mat (np.ndarray): Rotation matrix from ego to global
+ in shape (3, 3).
+ sensor_type (str): Sensor to calibrate. Default: "lidar".
+
+ Returns:
+ sweep (dict): Sweep information after transformation.
+ """
+ sd_rec = nusc.get("sample_data", sensor_token)
+ cs_record = nusc.get("calibrated_sensor", sd_rec["calibrated_sensor_token"])
+ pose_record = nusc.get("ego_pose", sd_rec["ego_pose_token"])
+ data_path = str(nusc.get_sample_data_path(sd_rec["token"]))
+ # if os.getcwd() in data_path: # path from lyftdataset is absolute path
+ # data_path = data_path.split(f"{os.getcwd()}/")[-1] # relative path
+ sweep = {
+ "data_path": data_path,
+ "type": sensor_type,
+ "sample_data_token": sd_rec["token"],
+ "sensor2ego_translation": cs_record["translation"],
+ "sensor2ego_rotation": cs_record["rotation"],
+ "ego2global_translation": pose_record["translation"],
+ "ego2global_rotation": pose_record["rotation"],
+ "timestamp": sd_rec["timestamp"],
+ }
+ l2e_r_s = sweep["sensor2ego_rotation"]
+ l2e_t_s = sweep["sensor2ego_translation"]
+ e2g_r_s = sweep["ego2global_rotation"]
+ e2g_t_s = sweep["ego2global_translation"]
+
+ # obtain the RT from sensor to Top LiDAR
+ # sweep->ego->global->ego'->lidar
+ l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
+ e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
+ R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
+ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+ )
+ T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
+ np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+ )
+ T -= (
+ e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+ + l2e_t @ np.linalg.inv(l2e_r_mat).T
+ ).squeeze(0)
+ sweep["sensor2lidar_rotation"] = R.T # points @ R.T + T
+ sweep["sensor2lidar_translation"] = T
+ return sweep
+
+
+def fill_trainval_infos(
+ data_path, nusc, train_scenes, test=False, max_sweeps=10, with_camera=False
+):
+ train_nusc_infos = []
+ val_nusc_infos = []
+ progress_bar = tqdm.tqdm(
+ total=len(nusc.sample), desc="create_info", dynamic_ncols=True
+ )
+
+ ref_chan = "LIDAR_TOP" # The radar channel from which we track back n sweeps to aggregate the point cloud.
+ chan = "LIDAR_TOP" # The reference channel of the current sample_rec that the point clouds are mapped to.
+
+ for index, sample in enumerate(nusc.sample):
+ progress_bar.update()
+
+ ref_sd_token = sample["data"][ref_chan]
+ ref_sd_rec = nusc.get("sample_data", ref_sd_token)
+ ref_cs_rec = nusc.get(
+ "calibrated_sensor", ref_sd_rec["calibrated_sensor_token"]
+ )
+ ref_pose_rec = nusc.get("ego_pose", ref_sd_rec["ego_pose_token"])
+ ref_time = 1e-6 * ref_sd_rec["timestamp"]
+
+ ref_lidar_path, ref_boxes, _ = get_sample_data(nusc, ref_sd_token)
+
+ ref_cam_front_token = sample["data"]["CAM_FRONT"]
+ ref_cam_path, _, ref_cam_intrinsic = nusc.get_sample_data(ref_cam_front_token)
+
+ # Homogeneous transform from ego car frame to reference frame
+ ref_from_car = transform_matrix(
+ ref_cs_rec["translation"], Quaternion(ref_cs_rec["rotation"]), inverse=True
+ )
+
+ # Homogeneous transformation matrix from global to _current_ ego car frame
+ car_from_global = transform_matrix(
+ ref_pose_rec["translation"],
+ Quaternion(ref_pose_rec["rotation"]),
+ inverse=True,
+ )
+ info = {
+ "lidar_path": Path(ref_lidar_path).relative_to(data_path).__str__(),
+ "lidar_token": ref_sd_token,
+ "cam_front_path": Path(ref_cam_path).relative_to(data_path).__str__(),
+ "cam_intrinsic": ref_cam_intrinsic,
+ "token": sample["token"],
+ "sweeps": [],
+ "ref_from_car": ref_from_car,
+ "car_from_global": car_from_global,
+ "timestamp": ref_time,
+ }
+ if with_camera:
+ info["cams"] = dict()
+ l2e_r = ref_cs_rec["rotation"]
+ l2e_t = (ref_cs_rec["translation"],)
+ e2g_r = ref_pose_rec["rotation"]
+ e2g_t = ref_pose_rec["translation"]
+ l2e_r_mat = Quaternion(l2e_r).rotation_matrix
+ e2g_r_mat = Quaternion(e2g_r).rotation_matrix
+
+ # obtain 6 image's information per frame
+ camera_types = [
+ "CAM_FRONT",
+ "CAM_FRONT_RIGHT",
+ "CAM_FRONT_LEFT",
+ "CAM_BACK",
+ "CAM_BACK_LEFT",
+ "CAM_BACK_RIGHT",
+ ]
+ for cam in camera_types:
+ cam_token = sample["data"][cam]
+ cam_path, _, camera_intrinsics = nusc.get_sample_data(cam_token)
+ cam_info = obtain_sensor2top(
+ nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam
+ )
+ cam_info["data_path"] = (
+ Path(cam_info["data_path"]).relative_to(data_path).__str__()
+ )
+ cam_info.update(camera_intrinsics=camera_intrinsics)
+ info["cams"].update({cam: cam_info})
+
+ sample_data_token = sample["data"][chan]
+ curr_sd_rec = nusc.get("sample_data", sample_data_token)
+ sweeps = []
+ while len(sweeps) < max_sweeps - 1:
+ if curr_sd_rec["prev"] == "":
+ if len(sweeps) == 0:
+ sweep = {
+ "lidar_path": Path(ref_lidar_path)
+ .relative_to(data_path)
+ .__str__(),
+ "sample_data_token": curr_sd_rec["token"],
+ "transform_matrix": None,
+ "time_lag": curr_sd_rec["timestamp"] * 0,
+ }
+ sweeps.append(sweep)
+ else:
+ sweeps.append(sweeps[-1])
+ else:
+ curr_sd_rec = nusc.get("sample_data", curr_sd_rec["prev"])
+
+ # Get past pose
+ current_pose_rec = nusc.get("ego_pose", curr_sd_rec["ego_pose_token"])
+ global_from_car = transform_matrix(
+ current_pose_rec["translation"],
+ Quaternion(current_pose_rec["rotation"]),
+ inverse=False,
+ )
+
+ # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.
+ current_cs_rec = nusc.get(
+ "calibrated_sensor", curr_sd_rec["calibrated_sensor_token"]
+ )
+ car_from_current = transform_matrix(
+ current_cs_rec["translation"],
+ Quaternion(current_cs_rec["rotation"]),
+ inverse=False,
+ )
+
+ tm = reduce(
+ np.dot,
+ [ref_from_car, car_from_global, global_from_car, car_from_current],
+ )
+
+ lidar_path = nusc.get_sample_data_path(curr_sd_rec["token"])
+
+ time_lag = ref_time - 1e-6 * curr_sd_rec["timestamp"]
+
+ sweep = {
+ "lidar_path": Path(lidar_path).relative_to(data_path).__str__(),
+ "sample_data_token": curr_sd_rec["token"],
+ "transform_matrix": tm,
+ "global_from_car": global_from_car,
+ "car_from_current": car_from_current,
+ "time_lag": time_lag,
+ }
+ sweeps.append(sweep)
+
+ info["sweeps"] = sweeps
+
+ assert len(info["sweeps"]) == max_sweeps - 1, (
+ f"sweep {curr_sd_rec['token']} only has {len(info['sweeps'])} sweeps, "
+ f"you should duplicate to sweep num {max_sweeps - 1}"
+ )
+
+ if not test:
+ # processing gt bbox
+ annotations = [
+ nusc.get("sample_annotation", token) for token in sample["anns"]
+ ]
+
+ # the filtering gives 0.5~1 map improvement
+ num_lidar_pts = np.array([anno["num_lidar_pts"] for anno in annotations])
+ num_radar_pts = np.array([anno["num_radar_pts"] for anno in annotations])
+ mask = num_lidar_pts + num_radar_pts > 0
+
+ locs = np.array([b.center for b in ref_boxes]).reshape(-1, 3)
+ dims = np.array([b.wlh for b in ref_boxes]).reshape(-1, 3)[
+ :, [1, 0, 2]
+ ] # wlh == > dxdydz (lwh)
+ velocity = np.array([b.velocity for b in ref_boxes]).reshape(-1, 3)
+ rots = np.array([quaternion_yaw(b.orientation) for b in ref_boxes]).reshape(
+ -1, 1
+ )
+ names = np.array([b.name for b in ref_boxes])
+ tokens = np.array([b.token for b in ref_boxes])
+ gt_boxes = np.concatenate([locs, dims, rots, velocity[:, :2]], axis=1)
+
+ assert len(annotations) == len(gt_boxes) == len(velocity)
+
+ info["gt_boxes"] = gt_boxes[mask, :]
+ info["gt_boxes_velocity"] = velocity[mask, :]
+ info["gt_names"] = np.array(
+ [map_name_from_general_to_detection[name] for name in names]
+ )[mask]
+ info["gt_boxes_token"] = tokens[mask]
+ info["num_lidar_pts"] = num_lidar_pts[mask]
+ info["num_radar_pts"] = num_radar_pts[mask]
+
+ # processing gt segment
+ segment_path = nusc.get("lidarseg", ref_sd_token)["filename"]
+ info["gt_segment_path"] = segment_path
+
+ if sample["scene_token"] in train_scenes:
+ train_nusc_infos.append(info)
+ else:
+ val_nusc_infos.append(info)
+
+ progress_bar.close()
+ return train_nusc_infos, val_nusc_infos
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root", required=True, help="Path to the nuScenes dataset."
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where processed information located.",
+ )
+ parser.add_argument(
+ "--max_sweeps", default=10, type=int, help="Max number of sweeps. Default: 10."
+ )
+ parser.add_argument(
+ "--with_camera",
+ action="store_true",
+ default=False,
+ help="Whether use camera or not.",
+ )
+ config = parser.parse_args()
+
+ print(f"Loading nuScenes tables for version v1.0-trainval...")
+ nusc_trainval = NuScenes(
+ version="v1.0-trainval", dataroot=config.dataset_root, verbose=False
+ )
+ available_scenes_trainval = get_available_scenes(nusc_trainval)
+ available_scene_names_trainval = [s["name"] for s in available_scenes_trainval]
+ print("total scene num:", len(nusc_trainval.scene))
+ print("exist scene num:", len(available_scenes_trainval))
+ assert len(available_scenes_trainval) == len(nusc_trainval.scene) == 850
+
+ print(f"Loading nuScenes tables for version v1.0-test...")
+ nusc_test = NuScenes(
+ version="v1.0-test", dataroot=config.dataset_root, verbose=False
+ )
+ available_scenes_test = get_available_scenes(nusc_test)
+ available_scene_names_test = [s["name"] for s in available_scenes_test]
+ print("total scene num:", len(nusc_test.scene))
+ print("exist scene num:", len(available_scenes_test))
+ assert len(available_scenes_test) == len(nusc_test.scene) == 150
+
+ train_scenes = splits.train
+ train_scenes = set(
+ [
+ available_scenes_trainval[available_scene_names_trainval.index(s)]["token"]
+ for s in train_scenes
+ ]
+ )
+ test_scenes = splits.test
+ test_scenes = set(
+ [
+ available_scenes_test[available_scene_names_test.index(s)]["token"]
+ for s in test_scenes
+ ]
+ )
+ print(f"Filling trainval information...")
+ train_nusc_infos, val_nusc_infos = fill_trainval_infos(
+ config.dataset_root,
+ nusc_trainval,
+ train_scenes,
+ test=False,
+ max_sweeps=config.max_sweeps,
+ with_camera=config.with_camera,
+ )
+ print(f"Filling test information...")
+ test_nusc_infos, _ = fill_trainval_infos(
+ config.dataset_root,
+ nusc_test,
+ test_scenes,
+ test=True,
+ max_sweeps=config.max_sweeps,
+ with_camera=config.with_camera,
+ )
+
+ print(f"Saving nuScenes information...")
+ os.makedirs(os.path.join(config.output_root, "info"), exist_ok=True)
+ print(
+ f"train sample: {len(train_nusc_infos)}, val sample: {len(val_nusc_infos)}, test sample: {len(test_nusc_infos)}"
+ )
+ with open(
+ os.path.join(
+ config.output_root,
+ "info",
+ f"nuscenes_infos_{config.max_sweeps}sweeps_train.pkl",
+ ),
+ "wb",
+ ) as f:
+ pickle.dump(train_nusc_infos, f)
+ with open(
+ os.path.join(
+ config.output_root,
+ "info",
+ f"nuscenes_infos_{config.max_sweeps}sweeps_val.pkl",
+ ),
+ "wb",
+ ) as f:
+ pickle.dump(val_nusc_infos, f)
+ with open(
+ os.path.join(
+ config.output_root,
+ "info",
+ f"nuscenes_infos_{config.max_sweeps}sweeps_test.pkl",
+ ),
+ "wb",
+ ) as f:
+ pickle.dump(test_nusc_infos, f)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py
new file mode 100644
index 0000000000000000000000000000000000000000..d770ad6317996c8a53cd13b2e12af3a536b6dca4
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py
@@ -0,0 +1,233 @@
+"""
+Preprocessing Script for S3DIS
+Parsing normal vectors has a large consumption of memory. Please reduce max_workers if memory is limited.
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import glob
+import numpy as np
+
+try:
+ import open3d
+except ImportError:
+ import warnings
+
+ warnings.warn("Please install open3d for parsing normal")
+
+try:
+ import trimesh
+except ImportError:
+ import warnings
+
+ warnings.warn("Please install trimesh for parsing normal")
+
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+area_mesh_dict = {}
+
+
+def parse_room(
+ room, angle, dataset_root, output_root, align_angle=True, parse_normal=False
+):
+ print("Parsing: {}".format(room))
+ classes = [
+ "ceiling",
+ "floor",
+ "wall",
+ "beam",
+ "column",
+ "window",
+ "door",
+ "table",
+ "chair",
+ "sofa",
+ "bookcase",
+ "board",
+ "clutter",
+ ]
+ class2label = {cls: i for i, cls in enumerate(classes)}
+ source_dir = os.path.join(dataset_root, room)
+ save_path = os.path.join(output_root, room)
+ os.makedirs(save_path, exist_ok=True)
+ object_path_list = sorted(glob.glob(os.path.join(source_dir, "Annotations/*.txt")))
+
+ room_coords = []
+ room_colors = []
+ room_normals = []
+ room_semantic_gt = []
+ room_instance_gt = []
+
+ for object_id, object_path in enumerate(object_path_list):
+ object_name = os.path.basename(object_path).split("_")[0]
+ obj = np.loadtxt(object_path)
+ coords = obj[:, :3]
+ colors = obj[:, 3:6]
+ # note: in some room there is 'stairs' class
+ class_name = object_name if object_name in classes else "clutter"
+ semantic_gt = np.repeat(class2label[class_name], coords.shape[0])
+ semantic_gt = semantic_gt.reshape([-1, 1])
+ instance_gt = np.repeat(object_id, coords.shape[0])
+ instance_gt = instance_gt.reshape([-1, 1])
+
+ room_coords.append(coords)
+ room_colors.append(colors)
+ room_semantic_gt.append(semantic_gt)
+ room_instance_gt.append(instance_gt)
+
+ room_coords = np.ascontiguousarray(np.vstack(room_coords))
+
+ if parse_normal:
+ x_min, z_max, y_min = np.min(room_coords, axis=0)
+ x_max, z_min, y_max = np.max(room_coords, axis=0)
+ z_max = -z_max
+ z_min = -z_min
+ max_bound = np.array([x_max, y_max, z_max]) + 0.1
+ min_bound = np.array([x_min, y_min, z_min]) - 0.1
+ bbox = open3d.geometry.AxisAlignedBoundingBox(
+ min_bound=min_bound, max_bound=max_bound
+ )
+ # crop room
+ room_mesh = (
+ area_mesh_dict[os.path.dirname(room)]
+ .crop(bbox)
+ .transform(
+ np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
+ )
+ )
+ vertices = np.array(room_mesh.vertices)
+ faces = np.array(room_mesh.triangles)
+ vertex_normals = np.array(room_mesh.vertex_normals)
+ room_mesh = trimesh.Trimesh(
+ vertices=vertices, faces=faces, vertex_normals=vertex_normals
+ )
+ (closest_points, distances, face_id) = room_mesh.nearest.on_surface(room_coords)
+ room_normals = room_mesh.face_normals[face_id]
+
+ if align_angle:
+ angle = (2 - angle / 180) * np.pi
+ rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+ rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+ room_center = (np.max(room_coords, axis=0) + np.min(room_coords, axis=0)) / 2
+ room_coords = (room_coords - room_center) @ np.transpose(rot_t) + room_center
+ if parse_normal:
+ room_normals = room_normals @ np.transpose(rot_t)
+
+ room_colors = np.ascontiguousarray(np.vstack(room_colors))
+ room_semantic_gt = np.ascontiguousarray(np.vstack(room_semantic_gt))
+ room_instance_gt = np.ascontiguousarray(np.vstack(room_instance_gt))
+ np.save(os.path.join(save_path, "coord.npy"), room_coords.astype(np.float32))
+ np.save(os.path.join(save_path, "color.npy"), room_colors.astype(np.uint8))
+ np.save(os.path.join(save_path, "segment.npy"), room_semantic_gt.astype(np.int16))
+ np.save(os.path.join(save_path, "instance.npy"), room_instance_gt.astype(np.int16))
+
+ if parse_normal:
+ np.save(os.path.join(save_path, "normal.npy"), room_normals.astype(np.float32))
+
+
+def main_process():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--splits",
+ required=True,
+ nargs="+",
+ choices=["Area_1", "Area_2", "Area_3", "Area_4", "Area_5", "Area_6"],
+ help="Splits need to process ([Area_1, Area_2, Area_3, Area_4, Area_5, Area_6]).",
+ )
+ parser.add_argument(
+ "--dataset_root", required=True, help="Path to Stanford3dDataset_v1.2 dataset"
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where area folders will be located",
+ )
+ parser.add_argument(
+ "--raw_root",
+ default=None,
+ help="Path to Stanford2d3dDataset_noXYZ dataset (optional)",
+ )
+ parser.add_argument(
+ "--align_angle", action="store_true", help="Whether align room angles"
+ )
+ parser.add_argument(
+ "--parse_normal", action="store_true", help="Whether process normal"
+ )
+ parser.add_argument(
+ "--num_workers", default=1, type=int, help="Num workers for preprocessing."
+ )
+ args = parser.parse_args()
+
+ if args.parse_normal:
+ assert args.raw_root is not None
+
+ room_list = []
+ angle_list = []
+
+ # Load room information
+ print("Loading room information ...")
+ for split in args.splits:
+ area_info = np.loadtxt(
+ os.path.join(
+ args.dataset_root,
+ split,
+ f"{split}_alignmentAngle.txt",
+ ),
+ dtype=str,
+ )
+ room_list += [os.path.join(split, room_info[0]) for room_info in area_info]
+ angle_list += [int(room_info[1]) for room_info in area_info]
+
+ if args.parse_normal:
+ # load raw mesh file to extract normal
+ print("Loading raw mesh file ...")
+ for split in args.splits:
+ if split != "Area_5":
+ mesh_dir = os.path.join(args.raw_root, split, "3d", "rgb.obj")
+ mesh = open3d.io.read_triangle_mesh(mesh_dir)
+ mesh.triangle_uvs.clear()
+ else:
+ mesh_a_dir = os.path.join(args.raw_root, f"{split}a", "3d", "rgb.obj")
+ mesh_b_dir = os.path.join(args.raw_root, f"{split}b", "3d", "rgb.obj")
+ mesh_a = open3d.io.read_triangle_mesh(mesh_a_dir)
+ mesh_a.triangle_uvs.clear()
+ mesh_b = open3d.io.read_triangle_mesh(mesh_b_dir)
+ mesh_b.triangle_uvs.clear()
+ mesh_b = mesh_b.transform(
+ np.array(
+ [
+ [0, 0, -1, -4.09703582],
+ [0, 1, 0, 0],
+ [1, 0, 0, -6.22617759],
+ [0, 0, 0, 1],
+ ]
+ )
+ )
+ mesh = mesh_a + mesh_b
+ area_mesh_dict[split] = mesh
+ print(f"{split} mesh is loaded")
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(
+ max_workers=args.num_workers
+ ) # peak 110G memory when parsing normal.
+ _ = list(
+ pool.map(
+ parse_room,
+ room_list,
+ angle_list,
+ repeat(args.dataset_root),
+ repeat(args.output_root),
+ repeat(args.align_angle),
+ repeat(args.parse_normal),
+ )
+ )
+
+
+if __name__ == "__main__":
+ main_process()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/sampling_chunking_data.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/sampling_chunking_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..96536d415370bf28c0f1cc89312b2fde719c9a58
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/sampling_chunking_data.py
@@ -0,0 +1,149 @@
+"""
+Chunking Data
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+
+def chunking_scene(
+ name,
+ dataset_root,
+ split,
+ grid_size=None,
+ chunk_range=(6, 6),
+ chunk_stride=(3, 3),
+ chunk_minimum_size=10000,
+):
+ print(f"Chunking scene {name} in {split} split")
+ dataset_root = Path(dataset_root)
+ scene_path = dataset_root / split / name
+ assets = os.listdir(scene_path)
+ data_dict = dict()
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ data_dict[asset[:-4]] = np.load(scene_path / asset)
+ coord = data_dict["coord"] - data_dict["coord"].min(axis=0)
+
+ if grid_size is not None:
+ grid_coord = np.floor(coord / grid_size).astype(int)
+ _, idx = np.unique(grid_coord, axis=0, return_index=True)
+ coord = coord[idx]
+ for key in data_dict.keys():
+ data_dict[key] = data_dict[key][idx]
+
+ bev_range = coord.max(axis=0)[:2]
+ x, y = np.meshgrid(
+ np.arange(0, bev_range[0] + chunk_stride[0] - chunk_range[0], chunk_stride[0]),
+ np.arange(0, bev_range[0] + chunk_stride[0] - chunk_range[0], chunk_stride[0]),
+ indexing="ij",
+ )
+ chunks = np.concatenate([x.reshape([-1, 1]), y.reshape([-1, 1])], axis=-1)
+ chunk_idx = 0
+ for chunk in chunks:
+ mask = (
+ (coord[:, 0] >= chunk[0])
+ & (coord[:, 0] < chunk[0] + chunk_range[0])
+ & (coord[:, 1] >= chunk[1])
+ & (coord[:, 1] < chunk[1] + chunk_range[1])
+ )
+ if np.sum(mask) < chunk_minimum_size:
+ continue
+
+ chunk_data_name = f"{name}_{chunk_idx}"
+ if grid_size is not None:
+ chunk_split_name = (
+ f"{split}_"
+ f"grid{grid_size * 100:.0f}mm_"
+ f"chunk{chunk_range[0]}x{chunk_range[1]}_"
+ f"stride{chunk_stride[0]}x{chunk_stride[1]}"
+ )
+ else:
+ chunk_split_name = (
+ f"{split}_"
+ f"chunk{chunk_range[0]}x{chunk_range[1]}_"
+ f"stride{chunk_stride[0]}x{chunk_stride[1]}"
+ )
+
+ chunk_save_path = dataset_root / chunk_split_name / chunk_data_name
+ chunk_save_path.mkdir(parents=True, exist_ok=True)
+ for key in data_dict.keys():
+ np.save(chunk_save_path / f"{key}.npy", data_dict[key][mask])
+ chunk_idx += 1
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the Pointcept processed ScanNet++ dataset.",
+ )
+ parser.add_argument(
+ "--split",
+ required=True,
+ default="train",
+ type=str,
+ help="Split need to process.",
+ )
+ parser.add_argument(
+ "--grid_size",
+ default=None,
+ type=float,
+ help="Grid size for initial grid sampling",
+ )
+ parser.add_argument(
+ "--chunk_range",
+ default=[6, 6],
+ type=int,
+ nargs="+",
+ help="Range of each chunk, e.g. --chunk_range 6 6",
+ )
+ parser.add_argument(
+ "--chunk_stride",
+ default=[3, 3],
+ type=int,
+ nargs="+",
+ help="Stride of each chunk, e.g. --chunk_stride 3 3",
+ )
+ parser.add_argument(
+ "--chunk_minimum_size",
+ default=10000,
+ type=int,
+ help="Minimum number of points in each chunk",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+
+ config = parser.parse_args()
+ config.dataset_root = Path(config.dataset_root)
+ data_list = os.listdir(config.dataset_root / config.split)
+
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ chunking_scene,
+ data_list,
+ repeat(config.dataset_root),
+ repeat(config.split),
+ repeat(config.grid_size),
+ repeat(config.chunk_range),
+ repeat(config.chunk_stride),
+ repeat(config.chunk_minimum_size),
+ )
+ )
+ pool.shutdown()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e53f5bcb2c1480f42ee9327940246258aa434f88
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt
@@ -0,0 +1,17 @@
+1 trash
+3 basket
+4 bathtub
+5 bed
+9 shelf
+13 cabinet
+18 chair
+20 keyboard
+22 tv
+30 lamp
+31 laptop
+35 microwave
+39 pillow
+42 printer
+47 sofa
+48 stove
+49 table
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt
new file mode 100644
index 0000000000000000000000000000000000000000..48e228766391e0f0234c2eed086e31f738068a4b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt
@@ -0,0 +1,20 @@
+1 wall
+2 floor
+3 cabinet
+4 bed
+5 chair
+6 sofa
+7 table
+8 door
+9 window
+10 bookshelf
+11 picture
+12 counter
+14 desk
+16 curtain
+24 refridgerator
+28 shower curtain
+33 toilet
+34 sink
+36 bathtub
+39 otherfurniture
\ No newline at end of file
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..0404fd6aa8ad14ad729354ce184d4b51834bfd1b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py
@@ -0,0 +1,704 @@
+# ScanNet Benchmark constants
+VALID_CLASS_IDS_20 = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 12,
+ 14,
+ 16,
+ 24,
+ 28,
+ 33,
+ 34,
+ 36,
+ 39,
+)
+
+CLASS_LABELS_20 = (
+ "wall",
+ "floor",
+ "cabinet",
+ "bed",
+ "chair",
+ "sofa",
+ "table",
+ "door",
+ "window",
+ "bookshelf",
+ "picture",
+ "counter",
+ "desk",
+ "curtain",
+ "refrigerator",
+ "shower curtain",
+ "toilet",
+ "sink",
+ "bathtub",
+ "otherfurniture",
+)
+
+SCANNET_COLOR_MAP_20 = {
+ 0: (0.0, 0.0, 0.0),
+ 1: (174.0, 199.0, 232.0),
+ 2: (152.0, 223.0, 138.0),
+ 3: (31.0, 119.0, 180.0),
+ 4: (255.0, 187.0, 120.0),
+ 5: (188.0, 189.0, 34.0),
+ 6: (140.0, 86.0, 75.0),
+ 7: (255.0, 152.0, 150.0),
+ 8: (214.0, 39.0, 40.0),
+ 9: (197.0, 176.0, 213.0),
+ 10: (148.0, 103.0, 189.0),
+ 11: (196.0, 156.0, 148.0),
+ 12: (23.0, 190.0, 207.0),
+ 14: (247.0, 182.0, 210.0),
+ 15: (66.0, 188.0, 102.0),
+ 16: (219.0, 219.0, 141.0),
+ 17: (140.0, 57.0, 197.0),
+ 18: (202.0, 185.0, 52.0),
+ 19: (51.0, 176.0, 203.0),
+ 20: (200.0, 54.0, 131.0),
+ 21: (92.0, 193.0, 61.0),
+ 22: (78.0, 71.0, 183.0),
+ 23: (172.0, 114.0, 82.0),
+ 24: (255.0, 127.0, 14.0),
+ 25: (91.0, 163.0, 138.0),
+ 26: (153.0, 98.0, 156.0),
+ 27: (140.0, 153.0, 101.0),
+ 28: (158.0, 218.0, 229.0),
+ 29: (100.0, 125.0, 154.0),
+ 30: (178.0, 127.0, 135.0),
+ 32: (146.0, 111.0, 194.0),
+ 33: (44.0, 160.0, 44.0),
+ 34: (112.0, 128.0, 144.0),
+ 35: (96.0, 207.0, 209.0),
+ 36: (227.0, 119.0, 194.0),
+ 37: (213.0, 92.0, 176.0),
+ 38: (94.0, 106.0, 211.0),
+ 39: (82.0, 84.0, 163.0),
+ 40: (100.0, 85.0, 144.0),
+}
+
+# ScanNet200 Benchmark constants
+VALID_CLASS_IDS_200 = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 21,
+ 22,
+ 23,
+ 24,
+ 26,
+ 27,
+ 28,
+ 29,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 82,
+ 84,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 93,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 110,
+ 112,
+ 115,
+ 116,
+ 118,
+ 120,
+ 121,
+ 122,
+ 125,
+ 128,
+ 130,
+ 131,
+ 132,
+ 134,
+ 136,
+ 138,
+ 139,
+ 140,
+ 141,
+ 145,
+ 148,
+ 154,
+ 155,
+ 156,
+ 157,
+ 159,
+ 161,
+ 163,
+ 165,
+ 166,
+ 168,
+ 169,
+ 170,
+ 177,
+ 180,
+ 185,
+ 188,
+ 191,
+ 193,
+ 195,
+ 202,
+ 208,
+ 213,
+ 214,
+ 221,
+ 229,
+ 230,
+ 232,
+ 233,
+ 242,
+ 250,
+ 261,
+ 264,
+ 276,
+ 283,
+ 286,
+ 300,
+ 304,
+ 312,
+ 323,
+ 325,
+ 331,
+ 342,
+ 356,
+ 370,
+ 392,
+ 395,
+ 399,
+ 408,
+ 417,
+ 488,
+ 540,
+ 562,
+ 570,
+ 572,
+ 581,
+ 609,
+ 748,
+ 776,
+ 1156,
+ 1163,
+ 1164,
+ 1165,
+ 1166,
+ 1167,
+ 1168,
+ 1169,
+ 1170,
+ 1171,
+ 1172,
+ 1173,
+ 1174,
+ 1175,
+ 1176,
+ 1178,
+ 1179,
+ 1180,
+ 1181,
+ 1182,
+ 1183,
+ 1184,
+ 1185,
+ 1186,
+ 1187,
+ 1188,
+ 1189,
+ 1190,
+ 1191,
+)
+
+CLASS_LABELS_200 = (
+ "wall",
+ "chair",
+ "floor",
+ "table",
+ "door",
+ "couch",
+ "cabinet",
+ "shelf",
+ "desk",
+ "office chair",
+ "bed",
+ "pillow",
+ "sink",
+ "picture",
+ "window",
+ "toilet",
+ "bookshelf",
+ "monitor",
+ "curtain",
+ "book",
+ "armchair",
+ "coffee table",
+ "box",
+ "refrigerator",
+ "lamp",
+ "kitchen cabinet",
+ "towel",
+ "clothes",
+ "tv",
+ "nightstand",
+ "counter",
+ "dresser",
+ "stool",
+ "cushion",
+ "plant",
+ "ceiling",
+ "bathtub",
+ "end table",
+ "dining table",
+ "keyboard",
+ "bag",
+ "backpack",
+ "toilet paper",
+ "printer",
+ "tv stand",
+ "whiteboard",
+ "blanket",
+ "shower curtain",
+ "trash can",
+ "closet",
+ "stairs",
+ "microwave",
+ "stove",
+ "shoe",
+ "computer tower",
+ "bottle",
+ "bin",
+ "ottoman",
+ "bench",
+ "board",
+ "washing machine",
+ "mirror",
+ "copier",
+ "basket",
+ "sofa chair",
+ "file cabinet",
+ "fan",
+ "laptop",
+ "shower",
+ "paper",
+ "person",
+ "paper towel dispenser",
+ "oven",
+ "blinds",
+ "rack",
+ "plate",
+ "blackboard",
+ "piano",
+ "suitcase",
+ "rail",
+ "radiator",
+ "recycling bin",
+ "container",
+ "wardrobe",
+ "soap dispenser",
+ "telephone",
+ "bucket",
+ "clock",
+ "stand",
+ "light",
+ "laundry basket",
+ "pipe",
+ "clothes dryer",
+ "guitar",
+ "toilet paper holder",
+ "seat",
+ "speaker",
+ "column",
+ "bicycle",
+ "ladder",
+ "bathroom stall",
+ "shower wall",
+ "cup",
+ "jacket",
+ "storage bin",
+ "coffee maker",
+ "dishwasher",
+ "paper towel roll",
+ "machine",
+ "mat",
+ "windowsill",
+ "bar",
+ "toaster",
+ "bulletin board",
+ "ironing board",
+ "fireplace",
+ "soap dish",
+ "kitchen counter",
+ "doorframe",
+ "toilet paper dispenser",
+ "mini fridge",
+ "fire extinguisher",
+ "ball",
+ "hat",
+ "shower curtain rod",
+ "water cooler",
+ "paper cutter",
+ "tray",
+ "shower door",
+ "pillar",
+ "ledge",
+ "toaster oven",
+ "mouse",
+ "toilet seat cover dispenser",
+ "furniture",
+ "cart",
+ "storage container",
+ "scale",
+ "tissue box",
+ "light switch",
+ "crate",
+ "power outlet",
+ "decoration",
+ "sign",
+ "projector",
+ "closet door",
+ "vacuum cleaner",
+ "candle",
+ "plunger",
+ "stuffed animal",
+ "headphones",
+ "dish rack",
+ "broom",
+ "guitar case",
+ "range hood",
+ "dustpan",
+ "hair dryer",
+ "water bottle",
+ "handicap bar",
+ "purse",
+ "vent",
+ "shower floor",
+ "water pitcher",
+ "mailbox",
+ "bowl",
+ "paper bag",
+ "alarm clock",
+ "music stand",
+ "projector screen",
+ "divider",
+ "laundry detergent",
+ "bathroom counter",
+ "object",
+ "bathroom vanity",
+ "closet wall",
+ "laundry hamper",
+ "bathroom stall door",
+ "ceiling light",
+ "trash bin",
+ "dumbbell",
+ "stair rail",
+ "tube",
+ "bathroom cabinet",
+ "cd case",
+ "closet rod",
+ "coffee kettle",
+ "structure",
+ "shower head",
+ "keyboard piano",
+ "case of water bottles",
+ "coat rack",
+ "storage organizer",
+ "folded chair",
+ "fire alarm",
+ "power strip",
+ "calendar",
+ "poster",
+ "potted plant",
+ "luggage",
+ "mattress",
+)
+
+SCANNET_COLOR_MAP_200 = {
+ 0: (0.0, 0.0, 0.0),
+ 1: (174.0, 199.0, 232.0),
+ 2: (188.0, 189.0, 34.0),
+ 3: (152.0, 223.0, 138.0),
+ 4: (255.0, 152.0, 150.0),
+ 5: (214.0, 39.0, 40.0),
+ 6: (91.0, 135.0, 229.0),
+ 7: (31.0, 119.0, 180.0),
+ 8: (229.0, 91.0, 104.0),
+ 9: (247.0, 182.0, 210.0),
+ 10: (91.0, 229.0, 110.0),
+ 11: (255.0, 187.0, 120.0),
+ 13: (141.0, 91.0, 229.0),
+ 14: (112.0, 128.0, 144.0),
+ 15: (196.0, 156.0, 148.0),
+ 16: (197.0, 176.0, 213.0),
+ 17: (44.0, 160.0, 44.0),
+ 18: (148.0, 103.0, 189.0),
+ 19: (229.0, 91.0, 223.0),
+ 21: (219.0, 219.0, 141.0),
+ 22: (192.0, 229.0, 91.0),
+ 23: (88.0, 218.0, 137.0),
+ 24: (58.0, 98.0, 137.0),
+ 26: (177.0, 82.0, 239.0),
+ 27: (255.0, 127.0, 14.0),
+ 28: (237.0, 204.0, 37.0),
+ 29: (41.0, 206.0, 32.0),
+ 31: (62.0, 143.0, 148.0),
+ 32: (34.0, 14.0, 130.0),
+ 33: (143.0, 45.0, 115.0),
+ 34: (137.0, 63.0, 14.0),
+ 35: (23.0, 190.0, 207.0),
+ 36: (16.0, 212.0, 139.0),
+ 38: (90.0, 119.0, 201.0),
+ 39: (125.0, 30.0, 141.0),
+ 40: (150.0, 53.0, 56.0),
+ 41: (186.0, 197.0, 62.0),
+ 42: (227.0, 119.0, 194.0),
+ 44: (38.0, 100.0, 128.0),
+ 45: (120.0, 31.0, 243.0),
+ 46: (154.0, 59.0, 103.0),
+ 47: (169.0, 137.0, 78.0),
+ 48: (143.0, 245.0, 111.0),
+ 49: (37.0, 230.0, 205.0),
+ 50: (14.0, 16.0, 155.0),
+ 51: (196.0, 51.0, 182.0),
+ 52: (237.0, 80.0, 38.0),
+ 54: (138.0, 175.0, 62.0),
+ 55: (158.0, 218.0, 229.0),
+ 56: (38.0, 96.0, 167.0),
+ 57: (190.0, 77.0, 246.0),
+ 58: (208.0, 49.0, 84.0),
+ 59: (208.0, 193.0, 72.0),
+ 62: (55.0, 220.0, 57.0),
+ 63: (10.0, 125.0, 140.0),
+ 64: (76.0, 38.0, 202.0),
+ 65: (191.0, 28.0, 135.0),
+ 66: (211.0, 120.0, 42.0),
+ 67: (118.0, 174.0, 76.0),
+ 68: (17.0, 242.0, 171.0),
+ 69: (20.0, 65.0, 247.0),
+ 70: (208.0, 61.0, 222.0),
+ 71: (162.0, 62.0, 60.0),
+ 72: (210.0, 235.0, 62.0),
+ 73: (45.0, 152.0, 72.0),
+ 74: (35.0, 107.0, 149.0),
+ 75: (160.0, 89.0, 237.0),
+ 76: (227.0, 56.0, 125.0),
+ 77: (169.0, 143.0, 81.0),
+ 78: (42.0, 143.0, 20.0),
+ 79: (25.0, 160.0, 151.0),
+ 80: (82.0, 75.0, 227.0),
+ 82: (253.0, 59.0, 222.0),
+ 84: (240.0, 130.0, 89.0),
+ 86: (123.0, 172.0, 47.0),
+ 87: (71.0, 194.0, 133.0),
+ 88: (24.0, 94.0, 205.0),
+ 89: (134.0, 16.0, 179.0),
+ 90: (159.0, 32.0, 52.0),
+ 93: (213.0, 208.0, 88.0),
+ 95: (64.0, 158.0, 70.0),
+ 96: (18.0, 163.0, 194.0),
+ 97: (65.0, 29.0, 153.0),
+ 98: (177.0, 10.0, 109.0),
+ 99: (152.0, 83.0, 7.0),
+ 100: (83.0, 175.0, 30.0),
+ 101: (18.0, 199.0, 153.0),
+ 102: (61.0, 81.0, 208.0),
+ 103: (213.0, 85.0, 216.0),
+ 104: (170.0, 53.0, 42.0),
+ 105: (161.0, 192.0, 38.0),
+ 106: (23.0, 241.0, 91.0),
+ 107: (12.0, 103.0, 170.0),
+ 110: (151.0, 41.0, 245.0),
+ 112: (133.0, 51.0, 80.0),
+ 115: (184.0, 162.0, 91.0),
+ 116: (50.0, 138.0, 38.0),
+ 118: (31.0, 237.0, 236.0),
+ 120: (39.0, 19.0, 208.0),
+ 121: (223.0, 27.0, 180.0),
+ 122: (254.0, 141.0, 85.0),
+ 125: (97.0, 144.0, 39.0),
+ 128: (106.0, 231.0, 176.0),
+ 130: (12.0, 61.0, 162.0),
+ 131: (124.0, 66.0, 140.0),
+ 132: (137.0, 66.0, 73.0),
+ 134: (250.0, 253.0, 26.0),
+ 136: (55.0, 191.0, 73.0),
+ 138: (60.0, 126.0, 146.0),
+ 139: (153.0, 108.0, 234.0),
+ 140: (184.0, 58.0, 125.0),
+ 141: (135.0, 84.0, 14.0),
+ 145: (139.0, 248.0, 91.0),
+ 148: (53.0, 200.0, 172.0),
+ 154: (63.0, 69.0, 134.0),
+ 155: (190.0, 75.0, 186.0),
+ 156: (127.0, 63.0, 52.0),
+ 157: (141.0, 182.0, 25.0),
+ 159: (56.0, 144.0, 89.0),
+ 161: (64.0, 160.0, 250.0),
+ 163: (182.0, 86.0, 245.0),
+ 165: (139.0, 18.0, 53.0),
+ 166: (134.0, 120.0, 54.0),
+ 168: (49.0, 165.0, 42.0),
+ 169: (51.0, 128.0, 133.0),
+ 170: (44.0, 21.0, 163.0),
+ 177: (232.0, 93.0, 193.0),
+ 180: (176.0, 102.0, 54.0),
+ 185: (116.0, 217.0, 17.0),
+ 188: (54.0, 209.0, 150.0),
+ 191: (60.0, 99.0, 204.0),
+ 193: (129.0, 43.0, 144.0),
+ 195: (252.0, 100.0, 106.0),
+ 202: (187.0, 196.0, 73.0),
+ 208: (13.0, 158.0, 40.0),
+ 213: (52.0, 122.0, 152.0),
+ 214: (128.0, 76.0, 202.0),
+ 221: (187.0, 50.0, 115.0),
+ 229: (180.0, 141.0, 71.0),
+ 230: (77.0, 208.0, 35.0),
+ 232: (72.0, 183.0, 168.0),
+ 233: (97.0, 99.0, 203.0),
+ 242: (172.0, 22.0, 158.0),
+ 250: (155.0, 64.0, 40.0),
+ 261: (118.0, 159.0, 30.0),
+ 264: (69.0, 252.0, 148.0),
+ 276: (45.0, 103.0, 173.0),
+ 283: (111.0, 38.0, 149.0),
+ 286: (184.0, 9.0, 49.0),
+ 300: (188.0, 174.0, 67.0),
+ 304: (53.0, 206.0, 53.0),
+ 312: (97.0, 235.0, 252.0),
+ 323: (66.0, 32.0, 182.0),
+ 325: (236.0, 114.0, 195.0),
+ 331: (241.0, 154.0, 83.0),
+ 342: (133.0, 240.0, 52.0),
+ 356: (16.0, 205.0, 144.0),
+ 370: (75.0, 101.0, 198.0),
+ 392: (237.0, 95.0, 251.0),
+ 395: (191.0, 52.0, 49.0),
+ 399: (227.0, 254.0, 54.0),
+ 408: (49.0, 206.0, 87.0),
+ 417: (48.0, 113.0, 150.0),
+ 488: (125.0, 73.0, 182.0),
+ 540: (229.0, 32.0, 114.0),
+ 562: (158.0, 119.0, 28.0),
+ 570: (60.0, 205.0, 27.0),
+ 572: (18.0, 215.0, 201.0),
+ 581: (79.0, 76.0, 153.0),
+ 609: (134.0, 13.0, 116.0),
+ 748: (192.0, 97.0, 63.0),
+ 776: (108.0, 163.0, 18.0),
+ 1156: (95.0, 220.0, 156.0),
+ 1163: (98.0, 141.0, 208.0),
+ 1164: (144.0, 19.0, 193.0),
+ 1165: (166.0, 36.0, 57.0),
+ 1166: (212.0, 202.0, 34.0),
+ 1167: (23.0, 206.0, 34.0),
+ 1168: (91.0, 211.0, 236.0),
+ 1169: (79.0, 55.0, 137.0),
+ 1170: (182.0, 19.0, 117.0),
+ 1171: (134.0, 76.0, 14.0),
+ 1172: (87.0, 185.0, 28.0),
+ 1173: (82.0, 224.0, 187.0),
+ 1174: (92.0, 110.0, 214.0),
+ 1175: (168.0, 80.0, 171.0),
+ 1176: (197.0, 63.0, 51.0),
+ 1178: (175.0, 199.0, 77.0),
+ 1179: (62.0, 180.0, 98.0),
+ 1180: (8.0, 91.0, 150.0),
+ 1181: (77.0, 15.0, 130.0),
+ 1182: (154.0, 65.0, 96.0),
+ 1183: (197.0, 152.0, 11.0),
+ 1184: (59.0, 155.0, 45.0),
+ 1185: (12.0, 147.0, 145.0),
+ 1186: (54.0, 35.0, 219.0),
+ 1187: (210.0, 73.0, 181.0),
+ 1188: (221.0, 124.0, 77.0),
+ 1189: (149.0, 214.0, 66.0),
+ 1190: (72.0, 185.0, 134.0),
+ 1191: (42.0, 94.0, 198.0),
+}
+
+# For instance segmentation the non-object categories
+VALID_PANOPTIC_IDS = (1, 3)
+
+CLASS_LABELS_PANOPTIC = ("wall", "floor")
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..39ccc3c60bf289199342332e455fadb5b22129ee
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py
@@ -0,0 +1,625 @@
+# This file contains the HEAD - COMMON - TAIL split category ids for ScanNet 200
+
+HEAD_CATS_SCANNET_200 = [
+ "tv stand",
+ "curtain",
+ "blinds",
+ "shower curtain",
+ "bookshelf",
+ "tv",
+ "kitchen cabinet",
+ "pillow",
+ "lamp",
+ "dresser",
+ "monitor",
+ "object",
+ "ceiling",
+ "board",
+ "stove",
+ "closet wall",
+ "couch",
+ "office chair",
+ "kitchen counter",
+ "shower",
+ "closet",
+ "doorframe",
+ "sofa chair",
+ "mailbox",
+ "nightstand",
+ "washing machine",
+ "picture",
+ "book",
+ "sink",
+ "recycling bin",
+ "table",
+ "backpack",
+ "shower wall",
+ "toilet",
+ "copier",
+ "counter",
+ "stool",
+ "refrigerator",
+ "window",
+ "file cabinet",
+ "chair",
+ "wall",
+ "plant",
+ "coffee table",
+ "stairs",
+ "armchair",
+ "cabinet",
+ "bathroom vanity",
+ "bathroom stall",
+ "mirror",
+ "blackboard",
+ "trash can",
+ "stair rail",
+ "box",
+ "towel",
+ "door",
+ "clothes",
+ "whiteboard",
+ "bed",
+ "floor",
+ "bathtub",
+ "desk",
+ "wardrobe",
+ "clothes dryer",
+ "radiator",
+ "shelf",
+]
+COMMON_CATS_SCANNET_200 = [
+ "cushion",
+ "end table",
+ "dining table",
+ "keyboard",
+ "bag",
+ "toilet paper",
+ "printer",
+ "blanket",
+ "microwave",
+ "shoe",
+ "computer tower",
+ "bottle",
+ "bin",
+ "ottoman",
+ "bench",
+ "basket",
+ "fan",
+ "laptop",
+ "person",
+ "paper towel dispenser",
+ "oven",
+ "rack",
+ "piano",
+ "suitcase",
+ "rail",
+ "container",
+ "telephone",
+ "stand",
+ "light",
+ "laundry basket",
+ "pipe",
+ "seat",
+ "column",
+ "bicycle",
+ "ladder",
+ "jacket",
+ "storage bin",
+ "coffee maker",
+ "dishwasher",
+ "machine",
+ "mat",
+ "windowsill",
+ "bulletin board",
+ "fireplace",
+ "mini fridge",
+ "water cooler",
+ "shower door",
+ "pillar",
+ "ledge",
+ "furniture",
+ "cart",
+ "decoration",
+ "closet door",
+ "vacuum cleaner",
+ "dish rack",
+ "range hood",
+ "projector screen",
+ "divider",
+ "bathroom counter",
+ "laundry hamper",
+ "bathroom stall door",
+ "ceiling light",
+ "trash bin",
+ "bathroom cabinet",
+ "structure",
+ "storage organizer",
+ "potted plant",
+ "mattress",
+]
+TAIL_CATS_SCANNET_200 = [
+ "paper",
+ "plate",
+ "soap dispenser",
+ "bucket",
+ "clock",
+ "guitar",
+ "toilet paper holder",
+ "speaker",
+ "cup",
+ "paper towel roll",
+ "bar",
+ "toaster",
+ "ironing board",
+ "soap dish",
+ "toilet paper dispenser",
+ "fire extinguisher",
+ "ball",
+ "hat",
+ "shower curtain rod",
+ "paper cutter",
+ "tray",
+ "toaster oven",
+ "mouse",
+ "toilet seat cover dispenser",
+ "storage container",
+ "scale",
+ "tissue box",
+ "light switch",
+ "crate",
+ "power outlet",
+ "sign",
+ "projector",
+ "candle",
+ "plunger",
+ "stuffed animal",
+ "headphones",
+ "broom",
+ "guitar case",
+ "dustpan",
+ "hair dryer",
+ "water bottle",
+ "handicap bar",
+ "purse",
+ "vent",
+ "shower floor",
+ "water pitcher",
+ "bowl",
+ "paper bag",
+ "alarm clock",
+ "music stand",
+ "laundry detergent",
+ "dumbbell",
+ "tube",
+ "cd case",
+ "closet rod",
+ "coffee kettle",
+ "shower head",
+ "keyboard piano",
+ "case of water bottles",
+ "coat rack",
+ "folded chair",
+ "fire alarm",
+ "power strip",
+ "calendar",
+ "poster",
+ "luggage",
+]
+
+
+# Given the different size of the official train and val sets, not all ScanNet200 categories are present in the validation set.
+# Here we list of categories with labels and IDs present in both train and validation set, and the remaining categories those are present in train, but not in val
+# We dont evaluate on unseen validation categories in this benchmark
+
+VALID_CLASS_IDS_200_VALIDATION = (
+ "wall",
+ "chair",
+ "floor",
+ "table",
+ "door",
+ "couch",
+ "cabinet",
+ "shelf",
+ "desk",
+ "office chair",
+ "bed",
+ "pillow",
+ "sink",
+ "picture",
+ "window",
+ "toilet",
+ "bookshelf",
+ "monitor",
+ "curtain",
+ "book",
+ "armchair",
+ "coffee table",
+ "box",
+ "refrigerator",
+ "lamp",
+ "kitchen cabinet",
+ "towel",
+ "clothes",
+ "tv",
+ "nightstand",
+ "counter",
+ "dresser",
+ "stool",
+ "cushion",
+ "plant",
+ "ceiling",
+ "bathtub",
+ "end table",
+ "dining table",
+ "keyboard",
+ "bag",
+ "backpack",
+ "toilet paper",
+ "printer",
+ "tv stand",
+ "whiteboard",
+ "blanket",
+ "shower curtain",
+ "trash can",
+ "closet",
+ "stairs",
+ "microwave",
+ "stove",
+ "shoe",
+ "computer tower",
+ "bottle",
+ "bin",
+ "ottoman",
+ "bench",
+ "board",
+ "washing machine",
+ "mirror",
+ "copier",
+ "basket",
+ "sofa chair",
+ "file cabinet",
+ "fan",
+ "laptop",
+ "shower",
+ "paper",
+ "person",
+ "paper towel dispenser",
+ "oven",
+ "blinds",
+ "rack",
+ "plate",
+ "blackboard",
+ "piano",
+ "suitcase",
+ "rail",
+ "radiator",
+ "recycling bin",
+ "container",
+ "wardrobe",
+ "soap dispenser",
+ "telephone",
+ "bucket",
+ "clock",
+ "stand",
+ "light",
+ "laundry basket",
+ "pipe",
+ "clothes dryer",
+ "guitar",
+ "toilet paper holder",
+ "seat",
+ "speaker",
+ "column",
+ "ladder",
+ "bathroom stall",
+ "shower wall",
+ "cup",
+ "jacket",
+ "storage bin",
+ "coffee maker",
+ "dishwasher",
+ "paper towel roll",
+ "machine",
+ "mat",
+ "windowsill",
+ "bar",
+ "toaster",
+ "bulletin board",
+ "ironing board",
+ "fireplace",
+ "soap dish",
+ "kitchen counter",
+ "doorframe",
+ "toilet paper dispenser",
+ "mini fridge",
+ "fire extinguisher",
+ "ball",
+ "hat",
+ "shower curtain rod",
+ "water cooler",
+ "paper cutter",
+ "tray",
+ "shower door",
+ "pillar",
+ "ledge",
+ "toaster oven",
+ "mouse",
+ "toilet seat cover dispenser",
+ "furniture",
+ "cart",
+ "scale",
+ "tissue box",
+ "light switch",
+ "crate",
+ "power outlet",
+ "decoration",
+ "sign",
+ "projector",
+ "closet door",
+ "vacuum cleaner",
+ "plunger",
+ "stuffed animal",
+ "headphones",
+ "dish rack",
+ "broom",
+ "range hood",
+ "dustpan",
+ "hair dryer",
+ "water bottle",
+ "handicap bar",
+ "vent",
+ "shower floor",
+ "water pitcher",
+ "mailbox",
+ "bowl",
+ "paper bag",
+ "projector screen",
+ "divider",
+ "laundry detergent",
+ "bathroom counter",
+ "object",
+ "bathroom vanity",
+ "closet wall",
+ "laundry hamper",
+ "bathroom stall door",
+ "ceiling light",
+ "trash bin",
+ "dumbbell",
+ "stair rail",
+ "tube",
+ "bathroom cabinet",
+ "closet rod",
+ "coffee kettle",
+ "shower head",
+ "keyboard piano",
+ "case of water bottles",
+ "coat rack",
+ "folded chair",
+ "fire alarm",
+ "power strip",
+ "calendar",
+ "poster",
+ "potted plant",
+ "mattress",
+)
+
+CLASS_LABELS_200_VALIDATION = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 21,
+ 22,
+ 23,
+ 24,
+ 26,
+ 27,
+ 28,
+ 29,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 82,
+ 84,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ 93,
+ 95,
+ 96,
+ 97,
+ 98,
+ 99,
+ 100,
+ 101,
+ 102,
+ 103,
+ 104,
+ 105,
+ 106,
+ 107,
+ 110,
+ 112,
+ 115,
+ 116,
+ 118,
+ 120,
+ 122,
+ 125,
+ 128,
+ 130,
+ 131,
+ 132,
+ 134,
+ 136,
+ 138,
+ 139,
+ 140,
+ 141,
+ 145,
+ 148,
+ 154,
+ 155,
+ 156,
+ 157,
+ 159,
+ 161,
+ 163,
+ 165,
+ 166,
+ 168,
+ 169,
+ 170,
+ 177,
+ 180,
+ 185,
+ 188,
+ 191,
+ 193,
+ 195,
+ 202,
+ 208,
+ 213,
+ 214,
+ 229,
+ 230,
+ 232,
+ 233,
+ 242,
+ 250,
+ 261,
+ 264,
+ 276,
+ 283,
+ 300,
+ 304,
+ 312,
+ 323,
+ 325,
+ 342,
+ 356,
+ 370,
+ 392,
+ 395,
+ 408,
+ 417,
+ 488,
+ 540,
+ 562,
+ 570,
+ 609,
+ 748,
+ 776,
+ 1156,
+ 1163,
+ 1164,
+ 1165,
+ 1166,
+ 1167,
+ 1168,
+ 1169,
+ 1170,
+ 1171,
+ 1172,
+ 1173,
+ 1175,
+ 1176,
+ 1179,
+ 1180,
+ 1181,
+ 1182,
+ 1184,
+ 1185,
+ 1186,
+ 1187,
+ 1188,
+ 1189,
+ 1191,
+)
+
+VALID_CLASS_IDS_200_TRAIN_ONLY = (
+ "bicycle",
+ "storage container",
+ "candle",
+ "guitar case",
+ "purse",
+ "alarm clock",
+ "music stand",
+ "cd case",
+ "structure",
+ "storage organizer",
+ "luggage",
+)
+
+CLASS_LABELS_200_TRAIN_ONLY = (
+ 121,
+ 221,
+ 286,
+ 331,
+ 399,
+ 572,
+ 581,
+ 1174,
+ 1178,
+ 1183,
+ 1190,
+)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz
new file mode 100644
index 0000000000000000000000000000000000000000..d9bbb4f7c3b72dbe81fbeb86f594066b883fafaf
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df5c2bd40e8518e982c7d7b4b39020b07ac774695038bf49cb28b44e5760457e
+size 676
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_test.txt b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9e7d9205321e8ca047a527466f4b7100c9c9d2c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_test.txt
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index 0000000000000000000000000000000000000000..7520948c8170df9ae1a9e8a40bc444fcc7cc0772
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv1_train.txt
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+815 media center media center 1 3 6 cabinet cabinet Furniture cabinet 2933112 n02933112 cabinet.n.01 cabinet 7
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+1348 dryer sheets dryer sheets 1 40 7 otherprop Objects objects 39
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+1349 air mattress air mattress 1 4 1 bed bed Bed bed bed bed 2818832 n02690809 air_mattress.n.01 bed 11
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+1301 socks sock 1 21 7 clothes clothes Objects n04254777 sock.n.01 clothes 38
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+1199 clothes hangers clothes hanger 1 40 7 otherprop Objects n03057920 coat_hanger.n.01 misc 40
+1357 starbucks cup starbucks cup 1 40 7 cup otherprop Objects cup cup or mug 3797390 n03797390 mug.n.04 objects 39
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..cff61b132f3ebf4edd513445b76fd39db54462d2
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv
@@ -0,0 +1,608 @@
+id raw_category category count nyu40id eigen13id nyuClass nyu40class eigen13class ModelNet40 ModelNet10 ShapeNetCore55 synsetoffset wnsynsetid wnsynsetkey mpcat40 mpcat40index
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+2 chair chair 4646 5 4 chair chair Chair chair chair chair 3001627 n03001627 chair.n.01 chair 3
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+3 floor floor 1553 2 5 floor floor Floor n03365592 floor.n.01 floor 2
+5 door door 1483 8 12 door door Wall door n03221720 door.n.01 door 4
+1163 object object 1313 40 7 otherprop Objects objects 39
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+4 table table 1170 7 10 table table Table table table table 4379243 n04379243 table.n.02 table 5
+56 trash can trash can 1090 39 6 garbage bin otherfurniture Furniture trash_bin 2747177 n02747177 ashcan.n.01 objects 39
+13 pillow pillow 937 18 7 pillow pillow Objects pillow 3938244 n03938244 pillow.n.01 cushion 8
+15 picture picture 862 11 8 picture picture Picture n03931044 picture.n.01 picture 6
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+19 monitor monitor 765 40 7 monitor otherprop Objects monitor monitor tv or monitor 3211117 n03782190 monitor.n.04 objects 39
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+9 desk desk 680 14 10 desk desk Table desk desk table 4379243 n03179701 desk.n.01 table 5
+8 shelf shelf 641 15 6 shelves shelves Furniture bookshelf bookshelf 2871439 n02871439 bookshelf.n.01 shelving 31
+10 office chair office chair 595 5 4 chair chair Chair chair chair chair 3001627 n04373704 swivel_chair.n.01 chair 3
+31 towel towel 570 27 7 towel towel Objects n04459362 towel.n.01 towel 20
+6 couch couch 502 6 9 sofa sofa Sofa sofa sofa sofa 4256520 n04256520 sofa.n.01 sofa 10
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+28 lamp lamp 419 35 7 lamp lamp Objects lamp lamp 3636649 n03636649 lamp.n.02 lighting 28
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+21 curtain curtain 347 16 13 curtain curtain Window curtain n03151077 curtain.n.01 curtain 12
+40 plant plant 331 40 7 plant otherprop Objects plant n00017222 plant.n.02 plant 14
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+96 radiator radiator 322 39 6 radiator otherfurniture Furniture n04041069 radiator.n.02 misc 40
+22 book book 318 23 2 book books Books n02870526 book.n.11 objects 39
+29 kitchen cabinet kitchen cabinet 310 3 6 cabinet cabinet Furniture n02933112 cabinet.n.01 cabinet 7
+49 toilet paper toilet paper 291 40 7 toilet paper otherprop Objects n15075141 toilet_tissue.n.01 objects 39
+29 kitchen cabinets kitchen cabinet 289 3 6 cabinet cabinet Furniture cabinet 2933112 n02933112 cabinet.n.01 cabinet 7
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+63 shoes shoe 272 40 7 shoe otherprop Objects n04199027 shoe.n.01 clothes 38
+24 coffee table coffee table 258 7 10 coffee table table Table table table table 4379243 n03063968 coffee_table.n.01 table 5
+17 toilet toilet 256 33 7 toilet toilet Objects toilet toilet n04446276 toilet.n.01 toilet 18
+47 bag bag 252 37 7 bag bag Objects suitcase 2773838 n02773838 bag.n.06 objects 39
+32 clothes clothes 248 21 7 clothes clothes Objects n02728440 apparel.n.01 clothes 38
+46 keyboard keyboard 246 40 7 keyboard otherprop Objects keyboard computer keyboard 3085013 n03085013 computer_keyboard.n.01 objects 39
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+97 recycling bin recycling bin 225 39 6 garbage bin otherfurniture Furniture trash_bin 2747177 n02747177 ashcan.n.01 objects 39
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+38 stool stool 221 40 7 stool otherprop Objects stool n04326896 stool.n.01 stool 19
+33 tv tv 219 25 11 television television TV tv or monitor 3211117 n03211117 display.n.06 tv_monitor 22
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+1164 bathroom vanity bathroom vanity 126 3 6 cabinet cabinet Furniture cabinet 2933112 n02933112 cabinet.n.01 table 5
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+50 printer printer 106 40 7 printer otherprop Objects printer 4004475 n04004475 printer.n.03 appliances 37
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+80 person person 46 31 7 person person Objects person n05217688 person.n.02 misc 40
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+57 closet closet 45 39 6 wardrobe otherfurniture Furniture wardrobe misc 40
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+118 speaker speaker 43 40 7 speaker otherprop Objects speaker 3691459 n03691459 loudspeaker.n.01 objects 39
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+570 paper bag paper bag 39 37 7 bag bag Objects n04122825 sack.n.01 objects 39
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+106 laundry basket laundry basket 37 40 7 laundry basket otherprop Objects basket 2801938 n03050864 clothes_hamper.n.01 objects 39
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+276 closet door closet door 35 8 12 door door Wall door n03221720 door.n.01 door 4
+323 dish rack dish rack 35 40 7 dish rack otherprop Objects n03207630 dish_rack.n.01 objects 39
+58 stairs stairs 35 38 7 stairs otherstructure Objects n04298308 stairway.n.01 stairs 16
+86 blinds blinds 35 13 13 blinds blinds Window n02851099 blind.n.03 blinds 32
+2 stack of chairs chair 35 5 4 chair chair Chair chair chair chair 3001627 n03001627 chair.n.01 chair 3
+399 purse purse 34 40 7 purse otherprop Objects n02774152 bag.n.04 objects 39
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+66 bin bin 28 40 7 bin otherprop Objects n02839910 bin.n.01 objects 39
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+540 mailboxes mailbox 28 29 7 box box Objects mailbox 3710193 n03710193 mailbox.n.01 misc 40
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+120 column column 26 38 7 column otherstructure Objects n03074380 column.n.06 column 24
+107 pipe pipe 25 40 7 pipe otherprop Objects n03944672 pipe.n.02 misc 40
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+90 piano piano 24 39 6 piano otherfurniture Furniture piano piano 3928116 n03928116 piano.n.01 furniture 36
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+1174 cd case cd case 24 40 7 otherprop Objects objects 39
+562 bowl bowl 24 40 7 bowl otherprop Objects bowl bowl 2880940 n02880940 bowl.n.03 objects 39
+1175 closet rod closet rod 24 40 7 otherprop Objects n04100174 rod.n.01 misc 40
+1156 bathroom counter bathroom counter 24 12 6 counter counter Furniture table table table 4379243 n03116530 counter.n.01 counter 26
+84 oven oven 23 38 7 oven otherstructure Objects n03862676 oven.n.01 appliances 37
+104 stand stand 23 39 6 stand otherfurniture Furniture table table table 4379243 n04301000 stand.n.04 table 5
+229 scale scale 23 40 7 scale otherprop Objects n04141975 scale.n.07 objects 39
+70 washing machine washing machine 23 39 6 washing machine otherfurniture Furniture washing_machine 4554684 n04554684 washer.n.03 appliances 37
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+169 hat hat 22 40 7 hat otherprop Objects n03497657 hat.n.01 clothes 38
+128 shower wall shower wall 22 1 12 wall wall Wall n04208936 shower.n.01 wall 1
+331 guitar case guitar case 21 40 7 guitar case otherprop Objects objects 39
+87 rack rack 21 39 6 stand otherfurniture Furniture n04038440 rack.n.05 shelving 31
+488 water pitcher water pitcher 21 40 7 pitcher otherprop Objects n03950228 pitcher.n.02 objects 39
+776 laundry detergent laundry detergent 21 40 7 otherprop Objects objects 39
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+242 power outlet power outlet 19 40 7 otherprop Objects misc 40
+45 dining table dining table 19 7 10 table table Table table table table 4379243 n04379243 table.n.02 table 5
+417 shower floor shower floor 19 2 5 floor floor Floor n04208936 shower.n.01 floor 2
+70 washing machines washing machine 19 39 6 washing machine otherfurniture Furniture washing_machine 4554684 n04554684 washer.n.03 appliances 37
+188 shower door shower door 19 8 12 door door Wall door n04208936 shower.n.01 door 4
+1176 coffee kettle coffee kettle 18 40 7 pot otherprop Objects n03612814 kettle.n.01 objects 39
+1177 wardrobe cabinet wardrobe 18 39 6 wardrobe otherfurniture Furniture wardrobe n04550184 wardrobe.n.01 furniture 36
+1178 structure structure 18 38 7 otherstructure Objects misc 40
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+110 clothes dryer clothes dryer 17 39 6 otherfurniture Furniture n03251766 dryer.n.01 appliances 37
+148 toaster toaster 17 40 7 toaster otherprop Objects n04442312 toaster.n.02 appliances 37
+63 shoe shoe 17 40 7 shoe otherprop Objects n04199027 shoe.n.01 clothes 38
+155 ironing board ironing board 16 39 6 ironing board otherfurniture Furniture n03586090 ironing_board.n.01 objects 39
+572 alarm clock alarm clock 16 40 7 alarm clock otherprop Objects clock 3046257 n02694662 alarm_clock.n.01 objects 39
+1179 shower head shower head 15 38 7 otherstructure Objects shower 23
+28 lamp base lamp 15 35 7 lamp lamp Objects lamp lamp 3636649 n03636649 lamp.n.02 lighting 28
+392 water bottle water bottle 15 40 7 bottle otherprop Objects bottle bottle 2876657 n04557648 water_bottle.n.01 objects 39
+1180 keyboard piano keyboard piano 15 39 6 piano otherfurniture Furniture piano piano 3928116 n03928116 piano.n.01 furniture 36
+609 projector screen projector screen 15 38 7 projector screen otherstructure Objects misc 40
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diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_test.txt b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_test.txt
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diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9e7d9205321e8ca047a527466f4b7100c9c9d2c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt
@@ -0,0 +1,312 @@
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diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaccf1f0ee5255749100dfd93cf31d886ec9c699
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py
@@ -0,0 +1,255 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+import argparse
+import glob
+import json
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+# Load external constants
+from meta_data.scannet200_constants import VALID_CLASS_IDS_200, VALID_CLASS_IDS_20
+
+CLOUD_FILE_PFIX = "_vh_clean_2"
+SEGMENTS_FILE_PFIX = ".0.010000.segs.json"
+AGGREGATIONS_FILE_PFIX = ".aggregation.json"
+CLASS_IDS200 = VALID_CLASS_IDS_200
+CLASS_IDS20 = VALID_CLASS_IDS_20
+IGNORE_INDEX = -1
+
+
+def read_plymesh(filepath):
+ """Read ply file and return it as numpy array. Returns None if emtpy."""
+ with open(filepath, "rb") as f:
+ plydata = plyfile.PlyData.read(f)
+ if plydata.elements:
+ vertices = pd.DataFrame(plydata["vertex"].data).values
+ faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+ return vertices, faces
+
+
+# Map the raw category id to the point cloud
+def point_indices_from_group(seg_indices, group, labels_pd):
+ group_segments = np.array(group["segments"])
+ label = group["label"]
+
+ # Map the category name to id
+ label_id20 = labels_pd[labels_pd["raw_category"] == label]["nyu40id"]
+ label_id20 = int(label_id20.iloc[0]) if len(label_id20) > 0 else 0
+ label_id200 = labels_pd[labels_pd["raw_category"] == label]["id"]
+ label_id200 = int(label_id200.iloc[0]) if len(label_id200) > 0 else 0
+
+ # Only store for the valid categories
+ if label_id20 in CLASS_IDS20:
+ label_id20 = CLASS_IDS20.index(label_id20)
+ else:
+ label_id20 = IGNORE_INDEX
+
+ if label_id200 in CLASS_IDS200:
+ label_id200 = CLASS_IDS200.index(label_id200)
+ else:
+ label_id200 = IGNORE_INDEX
+
+ # get points, where segment indices (points labelled with segment ids) are in the group segment list
+ point_idx = np.where(np.isin(seg_indices, group_segments))[0]
+ return point_idx, label_id20, label_id200
+
+
+def face_normal(vertex, face):
+ v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+ v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+ vec = np.cross(v01, v02)
+ length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+ nf = vec / length
+ area = length * 0.5
+ return nf, area
+
+
+def vertex_normal(vertex, face):
+ nf, area = face_normal(vertex, face)
+ nf = nf * area
+
+ nv = np.zeros_like(vertex)
+ for i in range(face.shape[0]):
+ nv[face[i]] += nf[i]
+
+ length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+ nv = nv / length
+ return nv
+
+
+def handle_process(
+ scene_path, output_path, labels_pd, train_scenes, val_scenes, parse_normals=True
+):
+ scene_id = os.path.basename(scene_path)
+ mesh_path = os.path.join(scene_path, f"{scene_id}{CLOUD_FILE_PFIX}.ply")
+ segments_file = os.path.join(
+ scene_path, f"{scene_id}{CLOUD_FILE_PFIX}{SEGMENTS_FILE_PFIX}"
+ )
+ aggregations_file = os.path.join(scene_path, f"{scene_id}{AGGREGATIONS_FILE_PFIX}")
+ info_file = os.path.join(scene_path, f"{scene_id}.txt")
+
+ if scene_id in train_scenes:
+ output_path = os.path.join(output_path, "train", f"{scene_id}")
+ split_name = "train"
+ elif scene_id in val_scenes:
+ output_path = os.path.join(output_path, "val", f"{scene_id}")
+ split_name = "val"
+ else:
+ output_path = os.path.join(output_path, "test", f"{scene_id}")
+ split_name = "test"
+
+ print(f"Processing: {scene_id} in {split_name}")
+
+ vertices, faces = read_plymesh(mesh_path)
+ coords = vertices[:, :3]
+ colors = vertices[:, 3:6]
+ save_dict = dict(
+ coord=coords.astype(np.float32),
+ color=colors.astype(np.uint8),
+ )
+
+ # # Rotating the mesh to axis aligned
+ # info_dict = {}
+ # with open(info_file) as f:
+ # for line in f:
+ # (key, val) = line.split(" = ")
+ # info_dict[key] = np.fromstring(val, sep=' ')
+ #
+ # if 'axisAlignment' not in info_dict:
+ # rot_matrix = np.identity(4)
+ # else:
+ # rot_matrix = info_dict['axisAlignment'].reshape(4, 4)
+ # r_coords = coords.transpose()
+ # r_coords = np.append(r_coords, np.ones((1, r_coords.shape[1])), axis=0)
+ # r_coords = np.dot(rot_matrix, r_coords)
+ # coords = r_coords
+
+ # Parse Normals
+ if parse_normals:
+ save_dict["normal"] = vertex_normal(coords, faces).astype(np.float32)
+
+ # Load segments file
+ if split_name != "test":
+ with open(segments_file) as f:
+ segments = json.load(f)
+ seg_indices = np.array(segments["segIndices"])
+
+ # Load Aggregations file
+ with open(aggregations_file) as f:
+ aggregation = json.load(f)
+ seg_groups = np.array(aggregation["segGroups"])
+
+ # Generate new labels
+ semantic_gt20 = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+ semantic_gt200 = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+ instance_ids = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+ for group in seg_groups:
+ point_idx, label_id20, label_id200 = point_indices_from_group(
+ seg_indices, group, labels_pd
+ )
+
+ semantic_gt20[point_idx] = label_id20
+ semantic_gt200[point_idx] = label_id200
+ instance_ids[point_idx] = group["id"]
+
+ semantic_gt20 = semantic_gt20.astype(int)
+ semantic_gt200 = semantic_gt200.astype(int)
+ instance_ids = instance_ids.astype(int)
+
+ save_dict["segment20"] = semantic_gt20
+ save_dict["segment200"] = semantic_gt200
+ save_dict["instance"] = instance_ids
+
+ # Concatenate with original cloud
+ processed_vertices = np.hstack((semantic_gt200, instance_ids))
+
+ if np.any(np.isnan(processed_vertices)) or not np.all(
+ np.isfinite(processed_vertices)
+ ):
+ raise ValueError(f"Find NaN in Scene: {scene_id}")
+
+ # Save processed data
+ os.makedirs(output_path, exist_ok=True)
+ for key in save_dict.keys():
+ np.save(os.path.join(output_path, f"{key}.npy"), save_dict[key])
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ parser.add_argument(
+ "--parse_normals", default=True, type=bool, help="Whether parse point normals"
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ config = parser.parse_args()
+
+ # Load label map
+ labels_pd = pd.read_csv(
+ "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv",
+ sep="\t",
+ header=0,
+ )
+
+ # Load train/val splits
+ with open(
+ "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_train.txt"
+ ) as train_file:
+ train_scenes = train_file.read().splitlines()
+ with open(
+ "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt"
+ ) as val_file:
+ val_scenes = val_file.read().splitlines()
+
+ # Create output directories
+ train_output_dir = os.path.join(config.output_root, "train")
+ os.makedirs(train_output_dir, exist_ok=True)
+ val_output_dir = os.path.join(config.output_root, "val")
+ os.makedirs(val_output_dir, exist_ok=True)
+ test_output_dir = os.path.join(config.output_root, "test")
+ os.makedirs(test_output_dir, exist_ok=True)
+
+ # Load scene paths
+ scene_paths = sorted(glob.glob(config.dataset_root + "/scans*/scene*"))
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ handle_process,
+ scene_paths,
+ repeat(config.output_root),
+ repeat(labels_pd),
+ repeat(train_scenes),
+ repeat(val_scenes),
+ repeat(config.parse_normals),
+ )
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py
new file mode 100644
index 0000000000000000000000000000000000000000..d90c8770e812f782e4735cc7095c100cd6258bf6
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py
@@ -0,0 +1,183 @@
+import os, struct
+import numpy as np
+import zlib
+import imageio
+import cv2
+
+COMPRESSION_TYPE_COLOR = {-1: "unknown", 0: "raw", 1: "png", 2: "jpeg"}
+COMPRESSION_TYPE_DEPTH = {
+ -1: "unknown",
+ 0: "raw_ushort",
+ 1: "zlib_ushort",
+ 2: "occi_ushort",
+}
+
+
+class RGBDFrame:
+ def load(self, file_handle):
+ self.camera_to_world = np.asarray(
+ struct.unpack("f" * 16, file_handle.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.timestamp_color = struct.unpack("Q", file_handle.read(8))[0]
+ self.timestamp_depth = struct.unpack("Q", file_handle.read(8))[0]
+ self.color_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+ self.depth_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+ self.color_data = b"".join(
+ struct.unpack(
+ "c" * self.color_size_bytes, file_handle.read(self.color_size_bytes)
+ )
+ )
+ self.depth_data = b"".join(
+ struct.unpack(
+ "c" * self.depth_size_bytes, file_handle.read(self.depth_size_bytes)
+ )
+ )
+
+ def decompress_depth(self, compression_type):
+ if compression_type == "zlib_ushort":
+ return self.decompress_depth_zlib()
+ else:
+ raise
+
+ def decompress_depth_zlib(self):
+ return zlib.decompress(self.depth_data)
+
+ def decompress_color(self, compression_type):
+ if compression_type == "jpeg":
+ return self.decompress_color_jpeg()
+ else:
+ raise
+
+ def decompress_color_jpeg(self):
+ return imageio.imread(self.color_data)
+
+
+class SensorData:
+ def __init__(self, filename):
+ self.version = 4
+ self.load(filename)
+
+ def load(self, filename):
+ with open(filename, "rb") as f:
+ version = struct.unpack("I", f.read(4))[0]
+ assert self.version == version
+ strlen = struct.unpack("Q", f.read(8))[0]
+ self.sensor_name = b"".join(struct.unpack("c" * strlen, f.read(strlen)))
+ self.intrinsic_color = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.extrinsic_color = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.intrinsic_depth = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.extrinsic_depth = np.asarray(
+ struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+ ).reshape(4, 4)
+ self.color_compression_type = COMPRESSION_TYPE_COLOR[
+ struct.unpack("i", f.read(4))[0]
+ ]
+ self.depth_compression_type = COMPRESSION_TYPE_DEPTH[
+ struct.unpack("i", f.read(4))[0]
+ ]
+ self.color_width = struct.unpack("I", f.read(4))[0]
+ self.color_height = struct.unpack("I", f.read(4))[0]
+ self.depth_width = struct.unpack("I", f.read(4))[0]
+ self.depth_height = struct.unpack("I", f.read(4))[0]
+ self.depth_shift = struct.unpack("f", f.read(4))[0]
+ num_frames = struct.unpack("Q", f.read(8))[0]
+ self.frames = []
+ for i in range(num_frames):
+ frame = RGBDFrame()
+ frame.load(f)
+ self.frames.append(frame)
+
+ def export_depth_images(self, output_path, image_size=None, frame_skip=1):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print(
+ "exporting", len(self.frames) // frame_skip, " depth frames to", output_path
+ )
+ for f in range(0, len(self.frames), frame_skip):
+ if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+ continue
+ if f % 100 == 0:
+ print(
+ "exporting",
+ f,
+ "th depth frames to",
+ os.path.join(output_path, str(f) + ".png"),
+ )
+
+ depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
+ depth = np.fromstring(depth_data, dtype=np.uint16).reshape(
+ self.depth_height, self.depth_width
+ )
+ if image_size is not None:
+ depth = cv2.resize(
+ depth,
+ (image_size[1], image_size[0]),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ imageio.imwrite(os.path.join(output_path, str(f) + ".png"), depth)
+
+ def export_color_images(self, output_path, image_size=None, frame_skip=1):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print(
+ "exporting", len(self.frames) // frame_skip, "color frames to", output_path
+ )
+ for f in range(0, len(self.frames), frame_skip):
+ if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+ continue
+ if f % 100 == 0:
+ print(
+ "exporting",
+ f,
+ "th color frames to",
+ os.path.join(output_path, str(f) + ".png"),
+ )
+ color = self.frames[f].decompress_color(self.color_compression_type)
+ if image_size is not None:
+ color = cv2.resize(
+ color,
+ (image_size[1], image_size[0]),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ # imageio.imwrite(os.path.join(output_path, str(f) + '.jpg'), color)
+ imageio.imwrite(os.path.join(output_path, str(f) + ".png"), color)
+
+ def save_mat_to_file(self, matrix, filename):
+ with open(filename, "w") as f:
+ for line in matrix:
+ np.savetxt(f, line[np.newaxis], fmt="%f")
+
+ def export_poses(self, output_path, frame_skip=1):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print(
+ "exporting", len(self.frames) // frame_skip, "camera poses to", output_path
+ )
+ for f in range(0, len(self.frames), frame_skip):
+ self.save_mat_to_file(
+ self.frames[f].camera_to_world,
+ os.path.join(output_path, str(f) + ".txt"),
+ )
+
+ def export_intrinsics(self, output_path):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+ print("exporting camera intrinsics to", output_path)
+ self.save_mat_to_file(
+ self.intrinsic_color, os.path.join(output_path, "intrinsic_color.txt")
+ )
+ self.save_mat_to_file(
+ self.extrinsic_color, os.path.join(output_path, "extrinsic_color.txt")
+ )
+ self.save_mat_to_file(
+ self.intrinsic_depth, os.path.join(output_path, "intrinsic_depth.txt")
+ )
+ self.save_mat_to_file(
+ self.extrinsic_depth, os.path.join(output_path, "extrinsic_depth.txt")
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6b407eebad280f2817805d15ec43b9f7f6afbf4
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py
@@ -0,0 +1,91 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import torch
+import numpy as np
+import math
+import glob, os
+import argparse
+import open3d as o3d
+
+
+def make_open3d_point_cloud(xyz, color=None, voxel_size=None):
+ if np.isnan(xyz).any():
+ return None
+
+ xyz = xyz[:, :3]
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(xyz)
+ if color is not None:
+ pcd.colors = o3d.utility.Vector3dVector(color)
+ if voxel_size is not None:
+ pcd = pcd.voxel_down_sample(voxel_size)
+
+ return pcd
+
+
+def compute_overlap_ratio(pcd0, pcd1, voxel_size):
+ pcd0_down = pcd0.voxel_down_sample(voxel_size)
+ pcd1_down = pcd1.voxel_down_sample(voxel_size)
+ matching01 = get_matching_indices(pcd0_down, pcd1_down, voxel_size * 1.5, 1)
+ matching10 = get_matching_indices(pcd1_down, pcd0_down, voxel_size * 1.5, 1)
+ overlap0 = float(len(matching01)) / float(len(pcd0_down.points))
+ overlap1 = float(len(matching10)) / float(len(pcd1_down.points))
+ return max(overlap0, overlap1)
+
+
+def get_matching_indices(source, pcd_tree, search_voxel_size, K=None):
+ match_inds = []
+ for i, point in enumerate(source.points):
+ [_, idx, _] = pcd_tree.search_radius_vector_3d(point, search_voxel_size)
+ if K is not None:
+ idx = idx[:K]
+ for j in idx:
+ match_inds.append((i, j))
+ return match_inds
+
+
+def compute_full_overlapping(data_root, scene_id, voxel_size=0.05):
+ _points = [
+ (
+ pcd_name,
+ make_open3d_point_cloud(
+ torch.load(pcd_name)["coord"], voxel_size=voxel_size
+ ),
+ )
+ for pcd_name in glob.glob(os.path.join(data_root, scene_id, "pcd", "*.pth"))
+ ]
+ points = [(pcd_name, pcd) for (pcd_name, pcd) in _points if pcd is not None]
+ print(
+ "load {} point clouds ({} invalid has been filtered), computing matching/overlapping".format(
+ len(points), len(_points) - len(points)
+ )
+ )
+
+ matching_matrix = np.zeros((len(points), len(points)))
+ for i, (pcd0_name, pcd0) in enumerate(points):
+ print("matching to...{}".format(pcd0_name))
+ pcd0_tree = o3d.geometry.KDTreeFlann(copy.deepcopy(pcd0))
+ for j, (pcd1_name, pcd1) in enumerate(points):
+ if i == j:
+ continue
+ matching_matrix[i, j] = float(
+ len(get_matching_indices(pcd1, pcd0_tree, 1.5 * voxel_size, 1))
+ ) / float(len(pcd1.points))
+
+ # write to file
+ with open(os.path.join(data_root, scene_id, "pcd", "overlap.txt"), "w") as f:
+ for i, (pcd0_name, pcd0) in enumerate(points):
+ for j, (pcd1_name, pcd1) in enumerate(points):
+ if i < j:
+ overlap = max(matching_matrix[i, j], matching_matrix[j, i])
+ f.write(
+ "{} {} {}\n".format(
+ pcd0_name.replace(data_root, ""),
+ pcd1_name.replace(data_root, ""),
+ overlap,
+ )
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8943ba040cc24fc8d3130bd8784052cb57ce6c9
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py
@@ -0,0 +1,33 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import argparse
+import glob, os, sys
+
+from SensorData import SensorData
+
+# params
+parser = argparse.ArgumentParser()
+# data paths
+parser.add_argument("--target_dir", required=True, help="path to the target dir")
+
+opt = parser.parse_args()
+print(opt)
+
+
+def main():
+ overlaps = glob.glob(os.path.join(opt.target_dir, "*/pcd/overlap.txt"))
+ with open(os.path.join(opt.target_dir, "overlap30.txt"), "w") as f:
+ for fo in overlaps:
+ for line in open(fo):
+ pcd0, pcd1, op = line.strip().split()
+ if float(op) >= 0.3:
+ print("{} {} {}".format(pcd0, pcd1, op), file=f)
+ print("done")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..17400c4bd28764829d248e90dc141182fa1d8f03
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py
@@ -0,0 +1,894 @@
+# Copyright 2014 Darsh Ranjan
+#
+# This file is part of python-plyfile.
+#
+# python-plyfile is free software: you can redistribute it and/or
+# modify it under the terms of the GNU General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# python-plyfile is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with python-plyfile. If not, see
+# .
+
+from itertools import islice as _islice
+
+import numpy as _np
+from sys import byteorder as _byteorder
+
+
+try:
+ _range = xrange
+except NameError:
+ _range = range
+
+
+# Many-many relation
+_data_type_relation = [
+ ("int8", "i1"),
+ ("char", "i1"),
+ ("uint8", "u1"),
+ ("uchar", "b1"),
+ ("uchar", "u1"),
+ ("int16", "i2"),
+ ("short", "i2"),
+ ("uint16", "u2"),
+ ("ushort", "u2"),
+ ("int32", "i4"),
+ ("int", "i4"),
+ ("uint32", "u4"),
+ ("uint", "u4"),
+ ("float32", "f4"),
+ ("float", "f4"),
+ ("float64", "f8"),
+ ("double", "f8"),
+]
+
+_data_types = dict(_data_type_relation)
+_data_type_reverse = dict((b, a) for (a, b) in _data_type_relation)
+
+_types_list = []
+_types_set = set()
+for _a, _b in _data_type_relation:
+ if _a not in _types_set:
+ _types_list.append(_a)
+ _types_set.add(_a)
+ if _b not in _types_set:
+ _types_list.append(_b)
+ _types_set.add(_b)
+
+
+_byte_order_map = {"ascii": "=", "binary_little_endian": "<", "binary_big_endian": ">"}
+
+_byte_order_reverse = {"<": "binary_little_endian", ">": "binary_big_endian"}
+
+_native_byte_order = {"little": "<", "big": ">"}[_byteorder]
+
+
+def _lookup_type(type_str):
+ if type_str not in _data_type_reverse:
+ try:
+ type_str = _data_types[type_str]
+ except KeyError:
+ raise ValueError("field type %r not in %r" % (type_str, _types_list))
+
+ return _data_type_reverse[type_str]
+
+
+def _split_line(line, n):
+ fields = line.split(None, n)
+ if len(fields) == n:
+ fields.append("")
+
+ assert len(fields) == n + 1
+
+ return fields
+
+
+def make2d(array, cols=None, dtype=None):
+ """
+ Make a 2D array from an array of arrays. The `cols' and `dtype'
+ arguments can be omitted if the array is not empty.
+
+ """
+ if (cols is None or dtype is None) and not len(array):
+ raise RuntimeError("cols and dtype must be specified for empty " "array")
+
+ if cols is None:
+ cols = len(array[0])
+
+ if dtype is None:
+ dtype = array[0].dtype
+
+ return _np.fromiter(array, [("_", dtype, (cols,))], count=len(array))["_"]
+
+
+class PlyParseError(Exception):
+ """
+ Raised when a PLY file cannot be parsed.
+
+ The attributes `element', `row', `property', and `message' give
+ additional information.
+
+ """
+
+ def __init__(self, message, element=None, row=None, prop=None):
+ self.message = message
+ self.element = element
+ self.row = row
+ self.prop = prop
+
+ s = ""
+ if self.element:
+ s += "element %r: " % self.element.name
+ if self.row is not None:
+ s += "row %d: " % self.row
+ if self.prop:
+ s += "property %r: " % self.prop.name
+ s += self.message
+
+ Exception.__init__(self, s)
+
+ def __repr__(self):
+ return (
+ "PlyParseError(%r, element=%r, row=%r, prop=%r)" % self.message,
+ self.element,
+ self.row,
+ self.prop,
+ )
+
+
+class PlyData(object):
+ """
+ PLY file header and data.
+
+ A PlyData instance is created in one of two ways: by the static
+ method PlyData.read (to read a PLY file), or directly from __init__
+ given a sequence of elements (which can then be written to a PLY
+ file).
+
+ """
+
+ def __init__(
+ self, elements=[], text=False, byte_order="=", comments=[], obj_info=[]
+ ):
+ """
+ elements: sequence of PlyElement instances.
+
+ text: whether the resulting PLY file will be text (True) or
+ binary (False).
+
+ byte_order: '<' for little-endian, '>' for big-endian, or '='
+ for native. This is only relevant if `text' is False.
+
+ comments: sequence of strings that will be placed in the header
+ between the 'ply' and 'format ...' lines.
+
+ obj_info: like comments, but will be placed in the header with
+ "obj_info ..." instead of "comment ...".
+
+ """
+ if byte_order == "=" and not text:
+ byte_order = _native_byte_order
+
+ self.byte_order = byte_order
+ self.text = text
+
+ self.comments = list(comments)
+ self.obj_info = list(obj_info)
+ self.elements = elements
+
+ def _get_elements(self):
+ return self._elements
+
+ def _set_elements(self, elements):
+ self._elements = tuple(elements)
+ self._index()
+
+ elements = property(_get_elements, _set_elements)
+
+ def _get_byte_order(self):
+ return self._byte_order
+
+ def _set_byte_order(self, byte_order):
+ if byte_order not in ["<", ">", "="]:
+ raise ValueError("byte order must be '<', '>', or '='")
+
+ self._byte_order = byte_order
+
+ byte_order = property(_get_byte_order, _set_byte_order)
+
+ def _index(self):
+ self._element_lookup = dict((elt.name, elt) for elt in self._elements)
+ if len(self._element_lookup) != len(self._elements):
+ raise ValueError("two elements with same name")
+
+ @staticmethod
+ def _parse_header(stream):
+ """
+ Parse a PLY header from a readable file-like stream.
+
+ """
+ lines = []
+ comments = {"comment": [], "obj_info": []}
+ while True:
+ line = stream.readline().decode("ascii").strip()
+ fields = _split_line(line, 1)
+
+ if fields[0] == "end_header":
+ break
+
+ elif fields[0] in comments.keys():
+ lines.append(fields)
+ else:
+ lines.append(line.split())
+
+ a = 0
+ if lines[a] != ["ply"]:
+ raise PlyParseError("expected 'ply'")
+
+ a += 1
+ while lines[a][0] in comments.keys():
+ comments[lines[a][0]].append(lines[a][1])
+ a += 1
+
+ if lines[a][0] != "format":
+ raise PlyParseError("expected 'format'")
+
+ if lines[a][2] != "1.0":
+ raise PlyParseError("expected version '1.0'")
+
+ if len(lines[a]) != 3:
+ raise PlyParseError("too many fields after 'format'")
+
+ fmt = lines[a][1]
+
+ if fmt not in _byte_order_map:
+ raise PlyParseError("don't understand format %r" % fmt)
+
+ byte_order = _byte_order_map[fmt]
+ text = fmt == "ascii"
+
+ a += 1
+ while a < len(lines) and lines[a][0] in comments.keys():
+ comments[lines[a][0]].append(lines[a][1])
+ a += 1
+
+ return PlyData(
+ PlyElement._parse_multi(lines[a:]),
+ text,
+ byte_order,
+ comments["comment"],
+ comments["obj_info"],
+ )
+
+ @staticmethod
+ def read(stream):
+ """
+ Read PLY data from a readable file-like object or filename.
+
+ """
+ (must_close, stream) = _open_stream(stream, "read")
+ try:
+ data = PlyData._parse_header(stream)
+ for elt in data:
+ elt._read(stream, data.text, data.byte_order)
+ finally:
+ if must_close:
+ stream.close()
+
+ return data
+
+ def write(self, stream):
+ """
+ Write PLY data to a writeable file-like object or filename.
+
+ """
+ (must_close, stream) = _open_stream(stream, "write")
+ try:
+ stream.write(self.header.encode("ascii"))
+ stream.write(b"\r\n")
+ for elt in self:
+ elt._write(stream, self.text, self.byte_order)
+ finally:
+ if must_close:
+ stream.close()
+
+ @property
+ def header(self):
+ """
+ Provide PLY-formatted metadata for the instance.
+
+ """
+ lines = ["ply"]
+
+ if self.text:
+ lines.append("format ascii 1.0")
+ else:
+ lines.append("format " + _byte_order_reverse[self.byte_order] + " 1.0")
+
+ # Some information is lost here, since all comments are placed
+ # between the 'format' line and the first element.
+ for c in self.comments:
+ lines.append("comment " + c)
+
+ for c in self.obj_info:
+ lines.append("obj_info " + c)
+
+ lines.extend(elt.header for elt in self.elements)
+ lines.append("end_header")
+ return "\r\n".join(lines)
+
+ def __iter__(self):
+ return iter(self.elements)
+
+ def __len__(self):
+ return len(self.elements)
+
+ def __contains__(self, name):
+ return name in self._element_lookup
+
+ def __getitem__(self, name):
+ return self._element_lookup[name]
+
+ def __str__(self):
+ return self.header
+
+ def __repr__(self):
+ return "PlyData(%r, text=%r, byte_order=%r, " "comments=%r, obj_info=%r)" % (
+ self.elements,
+ self.text,
+ self.byte_order,
+ self.comments,
+ self.obj_info,
+ )
+
+
+def _open_stream(stream, read_or_write):
+ if hasattr(stream, read_or_write):
+ return (False, stream)
+ try:
+ return (True, open(stream, read_or_write[0] + "b"))
+ except TypeError:
+ raise RuntimeError("expected open file or filename")
+
+
+class PlyElement(object):
+ """
+ PLY file element.
+
+ A client of this library doesn't normally need to instantiate this
+ directly, so the following is only for the sake of documenting the
+ internals.
+
+ Creating a PlyElement instance is generally done in one of two ways:
+ as a byproduct of PlyData.read (when reading a PLY file) and by
+ PlyElement.describe (before writing a PLY file).
+
+ """
+
+ def __init__(self, name, properties, count, comments=[]):
+ """
+ This is not part of the public interface. The preferred methods
+ of obtaining PlyElement instances are PlyData.read (to read from
+ a file) and PlyElement.describe (to construct from a numpy
+ array).
+
+ """
+ self._name = str(name)
+ self._check_name()
+ self._count = count
+
+ self._properties = tuple(properties)
+ self._index()
+
+ self.comments = list(comments)
+
+ self._have_list = any(isinstance(p, PlyListProperty) for p in self.properties)
+
+ @property
+ def count(self):
+ return self._count
+
+ def _get_data(self):
+ return self._data
+
+ def _set_data(self, data):
+ self._data = data
+ self._count = len(data)
+ self._check_sanity()
+
+ data = property(_get_data, _set_data)
+
+ def _check_sanity(self):
+ for prop in self.properties:
+ if prop.name not in self._data.dtype.fields:
+ raise ValueError("dangling property %r" % prop.name)
+
+ def _get_properties(self):
+ return self._properties
+
+ def _set_properties(self, properties):
+ self._properties = tuple(properties)
+ self._check_sanity()
+ self._index()
+
+ properties = property(_get_properties, _set_properties)
+
+ def _index(self):
+ self._property_lookup = dict((prop.name, prop) for prop in self._properties)
+ if len(self._property_lookup) != len(self._properties):
+ raise ValueError("two properties with same name")
+
+ def ply_property(self, name):
+ return self._property_lookup[name]
+
+ @property
+ def name(self):
+ return self._name
+
+ def _check_name(self):
+ if any(c.isspace() for c in self._name):
+ msg = "element name %r contains spaces" % self._name
+ raise ValueError(msg)
+
+ def dtype(self, byte_order="="):
+ """
+ Return the numpy dtype of the in-memory representation of the
+ data. (If there are no list properties, and the PLY format is
+ binary, then this also accurately describes the on-disk
+ representation of the element.)
+
+ """
+ return [(prop.name, prop.dtype(byte_order)) for prop in self.properties]
+
+ @staticmethod
+ def _parse_multi(header_lines):
+ """
+ Parse a list of PLY element definitions.
+
+ """
+ elements = []
+ while header_lines:
+ (elt, header_lines) = PlyElement._parse_one(header_lines)
+ elements.append(elt)
+
+ return elements
+
+ @staticmethod
+ def _parse_one(lines):
+ """
+ Consume one element definition. The unconsumed input is
+ returned along with a PlyElement instance.
+
+ """
+ a = 0
+ line = lines[a]
+
+ if line[0] != "element":
+ raise PlyParseError("expected 'element'")
+ if len(line) > 3:
+ raise PlyParseError("too many fields after 'element'")
+ if len(line) < 3:
+ raise PlyParseError("too few fields after 'element'")
+
+ (name, count) = (line[1], int(line[2]))
+
+ comments = []
+ properties = []
+ while True:
+ a += 1
+ if a >= len(lines):
+ break
+
+ if lines[a][0] == "comment":
+ comments.append(lines[a][1])
+ elif lines[a][0] == "property":
+ properties.append(PlyProperty._parse_one(lines[a]))
+ else:
+ break
+
+ return (PlyElement(name, properties, count, comments), lines[a:])
+
+ @staticmethod
+ def describe(data, name, len_types={}, val_types={}, comments=[]):
+ """
+ Construct a PlyElement from an array's metadata.
+
+ len_types and val_types can be given as mappings from list
+ property names to type strings (like 'u1', 'f4', etc., or
+ 'int8', 'float32', etc.). These can be used to define the length
+ and value types of list properties. List property lengths
+ always default to type 'u1' (8-bit unsigned integer), and value
+ types default to 'i4' (32-bit integer).
+
+ """
+ if not isinstance(data, _np.ndarray):
+ raise TypeError("only numpy arrays are supported")
+
+ if len(data.shape) != 1:
+ raise ValueError("only one-dimensional arrays are " "supported")
+
+ count = len(data)
+
+ properties = []
+ descr = data.dtype.descr
+
+ for t in descr:
+ if not isinstance(t[1], str):
+ raise ValueError("nested records not supported")
+
+ if not t[0]:
+ raise ValueError("field with empty name")
+
+ if len(t) != 2 or t[1][1] == "O":
+ # non-scalar field, which corresponds to a list
+ # property in PLY.
+
+ if t[1][1] == "O":
+ if len(t) != 2:
+ raise ValueError("non-scalar object fields not " "supported")
+
+ len_str = _data_type_reverse[len_types.get(t[0], "u1")]
+ if t[1][1] == "O":
+ val_type = val_types.get(t[0], "i4")
+ val_str = _lookup_type(val_type)
+ else:
+ val_str = _lookup_type(t[1][1:])
+
+ prop = PlyListProperty(t[0], len_str, val_str)
+ else:
+ val_str = _lookup_type(t[1][1:])
+ prop = PlyProperty(t[0], val_str)
+
+ properties.append(prop)
+
+ elt = PlyElement(name, properties, count, comments)
+ elt.data = data
+
+ return elt
+
+ def _read(self, stream, text, byte_order):
+ """
+ Read the actual data from a PLY file.
+
+ """
+ if text:
+ self._read_txt(stream)
+ else:
+ if self._have_list:
+ # There are list properties, so a simple load is
+ # impossible.
+ self._read_bin(stream, byte_order)
+ else:
+ # There are no list properties, so loading the data is
+ # much more straightforward.
+ self._data = _np.fromfile(stream, self.dtype(byte_order), self.count)
+
+ if len(self._data) < self.count:
+ k = len(self._data)
+ del self._data
+ raise PlyParseError("early end-of-file", self, k)
+
+ self._check_sanity()
+
+ def _write(self, stream, text, byte_order):
+ """
+ Write the data to a PLY file.
+
+ """
+ if text:
+ self._write_txt(stream)
+ else:
+ if self._have_list:
+ # There are list properties, so serialization is
+ # slightly complicated.
+ self._write_bin(stream, byte_order)
+ else:
+ # no list properties, so serialization is
+ # straightforward.
+ self.data.astype(self.dtype(byte_order), copy=False).tofile(stream)
+
+ def _read_txt(self, stream):
+ """
+ Load a PLY element from an ASCII-format PLY file. The element
+ may contain list properties.
+
+ """
+ self._data = _np.empty(self.count, dtype=self.dtype())
+
+ k = 0
+ for line in _islice(iter(stream.readline, b""), self.count):
+ fields = iter(line.strip().split())
+ for prop in self.properties:
+ try:
+ self._data[prop.name][k] = prop._from_fields(fields)
+ except StopIteration:
+ raise PlyParseError("early end-of-line", self, k, prop)
+ except ValueError:
+ raise PlyParseError("malformed input", self, k, prop)
+ try:
+ next(fields)
+ except StopIteration:
+ pass
+ else:
+ raise PlyParseError("expected end-of-line", self, k)
+ k += 1
+
+ if k < self.count:
+ del self._data
+ raise PlyParseError("early end-of-file", self, k)
+
+ def _write_txt(self, stream):
+ """
+ Save a PLY element to an ASCII-format PLY file. The element may
+ contain list properties.
+
+ """
+ for rec in self.data:
+ fields = []
+ for prop in self.properties:
+ fields.extend(prop._to_fields(rec[prop.name]))
+
+ _np.savetxt(stream, [fields], "%.18g", newline="\r\n")
+
+ def _read_bin(self, stream, byte_order):
+ """
+ Load a PLY element from a binary PLY file. The element may
+ contain list properties.
+
+ """
+ self._data = _np.empty(self.count, dtype=self.dtype(byte_order))
+
+ for k in _range(self.count):
+ for prop in self.properties:
+ try:
+ self._data[prop.name][k] = prop._read_bin(stream, byte_order)
+ except StopIteration:
+ raise PlyParseError("early end-of-file", self, k, prop)
+
+ def _write_bin(self, stream, byte_order):
+ """
+ Save a PLY element to a binary PLY file. The element may
+ contain list properties.
+
+ """
+ for rec in self.data:
+ for prop in self.properties:
+ prop._write_bin(rec[prop.name], stream, byte_order)
+
+ @property
+ def header(self):
+ """
+ Format this element's metadata as it would appear in a PLY
+ header.
+
+ """
+ lines = ["element %s %d" % (self.name, self.count)]
+
+ # Some information is lost here, since all comments are placed
+ # between the 'element' line and the first property definition.
+ for c in self.comments:
+ lines.append("comment " + c)
+
+ lines.extend(list(map(str, self.properties)))
+
+ return "\r\n".join(lines)
+
+ def __getitem__(self, key):
+ return self.data[key]
+
+ def __setitem__(self, key, value):
+ self.data[key] = value
+
+ def __str__(self):
+ return self.header
+
+ def __repr__(self):
+ return "PlyElement(%r, %r, count=%d, comments=%r)" % (
+ self.name,
+ self.properties,
+ self.count,
+ self.comments,
+ )
+
+
+class PlyProperty(object):
+ """
+ PLY property description. This class is pure metadata; the data
+ itself is contained in PlyElement instances.
+
+ """
+
+ def __init__(self, name, val_dtype):
+ self._name = str(name)
+ self._check_name()
+ self.val_dtype = val_dtype
+
+ def _get_val_dtype(self):
+ return self._val_dtype
+
+ def _set_val_dtype(self, val_dtype):
+ self._val_dtype = _data_types[_lookup_type(val_dtype)]
+
+ val_dtype = property(_get_val_dtype, _set_val_dtype)
+
+ @property
+ def name(self):
+ return self._name
+
+ def _check_name(self):
+ if any(c.isspace() for c in self._name):
+ msg = "Error: property name %r contains spaces" % self._name
+ raise RuntimeError(msg)
+
+ @staticmethod
+ def _parse_one(line):
+ assert line[0] == "property"
+
+ if line[1] == "list":
+ if len(line) > 5:
+ raise PlyParseError("too many fields after " "'property list'")
+ if len(line) < 5:
+ raise PlyParseError("too few fields after " "'property list'")
+
+ return PlyListProperty(line[4], line[2], line[3])
+
+ else:
+ if len(line) > 3:
+ raise PlyParseError("too many fields after " "'property'")
+ if len(line) < 3:
+ raise PlyParseError("too few fields after " "'property'")
+
+ return PlyProperty(line[2], line[1])
+
+ def dtype(self, byte_order="="):
+ """
+ Return the numpy dtype description for this property (as a tuple
+ of strings).
+
+ """
+ return byte_order + self.val_dtype
+
+ def _from_fields(self, fields):
+ """
+ Parse from generator. Raise StopIteration if the property could
+ not be read.
+
+ """
+ return _np.dtype(self.dtype()).type(next(fields))
+
+ def _to_fields(self, data):
+ """
+ Return generator over one item.
+
+ """
+ yield _np.dtype(self.dtype()).type(data)
+
+ def _read_bin(self, stream, byte_order):
+ """
+ Read data from a binary stream. Raise StopIteration if the
+ property could not be read.
+
+ """
+ try:
+ return _np.fromfile(stream, self.dtype(byte_order), 1)[0]
+ except IndexError:
+ raise StopIteration
+
+ def _write_bin(self, data, stream, byte_order):
+ """
+ Write data to a binary stream.
+
+ """
+ _np.dtype(self.dtype(byte_order)).type(data).tofile(stream)
+
+ def __str__(self):
+ val_str = _data_type_reverse[self.val_dtype]
+ return "property %s %s" % (val_str, self.name)
+
+ def __repr__(self):
+ return "PlyProperty(%r, %r)" % (self.name, _lookup_type(self.val_dtype))
+
+
+class PlyListProperty(PlyProperty):
+ """
+ PLY list property description.
+
+ """
+
+ def __init__(self, name, len_dtype, val_dtype):
+ PlyProperty.__init__(self, name, val_dtype)
+
+ self.len_dtype = len_dtype
+
+ def _get_len_dtype(self):
+ return self._len_dtype
+
+ def _set_len_dtype(self, len_dtype):
+ self._len_dtype = _data_types[_lookup_type(len_dtype)]
+
+ len_dtype = property(_get_len_dtype, _set_len_dtype)
+
+ def dtype(self, byte_order="="):
+ """
+ List properties always have a numpy dtype of "object".
+
+ """
+ return "|O"
+
+ def list_dtype(self, byte_order="="):
+ """
+ Return the pair (len_dtype, val_dtype) (both numpy-friendly
+ strings).
+
+ """
+ return (byte_order + self.len_dtype, byte_order + self.val_dtype)
+
+ def _from_fields(self, fields):
+ (len_t, val_t) = self.list_dtype()
+
+ n = int(_np.dtype(len_t).type(next(fields)))
+
+ data = _np.loadtxt(list(_islice(fields, n)), val_t, ndmin=1)
+ if len(data) < n:
+ raise StopIteration
+
+ return data
+
+ def _to_fields(self, data):
+ """
+ Return generator over the (numerical) PLY representation of the
+ list data (length followed by actual data).
+
+ """
+ (len_t, val_t) = self.list_dtype()
+
+ data = _np.asarray(data, dtype=val_t).ravel()
+
+ yield _np.dtype(len_t).type(data.size)
+ for x in data:
+ yield x
+
+ def _read_bin(self, stream, byte_order):
+ (len_t, val_t) = self.list_dtype(byte_order)
+
+ try:
+ n = _np.fromfile(stream, len_t, 1)[0]
+ except IndexError:
+ raise StopIteration
+
+ data = _np.fromfile(stream, val_t, n)
+ if len(data) < n:
+ raise StopIteration
+
+ return data
+
+ def _write_bin(self, data, stream, byte_order):
+ """
+ Write data to a binary stream.
+
+ """
+ (len_t, val_t) = self.list_dtype(byte_order)
+
+ data = _np.asarray(data, dtype=val_t).ravel()
+
+ _np.array(data.size, dtype=len_t).tofile(stream)
+ data.tofile(stream)
+
+ def __str__(self):
+ len_str = _data_type_reverse[self.len_dtype]
+ val_str = _data_type_reverse[self.val_dtype]
+ return "property list %s %s %s" % (len_str, val_str, self.name)
+
+ def __repr__(self):
+ return "PlyListProperty(%r, %r, %r)" % (
+ self.name,
+ _lookup_type(self.len_dtype),
+ _lookup_type(self.val_dtype),
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cbff78d9453bac7efe8359448dabcd6edb60452
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py
@@ -0,0 +1,98 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import glob, os
+import numpy as np
+import cv2
+import torch
+
+
+def extractor(input_path, output_path):
+ if not os.path.exists(output_path):
+ os.mkdir(output_path)
+
+ # Load Depth Camera Intrinsic
+ depth_intrinsic = np.loadtxt(input_path + "/intrinsic/intrinsic_depth.txt")
+ print("Depth intrinsic: ")
+ print(depth_intrinsic)
+
+ # Compute Camrea Distance (just for demo, so you can choose the camera distance in frame sampling)
+ poses = sorted(
+ glob.glob(input_path + "/pose/*.txt"),
+ key=lambda a: int(os.path.basename(a).split(".")[0]),
+ )
+ depths = sorted(
+ glob.glob(input_path + "/depth/*.png"),
+ key=lambda a: int(os.path.basename(a).split(".")[0]),
+ )
+ colors = sorted(
+ glob.glob(input_path + "/color/*.png"),
+ key=lambda a: int(os.path.basename(a).split(".")[0]),
+ )
+
+ # # Get Aligned Point Clouds.
+ for ind, (pose, depth, color) in enumerate(zip(poses, depths, colors)):
+ name = os.path.basename(pose).split(".")[0]
+
+ if os.path.exists(output_path + "/{}.npz".format(name)):
+ continue
+
+ try:
+ print("=" * 50, ": {}".format(pose))
+ depth_img = cv2.imread(depth, -1) # read 16bit grayscale image
+ mask = depth_img != 0
+ color_image = cv2.imread(color)
+ color_image = cv2.resize(color_image, (640, 480))
+ color_image = np.reshape(color_image[mask], [-1, 3])
+ colors = np.zeros_like(color_image)
+ colors[:, 0] = color_image[:, 2]
+ colors[:, 1] = color_image[:, 1]
+ colors[:, 2] = color_image[:, 0]
+
+ pose = np.loadtxt(poses[ind])
+ print("Camera pose: ")
+ print(pose)
+
+ depth_shift = 1000.0
+ x, y = np.meshgrid(
+ np.linspace(0, depth_img.shape[1] - 1, depth_img.shape[1]),
+ np.linspace(0, depth_img.shape[0] - 1, depth_img.shape[0]),
+ )
+ uv_depth = np.zeros((depth_img.shape[0], depth_img.shape[1], 3))
+ uv_depth[:, :, 0] = x
+ uv_depth[:, :, 1] = y
+ uv_depth[:, :, 2] = depth_img / depth_shift
+ uv_depth = np.reshape(uv_depth, [-1, 3])
+ uv_depth = uv_depth[np.where(uv_depth[:, 2] != 0), :].squeeze()
+
+ intrinsic_inv = np.linalg.inv(depth_intrinsic)
+ fx = depth_intrinsic[0, 0]
+ fy = depth_intrinsic[1, 1]
+ cx = depth_intrinsic[0, 2]
+ cy = depth_intrinsic[1, 2]
+ bx = depth_intrinsic[0, 3]
+ by = depth_intrinsic[1, 3]
+ point_list = []
+ n = uv_depth.shape[0]
+ points = np.ones((n, 4))
+ X = (uv_depth[:, 0] - cx) * uv_depth[:, 2] / fx + bx
+ Y = (uv_depth[:, 1] - cy) * uv_depth[:, 2] / fy + by
+ points[:, 0] = X
+ points[:, 1] = Y
+ points[:, 2] = uv_depth[:, 2]
+ points_world = np.dot(points, np.transpose(pose))
+ print(points_world.shape)
+
+ pcd = dict(coord=points_world[:, :3], color=colors)
+ # pcd_save = np.zeros((points_world.shape[0], 7))
+ # pcd_save[:, :3] = points_world[:, :3]
+ # pcd_save[:, 3:6] = colors
+
+ # print('Saving npz file...')
+ # np.savez(output_path + '/{}.npz'.format(name), pcd=pcd_save)
+ torch.save(pcd, output_path + "/{}.pth".format(name))
+ except:
+ continue
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..818c369fa60b841736012950895c6dbaebdbef86
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py
@@ -0,0 +1,51 @@
+import os
+import argparse
+import glob
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from reader import reader
+from point_cloud_extractor import extractor
+from compute_full_overlapping import compute_full_overlapping
+
+
+frame_skip = 25
+
+
+def parse_sens(sens_dir, output_dir):
+ scene_id = os.path.basename(os.path.dirname(sens_dir))
+ print(f"Parsing sens data{sens_dir}")
+ reader(
+ sens_dir,
+ os.path.join(output_dir, scene_id),
+ frame_skip,
+ export_color_images=True,
+ export_depth_images=True,
+ export_poses=True,
+ export_intrinsics=True,
+ )
+ extractor(
+ os.path.join(output_dir, scene_id), os.path.join(output_dir, scene_id, "pcd")
+ )
+ compute_full_overlapping(output_dir, scene_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ opt = parser.parse_args()
+ sens_list = sorted(glob.glob(os.path.join(opt.dataset_root, "scans/scene*/*.sens")))
+ # Preprocess data.
+ pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+ # pool = ProcessPoolExecutor(max_workers=1)
+ print("Processing scenes...")
+ _ = list(pool.map(parse_sens, sens_list, repeat(opt.output_root)))
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..d21aa0ce88006f34775edc9d9aaf4e750d523197
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py
@@ -0,0 +1,33 @@
+import argparse
+import os, sys
+
+from SensorData import SensorData
+
+
+def reader(
+ filename,
+ output_path,
+ frame_skip,
+ export_color_images=False,
+ export_depth_images=False,
+ export_poses=False,
+ export_intrinsics=False,
+):
+ if not os.path.exists(output_path):
+ os.makedirs(output_path)
+
+ # load the data
+ print("loading %s..." % filename)
+ sd = SensorData(filename)
+ if export_depth_images:
+ sd.export_depth_images(
+ os.path.join(output_path, "depth"), frame_skip=frame_skip
+ )
+ if export_color_images:
+ sd.export_color_images(
+ os.path.join(output_path, "color"), frame_skip=frame_skip
+ )
+ if export_poses:
+ sd.export_poses(os.path.join(output_path, "pose"), frame_skip=frame_skip)
+ if export_intrinsics:
+ sd.export_intrinsics(os.path.join(output_path, "intrinsic"))
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad820029016ff16c53ab0ad872ede9449add6a7b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py
@@ -0,0 +1,252 @@
+"""
+Preprocessing Script for ScanNet++
+modified from official preprocess code.
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import open3d as o3d
+import multiprocessing as mp
+from collections import OrderedDict
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+
+def parse_scene(
+ name,
+ split,
+ dataset_root,
+ output_root,
+ label_mapping,
+ class2idx,
+ ignore_index=-1,
+):
+ print(f"Parsing scene {name} in {split} split")
+ dataset_root = Path(dataset_root)
+ output_root = Path(output_root)
+ scene_path = dataset_root / "data" / name / "scans"
+ mesh_path = scene_path / "mesh_aligned_0.05.ply"
+ segs_path = scene_path / "segments.json"
+ anno_path = scene_path / "segments_anno.json"
+
+ # load mesh vertices and colors
+ mesh = o3d.io.read_triangle_mesh(str(mesh_path))
+
+ # extract mesh information
+ mesh.compute_vertex_normals(normalized=True)
+ coord = np.array(mesh.vertices).astype(np.float32)
+ color = (np.array(mesh.vertex_colors) * 255).astype(np.uint8)
+ normal = np.array(mesh.vertex_normals).astype(np.float32)
+
+ save_path = output_root / split / name
+ save_path.mkdir(parents=True, exist_ok=True)
+ np.save(save_path / "coord.npy", coord)
+ np.save(save_path / "color.npy", color)
+ np.save(save_path / "normal.npy", normal)
+
+ if split == "test":
+ return
+
+ # get label on vertices
+ # load segments = vertices per segment ID
+ with open(segs_path) as f:
+ segments = json.load(f)
+ # load anno = (instance, groups of segments)
+ with open(anno_path) as f:
+ anno = json.load(f)
+ seg_indices = np.array(segments["segIndices"], dtype=np.uint32)
+ num_vertices = len(seg_indices)
+ assert num_vertices == len(coord)
+ semantic_gt = np.ones((num_vertices, 3), dtype=np.int16) * ignore_index
+ instance_gt = np.ones((num_vertices, 3), dtype=np.int16) * ignore_index
+
+ # number of labels are used per vertex. initially 0
+ # increment each time a new label is added
+ instance_size = np.ones((num_vertices, 3), dtype=np.int16) * np.inf
+
+ # keep track of the size of the instance (#vertices) assigned to each vertex
+ # later, keep the label of the smallest instance for major label of vertices
+ # store inf initially so that we can pick the smallest instance
+ labels_used = np.zeros(num_vertices, dtype=np.int16)
+
+ for idx, instance in enumerate(anno["segGroups"]):
+ label = instance["label"]
+ instance["label_orig"] = label
+ # remap label
+ instance["label"] = label_mapping.get(label, None)
+ instance["label_index"] = class2idx.get(label, ignore_index)
+
+ if instance["label_index"] == ignore_index:
+ continue
+ # get all the vertices with segment index in this instance
+ # and max number of labels not yet applied
+ mask = np.isin(seg_indices, instance["segments"]) & (labels_used < 3)
+ size = mask.sum()
+ if size == 0:
+ continue
+
+ # get the position to add the label - 0, 1, 2
+ label_position = labels_used[mask]
+ semantic_gt[mask, label_position] = instance["label_index"]
+ # store all valid instance (include ignored instance)
+ instance_gt[mask, label_position] = instance["objectId"]
+ instance_size[mask, label_position] = size
+ labels_used[mask] += 1
+
+ # major label is the label of smallest instance for each vertex
+ # use major label for single class segmentation
+ # shift major label to the first column
+ mask = labels_used > 1
+ if mask.sum() > 0:
+ major_label_position = np.argmin(instance_size[mask], axis=1)
+
+ major_semantic_label = semantic_gt[mask, major_label_position]
+ semantic_gt[mask, major_label_position] = semantic_gt[:, 0][mask]
+ semantic_gt[:, 0][mask] = major_semantic_label
+
+ major_instance_label = instance_gt[mask, major_label_position]
+ instance_gt[mask, major_label_position] = instance_gt[:, 0][mask]
+ instance_gt[:, 0][mask] = major_instance_label
+
+ np.save(save_path / "segment.npy", semantic_gt)
+ np.save(save_path / "instance.npy", instance_gt)
+
+
+def filter_map_classes(mapping, count_thresh, count_type, mapping_type):
+ mapping = mapping[mapping[count_type] >= count_thresh]
+ if mapping_type == "semantic":
+ map_key = "semantic_map_to"
+ elif mapping_type == "instance":
+ map_key = "instance_map_to"
+ else:
+ raise NotImplementedError
+ # create a dict with classes to be mapped
+ # classes that don't have mapping are entered as x->x
+ # otherwise x->y
+ map_dict = OrderedDict()
+
+ for i in range(mapping.shape[0]):
+ row = mapping.iloc[i]
+ class_name = row["class"]
+ map_target = row[map_key]
+
+ # map to None or some other label -> don't add this class to the label list
+ try:
+ if len(map_target) > 0:
+ # map to None -> don't use this class
+ if map_target == "None":
+ pass
+ else:
+ # map to something else -> use this class
+ map_dict[class_name] = map_target
+ except TypeError:
+ # nan values -> no mapping, keep label as is
+ if class_name not in map_dict:
+ map_dict[class_name] = class_name
+
+ return map_dict
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet++ dataset containing data/metadata/splits.",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val/test folders will be located.",
+ )
+ parser.add_argument(
+ "--ignore_index",
+ default=-1,
+ type=int,
+ help="Default ignore index.",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ config = parser.parse_args()
+
+ print("Loading meta data...")
+ config.dataset_root = Path(config.dataset_root)
+ config.output_root = Path(config.output_root)
+
+ train_list = np.loadtxt(
+ config.dataset_root / "splits" / "nvs_sem_train.txt",
+ dtype=str,
+ )
+ print("Num samples in training split:", len(train_list))
+
+ val_list = np.loadtxt(
+ config.dataset_root / "splits" / "nvs_sem_val.txt",
+ dtype=str,
+ )
+ print("Num samples in validation split:", len(val_list))
+
+ test_list = np.loadtxt(
+ config.dataset_root / "splits" / "sem_test.txt",
+ dtype=str,
+ )
+ print("Num samples in testing split:", len(test_list))
+
+ data_list = np.concatenate([train_list, val_list, test_list])
+ split_list = np.concatenate(
+ [
+ np.full_like(train_list, "train"),
+ np.full_like(val_list, "val"),
+ np.full_like(test_list, "test"),
+ ]
+ )
+
+ # Parsing label information and mapping
+ segment_class_names = np.loadtxt(
+ config.dataset_root / "metadata" / "semantic_benchmark" / "top100.txt",
+ dtype=str,
+ delimiter=".", # dummy delimiter to replace " "
+ )
+ print("Num classes in segment class list:", len(segment_class_names))
+
+ instance_class_names = np.loadtxt(
+ config.dataset_root / "metadata" / "semantic_benchmark" / "top100_instance.txt",
+ dtype=str,
+ delimiter=".", # dummy delimiter to replace " "
+ )
+ print("Num classes in instance class list:", len(instance_class_names))
+
+ label_mapping = pd.read_csv(
+ config.dataset_root / "metadata" / "semantic_benchmark" / "map_benchmark.csv"
+ )
+ label_mapping = filter_map_classes(
+ label_mapping, count_thresh=0, count_type="count", mapping_type="semantic"
+ )
+ class2idx = {
+ class_name: idx for (idx, class_name) in enumerate(segment_class_names)
+ }
+
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ parse_scene,
+ data_list,
+ split_list,
+ repeat(config.dataset_root),
+ repeat(config.output_root),
+ repeat(label_mapping),
+ repeat(class2idx),
+ repeat(config.ignore_index),
+ )
+ )
+ pool.shutdown()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..6924dc9abf3a3c253ee80dd3b3d85454e3df35d2
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py
@@ -0,0 +1,420 @@
+"""
+Preprocessing Script for Structured3D
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import io
+import os
+import PIL
+from PIL import Image
+import cv2
+import zipfile
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+
+VALID_CLASS_IDS_25 = (
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 11,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 22,
+ 24,
+ 25,
+ 32,
+ 34,
+ 35,
+ 38,
+ 39,
+ 40,
+)
+CLASS_LABELS_25 = (
+ "wall",
+ "floor",
+ "cabinet",
+ "bed",
+ "chair",
+ "sofa",
+ "table",
+ "door",
+ "window",
+ "picture",
+ "desk",
+ "shelves",
+ "curtain",
+ "dresser",
+ "pillow",
+ "mirror",
+ "ceiling",
+ "refrigerator",
+ "television",
+ "nightstand",
+ "sink",
+ "lamp",
+ "otherstructure",
+ "otherfurniture",
+ "otherprop",
+)
+
+
+def normal_from_cross_product(points_2d: np.ndarray) -> np.ndarray:
+ xyz_points_pad = np.pad(points_2d, ((0, 1), (0, 1), (0, 0)), mode="symmetric")
+ xyz_points_ver = (xyz_points_pad[:, :-1, :] - xyz_points_pad[:, 1:, :])[:-1, :, :]
+ xyz_points_hor = (xyz_points_pad[:-1, :, :] - xyz_points_pad[1:, :, :])[:, :-1, :]
+ xyz_normal = np.cross(xyz_points_hor, xyz_points_ver)
+ xyz_dist = np.linalg.norm(xyz_normal, axis=-1, keepdims=True)
+ xyz_normal = np.divide(
+ xyz_normal, xyz_dist, out=np.zeros_like(xyz_normal), where=xyz_dist != 0
+ )
+ return xyz_normal
+
+
+class Structured3DReader:
+ def __init__(self, files):
+ super().__init__()
+ if isinstance(files, str):
+ files = [files]
+ self.readers = [zipfile.ZipFile(f, "r") for f in files]
+ self.names_mapper = dict()
+ for idx, reader in enumerate(self.readers):
+ for name in reader.namelist():
+ self.names_mapper[name] = idx
+
+ def filelist(self):
+ return list(self.names_mapper.keys())
+
+ def listdir(self, dir_name):
+ dir_name = dir_name.lstrip(os.path.sep).rstrip(os.path.sep)
+ file_list = list(
+ np.unique(
+ [
+ f.replace(dir_name + os.path.sep, "", 1).split(os.path.sep)[0]
+ for f in self.filelist()
+ if f.startswith(dir_name + os.path.sep)
+ ]
+ )
+ )
+ if "" in file_list:
+ file_list.remove("")
+ return file_list
+
+ def read(self, file_name):
+ split = self.names_mapper[file_name]
+ return self.readers[split].read(file_name)
+
+ def read_camera(self, camera_path):
+ z2y_top_m = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=np.float32)
+ cam_extr = np.fromstring(self.read(camera_path), dtype=np.float32, sep=" ")
+ cam_t = np.matmul(z2y_top_m, cam_extr[:3] / 1000)
+ if cam_extr.shape[0] > 3:
+ cam_front, cam_up = cam_extr[3:6], cam_extr[6:9]
+ cam_n = np.cross(cam_front, cam_up)
+ cam_r = np.stack((cam_front, cam_up, cam_n), axis=1).astype(np.float32)
+ cam_r = np.matmul(z2y_top_m, cam_r)
+ cam_f = cam_extr[9:11]
+ else:
+ cam_r = np.eye(3, dtype=np.float32)
+ cam_f = None
+ return cam_r, cam_t, cam_f
+
+ def read_depth(self, depth_path):
+ depth = cv2.imdecode(
+ np.frombuffer(self.read(depth_path), np.uint8), cv2.IMREAD_UNCHANGED
+ )[..., np.newaxis]
+ depth[depth == 0] = 65535
+ return depth
+
+ def read_color(self, color_path):
+ color = cv2.imdecode(
+ np.frombuffer(self.read(color_path), np.uint8), cv2.IMREAD_UNCHANGED
+ )[..., :3][..., ::-1]
+ return color
+
+ def read_segment(self, segment_path):
+ segment = np.array(PIL.Image.open(io.BytesIO(self.read(segment_path))))[
+ ..., np.newaxis
+ ]
+ return segment
+
+
+def parse_scene(
+ scene,
+ dataset_root,
+ output_root,
+ ignore_index=-1,
+ grid_size=None,
+ fuse_prsp=True,
+ fuse_pano=True,
+ vis=False,
+):
+ assert fuse_prsp or fuse_pano
+ reader = Structured3DReader(
+ [
+ os.path.join(dataset_root, f)
+ for f in os.listdir(dataset_root)
+ if f.endswith(".zip")
+ ]
+ )
+ scene_id = int(os.path.basename(scene).split("_")[-1])
+ if scene_id < 3000:
+ split = "train"
+ elif 3000 <= scene_id < 3250:
+ split = "val"
+ else:
+ split = "test"
+
+ print(f"Processing: {scene} in {split}")
+ rooms = reader.listdir(os.path.join("Structured3D", scene, "2D_rendering"))
+ for room in rooms:
+ room_path = os.path.join("Structured3D", scene, "2D_rendering", room)
+ coord_list = list()
+ color_list = list()
+ normal_list = list()
+ segment_list = list()
+ if fuse_prsp:
+ prsp_path = os.path.join(room_path, "perspective", "full")
+ frames = reader.listdir(prsp_path)
+
+ for frame in frames:
+ try:
+ cam_r, cam_t, cam_f = reader.read_camera(
+ os.path.join(prsp_path, frame, "camera_pose.txt")
+ )
+ depth = reader.read_depth(
+ os.path.join(prsp_path, frame, "depth.png")
+ )
+ color = reader.read_color(
+ os.path.join(prsp_path, frame, "rgb_rawlight.png")
+ )
+ segment = reader.read_segment(
+ os.path.join(prsp_path, frame, "semantic.png")
+ )
+ except:
+ print(
+ f"Skipping {scene}_room{room}_frame{frame} perspective view due to loading error"
+ )
+ else:
+ fx, fy = cam_f
+ height, width = depth.shape[0], depth.shape[1]
+ pixel = np.transpose(np.indices((width, height)), (2, 1, 0))
+ pixel = pixel.reshape((-1, 2))
+ pixel = np.hstack((pixel, np.ones((pixel.shape[0], 1))))
+ k = np.diag([1.0, 1.0, 1.0])
+
+ k[0, 2] = width / 2
+ k[1, 2] = height / 2
+
+ k[0, 0] = k[0, 2] / np.tan(fx)
+ k[1, 1] = k[1, 2] / np.tan(fy)
+ coord = (
+ depth.reshape((-1, 1)) * (np.linalg.inv(k) @ pixel.T).T
+ ).reshape(height, width, 3)
+ coord = coord @ np.array([[0, 0, 1], [0, -1, 0], [1, 0, 0]])
+ normal = normal_from_cross_product(coord)
+
+ # Filtering invalid points
+ view_dist = np.maximum(
+ np.linalg.norm(coord, axis=-1, keepdims=True), float(10e-5)
+ )
+ cosine_dist = np.sum(
+ (coord * normal / view_dist), axis=-1, keepdims=True
+ )
+ cosine_dist = np.abs(cosine_dist)
+ mask = ((cosine_dist > 0.15) & (depth < 65535) & (segment > 0))[
+ ..., 0
+ ].reshape(-1)
+
+ coord = np.matmul(coord / 1000, cam_r.T) + cam_t
+ normal = normal_from_cross_product(coord)
+
+ if sum(mask) > 0:
+ coord_list.append(coord.reshape(-1, 3)[mask])
+ color_list.append(color.reshape(-1, 3)[mask])
+ normal_list.append(normal.reshape(-1, 3)[mask])
+ segment_list.append(segment.reshape(-1, 1)[mask])
+ else:
+ print(
+ f"Skipping {scene}_room{room}_frame{frame} perspective view due to all points are filtered out"
+ )
+
+ if fuse_pano:
+ pano_path = os.path.join(room_path, "panorama")
+ try:
+ _, cam_t, _ = reader.read_camera(
+ os.path.join(pano_path, "camera_xyz.txt")
+ )
+ depth = reader.read_depth(os.path.join(pano_path, "full", "depth.png"))
+ color = reader.read_color(
+ os.path.join(pano_path, "full", "rgb_rawlight.png")
+ )
+ segment = reader.read_segment(
+ os.path.join(pano_path, "full", "semantic.png")
+ )
+ except:
+ print(f"Skipping {scene}_room{room} panorama view due to loading error")
+ else:
+ p_h, p_w = depth.shape[:2]
+ p_a = np.arange(p_w, dtype=np.float32) / p_w * 2 * np.pi - np.pi
+ p_b = np.arange(p_h, dtype=np.float32) / p_h * np.pi * -1 + np.pi / 2
+ p_a = np.tile(p_a[None], [p_h, 1])[..., np.newaxis]
+ p_b = np.tile(p_b[:, None], [1, p_w])[..., np.newaxis]
+ p_a_sin, p_a_cos, p_b_sin, p_b_cos = (
+ np.sin(p_a),
+ np.cos(p_a),
+ np.sin(p_b),
+ np.cos(p_b),
+ )
+ x = depth * p_a_cos * p_b_cos
+ y = depth * p_b_sin
+ z = depth * p_a_sin * p_b_cos
+ coord = np.concatenate([x, y, z], axis=-1) / 1000
+ normal = normal_from_cross_product(coord)
+
+ # Filtering invalid points
+ view_dist = np.maximum(
+ np.linalg.norm(coord, axis=-1, keepdims=True), float(10e-5)
+ )
+ cosine_dist = np.sum(
+ (coord * normal / view_dist), axis=-1, keepdims=True
+ )
+ cosine_dist = np.abs(cosine_dist)
+ mask = ((cosine_dist > 0.15) & (depth < 65535) & (segment > 0))[
+ ..., 0
+ ].reshape(-1)
+ coord = coord + cam_t
+
+ if sum(mask) > 0:
+ coord_list.append(coord.reshape(-1, 3)[mask])
+ color_list.append(color.reshape(-1, 3)[mask])
+ normal_list.append(normal.reshape(-1, 3)[mask])
+ segment_list.append(segment.reshape(-1, 1)[mask])
+ else:
+ print(
+ f"Skipping {scene}_room{room} panorama view due to all points are filtered out"
+ )
+
+ if len(coord_list) > 0:
+ coord = np.concatenate(coord_list, axis=0)
+ coord = coord @ np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+ color = np.concatenate(color_list, axis=0)
+ normal = np.concatenate(normal_list, axis=0)
+ normal = normal @ np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+ segment = np.concatenate(segment_list, axis=0)
+ segment25 = np.ones_like(segment, dtype=np.int64) * ignore_index
+ for idx, value in enumerate(VALID_CLASS_IDS_25):
+ mask = np.all(segment == value, axis=-1)
+ segment25[mask] = idx
+
+ data_dict = dict(
+ coord=coord.astype(np.float32),
+ color=color.astype(np.uint8),
+ normal=normal.astype(np.float32),
+ segment=segment25.astype(np.int16),
+ )
+ # Grid sampling data
+ if grid_size is not None:
+ grid_coord = np.floor(coord / grid_size).astype(int)
+ _, idx = np.unique(grid_coord, axis=0, return_index=True)
+ coord = coord[idx]
+ for key in data_dict.keys():
+ data_dict[key] = data_dict[key][idx]
+
+ # Save data
+ save_path = os.path.join(
+ output_root, split, os.path.basename(scene), f"room_{room}"
+ )
+ os.makedirs(save_path, exist_ok=True)
+ for key in data_dict.keys():
+ np.save(os.path.join(save_path, f"{key}.npy"), data_dict[key])
+
+ if vis:
+ from pointcept.utils.visualization import save_point_cloud
+
+ os.makedirs("./vis", exist_ok=True)
+ save_point_cloud(
+ coord, color / 255, f"./vis/{scene}_room{room}_color.ply"
+ )
+ save_point_cloud(
+ coord, (normal + 1) / 2, f"./vis/{scene}_room{room}_normal.ply"
+ )
+ else:
+ print(f"Skipping {scene}_room{room} due to no valid points")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the ScanNet dataset containing scene folders.",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located.",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ parser.add_argument(
+ "--grid_size", default=None, type=float, help="Grid size for grid sampling."
+ )
+ parser.add_argument("--ignore_index", default=-1, type=float, help="Ignore index.")
+ parser.add_argument(
+ "--fuse_prsp", action="store_true", help="Whether fuse perspective view."
+ )
+ parser.add_argument(
+ "--fuse_pano", action="store_true", help="Whether fuse panorama view."
+ )
+ config = parser.parse_args()
+
+ reader = Structured3DReader(
+ [
+ os.path.join(config.dataset_root, f)
+ for f in os.listdir(config.dataset_root)
+ if f.endswith(".zip")
+ ]
+ )
+
+ scenes_list = reader.listdir("Structured3D")
+ scenes_list = sorted(scenes_list)
+ os.makedirs(os.path.join(config.output_root, "train"), exist_ok=True)
+ os.makedirs(os.path.join(config.output_root, "val"), exist_ok=True)
+ os.makedirs(os.path.join(config.output_root, "test"), exist_ok=True)
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ parse_scene,
+ scenes_list,
+ repeat(config.dataset_root),
+ repeat(config.output_root),
+ repeat(config.ignore_index),
+ repeat(config.grid_size),
+ repeat(config.fuse_prsp),
+ repeat(config.fuse_pano),
+ )
+ )
+ pool.shutdown()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/waymo/3d_semseg_test_set_frames.txt b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/waymo/3d_semseg_test_set_frames.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e25dc615331aaff261c25bfc3e6b930fccf880ac
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/waymo/3d_semseg_test_set_frames.txt
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diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py
new file mode 100644
index 0000000000000000000000000000000000000000..33a309fc2a03c52b63a43863ac40632ad5daa0a6
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py
@@ -0,0 +1,387 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+import argparse
+import numpy as np
+import tensorflow.compat.v1 as tf
+from pathlib import Path
+from waymo_open_dataset.utils import frame_utils
+from waymo_open_dataset.utils import transform_utils
+from waymo_open_dataset.utils import range_image_utils
+from waymo_open_dataset import dataset_pb2 as open_dataset
+import glob
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+
+def create_lidar(frame):
+ """Parse and save the lidar data in psd format.
+ Args:
+ frame (:obj:`Frame`): Open dataset frame proto.
+ """
+ (
+ range_images,
+ camera_projections,
+ segmentation_labels,
+ range_image_top_pose,
+ ) = frame_utils.parse_range_image_and_camera_projection(frame)
+
+ points, cp_points, valid_masks = convert_range_image_to_point_cloud(
+ frame,
+ range_images,
+ camera_projections,
+ range_image_top_pose,
+ keep_polar_features=True,
+ )
+ points_ri2, cp_points_ri2, valid_masks_ri2 = convert_range_image_to_point_cloud(
+ frame,
+ range_images,
+ camera_projections,
+ range_image_top_pose,
+ ri_index=1,
+ keep_polar_features=True,
+ )
+
+ # 3d points in vehicle frame.
+ points_all = np.concatenate(points, axis=0)
+ points_all_ri2 = np.concatenate(points_ri2, axis=0)
+ # point labels.
+
+ points_all = np.concatenate([points_all, points_all_ri2], axis=0)
+
+ velodyne = np.c_[points_all[:, 3:6], points_all[:, 1]]
+ velodyne = velodyne.reshape((velodyne.shape[0] * velodyne.shape[1]))
+
+ valid_masks = [valid_masks, valid_masks_ri2]
+ return velodyne, valid_masks
+
+
+def create_label(frame):
+ (
+ range_images,
+ camera_projections,
+ segmentation_labels,
+ range_image_top_pose,
+ ) = frame_utils.parse_range_image_and_camera_projection(frame)
+
+ point_labels = convert_range_image_to_point_cloud_labels(
+ frame, range_images, segmentation_labels
+ )
+ point_labels_ri2 = convert_range_image_to_point_cloud_labels(
+ frame, range_images, segmentation_labels, ri_index=1
+ )
+
+ # point labels.
+ point_labels_all = np.concatenate(point_labels, axis=0)
+ point_labels_all_ri2 = np.concatenate(point_labels_ri2, axis=0)
+ point_labels_all = np.concatenate([point_labels_all, point_labels_all_ri2], axis=0)
+
+ labels = point_labels_all
+ return labels
+
+
+def convert_range_image_to_cartesian(
+ frame, range_images, range_image_top_pose, ri_index=0, keep_polar_features=False
+):
+ """Convert range images from polar coordinates to Cartesian coordinates.
+
+ Args:
+ frame: open dataset frame
+ range_images: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ range_image_top_pose: range image pixel pose for top lidar.
+ ri_index: 0 for the first return, 1 for the second return.
+ keep_polar_features: If true, keep the features from the polar range image
+ (i.e. range, intensity, and elongation) as the first features in the
+ output range image.
+
+ Returns:
+ dict of {laser_name, (H, W, D)} range images in Cartesian coordinates. D
+ will be 3 if keep_polar_features is False (x, y, z) and 6 if
+ keep_polar_features is True (range, intensity, elongation, x, y, z).
+ """
+ cartesian_range_images = {}
+ frame_pose = tf.convert_to_tensor(
+ value=np.reshape(np.array(frame.pose.transform), [4, 4])
+ )
+
+ # [H, W, 6]
+ range_image_top_pose_tensor = tf.reshape(
+ tf.convert_to_tensor(value=range_image_top_pose.data),
+ range_image_top_pose.shape.dims,
+ )
+ # [H, W, 3, 3]
+ range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(
+ range_image_top_pose_tensor[..., 0],
+ range_image_top_pose_tensor[..., 1],
+ range_image_top_pose_tensor[..., 2],
+ )
+ range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]
+ range_image_top_pose_tensor = transform_utils.get_transform(
+ range_image_top_pose_tensor_rotation, range_image_top_pose_tensor_translation
+ )
+
+ for c in frame.context.laser_calibrations:
+ range_image = range_images[c.name][ri_index]
+ if len(c.beam_inclinations) == 0: # pylint: disable=g-explicit-length-test
+ beam_inclinations = range_image_utils.compute_inclination(
+ tf.constant([c.beam_inclination_min, c.beam_inclination_max]),
+ height=range_image.shape.dims[0],
+ )
+ else:
+ beam_inclinations = tf.constant(c.beam_inclinations)
+
+ beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])
+ extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])
+
+ range_image_tensor = tf.reshape(
+ tf.convert_to_tensor(value=range_image.data), range_image.shape.dims
+ )
+ pixel_pose_local = None
+ frame_pose_local = None
+ if c.name == open_dataset.LaserName.TOP:
+ pixel_pose_local = range_image_top_pose_tensor
+ pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)
+ frame_pose_local = tf.expand_dims(frame_pose, axis=0)
+ range_image_cartesian = range_image_utils.extract_point_cloud_from_range_image(
+ tf.expand_dims(range_image_tensor[..., 0], axis=0),
+ tf.expand_dims(extrinsic, axis=0),
+ tf.expand_dims(tf.convert_to_tensor(value=beam_inclinations), axis=0),
+ pixel_pose=pixel_pose_local,
+ frame_pose=frame_pose_local,
+ )
+
+ range_image_cartesian = tf.squeeze(range_image_cartesian, axis=0)
+
+ if keep_polar_features:
+ # If we want to keep the polar coordinate features of range, intensity,
+ # and elongation, concatenate them to be the initial dimensions of the
+ # returned Cartesian range image.
+ range_image_cartesian = tf.concat(
+ [range_image_tensor[..., 0:3], range_image_cartesian], axis=-1
+ )
+
+ cartesian_range_images[c.name] = range_image_cartesian
+
+ return cartesian_range_images
+
+
+def convert_range_image_to_point_cloud(
+ frame,
+ range_images,
+ camera_projections,
+ range_image_top_pose,
+ ri_index=0,
+ keep_polar_features=False,
+):
+ """Convert range images to point cloud.
+
+ Args:
+ frame: open dataset frame
+ range_images: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ camera_projections: A dict of {laser_name,
+ [camera_projection_from_first_return,
+ camera_projection_from_second_return]}.
+ range_image_top_pose: range image pixel pose for top lidar.
+ ri_index: 0 for the first return, 1 for the second return.
+ keep_polar_features: If true, keep the features from the polar range image
+ (i.e. range, intensity, and elongation) as the first features in the
+ output range image.
+
+ Returns:
+ points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).
+ (NOTE: Will be {[N, 6]} if keep_polar_features is true.
+ cp_points: {[N, 6]} list of camera projections of length 5
+ (number of lidars).
+ """
+ calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+ points = []
+ cp_points = []
+ valid_masks = []
+
+ cartesian_range_images = convert_range_image_to_cartesian(
+ frame, range_images, range_image_top_pose, ri_index, keep_polar_features
+ )
+
+ for c in calibrations:
+ range_image = range_images[c.name][ri_index]
+ range_image_tensor = tf.reshape(
+ tf.convert_to_tensor(value=range_image.data), range_image.shape.dims
+ )
+ range_image_mask = range_image_tensor[..., 0] > 0
+
+ range_image_cartesian = cartesian_range_images[c.name]
+ points_tensor = tf.gather_nd(
+ range_image_cartesian, tf.compat.v1.where(range_image_mask)
+ )
+
+ cp = camera_projections[c.name][ri_index]
+ cp_tensor = tf.reshape(tf.convert_to_tensor(value=cp.data), cp.shape.dims)
+ cp_points_tensor = tf.gather_nd(cp_tensor, tf.compat.v1.where(range_image_mask))
+ points.append(points_tensor.numpy())
+ cp_points.append(cp_points_tensor.numpy())
+ valid_masks.append(range_image_mask.numpy())
+
+ return points, cp_points, valid_masks
+
+
+def convert_range_image_to_point_cloud_labels(
+ frame, range_images, segmentation_labels, ri_index=0
+):
+ """Convert segmentation labels from range images to point clouds.
+
+ Args:
+ frame: open dataset frame
+ range_images: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ segmentation_labels: A dict of {laser_name, [range_image_first_return,
+ range_image_second_return]}.
+ ri_index: 0 for the first return, 1 for the second return.
+
+ Returns:
+ point_labels: {[N, 2]} list of 3d lidar points's segmentation labels. 0 for
+ points that are not labeled.
+ """
+ calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+ point_labels = []
+ for c in calibrations:
+ range_image = range_images[c.name][ri_index]
+ range_image_tensor = tf.reshape(
+ tf.convert_to_tensor(range_image.data), range_image.shape.dims
+ )
+ range_image_mask = range_image_tensor[..., 0] > 0
+
+ if c.name in segmentation_labels:
+ sl = segmentation_labels[c.name][ri_index]
+ sl_tensor = tf.reshape(tf.convert_to_tensor(sl.data), sl.shape.dims)
+ sl_points_tensor = tf.gather_nd(sl_tensor, tf.where(range_image_mask))
+ else:
+ num_valid_point = tf.math.reduce_sum(tf.cast(range_image_mask, tf.int32))
+ sl_points_tensor = tf.zeros([num_valid_point, 2], dtype=tf.int32)
+
+ point_labels.append(sl_points_tensor.numpy())
+ return point_labels
+
+
+def handle_process(file_path, output_root, test_frame_list):
+ file = os.path.basename(file_path)
+ split = os.path.basename(os.path.dirname(file_path))
+ print(f"Parsing {split}/{file}")
+ save_path = Path(output_root) / split / file.split(".")[0]
+
+ data_group = tf.data.TFRecordDataset(file_path, compression_type="")
+ for data in data_group:
+ frame = open_dataset.Frame()
+ frame.ParseFromString(bytearray(data.numpy()))
+ context_name = frame.context.name
+ timestamp = str(frame.timestamp_micros)
+
+ if split != "testing":
+ # for training and validation frame, extract labelled frame
+ if not frame.lasers[0].ri_return1.segmentation_label_compressed:
+ continue
+ else:
+ # for testing frame, extract frame in test_frame_list
+ if f"{context_name},{timestamp}" not in test_frame_list:
+ continue
+
+ os.makedirs(save_path / timestamp, exist_ok=True)
+
+ # extract frame pass above check
+ point_cloud, valid_masks = create_lidar(frame)
+ point_cloud = point_cloud.reshape(-1, 4)
+ coord = point_cloud[:, :3]
+ strength = np.tanh(point_cloud[:, -1].reshape([-1, 1]))
+ pose = np.array(frame.pose.transform, np.float32).reshape(4, 4)
+ mask = np.array(valid_masks, dtype=object)
+
+ np.save(save_path / timestamp / "coord.npy", coord)
+ np.save(save_path / timestamp / "strength.npy", strength)
+ np.save(save_path / timestamp / "pose.npy", pose)
+
+ # save mask for reverse prediction
+ if split != "training":
+ np.save(save_path / timestamp / "mask.npy", mask)
+
+ # save label
+ if split != "testing":
+ # ignore TYPE_UNDEFINED, ignore_index 0 -> -1
+ label = create_label(frame)[:, 1].reshape([-1]) - 1
+ np.save(save_path / timestamp / "segment.npy", label)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dataset_root",
+ required=True,
+ help="Path to the Waymo dataset",
+ )
+ parser.add_argument(
+ "--output_root",
+ required=True,
+ help="Output path where train/val folders will be located",
+ )
+ parser.add_argument(
+ "--splits",
+ required=True,
+ nargs="+",
+ choices=["training", "validation", "testing"],
+ help="Splits need to process ([training, validation, testing]).",
+ )
+ parser.add_argument(
+ "--num_workers",
+ default=mp.cpu_count(),
+ type=int,
+ help="Num workers for preprocessing.",
+ )
+ config = parser.parse_args()
+
+ # load file list
+ file_list = glob.glob(
+ os.path.join(os.path.abspath(config.dataset_root), "*", "*.tfrecord")
+ )
+ assert len(file_list) == 1150
+
+ # Create output directories
+ for split in config.splits:
+ os.makedirs(os.path.join(config.output_root, split), exist_ok=True)
+
+ file_list = [
+ file
+ for file in file_list
+ if os.path.basename(os.path.dirname(file)) in config.splits
+ ]
+
+ # Load test frame list
+ test_frame_file = os.path.join(
+ os.path.dirname(__file__), "3d_semseg_test_set_frames.txt"
+ )
+ test_frame_list = [x.rstrip() for x in (open(test_frame_file, "r").readlines())]
+
+ # Preprocess data.
+ print("Processing scenes...")
+ pool = ProcessPoolExecutor(max_workers=config.num_workers)
+ _ = list(
+ pool.map(
+ handle_process,
+ file_list,
+ repeat(config.output_root),
+ repeat(test_frame_list),
+ )
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/s3dis.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/s3dis.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cfc176380caaa42d35bbf517ae2f13bf5c86f90
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/s3dis.py
@@ -0,0 +1,18 @@
+"""
+S3DIS Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from .defaults import DefaultDataset
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class S3DISDataset(DefaultDataset):
+ def get_data_name(self, idx):
+ remain, room_name = os.path.split(self.data_list[idx % len(self.data_list)])
+ remain, area_name = os.path.split(remain)
+ return f"{area_name}-{room_name}"
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannet.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannet.py
new file mode 100644
index 0000000000000000000000000000000000000000..035043d03bcbc19fcd9b1542f4061dfe01a6dbf2
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannet.py
@@ -0,0 +1,189 @@
+"""
+ScanNet20 / ScanNet200 / ScanNet Data Efficient Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+from collections.abc import Sequence
+
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.cache import shared_dict
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+from .preprocessing.scannet.meta_data.scannet200_constants import (
+ VALID_CLASS_IDS_20,
+ VALID_CLASS_IDS_200,
+)
+
+
+@DATASETS.register_module()
+class ScanNetDataset(Dataset):
+ class2id = np.array(VALID_CLASS_IDS_20)
+
+ def __init__(
+ self,
+ split="train",
+ data_root="data/scannet",
+ transform=None,
+ lr_file=None,
+ la_file=None,
+ ignore_index=-1,
+ test_mode=False,
+ test_cfg=None,
+ cache=False,
+ loop=1,
+ ):
+ super(ScanNetDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.cache = cache
+ self.loop = loop if not test_mode else 1 # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+
+ if test_mode:
+ self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+ self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ if lr_file:
+ self.data_list = [os.path.join(data_root, "train", name + ".pth") for name in np.loadtxt(lr_file, dtype=str)]
+ else:
+ self.data_list = self.get_data_list()
+ self.la = torch.load(la_file) if la_file else None
+ self.ignore_index = ignore_index
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ if not self.cache:
+ data = torch.load(data_path)
+ else:
+ data_name = data_path.replace(os.path.dirname(self.data_root), "").split(".")[0]
+ cache_name = "pointcept" + data_name.replace(os.path.sep, "-")
+ data = shared_dict(cache_name)
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ scene_id = data["scene_id"]
+ if "semantic_gt20" in data.keys():
+ segment = data["semantic_gt20"].reshape([-1])
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ if "instance_gt" in data.keys():
+ instance = data["instance_gt"].reshape([-1])
+ else:
+ instance = np.ones(coord.shape[0]) * -1
+ data_dict = dict(
+ coord=coord,
+ normal=normal,
+ color=color,
+ segment=segment,
+ instance=instance,
+ scene_id=scene_id,
+ )
+ if self.la:
+ sampled_index = self.la[self.get_data_name(idx)]
+ mask = np.ones_like(segment).astype(np.bool)
+ mask[sampled_index] = False
+ segment[mask] = self.ignore_index
+ data_dict["segment"] = segment
+ data_dict["sampled_index"] = sampled_index
+ return data_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_dict = self.get_data(idx)
+ segment = data_dict.pop("segment")
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(aug(deepcopy(data_dict)))
+
+ input_dict_list = []
+ for data in data_dict_list:
+ data_part_list = self.test_voxelize(data)
+ for data_part in data_part_list:
+ if self.test_crop:
+ data_part = self.test_crop(data_part)
+ else:
+ data_part = [data_part]
+ input_dict_list += data_part
+
+ for i in range(len(input_dict_list)):
+ input_dict_list[i] = self.post_transform(input_dict_list[i])
+ data_dict = dict(fragment_list=input_dict_list, segment=segment, name=self.get_data_name(idx))
+ return data_dict
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ScanNet200Dataset(ScanNetDataset):
+ class2id = np.array(VALID_CLASS_IDS_200)
+
+ def get_data(self, idx):
+ data = torch.load(self.data_list[idx % len(self.data_list)])
+ coord = data["coord"]
+ color = data["color"]
+ normal = data["normal"]
+ scene_id = data["scene_id"]
+ if "semantic_gt200" in data.keys():
+ segment = data["semantic_gt200"].reshape([-1])
+ else:
+ segment = np.ones(coord.shape[0]) * -1
+ if "instance_gt" in data.keys():
+ instance = data["instance_gt"].reshape([-1])
+ else:
+ instance = np.ones(coord.shape[0]) * -1
+ data_dict = dict(
+ coord=coord,
+ normal=normal,
+ color=color,
+ segment=segment,
+ instance=instance,
+ scene_id=scene_id,
+ )
+ if self.la:
+ sampled_index = self.la[self.get_data_name(idx)]
+ segment[sampled_index] = self.ignore_index
+ data_dict["segment"] = segment
+ data_dict["sampled_index"] = sampled_index
+ return data_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannet_pair.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannet_pair.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7fdf199aa90b113bb4e8c7643e5549f62d0c51a
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannet_pair.py
@@ -0,0 +1,89 @@
+"""
+ScanNet Pair Dataset (Frame-level contrastive view)
+
+Refer PointContrast
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+
+
+@DATASETS.register_module()
+class ScanNetPairDataset(Dataset):
+ def __init__(
+ self,
+ data_root="data/scannet_pair",
+ overlap_threshold=0.3,
+ view1_transform=None,
+ view2_transform=None,
+ loop=1,
+ **kwargs
+ ):
+ super(ScanNetPairDataset, self).__init__()
+ self.data_root = data_root
+ self.overlap_threshold = overlap_threshold
+ self.view1_transform = Compose(view1_transform)
+ self.view2_transform = Compose(view2_transform)
+ self.loop = loop
+ self.data_list = self.get_data_list()
+ logger = get_root_logger()
+ logger.info("Totally {} x {} samples.".format(len(self.data_list), self.loop))
+
+ def get_data_list(self):
+ data_list = []
+ overlap_list = glob.glob(
+ os.path.join(self.data_root, "*", "pcd", "overlap.txt")
+ )
+ for overlap_file in overlap_list:
+ with open(overlap_file) as f:
+ overlap = f.readlines()
+ overlap = [pair.strip().split() for pair in overlap]
+ data_list.extend(
+ [
+ pair[:2]
+ for pair in overlap
+ if float(pair[2]) > self.overlap_threshold
+ ]
+ )
+ return data_list
+
+ def get_data(self, idx):
+ pair = self.data_list[idx % len(self.data_list)]
+ view1_dict = torch.load(self.data_root + pair[0])
+ view2_dict = torch.load(self.data_root + pair[1])
+ return view1_dict, view2_dict
+
+ def get_data_name(self, idx):
+ return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+ def prepare_train_data(self, idx):
+ # load data
+ view1_dict, view2_dict = self.get_data(idx)
+ view1_dict = self.view1_transform(view1_dict)
+ view2_dict = self.view2_transform(view2_dict)
+ data_dict = dict()
+ for key, value in view1_dict.items():
+ data_dict["view1_" + key] = value
+ for key, value in view2_dict.items():
+ data_dict["view2_" + key] = value
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ raise NotImplementedError
+
+ def __getitem__(self, idx):
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_list) * self.loop
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannetpp.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannetpp.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e3ef3bf65bd4f81fb0f65652ead6fbff62337e9
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/scannetpp.py
@@ -0,0 +1,78 @@
+"""
+ScanNet++ dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import glob
+
+from pointcept.utils.cache import shared_dict
+
+from .builder import DATASETS
+from .defaults import DefaultDatasetV2
+
+
+@DATASETS.register_module()
+class ScanNetPPDataset(DefaultDatasetV2):
+ VALID_ASSETS = [
+ "coord",
+ "color",
+ "normal",
+ "segment",
+ "instance",
+ ]
+
+ def __init__(
+ self,
+ multilabel=False,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.multilabel = multilabel
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ name = self.get_data_name(idx)
+ if self.cache:
+ cache_name = f"pointcept-{name}"
+ return shared_dict(cache_name)
+
+ data_dict = {}
+ assets = os.listdir(data_path)
+ for asset in assets:
+ if not asset.endswith(".npy"):
+ continue
+ if asset[:-4] not in self.VALID_ASSETS:
+ continue
+ data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+ data_dict["name"] = name
+
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"].astype(np.float32)
+
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"].astype(np.float32)
+
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+ if not self.multilabel:
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][:, 0].astype(np.int32)
+ else:
+ data_dict["segment"] = (
+ np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+ )
+
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][:, 0].astype(np.int32)
+ else:
+ data_dict["instance"] = (
+ np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+ )
+ else:
+ raise NotImplementedError
+ return data_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/semantic_kitti.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/semantic_kitti.py
new file mode 100644
index 0000000000000000000000000000000000000000..3729b4163a113be69f3874cad646f4bc6255cfba
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/semantic_kitti.py
@@ -0,0 +1,139 @@
+"""
+Semantic KITTI dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class SemanticKITTIDataset(DefaultDataset):
+ def __init__(self, ignore_index=-1, **kwargs):
+ self.ignore_index = ignore_index
+ self.learning_map = self.get_learning_map(ignore_index)
+ self.learning_map_inv = self.get_learning_map_inv(ignore_index)
+ super().__init__(ignore_index=ignore_index, **kwargs)
+
+ def get_data_list(self):
+ split2seq = dict(
+ train=[0, 1, 2, 3, 4, 5, 6, 7, 9, 10],
+ val=[8],
+ test=[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
+ )
+ if isinstance(self.split, str):
+ seq_list = split2seq[self.split]
+ elif isinstance(self.split, list):
+ seq_list = []
+ for split in self.split:
+ seq_list += split2seq[split]
+ else:
+ raise NotImplementedError
+
+ data_list = []
+ for seq in seq_list:
+ seq = str(seq).zfill(2)
+ seq_folder = os.path.join(self.data_root, "dataset", "sequences", seq)
+ seq_files = sorted(os.listdir(os.path.join(seq_folder, "velodyne")))
+ data_list += [
+ os.path.join(seq_folder, "velodyne", file) for file in seq_files
+ ]
+ return data_list
+
+ def get_data(self, idx):
+ data_path = self.data_list[idx % len(self.data_list)]
+ with open(data_path, "rb") as b:
+ scan = np.fromfile(b, dtype=np.float32).reshape(-1, 4)
+ coord = scan[:, :3]
+ strength = scan[:, -1].reshape([-1, 1])
+
+ label_file = data_path.replace("velodyne", "labels").replace(".bin", ".label")
+ if os.path.exists(label_file):
+ with open(label_file, "rb") as a:
+ segment = np.fromfile(a, dtype=np.int32).reshape(-1)
+ segment = np.vectorize(self.learning_map.__getitem__)(
+ segment & 0xFFFF
+ ).astype(np.int32)
+ else:
+ segment = np.zeros(scan.shape[0]).astype(np.int32)
+ data_dict = dict(coord=coord, strength=strength, segment=segment)
+ return data_dict
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ dir_path, file_name = os.path.split(file_path)
+ sequence_name = os.path.basename(os.path.dirname(dir_path))
+ frame_name = os.path.splitext(file_name)[0]
+ data_name = f"{sequence_name}_{frame_name}"
+ return data_name
+
+ @staticmethod
+ def get_learning_map(ignore_index):
+ learning_map = {
+ 0: ignore_index, # "unlabeled"
+ 1: ignore_index, # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 0, # "car"
+ 11: 1, # "bicycle"
+ 13: 4, # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 2, # "motorcycle"
+ 16: 4, # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 3, # "truck"
+ 20: 4, # "other-vehicle"
+ 30: 5, # "person"
+ 31: 6, # "bicyclist"
+ 32: 7, # "motorcyclist"
+ 40: 8, # "road"
+ 44: 9, # "parking"
+ 48: 10, # "sidewalk"
+ 49: 11, # "other-ground"
+ 50: 12, # "building"
+ 51: 13, # "fence"
+ 52: ignore_index, # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 8, # "lane-marking" to "road" ---------------------------------mapped
+ 70: 14, # "vegetation"
+ 71: 15, # "trunk"
+ 72: 16, # "terrain"
+ 80: 17, # "pole"
+ 81: 18, # "traffic-sign"
+ 99: ignore_index, # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 0, # "moving-car" to "car" ------------------------------------mapped
+ 253: 6, # "moving-bicyclist" to "bicyclist" ------------------------mapped
+ 254: 5, # "moving-person" to "person" ------------------------------mapped
+ 255: 7, # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+ 256: 4, # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+ 257: 4, # "moving-bus" mapped to "other-vehicle" -------------------mapped
+ 258: 3, # "moving-truck" to "truck" --------------------------------mapped
+ 259: 4, # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+ }
+ return learning_map
+
+ @staticmethod
+ def get_learning_map_inv(ignore_index):
+ learning_map_inv = {
+ ignore_index: ignore_index, # "unlabeled"
+ 0: 10, # "car"
+ 1: 11, # "bicycle"
+ 2: 15, # "motorcycle"
+ 3: 18, # "truck"
+ 4: 20, # "other-vehicle"
+ 5: 30, # "person"
+ 6: 31, # "bicyclist"
+ 7: 32, # "motorcyclist"
+ 8: 40, # "road"
+ 9: 44, # "parking"
+ 10: 48, # "sidewalk"
+ 11: 49, # "other-ground"
+ 12: 50, # "building"
+ 13: 51, # "fence"
+ 14: 70, # "vegetation"
+ 15: 71, # "trunk"
+ 16: 72, # "terrain"
+ 17: 80, # "pole"
+ 18: 81, # "traffic-sign"
+ }
+ return learning_map_inv
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/shapenet_part.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/shapenet_part.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca0c3d50adf98b643233e28b56631654479fde60
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/shapenet_part.py
@@ -0,0 +1,160 @@
+"""
+ShapeNet Part Dataset (Unmaintained)
+
+get processed shapenet part dataset
+at "https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip"
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import json
+import torch
+import numpy as np
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ShapeNetPartDataset(Dataset):
+ def __init__(
+ self,
+ split="train",
+ data_root="data/shapenetcore_partanno_segmentation_benchmark_v0_normal",
+ transform=None,
+ test_mode=False,
+ test_cfg=None,
+ loop=1,
+ ):
+ super(ShapeNetPartDataset, self).__init__()
+ self.data_root = data_root
+ self.split = split
+ self.transform = Compose(transform)
+ self.loop = (
+ loop if not test_mode else 1
+ ) # force make loop = 1 while in test mode
+ self.test_mode = test_mode
+ self.test_cfg = test_cfg if test_mode else None
+ self.cache = {}
+
+ # load categories file
+ self.categories = []
+ self.category2part = {
+ "Airplane": [0, 1, 2, 3],
+ "Bag": [4, 5],
+ "Cap": [6, 7],
+ "Car": [8, 9, 10, 11],
+ "Chair": [12, 13, 14, 15],
+ "Earphone": [16, 17, 18],
+ "Guitar": [19, 20, 21],
+ "Knife": [22, 23],
+ "Lamp": [24, 25, 26, 27],
+ "Laptop": [28, 29],
+ "Motorbike": [30, 31, 32, 33, 34, 35],
+ "Mug": [36, 37],
+ "Pistol": [38, 39, 40],
+ "Rocket": [41, 42, 43],
+ "Skateboard": [44, 45, 46],
+ "Table": [47, 48, 49],
+ }
+ self.token2category = {}
+ with open(os.path.join(self.data_root, "synsetoffset2category.txt"), "r") as f:
+ for line in f:
+ ls = line.strip().split()
+ self.token2category[ls[1]] = len(self.categories)
+ self.categories.append(ls[0])
+
+ if test_mode:
+ self.post_transform = Compose(self.test_cfg.post_transform)
+ self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+ # load data list
+ if isinstance(self.split, str):
+ self.data_list = self.load_data_list(self.split)
+ elif isinstance(self.split, list):
+ self.data_list = []
+ for s in self.split:
+ self.data_list += self.load_data_list(s)
+ else:
+ raise NotImplementedError
+
+ logger = get_root_logger()
+ logger.info(
+ "Totally {} x {} samples in {} set.".format(
+ len(self.data_idx), self.loop, split
+ )
+ )
+
+ def load_data_list(self, split):
+ split_file = os.path.join(
+ self.data_root,
+ "train_test_split",
+ "shuffled_{}_file_list.json".format(split),
+ )
+ if not os.path.isfile(split_file):
+ raise (RuntimeError("Split file do not exist: " + split_file + "\n"))
+ with open(split_file, "r") as f:
+ # drop "shape_data/" and append ".txt"
+ data_list = [
+ os.path.join(self.data_root, data[11:] + ".txt")
+ for data in json.load(f)
+ ]
+ return data_list
+
+ def prepare_train_data(self, idx):
+ # load data
+ data_idx = idx % len(self.data_list)
+ if data_idx in self.cache:
+ coord, norm, segment, cls_token = self.cache[data_idx]
+ else:
+ data = np.loadtxt(self.data_list[data_idx]).astype(np.float32)
+ cls_token = self.token2category[
+ os.path.basename(os.path.dirname(self.data_list[data_idx]))
+ ]
+ coord, norm, segment = (
+ data[:, :3],
+ data[:, 3:6],
+ data[:, 6].astype(np.int32),
+ )
+ self.cache[data_idx] = (coord, norm, segment, cls_token)
+
+ data_dict = dict(coord=coord, norm=norm, segment=segment, cls_token=cls_token)
+ data_dict = self.transform(data_dict)
+ return data_dict
+
+ def prepare_test_data(self, idx):
+ # load data
+ data_idx = self.data_idx[idx % len(self.data_idx)]
+ data = np.loadtxt(self.data_list[data_idx]).astype(np.float32)
+ cls_token = self.token2category[
+ os.path.basename(os.path.dirname(self.data_list[data_idx]))
+ ]
+ coord, norm, segment = data[:, :3], data[:, 3:6], data[:, 6].astype(np.int32)
+
+ data_dict = dict(coord=coord, norm=norm, cls_token=cls_token)
+ data_dict = self.transform(data_dict)
+ data_dict_list = []
+ for aug in self.aug_transform:
+ data_dict_list.append(self.post_transform(aug(deepcopy(data_dict))))
+ data_dict = dict(
+ fragment_list=data_dict_list, segment=segment, name=self.get_data_name(idx)
+ )
+ return data_dict
+
+ def get_data_name(self, idx):
+ data_idx = self.data_idx[idx % len(self.data_idx)]
+ return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+ def __getitem__(self, idx):
+ if self.test_mode:
+ return self.prepare_test_data(idx)
+ else:
+ return self.prepare_train_data(idx)
+
+ def __len__(self):
+ return len(self.data_idx) * self.loop
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/structure3d.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/structure3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..81649e3d83275f0419546f37da810e7508b327e0
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/structure3d.py
@@ -0,0 +1,38 @@
+"""
+Structured3D Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+from collections.abc import Sequence
+
+from .defaults import DefaultDataset
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class Structured3DDataset(DefaultDataset):
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(
+ os.path.join(self.data_root, self.split, "scene_*/room_*")
+ )
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(
+ os.path.join(self.data_root, split, "scene_*/room_*")
+ )
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ dir_path, room_name = os.path.split(file_path)
+ scene_name = os.path.basename(dir_path)
+ data_name = f"{scene_name}_{room_name}"
+ return data_name
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/transform.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1abfe4f3d88decdac17861b31647c34f9520292
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/transform.py
@@ -0,0 +1,1148 @@
+"""
+3D Point Cloud Augmentation
+
+Inspirited by chrischoy/SpatioTemporalSegmentation
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+import numbers
+import scipy
+import scipy.ndimage
+import scipy.interpolate
+import scipy.stats
+import numpy as np
+import torch
+import copy
+from collections.abc import Sequence, Mapping
+
+from pointcept.utils.registry import Registry
+
+TRANSFORMS = Registry("transforms")
+
+
+@TRANSFORMS.register_module()
+class Collect(object):
+ def __init__(self, keys, offset_keys_dict=None, **kwargs):
+ """
+ e.g. Collect(keys=[coord], feat_keys=[coord, color])
+ """
+ if offset_keys_dict is None:
+ offset_keys_dict = dict(offset="coord")
+ self.keys = keys
+ self.offset_keys = offset_keys_dict
+ self.kwargs = kwargs
+
+ def __call__(self, data_dict):
+ data = dict()
+ if isinstance(self.keys, str):
+ self.keys = [self.keys]
+ for key in self.keys:
+ data[key] = data_dict[key]
+ for key, value in self.offset_keys.items():
+ data[key] = torch.tensor([data_dict[value].shape[0]])
+ for name, keys in self.kwargs.items():
+ name = name.replace("_keys", "")
+ assert isinstance(keys, Sequence)
+ data[name] = torch.cat([data_dict[key].float() for key in keys], dim=1)
+ return data
+
+
+@TRANSFORMS.register_module()
+class Copy(object):
+ def __init__(self, keys_dict=None):
+ if keys_dict is None:
+ keys_dict = dict(coord="origin_coord", segment="origin_segment")
+ self.keys_dict = keys_dict
+
+ def __call__(self, data_dict):
+ for key, value in self.keys_dict.items():
+ if isinstance(data_dict[key], np.ndarray):
+ data_dict[value] = data_dict[key].copy()
+ elif isinstance(data_dict[key], torch.Tensor):
+ data_dict[value] = data_dict[key].clone().detach()
+ else:
+ data_dict[value] = copy.deepcopy(data_dict[key])
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ToTensor(object):
+ def __call__(self, data):
+ if isinstance(data, torch.Tensor):
+ return data
+ elif isinstance(data, str):
+ # note that str is also a kind of sequence, judgement should before sequence
+ return data
+ elif isinstance(data, int):
+ return torch.LongTensor([data])
+ elif isinstance(data, float):
+ return torch.FloatTensor([data])
+ elif isinstance(data, np.ndarray) and np.issubdtype(data.dtype, bool):
+ return torch.from_numpy(data)
+ elif isinstance(data, np.ndarray) and np.issubdtype(data.dtype, np.integer):
+ return torch.from_numpy(data).long()
+ elif isinstance(data, np.ndarray) and np.issubdtype(data.dtype, np.floating):
+ return torch.from_numpy(data).float()
+ elif isinstance(data, Mapping):
+ result = {sub_key: self(item) for sub_key, item in data.items()}
+ return result
+ elif isinstance(data, Sequence):
+ result = [self(item) for item in data]
+ return result
+ else:
+ raise TypeError(f"type {type(data)} cannot be converted to tensor.")
+
+
+@TRANSFORMS.register_module()
+class Add(object):
+ def __init__(self, keys_dict=None):
+ if keys_dict is None:
+ keys_dict = dict()
+ self.keys_dict = keys_dict
+
+ def __call__(self, data_dict):
+ for key, value in self.keys_dict.items():
+ data_dict[key] = value
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class NormalizeColor(object):
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"] / 127.5 - 1
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class NormalizeCoord(object):
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ # modified from pointnet2
+ centroid = np.mean(data_dict["coord"], axis=0)
+ data_dict["coord"] -= centroid
+ m = np.max(np.sqrt(np.sum(data_dict["coord"] ** 2, axis=1)))
+ data_dict["coord"] = data_dict["coord"] / m
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class PositiveShift(object):
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ coord_min = np.min(data_dict["coord"], 0)
+ data_dict["coord"] -= coord_min
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class CenterShift(object):
+ def __init__(self, apply_z=True):
+ self.apply_z = apply_z
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ x_min, y_min, z_min = data_dict["coord"].min(axis=0)
+ x_max, y_max, _ = data_dict["coord"].max(axis=0)
+ if self.apply_z:
+ shift = [(x_min + x_max) / 2, (y_min + y_max) / 2, z_min]
+ else:
+ shift = [(x_min + x_max) / 2, (y_min + y_max) / 2, 0]
+ data_dict["coord"] -= shift
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomShift(object):
+ def __init__(self, shift=((-0.2, 0.2), (-0.2, 0.2), (0, 0))):
+ self.shift = shift
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ shift_x = np.random.uniform(self.shift[0][0], self.shift[0][1])
+ shift_y = np.random.uniform(self.shift[1][0], self.shift[1][1])
+ shift_z = np.random.uniform(self.shift[2][0], self.shift[2][1])
+ data_dict["coord"] += [shift_x, shift_y, shift_z]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class PointClip(object):
+ def __init__(self, point_cloud_range=(-80, -80, -3, 80, 80, 1)):
+ self.point_cloud_range = point_cloud_range
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = np.clip(
+ data_dict["coord"],
+ a_min=self.point_cloud_range[:3],
+ a_max=self.point_cloud_range[3:],
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomDropout(object):
+ def __init__(self, dropout_ratio=0.2, dropout_application_ratio=0.5):
+ """
+ upright_axis: axis index among x,y,z, i.e. 2 for z
+ """
+ self.dropout_ratio = dropout_ratio
+ self.dropout_application_ratio = dropout_application_ratio
+
+ def __call__(self, data_dict):
+ if random.random() < self.dropout_application_ratio:
+ n = len(data_dict["coord"])
+ idx = np.random.choice(n, int(n * (1 - self.dropout_ratio)), replace=False)
+ if "sampled_index" in data_dict:
+ # for ScanNet data efficient, we need to make sure labeled point is sampled.
+ idx = np.unique(np.append(idx, data_dict["sampled_index"]))
+ mask = np.zeros_like(data_dict["segment"]).astype(bool)
+ mask[data_dict["sampled_index"]] = True
+ data_dict["sampled_index"] = np.where(mask[idx])[0]
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][idx]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][idx]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][idx]
+ if "strength" in data_dict.keys():
+ data_dict["strength"] = data_dict["strength"][idx]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][idx]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][idx]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomRotate(object):
+ def __init__(self, angle=None, center=None, axis="z", always_apply=False, p=0.5):
+ self.angle = [-1, 1] if angle is None else angle
+ self.axis = axis
+ self.always_apply = always_apply
+ self.p = p if not self.always_apply else 1
+ self.center = center
+
+ def __call__(self, data_dict):
+ if random.random() > self.p:
+ return data_dict
+ angle = np.random.uniform(self.angle[0], self.angle[1]) * np.pi
+ rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+ if self.axis == "x":
+ rot_t = np.array([[1, 0, 0], [0, rot_cos, -rot_sin], [0, rot_sin, rot_cos]])
+ elif self.axis == "y":
+ rot_t = np.array([[rot_cos, 0, rot_sin], [0, 1, 0], [-rot_sin, 0, rot_cos]])
+ elif self.axis == "z":
+ rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+ else:
+ raise NotImplementedError
+ if "coord" in data_dict.keys():
+ if self.center is None:
+ x_min, y_min, z_min = data_dict["coord"].min(axis=0)
+ x_max, y_max, z_max = data_dict["coord"].max(axis=0)
+ center = [(x_min + x_max) / 2, (y_min + y_max) / 2, (z_min + z_max) / 2]
+ else:
+ center = self.center
+ data_dict["coord"] -= center
+ data_dict["coord"] = np.dot(data_dict["coord"], np.transpose(rot_t))
+ data_dict["coord"] += center
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = np.dot(data_dict["normal"], np.transpose(rot_t))
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomRotateTargetAngle(object):
+ def __init__(
+ self, angle=(1 / 2, 1, 3 / 2), center=None, axis="z", always_apply=False, p=0.75
+ ):
+ self.angle = angle
+ self.axis = axis
+ self.always_apply = always_apply
+ self.p = p if not self.always_apply else 1
+ self.center = center
+
+ def __call__(self, data_dict):
+ if random.random() > self.p:
+ return data_dict
+ angle = np.random.choice(self.angle) * np.pi
+ rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+ if self.axis == "x":
+ rot_t = np.array([[1, 0, 0], [0, rot_cos, -rot_sin], [0, rot_sin, rot_cos]])
+ elif self.axis == "y":
+ rot_t = np.array([[rot_cos, 0, rot_sin], [0, 1, 0], [-rot_sin, 0, rot_cos]])
+ elif self.axis == "z":
+ rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+ else:
+ raise NotImplementedError
+ if "coord" in data_dict.keys():
+ if self.center is None:
+ x_min, y_min, z_min = data_dict["coord"].min(axis=0)
+ x_max, y_max, z_max = data_dict["coord"].max(axis=0)
+ center = [(x_min + x_max) / 2, (y_min + y_max) / 2, (z_min + z_max) / 2]
+ else:
+ center = self.center
+ data_dict["coord"] -= center
+ data_dict["coord"] = np.dot(data_dict["coord"], np.transpose(rot_t))
+ data_dict["coord"] += center
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = np.dot(data_dict["normal"], np.transpose(rot_t))
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomScale(object):
+ def __init__(self, scale=None, anisotropic=False):
+ self.scale = scale if scale is not None else [0.95, 1.05]
+ self.anisotropic = anisotropic
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ scale = np.random.uniform(
+ self.scale[0], self.scale[1], 3 if self.anisotropic else 1
+ )
+ data_dict["coord"] *= scale
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomFlip(object):
+ def __init__(self, p=0.5):
+ self.p = p
+
+ def __call__(self, data_dict):
+ if np.random.rand() < self.p:
+ if "coord" in data_dict.keys():
+ data_dict["coord"][:, 0] = -data_dict["coord"][:, 0]
+ if "normal" in data_dict.keys():
+ data_dict["normal"][:, 0] = -data_dict["normal"][:, 0]
+ if np.random.rand() < self.p:
+ if "coord" in data_dict.keys():
+ data_dict["coord"][:, 1] = -data_dict["coord"][:, 1]
+ if "normal" in data_dict.keys():
+ data_dict["normal"][:, 1] = -data_dict["normal"][:, 1]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomJitter(object):
+ def __init__(self, sigma=0.01, clip=0.05):
+ assert clip > 0
+ self.sigma = sigma
+ self.clip = clip
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ jitter = np.clip(
+ self.sigma * np.random.randn(data_dict["coord"].shape[0], 3),
+ -self.clip,
+ self.clip,
+ )
+ data_dict["coord"] += jitter
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ClipGaussianJitter(object):
+ def __init__(self, scalar=0.02, store_jitter=False):
+ self.scalar = scalar
+ self.mean = np.mean(3)
+ self.cov = np.identity(3)
+ self.quantile = 1.96
+ self.store_jitter = store_jitter
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys():
+ jitter = np.random.multivariate_normal(
+ self.mean, self.cov, data_dict["coord"].shape[0]
+ )
+ jitter = self.scalar * np.clip(jitter / 1.96, -1, 1)
+ data_dict["coord"] += jitter
+ if self.store_jitter:
+ data_dict["jitter"] = jitter
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ChromaticAutoContrast(object):
+ def __init__(self, p=0.2, blend_factor=None):
+ self.p = p
+ self.blend_factor = blend_factor
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ lo = np.min(data_dict["color"], 0, keepdims=True)
+ hi = np.max(data_dict["color"], 0, keepdims=True)
+ scale = 255 / (hi - lo)
+ contrast_feat = (data_dict["color"][:, :3] - lo) * scale
+ blend_factor = (
+ np.random.rand() if self.blend_factor is None else self.blend_factor
+ )
+ data_dict["color"][:, :3] = (1 - blend_factor) * data_dict["color"][
+ :, :3
+ ] + blend_factor * contrast_feat
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ChromaticTranslation(object):
+ def __init__(self, p=0.95, ratio=0.05):
+ self.p = p
+ self.ratio = ratio
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ tr = (np.random.rand(1, 3) - 0.5) * 255 * 2 * self.ratio
+ data_dict["color"][:, :3] = np.clip(tr + data_dict["color"][:, :3], 0, 255)
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ChromaticJitter(object):
+ def __init__(self, p=0.95, std=0.005):
+ self.p = p
+ self.std = std
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ noise = np.random.randn(data_dict["color"].shape[0], 3)
+ noise *= self.std * 255
+ data_dict["color"][:, :3] = np.clip(
+ noise + data_dict["color"][:, :3], 0, 255
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomColorGrayScale(object):
+ def __init__(self, p):
+ self.p = p
+
+ @staticmethod
+ def rgb_to_grayscale(color, num_output_channels=1):
+ if color.shape[-1] < 3:
+ raise TypeError(
+ "Input color should have at least 3 dimensions, but found {}".format(
+ color.shape[-1]
+ )
+ )
+
+ if num_output_channels not in (1, 3):
+ raise ValueError("num_output_channels should be either 1 or 3")
+
+ r, g, b = color[..., 0], color[..., 1], color[..., 2]
+ gray = (0.2989 * r + 0.587 * g + 0.114 * b).astype(color.dtype)
+ gray = np.expand_dims(gray, axis=-1)
+
+ if num_output_channels == 3:
+ gray = np.broadcast_to(gray, color.shape)
+
+ return gray
+
+ def __call__(self, data_dict):
+ if np.random.rand() < self.p:
+ data_dict["color"] = self.rgb_to_grayscale(data_dict["color"], 3)
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomColorJitter(object):
+ """
+ Random Color Jitter for 3D point cloud (refer torchvision)
+ """
+
+ def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, p=0.95):
+ self.brightness = self._check_input(brightness, "brightness")
+ self.contrast = self._check_input(contrast, "contrast")
+ self.saturation = self._check_input(saturation, "saturation")
+ self.hue = self._check_input(
+ hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False
+ )
+ self.p = p
+
+ @staticmethod
+ def _check_input(
+ value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True
+ ):
+ if isinstance(value, numbers.Number):
+ if value < 0:
+ raise ValueError(
+ "If {} is a single number, it must be non negative.".format(name)
+ )
+ value = [center - float(value), center + float(value)]
+ if clip_first_on_zero:
+ value[0] = max(value[0], 0.0)
+ elif isinstance(value, (tuple, list)) and len(value) == 2:
+ if not bound[0] <= value[0] <= value[1] <= bound[1]:
+ raise ValueError("{} values should be between {}".format(name, bound))
+ else:
+ raise TypeError(
+ "{} should be a single number or a list/tuple with length 2.".format(
+ name
+ )
+ )
+
+ # if value is 0 or (1., 1.) for brightness/contrast/saturation
+ # or (0., 0.) for hue, do nothing
+ if value[0] == value[1] == center:
+ value = None
+ return value
+
+ @staticmethod
+ def blend(color1, color2, ratio):
+ ratio = float(ratio)
+ bound = 255.0
+ return (
+ (ratio * color1 + (1.0 - ratio) * color2)
+ .clip(0, bound)
+ .astype(color1.dtype)
+ )
+
+ @staticmethod
+ def rgb2hsv(rgb):
+ r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
+ maxc = np.max(rgb, axis=-1)
+ minc = np.min(rgb, axis=-1)
+ eqc = maxc == minc
+ cr = maxc - minc
+ s = cr / (np.ones_like(maxc) * eqc + maxc * (1 - eqc))
+ cr_divisor = np.ones_like(maxc) * eqc + cr * (1 - eqc)
+ rc = (maxc - r) / cr_divisor
+ gc = (maxc - g) / cr_divisor
+ bc = (maxc - b) / cr_divisor
+
+ hr = (maxc == r) * (bc - gc)
+ hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc)
+ hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc)
+ h = hr + hg + hb
+ h = (h / 6.0 + 1.0) % 1.0
+ return np.stack((h, s, maxc), axis=-1)
+
+ @staticmethod
+ def hsv2rgb(hsv):
+ h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+ i = np.floor(h * 6.0)
+ f = (h * 6.0) - i
+ i = i.astype(np.int32)
+
+ p = np.clip((v * (1.0 - s)), 0.0, 1.0)
+ q = np.clip((v * (1.0 - s * f)), 0.0, 1.0)
+ t = np.clip((v * (1.0 - s * (1.0 - f))), 0.0, 1.0)
+ i = i % 6
+ mask = np.expand_dims(i, axis=-1) == np.arange(6)
+
+ a1 = np.stack((v, q, p, p, t, v), axis=-1)
+ a2 = np.stack((t, v, v, q, p, p), axis=-1)
+ a3 = np.stack((p, p, t, v, v, q), axis=-1)
+ a4 = np.stack((a1, a2, a3), axis=-1)
+
+ return np.einsum("...na, ...nab -> ...nb", mask.astype(hsv.dtype), a4)
+
+ def adjust_brightness(self, color, brightness_factor):
+ if brightness_factor < 0:
+ raise ValueError(
+ "brightness_factor ({}) is not non-negative.".format(brightness_factor)
+ )
+
+ return self.blend(color, np.zeros_like(color), brightness_factor)
+
+ def adjust_contrast(self, color, contrast_factor):
+ if contrast_factor < 0:
+ raise ValueError(
+ "contrast_factor ({}) is not non-negative.".format(contrast_factor)
+ )
+ mean = np.mean(RandomColorGrayScale.rgb_to_grayscale(color))
+ return self.blend(color, mean, contrast_factor)
+
+ def adjust_saturation(self, color, saturation_factor):
+ if saturation_factor < 0:
+ raise ValueError(
+ "saturation_factor ({}) is not non-negative.".format(saturation_factor)
+ )
+ gray = RandomColorGrayScale.rgb_to_grayscale(color)
+ return self.blend(color, gray, saturation_factor)
+
+ def adjust_hue(self, color, hue_factor):
+ if not (-0.5 <= hue_factor <= 0.5):
+ raise ValueError(
+ "hue_factor ({}) is not in [-0.5, 0.5].".format(hue_factor)
+ )
+ orig_dtype = color.dtype
+ hsv = self.rgb2hsv(color / 255.0)
+ h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+ h = (h + hue_factor) % 1.0
+ hsv = np.stack((h, s, v), axis=-1)
+ color_hue_adj = (self.hsv2rgb(hsv) * 255.0).astype(orig_dtype)
+ return color_hue_adj
+
+ @staticmethod
+ def get_params(brightness, contrast, saturation, hue):
+ fn_idx = torch.randperm(4)
+ b = (
+ None
+ if brightness is None
+ else np.random.uniform(brightness[0], brightness[1])
+ )
+ c = None if contrast is None else np.random.uniform(contrast[0], contrast[1])
+ s = (
+ None
+ if saturation is None
+ else np.random.uniform(saturation[0], saturation[1])
+ )
+ h = None if hue is None else np.random.uniform(hue[0], hue[1])
+ return fn_idx, b, c, s, h
+
+ def __call__(self, data_dict):
+ (
+ fn_idx,
+ brightness_factor,
+ contrast_factor,
+ saturation_factor,
+ hue_factor,
+ ) = self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
+
+ for fn_id in fn_idx:
+ if (
+ fn_id == 0
+ and brightness_factor is not None
+ and np.random.rand() < self.p
+ ):
+ data_dict["color"] = self.adjust_brightness(
+ data_dict["color"], brightness_factor
+ )
+ elif (
+ fn_id == 1 and contrast_factor is not None and np.random.rand() < self.p
+ ):
+ data_dict["color"] = self.adjust_contrast(
+ data_dict["color"], contrast_factor
+ )
+ elif (
+ fn_id == 2
+ and saturation_factor is not None
+ and np.random.rand() < self.p
+ ):
+ data_dict["color"] = self.adjust_saturation(
+ data_dict["color"], saturation_factor
+ )
+ elif fn_id == 3 and hue_factor is not None and np.random.rand() < self.p:
+ data_dict["color"] = self.adjust_hue(data_dict["color"], hue_factor)
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class HueSaturationTranslation(object):
+ @staticmethod
+ def rgb_to_hsv(rgb):
+ # Translated from source of colorsys.rgb_to_hsv
+ # r,g,b should be a numpy arrays with values between 0 and 255
+ # rgb_to_hsv returns an array of floats between 0.0 and 1.0.
+ rgb = rgb.astype("float")
+ hsv = np.zeros_like(rgb)
+ # in case an RGBA array was passed, just copy the A channel
+ hsv[..., 3:] = rgb[..., 3:]
+ r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
+ maxc = np.max(rgb[..., :3], axis=-1)
+ minc = np.min(rgb[..., :3], axis=-1)
+ hsv[..., 2] = maxc
+ mask = maxc != minc
+ hsv[mask, 1] = (maxc - minc)[mask] / maxc[mask]
+ rc = np.zeros_like(r)
+ gc = np.zeros_like(g)
+ bc = np.zeros_like(b)
+ rc[mask] = (maxc - r)[mask] / (maxc - minc)[mask]
+ gc[mask] = (maxc - g)[mask] / (maxc - minc)[mask]
+ bc[mask] = (maxc - b)[mask] / (maxc - minc)[mask]
+ hsv[..., 0] = np.select(
+ [r == maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc
+ )
+ hsv[..., 0] = (hsv[..., 0] / 6.0) % 1.0
+ return hsv
+
+ @staticmethod
+ def hsv_to_rgb(hsv):
+ # Translated from source of colorsys.hsv_to_rgb
+ # h,s should be a numpy arrays with values between 0.0 and 1.0
+ # v should be a numpy array with values between 0.0 and 255.0
+ # hsv_to_rgb returns an array of uints between 0 and 255.
+ rgb = np.empty_like(hsv)
+ rgb[..., 3:] = hsv[..., 3:]
+ h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
+ i = (h * 6.0).astype("uint8")
+ f = (h * 6.0) - i
+ p = v * (1.0 - s)
+ q = v * (1.0 - s * f)
+ t = v * (1.0 - s * (1.0 - f))
+ i = i % 6
+ conditions = [s == 0.0, i == 1, i == 2, i == 3, i == 4, i == 5]
+ rgb[..., 0] = np.select(conditions, [v, q, p, p, t, v], default=v)
+ rgb[..., 1] = np.select(conditions, [v, v, v, q, p, p], default=t)
+ rgb[..., 2] = np.select(conditions, [v, p, t, v, v, q], default=p)
+ return rgb.astype("uint8")
+
+ def __init__(self, hue_max=0.5, saturation_max=0.2):
+ self.hue_max = hue_max
+ self.saturation_max = saturation_max
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys():
+ # Assume color[:, :3] is rgb
+ hsv = HueSaturationTranslation.rgb_to_hsv(data_dict["color"][:, :3])
+ hue_val = (np.random.rand() - 0.5) * 2 * self.hue_max
+ sat_ratio = 1 + (np.random.rand() - 0.5) * 2 * self.saturation_max
+ hsv[..., 0] = np.remainder(hue_val + hsv[..., 0] + 1, 1)
+ hsv[..., 1] = np.clip(sat_ratio * hsv[..., 1], 0, 1)
+ data_dict["color"][:, :3] = np.clip(
+ HueSaturationTranslation.hsv_to_rgb(hsv), 0, 255
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class RandomColorDrop(object):
+ def __init__(self, p=0.2, color_augment=0.0):
+ self.p = p
+ self.color_augment = color_augment
+
+ def __call__(self, data_dict):
+ if "color" in data_dict.keys() and np.random.rand() < self.p:
+ data_dict["color"] *= self.color_augment
+ return data_dict
+
+ def __repr__(self):
+ return "RandomColorDrop(color_augment: {}, p: {})".format(
+ self.color_augment, self.p
+ )
+
+
+@TRANSFORMS.register_module()
+class ElasticDistortion(object):
+ def __init__(self, distortion_params=None):
+ self.distortion_params = (
+ [[0.2, 0.4], [0.8, 1.6]] if distortion_params is None else distortion_params
+ )
+
+ @staticmethod
+ def elastic_distortion(coords, granularity, magnitude):
+ """
+ Apply elastic distortion on sparse coordinate space.
+ pointcloud: numpy array of (number of points, at least 3 spatial dims)
+ granularity: size of the noise grid (in same scale[m/cm] as the voxel grid)
+ magnitude: noise multiplier
+ """
+ blurx = np.ones((3, 1, 1, 1)).astype("float32") / 3
+ blury = np.ones((1, 3, 1, 1)).astype("float32") / 3
+ blurz = np.ones((1, 1, 3, 1)).astype("float32") / 3
+ coords_min = coords.min(0)
+
+ # Create Gaussian noise tensor of the size given by granularity.
+ noise_dim = ((coords - coords_min).max(0) // granularity).astype(int) + 3
+ noise = np.random.randn(*noise_dim, 3).astype(np.float32)
+
+ # Smoothing.
+ for _ in range(2):
+ noise = scipy.ndimage.filters.convolve(
+ noise, blurx, mode="constant", cval=0
+ )
+ noise = scipy.ndimage.filters.convolve(
+ noise, blury, mode="constant", cval=0
+ )
+ noise = scipy.ndimage.filters.convolve(
+ noise, blurz, mode="constant", cval=0
+ )
+
+ # Trilinear interpolate noise filters for each spatial dimensions.
+ ax = [
+ np.linspace(d_min, d_max, d)
+ for d_min, d_max, d in zip(
+ coords_min - granularity,
+ coords_min + granularity * (noise_dim - 2),
+ noise_dim,
+ )
+ ]
+ interp = scipy.interpolate.RegularGridInterpolator(
+ ax, noise, bounds_error=False, fill_value=0
+ )
+ coords += interp(coords) * magnitude
+ return coords
+
+ def __call__(self, data_dict):
+ if "coord" in data_dict.keys() and self.distortion_params is not None:
+ if random.random() < 0.95:
+ for granularity, magnitude in self.distortion_params:
+ data_dict["coord"] = self.elastic_distortion(
+ data_dict["coord"], granularity, magnitude
+ )
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class GridSample(object):
+ def __init__(
+ self,
+ grid_size=0.05,
+ hash_type="fnv",
+ mode="train",
+ keys=("coord", "color", "normal", "segment"),
+ return_inverse=False,
+ return_grid_coord=False,
+ return_min_coord=False,
+ return_displacement=False,
+ project_displacement=False,
+ ):
+ self.grid_size = grid_size
+ self.hash = self.fnv_hash_vec if hash_type == "fnv" else self.ravel_hash_vec
+ assert mode in ["train", "test"]
+ self.mode = mode
+ self.keys = keys
+ self.return_inverse = return_inverse
+ self.return_grid_coord = return_grid_coord
+ self.return_min_coord = return_min_coord
+ self.return_displacement = return_displacement
+ self.project_displacement = project_displacement
+
+ def __call__(self, data_dict):
+ assert "coord" in data_dict.keys()
+ scaled_coord = data_dict["coord"] / np.array(self.grid_size)
+ grid_coord = np.floor(scaled_coord).astype(int)
+ min_coord = grid_coord.min(0)
+ grid_coord -= min_coord
+ scaled_coord -= min_coord
+ min_coord = min_coord * np.array(self.grid_size)
+ key = self.hash(grid_coord)
+ idx_sort = np.argsort(key)
+ key_sort = key[idx_sort]
+ _, inverse, count = np.unique(key_sort, return_inverse=True, return_counts=True)
+ if self.mode == "train": # train mode
+ idx_select = (
+ np.cumsum(np.insert(count, 0, 0)[0:-1])
+ + np.random.randint(0, count.max(), count.size) % count
+ )
+ idx_unique = idx_sort[idx_select]
+ if "sampled_index" in data_dict:
+ # for ScanNet data efficient, we need to make sure labeled point is sampled.
+ idx_unique = np.unique(
+ np.append(idx_unique, data_dict["sampled_index"])
+ )
+ mask = np.zeros_like(data_dict["segment"]).astype(bool)
+ mask[data_dict["sampled_index"]] = True
+ data_dict["sampled_index"] = np.where(mask[idx_unique])[0]
+ if self.return_inverse:
+ data_dict["inverse"] = np.zeros_like(inverse)
+ data_dict["inverse"][idx_sort] = inverse
+ if self.return_grid_coord:
+ data_dict["grid_coord"] = grid_coord[idx_unique]
+ if self.return_min_coord:
+ data_dict["min_coord"] = min_coord.reshape([1, 3])
+ if self.return_displacement:
+ displacement = (
+ scaled_coord - grid_coord - 0.5
+ ) # [0, 1] -> [-0.5, 0.5] displacement to center
+ if self.project_displacement:
+ displacement = np.sum(
+ displacement * data_dict["normal"], axis=-1, keepdims=True
+ )
+ data_dict["displacement"] = displacement[idx_unique]
+ for key in self.keys:
+ data_dict[key] = data_dict[key][idx_unique]
+ return data_dict
+
+ elif self.mode == "test": # test mode
+ data_part_list = []
+ for i in range(count.max()):
+ idx_select = np.cumsum(np.insert(count, 0, 0)[0:-1]) + i % count
+ idx_part = idx_sort[idx_select]
+ data_part = dict(index=idx_part)
+ if self.return_inverse:
+ data_dict["inverse"] = np.zeros_like(inverse)
+ data_dict["inverse"][idx_sort] = inverse
+ if self.return_grid_coord:
+ data_part["grid_coord"] = grid_coord[idx_part]
+ if self.return_min_coord:
+ data_part["min_coord"] = min_coord.reshape([1, 3])
+ if self.return_displacement:
+ displacement = (
+ scaled_coord - grid_coord - 0.5
+ ) # [0, 1] -> [-0.5, 0.5] displacement to center
+ if self.project_displacement:
+ displacement = np.sum(
+ displacement * data_dict["normal"], axis=-1, keepdims=True
+ )
+ data_dict["displacement"] = displacement[idx_part]
+ for key in data_dict.keys():
+ if key in self.keys:
+ data_part[key] = data_dict[key][idx_part]
+ else:
+ data_part[key] = data_dict[key]
+ data_part_list.append(data_part)
+ return data_part_list
+ else:
+ raise NotImplementedError
+
+ @staticmethod
+ def ravel_hash_vec(arr):
+ """
+ Ravel the coordinates after subtracting the min coordinates.
+ """
+ assert arr.ndim == 2
+ arr = arr.copy()
+ arr -= arr.min(0)
+ arr = arr.astype(np.uint64, copy=False)
+ arr_max = arr.max(0).astype(np.uint64) + 1
+
+ keys = np.zeros(arr.shape[0], dtype=np.uint64)
+ # Fortran style indexing
+ for j in range(arr.shape[1] - 1):
+ keys += arr[:, j]
+ keys *= arr_max[j + 1]
+ keys += arr[:, -1]
+ return keys
+
+ @staticmethod
+ def fnv_hash_vec(arr):
+ """
+ FNV64-1A
+ """
+ assert arr.ndim == 2
+ # Floor first for negative coordinates
+ arr = arr.copy()
+ arr = arr.astype(np.uint64, copy=False)
+ hashed_arr = np.uint64(14695981039346656037) * np.ones(
+ arr.shape[0], dtype=np.uint64
+ )
+ for j in range(arr.shape[1]):
+ hashed_arr *= np.uint64(1099511628211)
+ hashed_arr = np.bitwise_xor(hashed_arr, arr[:, j])
+ return hashed_arr
+
+
+@TRANSFORMS.register_module()
+class SphereCrop(object):
+ def __init__(self, point_max=80000, sample_rate=None, mode="random"):
+ self.point_max = point_max
+ self.sample_rate = sample_rate
+ assert mode in ["random", "center", "all"]
+ self.mode = mode
+
+ def __call__(self, data_dict):
+ point_max = (
+ int(self.sample_rate * data_dict["coord"].shape[0])
+ if self.sample_rate is not None
+ else self.point_max
+ )
+
+ assert "coord" in data_dict.keys()
+ if self.mode == "all":
+ # TODO: Optimize
+ if "index" not in data_dict.keys():
+ data_dict["index"] = np.arange(data_dict["coord"].shape[0])
+ data_part_list = []
+ # coord_list, color_list, dist2_list, idx_list, offset_list = [], [], [], [], []
+ if data_dict["coord"].shape[0] > point_max:
+ coord_p, idx_uni = np.random.rand(
+ data_dict["coord"].shape[0]
+ ) * 1e-3, np.array([])
+ while idx_uni.size != data_dict["index"].shape[0]:
+ init_idx = np.argmin(coord_p)
+ dist2 = np.sum(
+ np.power(data_dict["coord"] - data_dict["coord"][init_idx], 2),
+ 1,
+ )
+ idx_crop = np.argsort(dist2)[:point_max]
+
+ data_crop_dict = dict()
+ if "coord" in data_dict.keys():
+ data_crop_dict["coord"] = data_dict["coord"][idx_crop]
+ if "grid_coord" in data_dict.keys():
+ data_crop_dict["grid_coord"] = data_dict["grid_coord"][idx_crop]
+ if "normal" in data_dict.keys():
+ data_crop_dict["normal"] = data_dict["normal"][idx_crop]
+ if "color" in data_dict.keys():
+ data_crop_dict["color"] = data_dict["color"][idx_crop]
+ if "displacement" in data_dict.keys():
+ data_crop_dict["displacement"] = data_dict["displacement"][
+ idx_crop
+ ]
+ if "strength" in data_dict.keys():
+ data_crop_dict["strength"] = data_dict["strength"][idx_crop]
+ data_crop_dict["weight"] = dist2[idx_crop]
+ data_crop_dict["index"] = data_dict["index"][idx_crop]
+ data_part_list.append(data_crop_dict)
+
+ delta = np.square(
+ 1 - data_crop_dict["weight"] / np.max(data_crop_dict["weight"])
+ )
+ coord_p[idx_crop] += delta
+ idx_uni = np.unique(
+ np.concatenate((idx_uni, data_crop_dict["index"]))
+ )
+ else:
+ data_crop_dict = data_dict.copy()
+ data_crop_dict["weight"] = np.zeros(data_dict["coord"].shape[0])
+ data_crop_dict["index"] = data_dict["index"]
+ data_part_list.append(data_crop_dict)
+ return data_part_list
+ # mode is "random" or "center"
+ elif data_dict["coord"].shape[0] > point_max:
+ if self.mode == "random":
+ center = data_dict["coord"][
+ np.random.randint(data_dict["coord"].shape[0])
+ ]
+ elif self.mode == "center":
+ center = data_dict["coord"][data_dict["coord"].shape[0] // 2]
+ else:
+ raise NotImplementedError
+ idx_crop = np.argsort(np.sum(np.square(data_dict["coord"] - center), 1))[
+ :point_max
+ ]
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][idx_crop]
+ if "origin_coord" in data_dict.keys():
+ data_dict["origin_coord"] = data_dict["origin_coord"][idx_crop]
+ if "grid_coord" in data_dict.keys():
+ data_dict["grid_coord"] = data_dict["grid_coord"][idx_crop]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][idx_crop]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][idx_crop]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][idx_crop]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][idx_crop]
+ if "displacement" in data_dict.keys():
+ data_dict["displacement"] = data_dict["displacement"][idx_crop]
+ if "strength" in data_dict.keys():
+ data_dict["strength"] = data_dict["strength"][idx_crop]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ShufflePoint(object):
+ def __call__(self, data_dict):
+ assert "coord" in data_dict.keys()
+ shuffle_index = np.arange(data_dict["coord"].shape[0])
+ np.random.shuffle(shuffle_index)
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][shuffle_index]
+ if "grid_coord" in data_dict.keys():
+ data_dict["grid_coord"] = data_dict["grid_coord"][shuffle_index]
+ if "displacement" in data_dict.keys():
+ data_dict["displacement"] = data_dict["displacement"][shuffle_index]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][shuffle_index]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][shuffle_index]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][shuffle_index]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][shuffle_index]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class CropBoundary(object):
+ def __call__(self, data_dict):
+ assert "segment" in data_dict
+ segment = data_dict["segment"].flatten()
+ mask = (segment != 0) * (segment != 1)
+ if "coord" in data_dict.keys():
+ data_dict["coord"] = data_dict["coord"][mask]
+ if "grid_coord" in data_dict.keys():
+ data_dict["grid_coord"] = data_dict["grid_coord"][mask]
+ if "color" in data_dict.keys():
+ data_dict["color"] = data_dict["color"][mask]
+ if "normal" in data_dict.keys():
+ data_dict["normal"] = data_dict["normal"][mask]
+ if "segment" in data_dict.keys():
+ data_dict["segment"] = data_dict["segment"][mask]
+ if "instance" in data_dict.keys():
+ data_dict["instance"] = data_dict["instance"][mask]
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class ContrastiveViewsGenerator(object):
+ def __init__(
+ self,
+ view_keys=("coord", "color", "normal", "origin_coord"),
+ view_trans_cfg=None,
+ ):
+ self.view_keys = view_keys
+ self.view_trans = Compose(view_trans_cfg)
+
+ def __call__(self, data_dict):
+ view1_dict = dict()
+ view2_dict = dict()
+ for key in self.view_keys:
+ view1_dict[key] = data_dict[key].copy()
+ view2_dict[key] = data_dict[key].copy()
+ view1_dict = self.view_trans(view1_dict)
+ view2_dict = self.view_trans(view2_dict)
+ for key, value in view1_dict.items():
+ data_dict["view1_" + key] = value
+ for key, value in view2_dict.items():
+ data_dict["view2_" + key] = value
+ return data_dict
+
+
+@TRANSFORMS.register_module()
+class InstanceParser(object):
+ def __init__(self, segment_ignore_index=(-1, 0, 1), instance_ignore_index=-1):
+ self.segment_ignore_index = segment_ignore_index
+ self.instance_ignore_index = instance_ignore_index
+
+ def __call__(self, data_dict):
+ coord = data_dict["coord"]
+ segment = data_dict["segment"]
+ instance = data_dict["instance"]
+ mask = ~np.in1d(segment, self.segment_ignore_index)
+ # mapping ignored instance to ignore index
+ instance[~mask] = self.instance_ignore_index
+ # reorder left instance
+ unique, inverse = np.unique(instance[mask], return_inverse=True)
+ instance_num = len(unique)
+ instance[mask] = inverse
+ # init instance information
+ centroid = np.ones((coord.shape[0], 3)) * self.instance_ignore_index
+ bbox = np.ones((instance_num, 8)) * self.instance_ignore_index
+ vacancy = [
+ index for index in self.segment_ignore_index if index >= 0
+ ] # vacate class index
+
+ for instance_id in range(instance_num):
+ mask_ = instance == instance_id
+ coord_ = coord[mask_]
+ bbox_min = coord_.min(0)
+ bbox_max = coord_.max(0)
+ bbox_centroid = coord_.mean(0)
+ bbox_center = (bbox_max + bbox_min) / 2
+ bbox_size = bbox_max - bbox_min
+ bbox_theta = np.zeros(1, dtype=coord_.dtype)
+ bbox_class = np.array([segment[mask_][0]], dtype=coord_.dtype)
+ # shift class index to fill vacate class index caused by segment ignore index
+ bbox_class -= np.greater(bbox_class, vacancy).sum()
+
+ centroid[mask_] = bbox_centroid
+ bbox[instance_id] = np.concatenate(
+ [bbox_center, bbox_size, bbox_theta, bbox_class]
+ ) # 3 + 3 + 1 + 1 = 8
+ data_dict["instance"] = instance
+ data_dict["instance_centroid"] = centroid
+ data_dict["bbox"] = bbox
+ return data_dict
+
+
+class Compose(object):
+ def __init__(self, cfg=None):
+ self.cfg = cfg if cfg is not None else []
+ self.transforms = []
+ for t_cfg in self.cfg:
+ self.transforms.append(TRANSFORMS.build(t_cfg))
+
+ def __call__(self, data_dict):
+ for t in self.transforms:
+ data_dict = t(data_dict)
+ return data_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/utils.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3abb9bf88c81f5eae302468ffc91c62bd942a002
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/utils.py
@@ -0,0 +1,59 @@
+"""
+Utils for Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+from collections.abc import Mapping, Sequence
+import numpy as np
+import torch
+from torch.utils.data.dataloader import default_collate
+
+
+def collate_fn(batch):
+ """
+ collate function for point cloud which support dict and list,
+ 'coord' is necessary to determine 'offset'
+ """
+ if not isinstance(batch, Sequence):
+ raise TypeError(f"{batch.dtype} is not supported.")
+
+ if isinstance(batch[0], torch.Tensor):
+ return torch.cat(list(batch))
+ elif isinstance(batch[0], str):
+ # str is also a kind of Sequence, judgement should before Sequence
+ return list(batch)
+ elif isinstance(batch[0], Sequence):
+ for data in batch:
+ data.append(torch.tensor([data[0].shape[0]]))
+ batch = [collate_fn(samples) for samples in zip(*batch)]
+ batch[-1] = torch.cumsum(batch[-1], dim=0).int()
+ return batch
+ elif isinstance(batch[0], Mapping):
+ batch = {key: collate_fn([d[key] for d in batch]) for key in batch[0]}
+ for key in batch.keys():
+ if "offset" in key:
+ batch[key] = torch.cumsum(batch[key], dim=0)
+ return batch
+ else:
+ return default_collate(batch)
+
+
+def point_collate_fn(batch, mix_prob=0):
+ assert isinstance(
+ batch[0], Mapping
+ ) # currently, only support input_dict, rather than input_list
+ batch = collate_fn(batch)
+ if "offset" in batch.keys():
+ # Mix3d (https://arxiv.org/pdf/2110.02210.pdf)
+ if random.random() < mix_prob:
+ batch["offset"] = torch.cat(
+ [batch["offset"][1:-1:2], batch["offset"][-1].unsqueeze(0)], dim=0
+ )
+ return batch
+
+
+def gaussian_kernel(dist2: np.array, a: float = 1, c: float = 5):
+ return a * np.exp(-dist2 / (2 * c**2))
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/waymo.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/waymo.py
new file mode 100644
index 0000000000000000000000000000000000000000..93e7f7ee1a0f18f9c932248b5d1d192dcb78f5f5
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/datasets/waymo.py
@@ -0,0 +1,104 @@
+"""
+Waymo dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import glob
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class WaymoDataset(DefaultDataset):
+ def __init__(
+ self,
+ timestamp=(0,),
+ reference_label=True,
+ timing_embedding=False,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ assert timestamp[0] == 0
+ self.timestamp = timestamp
+ self.reference_label = reference_label
+ self.timing_embedding = timing_embedding
+ self.data_list = sorted(self.data_list)
+ _, self.sequence_offset, self.sequence_index = np.unique(
+ [os.path.dirname(data) for data in self.data_list],
+ return_index=True,
+ return_inverse=True,
+ )
+ self.sequence_offset = np.append(self.sequence_offset, len(self.data_list))
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ self.split = [self.split]
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split, "*", "*"))
+ return data_list
+
+ @staticmethod
+ def align_pose(coord, pose, target_pose):
+ coord = np.hstack((coord, np.ones_like(coord[:, :1])))
+ pose_align = np.matmul(np.linalg.inv(target_pose), pose)
+ coord = (pose_align @ coord.T).T[:, :3]
+ return coord
+
+ def get_single_frame(self, idx):
+ return super().get_data(idx)
+
+ def get_data(self, idx):
+ idx = idx % len(self.data_list)
+ if self.timestamp == (0,):
+ return self.get_single_frame(idx)
+
+ sequence_index = self.sequence_index[idx]
+ lower, upper = self.sequence_offset[[sequence_index, sequence_index + 1]]
+ major_frame = self.get_single_frame(idx)
+ name = major_frame.pop("name")
+ target_pose = major_frame.pop("pose")
+ for key in major_frame.keys():
+ major_frame[key] = [major_frame[key]]
+
+ for timestamp in self.timestamp[1:]:
+ refer_idx = timestamp + idx
+ if refer_idx < lower or upper <= refer_idx:
+ continue
+ refer_frame = self.get_single_frame(refer_idx)
+ refer_frame.pop("name")
+ pose = refer_frame.pop("pose")
+ refer_frame["coord"] = self.align_pose(
+ refer_frame["coord"], pose, target_pose
+ )
+ if not self.reference_label:
+ refer_frame["segment"] = (
+ np.ones_like(refer_frame["segment"]) * self.ignore_index
+ )
+
+ if self.timing_embedding:
+ refer_frame["strength"] = np.hstack(
+ (
+ refer_frame["strength"],
+ np.ones_like(refer_frame["strength"]) * timestamp,
+ )
+ )
+
+ for key in major_frame.keys():
+ major_frame[key].append(refer_frame[key])
+ for key in major_frame.keys():
+ major_frame[key] = np.concatenate(major_frame[key], axis=0)
+ major_frame["name"] = name
+ return major_frame
+
+ def get_data_name(self, idx):
+ file_path = self.data_list[idx % len(self.data_list)]
+ sequence_path, frame_name = os.path.split(file_path)
+ sequence_name = os.path.basename(sequence_path)
+ data_name = f"{sequence_name}_{frame_name}"
+ return data_name
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/defaults.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..d45e7925a50acb03bb510c46ec4c566f6815cc05
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/defaults.py
@@ -0,0 +1,152 @@
+"""
+Default training/testing logic
+
+modified from detectron2(https://github.com/facebookresearch/detectron2)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import sys
+import argparse
+import multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel
+
+
+import pointcept.utils.comm as comm
+from pointcept.utils.env import get_random_seed, set_seed
+from pointcept.utils.config import Config, DictAction
+
+
+def create_ddp_model(model, *, fp16_compression=False, **kwargs):
+ """
+ Create a DistributedDataParallel model if there are >1 processes.
+ Args:
+ model: a torch.nn.Module
+ fp16_compression: add fp16 compression hooks to the ddp object.
+ See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook
+ kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`.
+ """
+ if comm.get_world_size() == 1:
+ return model
+ # kwargs['find_unused_parameters'] = True
+ if "device_ids" not in kwargs:
+ kwargs["device_ids"] = [comm.get_local_rank()]
+ if "output_device" not in kwargs:
+ kwargs["output_device"] = [comm.get_local_rank()]
+ ddp = DistributedDataParallel(model, **kwargs)
+ if fp16_compression:
+ from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks
+
+ ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook)
+ return ddp
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+ """Worker init func for dataloader.
+
+ The seed of each worker equals to num_worker * rank + worker_id + user_seed
+
+ Args:
+ worker_id (int): Worker id.
+ num_workers (int): Number of workers.
+ rank (int): The rank of current process.
+ seed (int): The random seed to use.
+ """
+
+ worker_seed = num_workers * rank + worker_id + seed
+ set_seed(worker_seed)
+
+
+def default_argument_parser(epilog=None):
+ parser = argparse.ArgumentParser(
+ epilog=epilog
+ or f"""
+ Examples:
+ Run on single machine:
+ $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml
+ Change some config options:
+ $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001
+ Run on multiple machines:
+ (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags]
+ (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags]
+ """,
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ )
+ parser.add_argument(
+ "--config-file", default="", metavar="FILE", help="path to config file"
+ )
+ parser.add_argument(
+ "--num-gpus", type=int, default=1, help="number of gpus *per machine*"
+ )
+ parser.add_argument(
+ "--num-machines", type=int, default=1, help="total number of machines"
+ )
+ parser.add_argument(
+ "--machine-rank",
+ type=int,
+ default=0,
+ help="the rank of this machine (unique per machine)",
+ )
+ # PyTorch still may leave orphan processes in multi-gpu training.
+ # Therefore we use a deterministic way to obtain port,
+ # so that users are aware of orphan processes by seeing the port occupied.
+ # port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14
+ parser.add_argument(
+ "--dist-url",
+ # default="tcp://127.0.0.1:{}".format(port),
+ default="auto",
+ help="initialization URL for pytorch distributed backend. See "
+ "https://pytorch.org/docs/stable/distributed.html for details.",
+ )
+ parser.add_argument(
+ "--options", nargs="+", action=DictAction, help="custom options"
+ )
+ return parser
+
+
+def default_config_parser(file_path, options):
+ # config name protocol: dataset_name/model_name-exp_name
+ if os.path.isfile(file_path):
+ cfg = Config.fromfile(file_path)
+ else:
+ sep = file_path.find("-")
+ cfg = Config.fromfile(os.path.join(file_path[:sep], file_path[sep + 1 :]))
+
+ if options is not None:
+ cfg.merge_from_dict(options)
+
+ if cfg.seed is None:
+ cfg.seed = get_random_seed()
+
+ cfg.data.train.loop = cfg.epoch // cfg.eval_epoch
+
+ os.makedirs(os.path.join(cfg.save_path, "model"), exist_ok=True)
+ if not cfg.resume:
+ cfg.dump(os.path.join(cfg.save_path, "config.py"))
+ return cfg
+
+
+def default_setup(cfg):
+ # scalar by world size
+ world_size = comm.get_world_size()
+ cfg.num_worker = cfg.num_worker if cfg.num_worker is not None else mp.cpu_count()
+ cfg.num_worker_per_gpu = cfg.num_worker // world_size
+ assert cfg.batch_size % world_size == 0
+ assert cfg.batch_size_val is None or cfg.batch_size_val % world_size == 0
+ assert cfg.batch_size_test is None or cfg.batch_size_test % world_size == 0
+ cfg.batch_size_per_gpu = cfg.batch_size // world_size
+ cfg.batch_size_val_per_gpu = (
+ cfg.batch_size_val // world_size if cfg.batch_size_val is not None else 1
+ )
+ cfg.batch_size_test_per_gpu = (
+ cfg.batch_size_test // world_size if cfg.batch_size_test is not None else 1
+ )
+ # update data loop
+ assert cfg.epoch % cfg.eval_epoch == 0
+ # settle random seed
+ rank = comm.get_rank()
+ seed = None if cfg.seed is None else cfg.seed * cfg.num_worker_per_gpu + rank
+ set_seed(seed)
+ return cfg
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ab2c4beb7f1938d9703e572ad8619fe88bff223
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/__init__.py
@@ -0,0 +1,5 @@
+from .default import HookBase
+from .misc import *
+from .evaluator import *
+
+from .builder import build_hooks
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/builder.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f4cce4871b0e18f3adc1f7430a8d5410442c77c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/builder.py
@@ -0,0 +1,18 @@
+"""
+Hook Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+
+HOOKS = Registry("hooks")
+
+
+def build_hooks(cfg):
+ hooks = []
+ for hook_cfg in cfg:
+ hooks.append(HOOKS.build(hook_cfg))
+ return hooks
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/default.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..87a64415a5a66d2570dffbaa7b90707443be42e2
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/default.py
@@ -0,0 +1,32 @@
+"""
+Default Hook
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+
+class HookBase:
+ """
+ Base class for hooks that can be registered with :class:`TrainerBase`.
+ """
+
+ trainer = None # A weak reference to the trainer object.
+
+ def before_train(self):
+ pass
+
+ def before_epoch(self):
+ pass
+
+ def before_step(self):
+ pass
+
+ def after_step(self):
+ pass
+
+ def after_epoch(self):
+ pass
+
+ def after_train(self):
+ pass
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/evaluator.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..02b35b3abd83e0a7b59f532f1ca8aacf70afcce2
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/evaluator.py
@@ -0,0 +1,581 @@
+"""
+Evaluate Hook
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import pointops
+from uuid import uuid4
+
+import pointcept.utils.comm as comm
+from pointcept.utils.misc import intersection_and_union_gpu
+
+from .default import HookBase
+from .builder import HOOKS
+
+
+@HOOKS.register_module()
+class ClsEvaluator(HookBase):
+ def after_epoch(self):
+ if self.trainer.cfg.evaluate:
+ self.eval()
+
+ def eval(self):
+ self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ self.trainer.model.eval()
+ for i, input_dict in enumerate(self.trainer.val_loader):
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ output_dict = self.trainer.model(input_dict)
+ output = output_dict["cls_logits"]
+ loss = output_dict["loss"]
+ pred = output.max(1)[1]
+ label = input_dict["category"]
+ intersection, union, target = intersection_and_union_gpu(
+ pred,
+ label,
+ self.trainer.cfg.data.num_classes,
+ self.trainer.cfg.data.ignore_index,
+ )
+ if comm.get_world_size() > 1:
+ dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(
+ target
+ )
+ intersection, union, target = (
+ intersection.cpu().numpy(),
+ union.cpu().numpy(),
+ target.cpu().numpy(),
+ )
+ # Here there is no need to sync since sync happened in dist.all_reduce
+ self.trainer.storage.put_scalar("val_intersection", intersection)
+ self.trainer.storage.put_scalar("val_union", union)
+ self.trainer.storage.put_scalar("val_target", target)
+ self.trainer.storage.put_scalar("val_loss", loss.item())
+ self.trainer.logger.info(
+ "Test: [{iter}/{max_iter}] "
+ "Loss {loss:.4f} ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item()
+ )
+ )
+ loss_avg = self.trainer.storage.history("val_loss").avg
+ intersection = self.trainer.storage.history("val_intersection").total
+ union = self.trainer.storage.history("val_union").total
+ target = self.trainer.storage.history("val_target").total
+ iou_class = intersection / (union + 1e-10)
+ acc_class = intersection / (target + 1e-10)
+ m_iou = np.mean(iou_class)
+ m_acc = np.mean(acc_class)
+ all_acc = sum(intersection) / (sum(target) + 1e-10)
+ self.trainer.logger.info(
+ "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(
+ m_iou, m_acc, all_acc
+ )
+ )
+ for i in range(self.trainer.cfg.data.num_classes):
+ self.trainer.logger.info(
+ "Class_{idx}-{name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.trainer.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=acc_class[i],
+ )
+ )
+ current_epoch = self.trainer.epoch + 1
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch)
+ self.trainer.writer.add_scalar("val/mIoU", m_iou, current_epoch)
+ self.trainer.writer.add_scalar("val/mAcc", m_acc, current_epoch)
+ self.trainer.writer.add_scalar("val/allAcc", all_acc, current_epoch)
+ self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+ self.trainer.comm_info["current_metric_value"] = all_acc # save for saver
+ self.trainer.comm_info["current_metric_name"] = "allAcc" # save for saver
+
+ def after_train(self):
+ self.trainer.logger.info(
+ "Best {}: {:.4f}".format("allAcc", self.trainer.best_metric_value)
+ )
+
+
+@HOOKS.register_module()
+class SemSegEvaluator(HookBase):
+ def after_epoch(self):
+ if self.trainer.cfg.evaluate:
+ self.eval()
+
+ def eval(self):
+ self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ self.trainer.model.eval()
+ for i, input_dict in enumerate(self.trainer.val_loader):
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ output_dict = self.trainer.model(input_dict)
+ output = output_dict["seg_logits"]
+ loss = output_dict["loss"]
+ pred = output.max(1)[1]
+ segment = input_dict["segment"]
+ if "origin_coord" in input_dict.keys():
+ idx, _ = pointops.knn_query(
+ 1,
+ input_dict["coord"].float(),
+ input_dict["offset"].int(),
+ input_dict["origin_coord"].float(),
+ input_dict["origin_offset"].int(),
+ )
+ pred = pred[idx.flatten().long()]
+ segment = input_dict["origin_segment"]
+ intersection, union, target = intersection_and_union_gpu(
+ pred,
+ segment,
+ self.trainer.cfg.data.num_classes,
+ self.trainer.cfg.data.ignore_index,
+ )
+ if comm.get_world_size() > 1:
+ dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(
+ target
+ )
+ intersection, union, target = (
+ intersection.cpu().numpy(),
+ union.cpu().numpy(),
+ target.cpu().numpy(),
+ )
+ # Here there is no need to sync since sync happened in dist.all_reduce
+ self.trainer.storage.put_scalar("val_intersection", intersection)
+ self.trainer.storage.put_scalar("val_union", union)
+ self.trainer.storage.put_scalar("val_target", target)
+ self.trainer.storage.put_scalar("val_loss", loss.item())
+ info = "Test: [{iter}/{max_iter}] ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader)
+ )
+ if "origin_coord" in input_dict.keys():
+ info = "Interp. " + info
+ self.trainer.logger.info(
+ info
+ + "Loss {loss:.4f} ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item()
+ )
+ )
+ loss_avg = self.trainer.storage.history("val_loss").avg
+ intersection = self.trainer.storage.history("val_intersection").total
+ union = self.trainer.storage.history("val_union").total
+ target = self.trainer.storage.history("val_target").total
+ iou_class = intersection / (union + 1e-10)
+ acc_class = intersection / (target + 1e-10)
+ m_iou = np.mean(iou_class)
+ m_acc = np.mean(acc_class)
+ all_acc = sum(intersection) / (sum(target) + 1e-10)
+ self.trainer.logger.info(
+ "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(
+ m_iou, m_acc, all_acc
+ )
+ )
+ for i in range(self.trainer.cfg.data.num_classes):
+ self.trainer.logger.info(
+ "Class_{idx}-{name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.trainer.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=acc_class[i],
+ )
+ )
+ current_epoch = self.trainer.epoch + 1
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch)
+ self.trainer.writer.add_scalar("val/mIoU", m_iou, current_epoch)
+ self.trainer.writer.add_scalar("val/mAcc", m_acc, current_epoch)
+ self.trainer.writer.add_scalar("val/allAcc", all_acc, current_epoch)
+ self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+ self.trainer.comm_info["current_metric_value"] = m_iou # save for saver
+ self.trainer.comm_info["current_metric_name"] = "mIoU" # save for saver
+
+ def after_train(self):
+ self.trainer.logger.info(
+ "Best {}: {:.4f}".format("mIoU", self.trainer.best_metric_value)
+ )
+
+
+@HOOKS.register_module()
+class InsSegEvaluator(HookBase):
+ def __init__(self, segment_ignore_index=(-1,), instance_ignore_index=-1):
+ self.segment_ignore_index = segment_ignore_index
+ self.instance_ignore_index = instance_ignore_index
+
+ self.valid_class_names = None # update in before train
+ self.overlaps = np.append(np.arange(0.5, 0.95, 0.05), 0.25)
+ self.min_region_sizes = 100
+ self.distance_threshes = float("inf")
+ self.distance_confs = -float("inf")
+
+ def before_train(self):
+ self.valid_class_names = [
+ self.trainer.cfg.data.names[i]
+ for i in range(self.trainer.cfg.data.num_classes)
+ if i not in self.segment_ignore_index
+ ]
+
+ def after_epoch(self):
+ if self.trainer.cfg.evaluate:
+ self.eval()
+
+ def associate_instances(self, pred, segment, instance):
+ segment = segment.cpu().numpy()
+ instance = instance.cpu().numpy()
+ void_mask = np.in1d(segment, self.segment_ignore_index)
+
+ assert (
+ pred["pred_classes"].shape[0]
+ == pred["pred_scores"].shape[0]
+ == pred["pred_masks"].shape[0]
+ )
+ assert pred["pred_masks"].shape[1] == segment.shape[0] == instance.shape[0]
+ # get gt instances
+ gt_instances = dict()
+ for i in range(self.trainer.cfg.data.num_classes):
+ if i not in self.segment_ignore_index:
+ gt_instances[self.trainer.cfg.data.names[i]] = []
+ instance_ids, idx, counts = np.unique(
+ instance, return_index=True, return_counts=True
+ )
+ segment_ids = segment[idx]
+ for i in range(len(instance_ids)):
+ if instance_ids[i] == self.instance_ignore_index:
+ continue
+ if segment_ids[i] in self.segment_ignore_index:
+ continue
+ gt_inst = dict()
+ gt_inst["instance_id"] = instance_ids[i]
+ gt_inst["segment_id"] = segment_ids[i]
+ gt_inst["dist_conf"] = 0.0
+ gt_inst["med_dist"] = -1.0
+ gt_inst["vert_count"] = counts[i]
+ gt_inst["matched_pred"] = []
+ gt_instances[self.trainer.cfg.data.names[segment_ids[i]]].append(gt_inst)
+
+ # get pred instances and associate with gt
+ pred_instances = dict()
+ for i in range(self.trainer.cfg.data.num_classes):
+ if i not in self.segment_ignore_index:
+ pred_instances[self.trainer.cfg.data.names[i]] = []
+ instance_id = 0
+ for i in range(len(pred["pred_classes"])):
+ if pred["pred_classes"][i] in self.segment_ignore_index:
+ continue
+ pred_inst = dict()
+ pred_inst["uuid"] = uuid4()
+ pred_inst["instance_id"] = instance_id
+ pred_inst["segment_id"] = pred["pred_classes"][i]
+ pred_inst["confidence"] = pred["pred_scores"][i]
+ pred_inst["mask"] = np.not_equal(pred["pred_masks"][i], 0)
+ pred_inst["vert_count"] = np.count_nonzero(pred_inst["mask"])
+ pred_inst["void_intersection"] = np.count_nonzero(
+ np.logical_and(void_mask, pred_inst["mask"])
+ )
+ if pred_inst["vert_count"] < self.min_region_sizes:
+ continue # skip if empty
+ segment_name = self.trainer.cfg.data.names[pred_inst["segment_id"]]
+ matched_gt = []
+ for gt_idx, gt_inst in enumerate(gt_instances[segment_name]):
+ intersection = np.count_nonzero(
+ np.logical_and(
+ instance == gt_inst["instance_id"], pred_inst["mask"]
+ )
+ )
+ if intersection > 0:
+ gt_inst_ = gt_inst.copy()
+ pred_inst_ = pred_inst.copy()
+ gt_inst_["intersection"] = intersection
+ pred_inst_["intersection"] = intersection
+ matched_gt.append(gt_inst_)
+ gt_inst["matched_pred"].append(pred_inst_)
+ pred_inst["matched_gt"] = matched_gt
+ pred_instances[segment_name].append(pred_inst)
+ instance_id += 1
+ return gt_instances, pred_instances
+
+ def evaluate_matches(self, scenes):
+ overlaps = self.overlaps
+ min_region_sizes = [self.min_region_sizes]
+ dist_threshes = [self.distance_threshes]
+ dist_confs = [self.distance_confs]
+
+ # results: class x overlap
+ ap_table = np.zeros(
+ (len(dist_threshes), len(self.valid_class_names), len(overlaps)), float
+ )
+ for di, (min_region_size, distance_thresh, distance_conf) in enumerate(
+ zip(min_region_sizes, dist_threshes, dist_confs)
+ ):
+ for oi, overlap_th in enumerate(overlaps):
+ pred_visited = {}
+ for scene in scenes:
+ for _ in scene["pred"]:
+ for label_name in self.valid_class_names:
+ for p in scene["pred"][label_name]:
+ if "uuid" in p:
+ pred_visited[p["uuid"]] = False
+ for li, label_name in enumerate(self.valid_class_names):
+ y_true = np.empty(0)
+ y_score = np.empty(0)
+ hard_false_negatives = 0
+ has_gt = False
+ has_pred = False
+ for scene in scenes:
+ pred_instances = scene["pred"][label_name]
+ gt_instances = scene["gt"][label_name]
+ # filter groups in ground truth
+ gt_instances = [
+ gt
+ for gt in gt_instances
+ if gt["vert_count"] >= min_region_size
+ and gt["med_dist"] <= distance_thresh
+ and gt["dist_conf"] >= distance_conf
+ ]
+ if gt_instances:
+ has_gt = True
+ if pred_instances:
+ has_pred = True
+
+ cur_true = np.ones(len(gt_instances))
+ cur_score = np.ones(len(gt_instances)) * (-float("inf"))
+ cur_match = np.zeros(len(gt_instances), dtype=bool)
+ # collect matches
+ for gti, gt in enumerate(gt_instances):
+ found_match = False
+ for pred in gt["matched_pred"]:
+ # greedy assignments
+ if pred_visited[pred["uuid"]]:
+ continue
+ overlap = float(pred["intersection"]) / (
+ gt["vert_count"]
+ + pred["vert_count"]
+ - pred["intersection"]
+ )
+ if overlap > overlap_th:
+ confidence = pred["confidence"]
+ # if already have a prediction for this gt,
+ # the prediction with the lower score is automatically a false positive
+ if cur_match[gti]:
+ max_score = max(cur_score[gti], confidence)
+ min_score = min(cur_score[gti], confidence)
+ cur_score[gti] = max_score
+ # append false positive
+ cur_true = np.append(cur_true, 0)
+ cur_score = np.append(cur_score, min_score)
+ cur_match = np.append(cur_match, True)
+ # otherwise set score
+ else:
+ found_match = True
+ cur_match[gti] = True
+ cur_score[gti] = confidence
+ pred_visited[pred["uuid"]] = True
+ if not found_match:
+ hard_false_negatives += 1
+ # remove non-matched ground truth instances
+ cur_true = cur_true[cur_match]
+ cur_score = cur_score[cur_match]
+
+ # collect non-matched predictions as false positive
+ for pred in pred_instances:
+ found_gt = False
+ for gt in pred["matched_gt"]:
+ overlap = float(gt["intersection"]) / (
+ gt["vert_count"]
+ + pred["vert_count"]
+ - gt["intersection"]
+ )
+ if overlap > overlap_th:
+ found_gt = True
+ break
+ if not found_gt:
+ num_ignore = pred["void_intersection"]
+ for gt in pred["matched_gt"]:
+ if gt["segment_id"] in self.segment_ignore_index:
+ num_ignore += gt["intersection"]
+ # small ground truth instances
+ if (
+ gt["vert_count"] < min_region_size
+ or gt["med_dist"] > distance_thresh
+ or gt["dist_conf"] < distance_conf
+ ):
+ num_ignore += gt["intersection"]
+ proportion_ignore = (
+ float(num_ignore) / pred["vert_count"]
+ )
+ # if not ignored append false positive
+ if proportion_ignore <= overlap_th:
+ cur_true = np.append(cur_true, 0)
+ confidence = pred["confidence"]
+ cur_score = np.append(cur_score, confidence)
+
+ # append to overall results
+ y_true = np.append(y_true, cur_true)
+ y_score = np.append(y_score, cur_score)
+
+ # compute average precision
+ if has_gt and has_pred:
+ # compute precision recall curve first
+
+ # sorting and cumsum
+ score_arg_sort = np.argsort(y_score)
+ y_score_sorted = y_score[score_arg_sort]
+ y_true_sorted = y_true[score_arg_sort]
+ y_true_sorted_cumsum = np.cumsum(y_true_sorted)
+
+ # unique thresholds
+ (thresholds, unique_indices) = np.unique(
+ y_score_sorted, return_index=True
+ )
+ num_prec_recall = len(unique_indices) + 1
+
+ # prepare precision recall
+ num_examples = len(y_score_sorted)
+ # https://github.com/ScanNet/ScanNet/pull/26
+ # all predictions are non-matched but also all of them are ignored and not counted as FP
+ # y_true_sorted_cumsum is empty
+ # num_true_examples = y_true_sorted_cumsum[-1]
+ num_true_examples = (
+ y_true_sorted_cumsum[-1]
+ if len(y_true_sorted_cumsum) > 0
+ else 0
+ )
+ precision = np.zeros(num_prec_recall)
+ recall = np.zeros(num_prec_recall)
+
+ # deal with the first point
+ y_true_sorted_cumsum = np.append(y_true_sorted_cumsum, 0)
+ # deal with remaining
+ for idx_res, idx_scores in enumerate(unique_indices):
+ cumsum = y_true_sorted_cumsum[idx_scores - 1]
+ tp = num_true_examples - cumsum
+ fp = num_examples - idx_scores - tp
+ fn = cumsum + hard_false_negatives
+ p = float(tp) / (tp + fp)
+ r = float(tp) / (tp + fn)
+ precision[idx_res] = p
+ recall[idx_res] = r
+
+ # first point in curve is artificial
+ precision[-1] = 1.0
+ recall[-1] = 0.0
+
+ # compute average of precision-recall curve
+ recall_for_conv = np.copy(recall)
+ recall_for_conv = np.append(recall_for_conv[0], recall_for_conv)
+ recall_for_conv = np.append(recall_for_conv, 0.0)
+
+ stepWidths = np.convolve(
+ recall_for_conv, [-0.5, 0, 0.5], "valid"
+ )
+ # integrate is now simply a dot product
+ ap_current = np.dot(precision, stepWidths)
+
+ elif has_gt:
+ ap_current = 0.0
+ else:
+ ap_current = float("nan")
+ ap_table[di, li, oi] = ap_current
+ d_inf = 0
+ o50 = np.where(np.isclose(self.overlaps, 0.5))
+ o25 = np.where(np.isclose(self.overlaps, 0.25))
+ oAllBut25 = np.where(np.logical_not(np.isclose(self.overlaps, 0.25)))
+ ap_scores = dict()
+ ap_scores["all_ap"] = np.nanmean(ap_table[d_inf, :, oAllBut25])
+ ap_scores["all_ap_50%"] = np.nanmean(ap_table[d_inf, :, o50])
+ ap_scores["all_ap_25%"] = np.nanmean(ap_table[d_inf, :, o25])
+ ap_scores["classes"] = {}
+ for li, label_name in enumerate(self.valid_class_names):
+ ap_scores["classes"][label_name] = {}
+ ap_scores["classes"][label_name]["ap"] = np.average(
+ ap_table[d_inf, li, oAllBut25]
+ )
+ ap_scores["classes"][label_name]["ap50%"] = np.average(
+ ap_table[d_inf, li, o50]
+ )
+ ap_scores["classes"][label_name]["ap25%"] = np.average(
+ ap_table[d_inf, li, o25]
+ )
+ return ap_scores
+
+ def eval(self):
+ self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ self.trainer.model.eval()
+ scenes = []
+ for i, input_dict in enumerate(self.trainer.val_loader):
+ assert (
+ len(input_dict["offset"]) == 1
+ ) # currently only support bs 1 for each GPU
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ output_dict = self.trainer.model(input_dict)
+
+ loss = output_dict["loss"]
+
+ segment = input_dict["segment"]
+ instance = input_dict["instance"]
+ # map to origin
+ if "origin_coord" in input_dict.keys():
+ idx, _ = pointops.knn_query(
+ 1,
+ input_dict["coord"].float(),
+ input_dict["offset"].int(),
+ input_dict["origin_coord"].float(),
+ input_dict["origin_offset"].int(),
+ )
+ idx = idx.cpu().flatten().long()
+ output_dict["pred_masks"] = output_dict["pred_masks"][:, idx]
+ segment = input_dict["origin_segment"]
+ instance = input_dict["origin_instance"]
+
+ gt_instances, pred_instance = self.associate_instances(
+ output_dict, segment, instance
+ )
+ scenes.append(dict(gt=gt_instances, pred=pred_instance))
+
+ self.trainer.storage.put_scalar("val_loss", loss.item())
+ self.trainer.logger.info(
+ "Test: [{iter}/{max_iter}] "
+ "Loss {loss:.4f} ".format(
+ iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item()
+ )
+ )
+
+ loss_avg = self.trainer.storage.history("val_loss").avg
+ comm.synchronize()
+ scenes_sync = comm.gather(scenes, dst=0)
+ scenes = [scene for scenes_ in scenes_sync for scene in scenes_]
+ ap_scores = self.evaluate_matches(scenes)
+ all_ap = ap_scores["all_ap"]
+ all_ap_50 = ap_scores["all_ap_50%"]
+ all_ap_25 = ap_scores["all_ap_25%"]
+ self.trainer.logger.info(
+ "Val result: mAP/AP50/AP25 {:.4f}/{:.4f}/{:.4f}.".format(
+ all_ap, all_ap_50, all_ap_25
+ )
+ )
+ for i, label_name in enumerate(self.valid_class_names):
+ ap = ap_scores["classes"][label_name]["ap"]
+ ap_50 = ap_scores["classes"][label_name]["ap50%"]
+ ap_25 = ap_scores["classes"][label_name]["ap25%"]
+ self.trainer.logger.info(
+ "Class_{idx}-{name} Result: AP/AP50/AP25 {AP:.4f}/{AP50:.4f}/{AP25:.4f}".format(
+ idx=i, name=label_name, AP=ap, AP50=ap_50, AP25=ap_25
+ )
+ )
+ current_epoch = self.trainer.epoch + 1
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch)
+ self.trainer.writer.add_scalar("val/mAP", all_ap, current_epoch)
+ self.trainer.writer.add_scalar("val/AP50", all_ap_50, current_epoch)
+ self.trainer.writer.add_scalar("val/AP25", all_ap_25, current_epoch)
+ self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+ self.trainer.comm_info["current_metric_value"] = all_ap_50 # save for saver
+ self.trainer.comm_info["current_metric_name"] = "AP50" # save for saver
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/misc.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..155bf5541fc8e5406618a801ba4ccb1e369d4308
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/hooks/misc.py
@@ -0,0 +1,464 @@
+"""
+Misc Hook
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import sys
+import glob
+import os
+import shutil
+import time
+import torch
+import torch.utils.data
+from collections import OrderedDict
+
+if sys.version_info >= (3, 10):
+ from collections.abc import Sequence
+else:
+ from collections import Sequence
+from pointcept.utils.timer import Timer
+from pointcept.utils.comm import is_main_process, synchronize, get_world_size
+from pointcept.utils.cache import shared_dict
+import pointcept.utils.comm as comm
+from pointcept.engines.test import TESTERS
+
+from .default import HookBase
+from .builder import HOOKS
+
+
+@HOOKS.register_module()
+class IterationTimer(HookBase):
+ def __init__(self, warmup_iter=1):
+ self._warmup_iter = warmup_iter
+ self._start_time = time.perf_counter()
+ self._iter_timer = Timer()
+ self._remain_iter = 0
+
+ def before_train(self):
+ self._start_time = time.perf_counter()
+ self._remain_iter = self.trainer.max_epoch * len(self.trainer.train_loader)
+
+ def before_epoch(self):
+ self._iter_timer.reset()
+
+ def before_step(self):
+ data_time = self._iter_timer.seconds()
+ self.trainer.storage.put_scalar("data_time", data_time)
+
+ def after_step(self):
+ batch_time = self._iter_timer.seconds()
+ self._iter_timer.reset()
+ self.trainer.storage.put_scalar("batch_time", batch_time)
+ self._remain_iter -= 1
+ remain_time = self._remain_iter * self.trainer.storage.history("batch_time").avg
+ t_m, t_s = divmod(remain_time, 60)
+ t_h, t_m = divmod(t_m, 60)
+ remain_time = "{:02d}:{:02d}:{:02d}".format(int(t_h), int(t_m), int(t_s))
+ if "iter_info" in self.trainer.comm_info.keys():
+ info = (
+ "Data {data_time_val:.3f} ({data_time_avg:.3f}) "
+ "Batch {batch_time_val:.3f} ({batch_time_avg:.3f}) "
+ "Remain {remain_time} ".format(
+ data_time_val=self.trainer.storage.history("data_time").val,
+ data_time_avg=self.trainer.storage.history("data_time").avg,
+ batch_time_val=self.trainer.storage.history("batch_time").val,
+ batch_time_avg=self.trainer.storage.history("batch_time").avg,
+ remain_time=remain_time,
+ )
+ )
+ self.trainer.comm_info["iter_info"] += info
+ if self.trainer.comm_info["iter"] <= self._warmup_iter:
+ self.trainer.storage.history("data_time").reset()
+ self.trainer.storage.history("batch_time").reset()
+
+
+@HOOKS.register_module()
+class InformationWriter(HookBase):
+ def __init__(self):
+ self.curr_iter = 0
+ self.model_output_keys = []
+
+ def before_train(self):
+ self.trainer.comm_info["iter_info"] = ""
+ self.curr_iter = self.trainer.start_epoch * len(self.trainer.train_loader)
+
+ def before_step(self):
+ self.curr_iter += 1
+ # MSC pretrain do not have offset information. Comment the code for support MSC
+ # info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] " \
+ # "Scan {batch_size} ({points_num}) ".format(
+ # epoch=self.trainer.epoch + 1, max_epoch=self.trainer.max_epoch,
+ # iter=self.trainer.comm_info["iter"], max_iter=len(self.trainer.train_loader),
+ # batch_size=len(self.trainer.comm_info["input_dict"]["offset"]),
+ # points_num=self.trainer.comm_info["input_dict"]["offset"][-1]
+ # )
+ info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] ".format(
+ epoch=self.trainer.epoch + 1,
+ max_epoch=self.trainer.max_epoch,
+ iter=self.trainer.comm_info["iter"] + 1,
+ max_iter=len(self.trainer.train_loader),
+ )
+ self.trainer.comm_info["iter_info"] += info
+
+ def after_step(self):
+ if "model_output_dict" in self.trainer.comm_info.keys():
+ model_output_dict = self.trainer.comm_info["model_output_dict"]
+ self.model_output_keys = model_output_dict.keys()
+ for key in self.model_output_keys:
+ self.trainer.storage.put_scalar(key, model_output_dict[key].item())
+
+ for key in self.model_output_keys:
+ self.trainer.comm_info["iter_info"] += "{key}: {value:.4f} ".format(
+ key=key, value=self.trainer.storage.history(key).val
+ )
+ lr = self.trainer.optimizer.state_dict()["param_groups"][0]["lr"]
+ self.trainer.comm_info["iter_info"] += "Lr: {lr:.5f}".format(lr=lr)
+ self.trainer.logger.info(self.trainer.comm_info["iter_info"])
+ self.trainer.comm_info["iter_info"] = "" # reset iter info
+ if self.trainer.writer is not None:
+ self.trainer.writer.add_scalar("lr", lr, self.curr_iter)
+ for key in self.model_output_keys:
+ self.trainer.writer.add_scalar(
+ "train_batch/" + key,
+ self.trainer.storage.history(key).val,
+ self.curr_iter,
+ )
+
+ def after_epoch(self):
+ epoch_info = "Train result: "
+ for key in self.model_output_keys:
+ epoch_info += "{key}: {value:.4f} ".format(
+ key=key, value=self.trainer.storage.history(key).avg
+ )
+ self.trainer.logger.info(epoch_info)
+ if self.trainer.writer is not None:
+ for key in self.model_output_keys:
+ self.trainer.writer.add_scalar(
+ "train/" + key,
+ self.trainer.storage.history(key).avg,
+ self.trainer.epoch + 1,
+ )
+
+
+@HOOKS.register_module()
+class CheckpointSaver(HookBase):
+ def __init__(self, save_freq=None):
+ self.save_freq = save_freq # None or int, None indicate only save model last
+
+ def after_epoch(self):
+ if is_main_process():
+ is_best = False
+ if self.trainer.cfg.evaluate:
+ current_metric_value = self.trainer.comm_info["current_metric_value"]
+ current_metric_name = self.trainer.comm_info["current_metric_name"]
+ if current_metric_value > self.trainer.best_metric_value:
+ self.trainer.best_metric_value = current_metric_value
+ is_best = True
+ self.trainer.logger.info(
+ "Best validation {} updated to: {:.4f}".format(
+ current_metric_name, current_metric_value
+ )
+ )
+ self.trainer.logger.info(
+ "Currently Best {}: {:.4f}".format(
+ current_metric_name, self.trainer.best_metric_value
+ )
+ )
+
+ filename = os.path.join(
+ self.trainer.cfg.save_path, "model", "model_last.pth"
+ )
+ self.trainer.logger.info("Saving checkpoint to: " + filename)
+ torch.save(
+ {
+ "epoch": self.trainer.epoch + 1,
+ "state_dict": self.trainer.model.state_dict(),
+ "optimizer": self.trainer.optimizer.state_dict(),
+ "scheduler": self.trainer.scheduler.state_dict(),
+ "scaler": (
+ self.trainer.scaler.state_dict()
+ if self.trainer.cfg.enable_amp
+ else None
+ ),
+ "best_metric_value": self.trainer.best_metric_value,
+ },
+ filename + ".tmp",
+ )
+ os.replace(filename + ".tmp", filename)
+ if is_best:
+ shutil.copyfile(
+ filename,
+ os.path.join(self.trainer.cfg.save_path, "model", "model_best.pth"),
+ )
+ if self.save_freq and (self.trainer.epoch + 1) % self.save_freq == 0:
+ shutil.copyfile(
+ filename,
+ os.path.join(
+ self.trainer.cfg.save_path,
+ "model",
+ f"epoch_{self.trainer.epoch + 1}.pth",
+ ),
+ )
+
+
+@HOOKS.register_module()
+class CheckpointLoader(HookBase):
+ def __init__(self, keywords="", replacement=None, strict=False):
+ self.keywords = keywords
+ self.replacement = replacement if replacement is not None else keywords
+ self.strict = strict
+
+ def before_train(self):
+ self.trainer.logger.info("=> Loading checkpoint & weight ...")
+ if self.trainer.cfg.weight and os.path.isfile(self.trainer.cfg.weight):
+ self.trainer.logger.info(f"Loading weight at: {self.trainer.cfg.weight}")
+ checkpoint = torch.load(
+ self.trainer.cfg.weight,
+ map_location=lambda storage, loc: storage.cuda(),
+ )
+ self.trainer.logger.info(
+ f"Loading layer weights with keyword: {self.keywords}, "
+ f"replace keyword with: {self.replacement}"
+ )
+ weight = OrderedDict()
+ for key, value in checkpoint["state_dict"].items():
+ if not key.startswith("module."):
+ key = "module." + key # xxx.xxx -> module.xxx.xxx
+ # Now all keys contain "module." no matter DDP or not.
+ if self.keywords in key:
+ key = key.replace(self.keywords, self.replacement)
+ if comm.get_world_size() == 1:
+ key = key[7:] # module.xxx.xxx -> xxx.xxx
+ weight[key] = value
+ load_state_info = self.trainer.model.load_state_dict(
+ weight, strict=self.strict
+ )
+ self.trainer.logger.info(f"Missing keys: {load_state_info[0]}")
+ if self.trainer.cfg.resume:
+ self.trainer.logger.info(
+ f"Resuming train at eval epoch: {checkpoint['epoch']}"
+ )
+ self.trainer.start_epoch = checkpoint["epoch"]
+ self.trainer.best_metric_value = checkpoint["best_metric_value"]
+ self.trainer.optimizer.load_state_dict(checkpoint["optimizer"])
+ self.trainer.scheduler.load_state_dict(checkpoint["scheduler"])
+ if self.trainer.cfg.enable_amp:
+ self.trainer.scaler.load_state_dict(checkpoint["scaler"])
+ else:
+ self.trainer.logger.info(f"No weight found at: {self.trainer.cfg.weight}")
+
+
+@HOOKS.register_module()
+class PreciseEvaluator(HookBase):
+ def __init__(self, test_last=False):
+ self.test_last = test_last
+
+ def after_train(self):
+ self.trainer.logger.info(
+ ">>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>>"
+ )
+ torch.cuda.empty_cache()
+ cfg = self.trainer.cfg
+ tester = TESTERS.build(
+ dict(type=cfg.test.type, cfg=cfg, model=self.trainer.model)
+ )
+ if self.test_last:
+ self.trainer.logger.info("=> Testing on model_last ...")
+ else:
+ self.trainer.logger.info("=> Testing on model_best ...")
+ best_path = os.path.join(
+ self.trainer.cfg.save_path, "model", "model_best.pth"
+ )
+ checkpoint = torch.load(best_path)
+ state_dict = checkpoint["state_dict"]
+ tester.model.load_state_dict(state_dict, strict=True)
+ tester.test()
+
+
+@HOOKS.register_module()
+class DataCacheOperator(HookBase):
+ def __init__(self, data_root, split):
+ self.data_root = data_root
+ self.split = split
+ self.data_list = self.get_data_list()
+
+ def get_data_list(self):
+ if isinstance(self.split, str):
+ data_list = glob.glob(os.path.join(self.data_root, self.split))
+ elif isinstance(self.split, Sequence):
+ data_list = []
+ for split in self.split:
+ data_list += glob.glob(os.path.join(self.data_root, split))
+ else:
+ raise NotImplementedError
+ return data_list
+
+ def get_cache_name(self, data_path):
+ data_name = data_path.replace(os.path.dirname(self.data_root), "")
+ return "pointcept" + data_name.replace(os.path.sep, "-")
+
+ def before_train(self):
+ self.trainer.logger.info(
+ f"=> Caching dataset: {self.data_root}, split: {self.split} ..."
+ )
+ if is_main_process():
+ dataset = self.trainer.train_loader.dataset
+ for i in range(len(dataset)):
+ data_dict = dataset[i]
+ name = data_dict["name"]
+ shared_dict(f"Pointcept-{name}", data_dict)
+ synchronize()
+
+
+@HOOKS.register_module()
+class RuntimeProfiler(HookBase):
+ def __init__(
+ self,
+ forward=True,
+ backward=True,
+ interrupt=False,
+ warm_up=2,
+ sort_by="cuda_time_total",
+ row_limit=30,
+ ):
+ self.forward = forward
+ self.backward = backward
+ self.interrupt = interrupt
+ self.warm_up = warm_up
+ self.sort_by = sort_by
+ self.row_limit = row_limit
+
+ def before_train(self):
+ self.trainer.logger.info("Profiling runtime ...")
+ from torch.profiler import profile, record_function, ProfilerActivity
+
+ for i, input_dict in enumerate(self.trainer.train_loader):
+ if i == self.warm_up + 1:
+ break
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ if self.forward:
+ with profile(
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ ) as forward_prof:
+ with record_function("model_inference"):
+ output_dict = self.trainer.model(input_dict)
+ else:
+ output_dict = self.trainer.model(input_dict)
+ loss = output_dict["loss"]
+ if self.backward:
+ with profile(
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ ) as backward_prof:
+ with record_function("model_inference"):
+ loss.backward()
+ self.trainer.logger.info(f"Profile: [{i + 1}/{self.warm_up + 1}]")
+ if self.forward:
+ self.trainer.logger.info(
+ "Forward profile: \n"
+ + str(
+ forward_prof.key_averages().table(
+ sort_by=self.sort_by, row_limit=self.row_limit
+ )
+ )
+ )
+ forward_prof.export_chrome_trace(
+ os.path.join(self.trainer.cfg.save_path, "forward_trace.json")
+ )
+
+ if self.backward:
+ self.trainer.logger.info(
+ "Backward profile: \n"
+ + str(
+ backward_prof.key_averages().table(
+ sort_by=self.sort_by, row_limit=self.row_limit
+ )
+ )
+ )
+ backward_prof.export_chrome_trace(
+ os.path.join(self.trainer.cfg.save_path, "backward_trace.json")
+ )
+ if self.interrupt:
+ sys.exit(0)
+
+
+@HOOKS.register_module()
+class RuntimeProfilerV2(HookBase):
+ def __init__(
+ self,
+ interrupt=False,
+ wait=1,
+ warmup=1,
+ active=10,
+ repeat=1,
+ sort_by="cuda_time_total",
+ row_limit=30,
+ ):
+ self.interrupt = interrupt
+ self.wait = wait
+ self.warmup = warmup
+ self.active = active
+ self.repeat = repeat
+ self.sort_by = sort_by
+ self.row_limit = row_limit
+
+ def before_train(self):
+ self.trainer.logger.info("Profiling runtime ...")
+ from torch.profiler import (
+ profile,
+ record_function,
+ ProfilerActivity,
+ schedule,
+ tensorboard_trace_handler,
+ )
+
+ prof = profile(
+ activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+ schedule=schedule(
+ wait=self.wait,
+ warmup=self.warmup,
+ active=self.active,
+ repeat=self.repeat,
+ ),
+ on_trace_ready=tensorboard_trace_handler(self.trainer.cfg.save_path),
+ record_shapes=True,
+ profile_memory=True,
+ with_stack=True,
+ )
+ prof.start()
+ for i, input_dict in enumerate(self.trainer.train_loader):
+ if i >= (self.wait + self.warmup + self.active) * self.repeat:
+ break
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with record_function("model_forward"):
+ output_dict = self.trainer.model(input_dict)
+ loss = output_dict["loss"]
+ with record_function("model_backward"):
+ loss.backward()
+ prof.step()
+ self.trainer.logger.info(
+ f"Profile: [{i + 1}/{(self.wait + self.warmup + self.active) * self.repeat}]"
+ )
+ self.trainer.logger.info(
+ "Profile: \n"
+ + str(
+ prof.key_averages().table(
+ sort_by=self.sort_by, row_limit=self.row_limit
+ )
+ )
+ )
+ prof.stop()
+
+ if self.interrupt:
+ sys.exit(0)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/launch.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..99a8351fe5ab4393c1fab75c3bd546ba66641986
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/launch.py
@@ -0,0 +1,137 @@
+"""
+Launcher
+
+modified from detectron2(https://github.com/facebookresearch/detectron2)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import logging
+from datetime import timedelta
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from pointcept.utils import comm
+
+__all__ = ["DEFAULT_TIMEOUT", "launch"]
+
+DEFAULT_TIMEOUT = timedelta(minutes=60)
+
+
+def _find_free_port():
+ import socket
+
+ sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ # Binding to port 0 will cause the OS to find an available port for us
+ sock.bind(("", 0))
+ port = sock.getsockname()[1]
+ sock.close()
+ # NOTE: there is still a chance the port could be taken by other processes.
+ return port
+
+
+def launch(
+ main_func,
+ num_gpus_per_machine,
+ num_machines=1,
+ machine_rank=0,
+ dist_url=None,
+ cfg=(),
+ timeout=DEFAULT_TIMEOUT,
+):
+ """
+ Launch multi-gpu or distributed training.
+ This function must be called on all machines involved in the training.
+ It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine.
+ Args:
+ main_func: a function that will be called by `main_func(*args)`
+ num_gpus_per_machine (int): number of GPUs per machine
+ num_machines (int): the total number of machines
+ machine_rank (int): the rank of this machine
+ dist_url (str): url to connect to for distributed jobs, including protocol
+ e.g. "tcp://127.0.0.1:8686".
+ Can be set to "auto" to automatically select a free port on localhost
+ timeout (timedelta): timeout of the distributed workers
+ args (tuple): arguments passed to main_func
+ """
+ world_size = num_machines * num_gpus_per_machine
+ if world_size > 1:
+ if dist_url == "auto":
+ assert (
+ num_machines == 1
+ ), "dist_url=auto not supported in multi-machine jobs."
+ port = _find_free_port()
+ dist_url = f"tcp://127.0.0.1:{port}"
+ if num_machines > 1 and dist_url.startswith("file://"):
+ logger = logging.getLogger(__name__)
+ logger.warning(
+ "file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://"
+ )
+
+ mp.spawn(
+ _distributed_worker,
+ nprocs=num_gpus_per_machine,
+ args=(
+ main_func,
+ world_size,
+ num_gpus_per_machine,
+ machine_rank,
+ dist_url,
+ cfg,
+ timeout,
+ ),
+ daemon=False,
+ )
+ else:
+ main_func(*cfg)
+
+
+def _distributed_worker(
+ local_rank,
+ main_func,
+ world_size,
+ num_gpus_per_machine,
+ machine_rank,
+ dist_url,
+ cfg,
+ timeout=DEFAULT_TIMEOUT,
+):
+ assert (
+ torch.cuda.is_available()
+ ), "cuda is not available. Please check your installation."
+ global_rank = machine_rank * num_gpus_per_machine + local_rank
+ try:
+ dist.init_process_group(
+ backend="NCCL",
+ init_method=dist_url,
+ world_size=world_size,
+ rank=global_rank,
+ timeout=timeout,
+ )
+ except Exception as e:
+ logger = logging.getLogger(__name__)
+ logger.error("Process group URL: {}".format(dist_url))
+ raise e
+
+ # Setup the local process group (which contains ranks within the same machine)
+ assert comm._LOCAL_PROCESS_GROUP is None
+ num_machines = world_size // num_gpus_per_machine
+ for i in range(num_machines):
+ ranks_on_i = list(
+ range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine)
+ )
+ pg = dist.new_group(ranks_on_i)
+ if i == machine_rank:
+ comm._LOCAL_PROCESS_GROUP = pg
+
+ assert num_gpus_per_machine <= torch.cuda.device_count()
+ torch.cuda.set_device(local_rank)
+
+ # synchronize is needed here to prevent a possible timeout after calling init_process_group
+ # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172
+ comm.synchronize()
+
+ main_func(*cfg)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/test.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..04378738e269f432c8bf720b4e55fc80af3db280
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/test.py
@@ -0,0 +1,597 @@
+"""
+Tester
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import time
+import numpy as np
+from collections import OrderedDict
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+import torch.utils.data
+
+from .defaults import create_ddp_model
+import pointcept.utils.comm as comm
+from pointcept.datasets import build_dataset, collate_fn
+from pointcept.models import build_model
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.registry import Registry
+from pointcept.utils.misc import (
+ AverageMeter,
+ intersection_and_union,
+ intersection_and_union_gpu,
+ make_dirs,
+)
+
+
+TESTERS = Registry("testers")
+
+
+class TesterBase:
+ def __init__(self, cfg, model=None, test_loader=None, verbose=False) -> None:
+ torch.multiprocessing.set_sharing_strategy("file_system")
+ self.logger = get_root_logger(
+ log_file=os.path.join(cfg.save_path, "test.log"),
+ file_mode="a" if cfg.resume else "w",
+ )
+ self.logger.info("=> Loading config ...")
+ self.cfg = cfg
+ self.verbose = verbose
+ if self.verbose:
+ self.logger.info(f"Save path: {cfg.save_path}")
+ self.logger.info(f"Config:\n{cfg.pretty_text}")
+ if model is None:
+ self.logger.info("=> Building model ...")
+ self.model = self.build_model()
+ else:
+ self.model = model
+ if test_loader is None:
+ self.logger.info("=> Building test dataset & dataloader ...")
+ self.test_loader = self.build_test_loader()
+ else:
+ self.test_loader = test_loader
+
+ def build_model(self):
+ model = build_model(self.cfg.model)
+ n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ self.logger.info(f"Num params: {n_parameters}")
+ model = create_ddp_model(
+ model.cuda(),
+ broadcast_buffers=False,
+ find_unused_parameters=self.cfg.find_unused_parameters,
+ )
+ if os.path.isfile(self.cfg.weight):
+ self.logger.info(f"Loading weight at: {self.cfg.weight}")
+ checkpoint = torch.load(self.cfg.weight)
+ weight = OrderedDict()
+ for key, value in checkpoint["state_dict"].items():
+ if key.startswith("module."):
+ if comm.get_world_size() == 1:
+ key = key[7:] # module.xxx.xxx -> xxx.xxx
+ else:
+ if comm.get_world_size() > 1:
+ key = "module." + key # xxx.xxx -> module.xxx.xxx
+ weight[key] = value
+ model.load_state_dict(weight, strict=True)
+ self.logger.info("=> Loaded weight '{}' (epoch {})".format(self.cfg.weight, checkpoint["epoch"]))
+ else:
+ raise RuntimeError("=> No checkpoint found at '{}'".format(self.cfg.weight))
+ return model
+
+ def build_test_loader(self):
+ test_dataset = build_dataset(self.cfg.data.test)
+ if comm.get_world_size() > 1:
+ test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset)
+ else:
+ test_sampler = None
+ test_loader = torch.utils.data.DataLoader(
+ test_dataset,
+ batch_size=self.cfg.batch_size_test_per_gpu,
+ shuffle=False,
+ num_workers=self.cfg.batch_size_test_per_gpu,
+ pin_memory=True,
+ sampler=test_sampler,
+ collate_fn=self.__class__.collate_fn,
+ )
+ return test_loader
+
+ def test(self):
+ raise NotImplementedError
+
+ @staticmethod
+ def collate_fn(batch):
+ raise collate_fn(batch)
+
+
+@TESTERS.register_module()
+class SemSegTester(TesterBase):
+
+ def test(self):
+ assert self.test_loader.batch_size == 1
+ logger = get_root_logger()
+ logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+
+ batch_time = AverageMeter()
+ intersection_meter = AverageMeter()
+ union_meter = AverageMeter()
+ target_meter = AverageMeter()
+ self.model.eval()
+
+ save_path = os.path.join(self.cfg.save_path, "result")
+ make_dirs(save_path)
+ # create submit folder only on main process
+ if (
+ self.cfg.data.test.type == "ScanNetDataset"
+ or self.cfg.data.test.type == "ScanNet200Dataset"
+ or self.cfg.data.test.type == "ScanNetPPDataset"
+ ) and comm.is_main_process():
+ make_dirs(os.path.join(save_path, "submit"))
+ elif self.cfg.data.test.type == "SemanticKITTIDataset" and comm.is_main_process():
+ make_dirs(os.path.join(save_path, "submit"))
+ elif self.cfg.data.test.type == "NuScenesDataset" and comm.is_main_process():
+ import json
+
+ make_dirs(os.path.join(save_path, "submit", "lidarseg", "test"))
+ make_dirs(os.path.join(save_path, "submit", "test"))
+ submission = dict(
+ meta=dict(
+ use_camera=False,
+ use_lidar=True,
+ use_radar=False,
+ use_map=False,
+ use_external=False,
+ )
+ )
+ with open(os.path.join(save_path, "submit", "test", "submission.json"), "w") as f:
+ json.dump(submission, f, indent=4)
+ comm.synchronize()
+ record = {}
+ # fragment inference
+ for idx, data_dict in enumerate(self.test_loader):
+ end = time.time()
+ data_dict = data_dict[0] # current assume batch size is 1
+ fragment_list = data_dict.pop("fragment_list")
+ segment = data_dict.pop("segment")
+ data_name = data_dict.pop("name")
+ pred_save_path = os.path.join(save_path, "{}_pred.npy".format(data_name))
+ if os.path.isfile(pred_save_path):
+ logger.info("{}/{}: {}, loaded pred and label.".format(idx + 1, len(self.test_loader), data_name))
+ pred = np.load(pred_save_path)
+ if "origin_segment" in data_dict.keys():
+ segment = data_dict["origin_segment"]
+ else:
+ pred = torch.zeros((segment.size, self.cfg.data.num_classes)).cuda()
+ for i in range(len(fragment_list)):
+ fragment_batch_size = 1
+ s_i, e_i = i * fragment_batch_size, min((i + 1) * fragment_batch_size, len(fragment_list))
+ input_dict = collate_fn(fragment_list[s_i:e_i])
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ idx_part = input_dict["index"]
+ with torch.no_grad():
+ pred_part = self.model(input_dict)["seg_logits"] # (n, k)
+ pred_part = F.softmax(pred_part, -1)
+ if self.cfg.empty_cache:
+ torch.cuda.empty_cache()
+ bs = 0
+ for be in input_dict["offset"]:
+ pred[idx_part[bs:be], :] += pred_part[bs:be]
+ bs = be
+
+ logger.info(
+ "Test: {}/{}-{data_name}, Batch: {batch_idx}/{batch_num}".format(
+ idx + 1,
+ len(self.test_loader),
+ data_name=data_name,
+ batch_idx=i,
+ batch_num=len(fragment_list),
+ )
+ )
+ if self.cfg.data.test.type == "ScanNetPPDataset":
+ pred = pred.topk(3, dim=1)[1].data.cpu().numpy()
+ else:
+ pred = pred.max(1)[1].data.cpu().numpy()
+ if "origin_segment" in data_dict.keys():
+ assert "inverse" in data_dict.keys()
+ pred = pred[data_dict["inverse"]]
+ segment = data_dict["origin_segment"]
+ np.save(pred_save_path, pred)
+ if (
+ self.cfg.data.test.type == "ScanNetDataset"
+ or self.cfg.data.test.type == "ScanNet200Dataset"
+ ):
+ np.savetxt(
+ os.path.join(save_path, "submit", "{}.txt".format(data_name)),
+ self.test_loader.dataset.class2id[pred].reshape([-1, 1]),
+ fmt="%d",
+ )
+ elif self.cfg.data.test.type == "ScanNetPPDataset":
+ np.savetxt(
+ os.path.join(save_path, "submit", "{}.txt".format(data_name)),
+ pred.astype(np.int32),
+ delimiter=",",
+ fmt="%d",
+ )
+ pred = pred[:, 0] # for mIoU, TODO: support top3 mIoU
+ elif self.cfg.data.test.type == "SemanticKITTIDataset":
+ # 00_000000 -> 00, 000000
+ sequence_name, frame_name = data_name.split("_")
+ os.makedirs(
+ os.path.join(
+ save_path, "submit", "sequences", sequence_name, "predictions"
+ ),
+ exist_ok=True,
+ )
+ pred = pred.astype(np.uint32)
+ pred = np.vectorize(
+ self.test_loader.dataset.learning_map_inv.__getitem__
+ )(pred).astype(np.uint32)
+ pred.tofile(
+ os.path.join(
+ save_path,
+ "submit",
+ "sequences",
+ sequence_name,
+ "predictions",
+ f"{frame_name}.label",
+ )
+ )
+ elif self.cfg.data.test.type == "NuScenesDataset":
+ np.array(pred + 1).astype(np.uint8).tofile(
+ os.path.join(
+ save_path,
+ "submit",
+ "lidarseg",
+ "test",
+ "{}_lidarseg.bin".format(data_name),
+ )
+ )
+
+ intersection, union, target = intersection_and_union(
+ pred, segment, self.cfg.data.num_classes, self.cfg.data.ignore_index
+ )
+ intersection_meter.update(intersection)
+ union_meter.update(union)
+ target_meter.update(target)
+ record[data_name] = dict(intersection=intersection, union=union, target=target)
+
+ mask = union != 0
+ iou_class = intersection / (union + 1e-10)
+ iou = np.mean(iou_class[mask])
+ acc = sum(intersection) / (sum(target) + 1e-10)
+
+ m_iou = np.mean(intersection_meter.sum / (union_meter.sum + 1e-10))
+ m_acc = np.mean(intersection_meter.sum / (target_meter.sum + 1e-10))
+
+ batch_time.update(time.time() - end)
+ logger.info(
+ "Test: {} [{}/{}]-{} "
+ "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
+ "Accuracy {acc:.4f} ({m_acc:.4f}) "
+ "mIoU {iou:.4f} ({m_iou:.4f})".format(
+ data_name,
+ idx + 1,
+ len(self.test_loader),
+ segment.size,
+ batch_time=batch_time,
+ acc=acc,
+ m_acc=m_acc,
+ iou=iou,
+ m_iou=m_iou,
+ )
+ )
+
+ logger.info("Syncing ...")
+ comm.synchronize()
+ record_sync = comm.gather(record, dst=0)
+
+ if comm.is_main_process():
+ record = {}
+ for _ in range(len(record_sync)):
+ r = record_sync.pop()
+ record.update(r)
+ del r
+ intersection = np.sum([meters["intersection"] for _, meters in record.items()], axis=0)
+ union = np.sum([meters["union"] for _, meters in record.items()], axis=0)
+ target = np.sum([meters["target"] for _, meters in record.items()], axis=0)
+
+ if self.cfg.data.test.type == "S3DISDataset":
+ torch.save(
+ dict(intersection=intersection, union=union, target=target),
+ os.path.join(save_path, f"{self.test_loader.dataset.split}.pth"),
+ )
+
+ iou_class = intersection / (union + 1e-10)
+ accuracy_class = intersection / (target + 1e-10)
+ mIoU = np.mean(iou_class)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection) / (sum(target) + 1e-10)
+
+ logger.info("Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}".format(mIoU, mAcc, allAcc))
+ for i in range(self.cfg.data.num_classes):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+ logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+
+ @staticmethod
+ def collate_fn(batch):
+ return batch
+
+
+@TESTERS.register_module()
+class ClsTester(TesterBase):
+ def test(self):
+ logger = get_root_logger()
+ logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+ batch_time = AverageMeter()
+ intersection_meter = AverageMeter()
+ union_meter = AverageMeter()
+ target_meter = AverageMeter()
+ self.model.eval()
+
+ for i, input_dict in enumerate(self.test_loader):
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ end = time.time()
+ with torch.no_grad():
+ output_dict = self.model(input_dict)
+ output = output_dict["cls_logits"]
+ pred = output.max(1)[1]
+ label = input_dict["category"]
+ intersection, union, target = intersection_and_union_gpu(pred, label, self.cfg.data.num_classes, self.cfg.data.ignore_index)
+ if comm.get_world_size() > 1:
+ dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce(target)
+ intersection, union, target = (
+ intersection.cpu().numpy(),
+ union.cpu().numpy(),
+ target.cpu().numpy(),
+ )
+ intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)
+
+ accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)
+ batch_time.update(time.time() - end)
+
+ logger.info(
+ "Test: [{}/{}] "
+ "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
+ "Accuracy {accuracy:.4f} ".format(
+ i + 1,
+ len(self.test_loader),
+ batch_time=batch_time,
+ accuracy=accuracy,
+ )
+ )
+
+ iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
+ accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
+ mIoU = np.mean(iou_class)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
+ logger.info("Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(mIoU, mAcc, allAcc))
+
+ for i in range(self.cfg.data.num_classes):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=self.cfg.data.names[i],
+ iou=iou_class[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+ logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+
+ @staticmethod
+ def collate_fn(batch):
+ return collate_fn(batch)
+
+
+@TESTERS.register_module()
+class ClsVotingTester(TesterBase):
+ def __init__(
+ self,
+ num_repeat=100,
+ metric="allAcc",
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.num_repeat = num_repeat
+ self.metric = metric
+ self.best_idx = 0
+ self.best_record = None
+ self.best_metric = 0
+
+ def test(self):
+ for i in range(self.num_repeat):
+ logger = get_root_logger()
+ logger.info(f">>>>>>>>>>>>>>>> Start Evaluation {i + 1} >>>>>>>>>>>>>>>>")
+ record = self.test_once()
+ if comm.is_main_process():
+ if record[self.metric] > self.best_metric:
+ self.best_record = record
+ self.best_idx = i
+ self.best_metric = record[self.metric]
+ info = f"Current best record is Evaluation {i + 1}: "
+ for m in self.best_record.keys():
+ info += f"{m}: {self.best_record[m]:.4f} "
+ logger.info(info)
+
+ def test_once(self):
+ logger = get_root_logger()
+ batch_time = AverageMeter()
+ intersection_meter = AverageMeter()
+ target_meter = AverageMeter()
+ record = {}
+ self.model.eval()
+
+ for idx, data_dict in enumerate(self.test_loader):
+ end = time.time()
+ data_dict = data_dict[0] # current assume batch size is 1
+ voting_list = data_dict.pop("voting_list")
+ category = data_dict.pop("category")
+ data_name = data_dict.pop("name")
+ # pred = torch.zeros([1, self.cfg.data.num_classes]).cuda()
+ # for i in range(len(voting_list)):
+ # input_dict = voting_list[i]
+ # for key in input_dict.keys():
+ # if isinstance(input_dict[key], torch.Tensor):
+ # input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ # with torch.no_grad():
+ # pred += F.softmax(self.model(input_dict)["cls_logits"], -1)
+ input_dict = collate_fn(voting_list)
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ pred = F.softmax(self.model(input_dict)["cls_logits"], -1).sum(
+ 0, keepdim=True
+ )
+ pred = pred.max(1)[1].cpu().numpy()
+ intersection, union, target = intersection_and_union(
+ pred, category, self.cfg.data.num_classes, self.cfg.data.ignore_index
+ )
+ intersection_meter.update(intersection)
+ target_meter.update(target)
+ record[data_name] = dict(intersection=intersection, target=target)
+ acc = sum(intersection) / (sum(target) + 1e-10)
+ m_acc = np.mean(intersection_meter.sum / (target_meter.sum + 1e-10))
+ batch_time.update(time.time() - end)
+ logger.info(
+ "Test: {} [{}/{}] "
+ "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
+ "Accuracy {acc:.4f} ({m_acc:.4f}) ".format(
+ data_name,
+ idx + 1,
+ len(self.test_loader),
+ batch_time=batch_time,
+ acc=acc,
+ m_acc=m_acc,
+ )
+ )
+
+ logger.info("Syncing ...")
+ comm.synchronize()
+ record_sync = comm.gather(record, dst=0)
+
+ if comm.is_main_process():
+ record = {}
+ for _ in range(len(record_sync)):
+ r = record_sync.pop()
+ record.update(r)
+ del r
+ intersection = np.sum(
+ [meters["intersection"] for _, meters in record.items()], axis=0
+ )
+ target = np.sum([meters["target"] for _, meters in record.items()], axis=0)
+ accuracy_class = intersection / (target + 1e-10)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection) / (sum(target) + 1e-10)
+
+ logger.info("Val result: mAcc/allAcc {:.4f}/{:.4f}".format(mAcc, allAcc))
+ for i in range(self.cfg.data.num_classes):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {accuracy:.4f}".format(
+ idx=i,
+ name=self.cfg.data.names[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+ return dict(mAcc=mAcc, allAcc=allAcc)
+
+ @staticmethod
+ def collate_fn(batch):
+ return batch
+
+
+@TESTERS.register_module()
+class PartSegTester(TesterBase):
+ def test(self):
+ test_dataset = self.test_loader.dataset
+ logger = get_root_logger()
+ logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>")
+
+ batch_time = AverageMeter()
+
+ num_categories = len(self.test_loader.dataset.categories)
+ iou_category, iou_count = np.zeros(num_categories), np.zeros(num_categories)
+ self.model.eval()
+
+ save_path = os.path.join(self.cfg.save_path, "result", "test_epoch{}".format(self.cfg.test_epoch))
+ make_dirs(save_path)
+
+ for idx in range(len(test_dataset)):
+ end = time.time()
+ data_name = test_dataset.get_data_name(idx)
+
+ data_dict_list, label = test_dataset[idx]
+ pred = torch.zeros((label.size, self.cfg.data.num_classes)).cuda()
+ batch_num = int(np.ceil(len(data_dict_list) / self.cfg.batch_size_test))
+ for i in range(batch_num):
+ s_i, e_i = i * self.cfg.batch_size_test, min((i + 1) * self.cfg.batch_size_test, len(data_dict_list))
+ input_dict = collate_fn(data_dict_list[s_i:e_i])
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.no_grad():
+ pred_part = self.model(input_dict)["cls_logits"]
+ pred_part = F.softmax(pred_part, -1)
+ if self.cfg.empty_cache:
+ torch.cuda.empty_cache()
+ pred_part = pred_part.reshape(-1, label.size, self.cfg.data.num_classes)
+ pred = pred + pred_part.total(dim=0)
+ logger.info(
+ "Test: {} {}/{}, Batch: {batch_idx}/{batch_num}".format(
+ data_name,
+ idx + 1,
+ len(test_dataset),
+ batch_idx=i,
+ batch_num=batch_num,
+ )
+ )
+ pred = pred.max(1)[1].data.cpu().numpy()
+
+ category_index = data_dict_list[0]["cls_token"]
+ category = self.test_loader.dataset.categories[category_index]
+ parts_idx = self.test_loader.dataset.category2part[category]
+ parts_iou = np.zeros(len(parts_idx))
+ for j, part in enumerate(parts_idx):
+ if (np.sum(label == part) == 0) and (np.sum(pred == part) == 0):
+ parts_iou[j] = 1.0
+ else:
+ i = (label == part) & (pred == part)
+ u = (label == part) | (pred == part)
+ parts_iou[j] = np.sum(i) / (np.sum(u) + 1e-10)
+ iou_category[category_index] += parts_iou.mean()
+ iou_count[category_index] += 1
+
+ batch_time.update(time.time() - end)
+ logger.info("Test: {} [{}/{}] " "Batch {batch_time.val:.3f} " "({batch_time.avg:.3f}) ".format(data_name, idx + 1, len(self.test_loader), batch_time=batch_time))
+
+ ins_mIoU = iou_category.sum() / (iou_count.sum() + 1e-10)
+ cat_mIoU = (iou_category / (iou_count + 1e-10)).mean()
+ logger.info("Val result: ins.mIoU/cat.mIoU {:.4f}/{:.4f}.".format(ins_mIoU, cat_mIoU))
+ for i in range(num_categories):
+ logger.info(
+ "Class_{idx}-{name} Result: iou_cat/num_sample {iou_cat:.4f}/{iou_count:.4f}".format(
+ idx=i,
+ name=self.test_loader.dataset.categories[i],
+ iou_cat=iou_category[i] / (iou_count[i] + 1e-10),
+ iou_count=int(iou_count[i]),
+ )
+ )
+ logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<")
+
+ @staticmethod
+ def collate_fn(batch):
+ return collate_fn(batch)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/train.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0ca13e05743697c9bab6fcb63fb4d8c015c47e1
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/engines/train.py
@@ -0,0 +1,309 @@
+"""
+Trainer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import sys
+import weakref
+import torch
+import torch.nn as nn
+import torch.utils.data
+from functools import partial
+
+if sys.version_info >= (3, 10):
+ from collections.abc import Iterator
+else:
+ from collections import Iterator
+from tensorboardX import SummaryWriter
+
+from .defaults import create_ddp_model, worker_init_fn
+from .hooks import HookBase, build_hooks
+import pointcept.utils.comm as comm
+from pointcept.datasets import build_dataset, point_collate_fn, collate_fn
+from pointcept.models import build_model
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.optimizer import build_optimizer
+from pointcept.utils.scheduler import build_scheduler
+from pointcept.utils.events import EventStorage
+from pointcept.utils.registry import Registry
+
+
+TRAINERS = Registry("trainers")
+
+
+class TrainerBase:
+ def __init__(self) -> None:
+ self.hooks = []
+ self.epoch = 0
+ self.start_epoch = 0
+ self.max_epoch = 0
+ self.max_iter = 0
+ self.comm_info = dict()
+ self.data_iterator: Iterator = enumerate([])
+ self.storage: EventStorage
+ self.writer: SummaryWriter
+
+ def register_hooks(self, hooks) -> None:
+ hooks = build_hooks(hooks)
+ for h in hooks:
+ assert isinstance(h, HookBase)
+ # To avoid circular reference, hooks and trainer cannot own each other.
+ # This normally does not matter, but will cause memory leak if the
+ # involved objects contain __del__:
+ # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/
+ h.trainer = weakref.proxy(self)
+ self.hooks.extend(hooks)
+
+ def train(self):
+ with EventStorage() as self.storage:
+ # => before train
+ self.before_train()
+ for self.epoch in range(self.start_epoch, self.max_epoch):
+ # => before epoch
+ self.before_epoch()
+ # => run_epoch
+ for (
+ self.comm_info["iter"],
+ self.comm_info["input_dict"],
+ ) in self.data_iterator:
+ # => before_step
+ self.before_step()
+ # => run_step
+ self.run_step()
+ # => after_step
+ self.after_step()
+ # => after epoch
+ self.after_epoch()
+ # => after train
+ self.after_train()
+
+ def before_train(self):
+ for h in self.hooks:
+ h.before_train()
+
+ def before_epoch(self):
+ for h in self.hooks:
+ h.before_epoch()
+
+ def before_step(self):
+ for h in self.hooks:
+ h.before_step()
+
+ def run_step(self):
+ raise NotImplementedError
+
+ def after_step(self):
+ for h in self.hooks:
+ h.after_step()
+
+ def after_epoch(self):
+ for h in self.hooks:
+ h.after_epoch()
+ self.storage.reset_histories()
+
+ def after_train(self):
+ # Sync GPU before running train hooks
+ comm.synchronize()
+ for h in self.hooks:
+ h.after_train()
+ if comm.is_main_process():
+ self.writer.close()
+
+
+@TRAINERS.register_module("DefaultTrainer")
+class Trainer(TrainerBase):
+ def __init__(self, cfg):
+ super(Trainer, self).__init__()
+ self.epoch = 0
+ self.start_epoch = 0
+ self.max_epoch = cfg.eval_epoch
+ self.best_metric_value = -torch.inf
+ self.logger = get_root_logger(
+ log_file=os.path.join(cfg.save_path, "train.log"),
+ file_mode="a" if cfg.resume else "w",
+ )
+ self.logger.info("=> Loading config ...")
+ self.cfg = cfg
+ self.logger.info(f"Save path: {cfg.save_path}")
+ self.logger.info(f"Config:\n{cfg.pretty_text}")
+ self.logger.info("=> Building model ...")
+ self.model = self.build_model()
+ self.logger.info("=> Building writer ...")
+ self.writer = self.build_writer()
+ self.logger.info("=> Building train dataset & dataloader ...")
+ self.train_loader = self.build_train_loader()
+ self.logger.info("=> Building val dataset & dataloader ...")
+ self.val_loader = self.build_val_loader()
+ self.logger.info("=> Building optimize, scheduler, scaler(amp) ...")
+ self.optimizer = self.build_optimizer()
+ self.scheduler = self.build_scheduler()
+ self.scaler = self.build_scaler()
+ self.logger.info("=> Building hooks ...")
+ self.register_hooks(self.cfg.hooks)
+
+ def train(self):
+ with EventStorage() as self.storage:
+ # => before train
+ self.before_train()
+ self.logger.info(">>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>>")
+ for self.epoch in range(self.start_epoch, self.max_epoch):
+ # => before epoch
+ # TODO: optimize to iteration based
+ if comm.get_world_size() > 1:
+ self.train_loader.sampler.set_epoch(self.epoch)
+ self.model.train()
+ self.data_iterator = enumerate(self.train_loader)
+ self.before_epoch()
+ # => run_epoch
+ for (
+ self.comm_info["iter"],
+ self.comm_info["input_dict"],
+ ) in self.data_iterator:
+ # => before_step
+ self.before_step()
+ # => run_step
+ self.run_step()
+ # => after_step
+ self.after_step()
+ # => after epoch
+ self.after_epoch()
+ # => after train
+ self.after_train()
+
+ def run_step(self):
+ input_dict = self.comm_info["input_dict"]
+ for key in input_dict.keys():
+ if isinstance(input_dict[key], torch.Tensor):
+ input_dict[key] = input_dict[key].cuda(non_blocking=True)
+ with torch.cuda.amp.autocast(enabled=self.cfg.enable_amp):
+ output_dict = self.model(input_dict)
+ loss = output_dict["loss"]
+ self.optimizer.zero_grad()
+ if self.cfg.enable_amp:
+ self.scaler.scale(loss).backward()
+ self.scaler.step(self.optimizer)
+
+ # When enable amp, optimizer.step call are skipped if the loss scaling factor is too large.
+ # Fix torch warning scheduler step before optimizer step.
+ scaler = self.scaler.get_scale()
+ self.scaler.update()
+ if scaler <= self.scaler.get_scale():
+ self.scheduler.step()
+ else:
+ loss.backward()
+ self.optimizer.step()
+ self.scheduler.step()
+ if self.cfg.empty_cache:
+ torch.cuda.empty_cache()
+ self.comm_info["model_output_dict"] = output_dict
+
+ def after_epoch(self):
+ for h in self.hooks:
+ h.after_epoch()
+ self.storage.reset_histories()
+ if self.cfg.empty_cache_per_epoch:
+ torch.cuda.empty_cache()
+
+ def build_model(self):
+ model = build_model(self.cfg.model)
+ if self.cfg.sync_bn:
+ model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
+ n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ # logger.info(f"Model: \n{self.model}")
+ self.logger.info(f"Num params: {n_parameters}")
+ model = create_ddp_model(
+ model.cuda(),
+ broadcast_buffers=False,
+ find_unused_parameters=self.cfg.find_unused_parameters,
+ )
+ return model
+
+ def build_writer(self):
+ writer = SummaryWriter(self.cfg.save_path) if comm.is_main_process() else None
+ self.logger.info(f"Tensorboard writer logging dir: {self.cfg.save_path}")
+ return writer
+
+ def build_train_loader(self):
+ train_data = build_dataset(self.cfg.data.train)
+
+ if comm.get_world_size() > 1:
+ train_sampler = torch.utils.data.distributed.DistributedSampler(train_data)
+ else:
+ train_sampler = None
+
+ init_fn = (
+ partial(
+ worker_init_fn,
+ num_workers=self.cfg.num_worker_per_gpu,
+ rank=comm.get_rank(),
+ seed=self.cfg.seed,
+ )
+ if self.cfg.seed is not None
+ else None
+ )
+
+ train_loader = torch.utils.data.DataLoader(
+ train_data,
+ batch_size=self.cfg.batch_size_per_gpu,
+ shuffle=(train_sampler is None),
+ num_workers=self.cfg.num_worker_per_gpu,
+ sampler=train_sampler,
+ collate_fn=partial(point_collate_fn, mix_prob=self.cfg.mix_prob),
+ pin_memory=True,
+ worker_init_fn=init_fn,
+ drop_last=True,
+ persistent_workers=True,
+ )
+ return train_loader
+
+ def build_val_loader(self):
+ val_loader = None
+ if self.cfg.evaluate:
+ val_data = build_dataset(self.cfg.data.val)
+ if comm.get_world_size() > 1:
+ val_sampler = torch.utils.data.distributed.DistributedSampler(val_data)
+ else:
+ val_sampler = None
+ val_loader = torch.utils.data.DataLoader(
+ val_data,
+ batch_size=self.cfg.batch_size_val_per_gpu,
+ shuffle=False,
+ num_workers=self.cfg.num_worker_per_gpu,
+ pin_memory=True,
+ sampler=val_sampler,
+ collate_fn=collate_fn,
+ )
+ return val_loader
+
+ def build_optimizer(self):
+ return build_optimizer(self.cfg.optimizer, self.model, self.cfg.param_dicts)
+
+ def build_scheduler(self):
+ assert hasattr(self, "optimizer")
+ assert hasattr(self, "train_loader")
+ self.cfg.scheduler.total_steps = len(self.train_loader) * self.cfg.eval_epoch
+ return build_scheduler(self.cfg.scheduler, self.optimizer)
+
+ def build_scaler(self):
+ scaler = torch.cuda.amp.GradScaler() if self.cfg.enable_amp else None
+ return scaler
+
+
+@TRAINERS.register_module("MultiDatasetTrainer")
+class MultiDatasetTrainer(Trainer):
+ def build_train_loader(self):
+ from pointcept.datasets import MultiDatasetDataloader
+
+ train_data = build_dataset(self.cfg.data.train)
+ train_loader = MultiDatasetDataloader(
+ train_data,
+ self.cfg.batch_size_per_gpu,
+ self.cfg.num_worker_per_gpu,
+ self.cfg.mix_prob,
+ self.cfg.seed,
+ )
+ self.comm_info["iter_per_epoch"] = len(train_loader)
+ return train_loader
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbc53a338ce2ef7e0eb36aa415e40b6b095665a3
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/__init__.py
@@ -0,0 +1,24 @@
+from .builder import build_model
+from .default import DefaultSegmentor, DefaultClassifier
+
+# Backbones
+from .sparse_unet import *
+from .point_transformer import *
+from .point_transformer_v2 import *
+from .point_transformer_v3 import *
+from .stratified_transformer import *
+from .spvcnn import *
+# from .octformer import *
+# from .oacnns import *
+
+# from .swin3d import *
+
+# Semantic Segmentation
+from .context_aware_classifier import *
+
+# Instance Segmentation
+# from .point_group import *
+
+# Pretraining
+from .masked_scene_contrast import *
+from .point_prompt_training import *
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/builder.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbda24465a405a5a2094f8d0c420a53c50fe79cc
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/builder.py
@@ -0,0 +1,16 @@
+"""
+Model Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+MODELS = Registry("models")
+MODULES = Registry("modules")
+
+
+def build_model(cfg):
+ """Build models."""
+ return MODELS.build(cfg)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/context_aware_classifier/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/context_aware_classifier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8327900bbf92258fa242f4b99d5b235bc1ae44aa
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/context_aware_classifier/__init__.py
@@ -0,0 +1 @@
+from .context_aware_classifier_v1m1_base import CACSegmentor
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/context_aware_classifier/context_aware_classifier_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/context_aware_classifier/context_aware_classifier_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..893f4c8fc6d68a1fb7dc24e1ceb3b3ace11ebec6
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/context_aware_classifier/context_aware_classifier_v1m1_base.py
@@ -0,0 +1,275 @@
+"""
+Context-aware Classifier for Semantic Segmentation
+
+Author: Zhuotao Tian, Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pointcept.models.losses import build_criteria
+from pointcept.models.utils.structure import Point
+from pointcept.models.builder import MODELS, build_model
+
+
+@MODELS.register_module("CAC-v1m1")
+class CACSegmentor(nn.Module):
+ def __init__(
+ self,
+ num_classes,
+ backbone_out_channels,
+ backbone=None,
+ criteria=None,
+ cos_temp=15,
+ main_weight=1,
+ pre_weight=1,
+ pre_self_weight=1,
+ kl_weight=1,
+ conf_thresh=0,
+ detach_pre_logits=False,
+ ):
+ super().__init__()
+ self.num_classes = num_classes
+ self.cos_temp = cos_temp
+ self.main_weight = main_weight
+ self.pre_weight = pre_weight
+ self.pre_self_weight = pre_self_weight
+ self.kl_weight = kl_weight
+ self.conf_thresh = conf_thresh
+ self.detach_pre_logits = detach_pre_logits
+
+ # backbone
+ self.backbone = build_model(backbone)
+ # heads
+ self.seg_head = nn.Linear(backbone_out_channels, num_classes)
+ self.proj = nn.Sequential(
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels * 2, bias=False),
+ nn.ReLU(inplace=True),
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels),
+ )
+ self.apd_proj = nn.Sequential(
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels * 2, bias=False),
+ nn.ReLU(inplace=True),
+ nn.Linear(backbone_out_channels * 2, backbone_out_channels),
+ )
+ self.feat_proj_layer = nn.Sequential(
+ nn.Linear(backbone_out_channels, backbone_out_channels, bias=False),
+ nn.BatchNorm1d(backbone_out_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(backbone_out_channels, backbone_out_channels),
+ )
+ # Criteria
+ self.criteria = build_criteria(criteria)
+
+ @staticmethod
+ def get_pred(x, proto):
+ # x: [n,c]; proto: [cls, c]
+ x = F.normalize(x, 2, 1)
+ proto = F.normalize(proto, 2, 1)
+ pred = x @ proto.permute(1, 0) # [n,c] x [c, cls] -> [n, cls]
+ return pred
+
+ def get_adaptive_perspective(self, feat, target, new_proto, proto):
+ raw_feat = feat.clone()
+ # target: [n]
+ # feat: [n,c]
+ # proto: [cls, c]
+ unique_y = list(target.unique())
+ if -1 in unique_y:
+ unique_y.remove(-1)
+ target = target.unsqueeze(-1) # [n, 1]
+
+ for tmp_y in unique_y:
+ tmp_mask = (target == tmp_y).float()
+ tmp_proto = (feat * tmp_mask).sum(0) / (tmp_mask.sum(0) + 1e-4) # c
+ onehot_vec = torch.zeros(new_proto.shape[0], 1).cuda() # cls, 1
+ onehot_vec[tmp_y.long()] = 1
+ new_proto = (
+ new_proto * (1 - onehot_vec) + tmp_proto.unsqueeze(0) * onehot_vec
+ )
+
+ new_proto = torch.cat([new_proto, proto], -1)
+ new_proto = self.apd_proj(new_proto)
+ raw_feat = self.feat_proj_layer(raw_feat)
+ pred = self.get_pred(raw_feat, new_proto)
+ return pred
+
+ def post_refine_proto_batch(self, feat, pred, proto, offset=None):
+ # x: [n, c]; pred: [n, cls]; proto: [cls, c]
+ pred_list = []
+ x = feat
+ raw_x = x.clone()
+ if self.detach_pre_logits:
+ pred = pred.detach()
+ raw_pred = pred.clone()
+
+ if offset is None:
+ raw_x = x.clone()
+ n, n_cls = pred.shape[:]
+ pred = pred.view(n, n_cls)
+ pred = F.softmax(pred, 1).permute(1, 0) # [n, cls] -> [cls, n]
+ if self.conf_thresh > 0:
+ max_pred = (
+ (pred.max(0)[0] >= self.conf_thresh).float().unsqueeze(0)
+ ) # 1, n
+ pred = pred * max_pred
+ pred_proto = (pred / (pred.sum(-1).unsqueeze(-1) + 1e-7)) @ raw_x # cls, c
+
+ pred_proto = torch.cat([pred_proto, proto], -1) # cls, 2c
+ pred_proto = self.proj(pred_proto)
+ raw_x = self.feat_proj_layer(raw_x)
+ new_pred = self.get_pred(raw_x, pred_proto)
+ else:
+ for i in range(len(offset)):
+ if i == 0:
+ start = 0
+ end = offset[i]
+ else:
+ start, end = offset[i - 1], offset[i]
+ tmp_x = raw_x[start:end]
+ pred = raw_pred[start:end]
+ n, n_cls = pred.shape[:]
+ pred = pred.view(n, n_cls)
+ pred = F.softmax(pred, 1).permute(1, 0) # [n, cls] -> [cls, n]
+ if self.conf_thresh > 0:
+ max_pred = (
+ (pred.max(0)[0] >= self.conf_thresh).float().unsqueeze(0)
+ ) # 1, n
+ pred = pred * max_pred
+ pred_proto = (
+ pred / (pred.sum(-1).unsqueeze(-1) + 1e-7)
+ ) @ tmp_x # cls, c
+
+ pred_proto = torch.cat([pred_proto, proto], -1) # cls, 2c
+ pred_proto = self.proj(pred_proto)
+ tmp_x = self.feat_proj_layer(tmp_x)
+ new_pred = self.get_pred(tmp_x, pred_proto)
+ pred_list.append(new_pred)
+ new_pred = torch.cat(pred_list, 0)
+ return new_pred
+
+ @staticmethod
+ def get_distill_loss(pred, soft, target, smoothness=0.5, eps=0):
+ """
+ knowledge distillation loss
+ """
+ n, c = soft.shape[:]
+ soft = soft.detach()
+ target = target.unsqueeze(-1) # n, 1
+ onehot = target.view(-1, 1) # n, 1
+ ignore_mask = (onehot == -1).float()
+ sm_soft = F.softmax(soft / 1, 1) # n, c
+
+ onehot = onehot * (1 - ignore_mask)
+ onehot = torch.zeros(n, c).cuda().scatter_(1, onehot.long(), 1) # n, c
+ smoothed_label = smoothness * sm_soft + (1 - smoothness) * onehot
+ if eps > 0:
+ smoothed_label = smoothed_label * (1 - eps) + (1 - smoothed_label) * eps / (
+ smoothed_label.shape[1] - 1
+ )
+
+ loss = torch.mul(-1 * F.log_softmax(pred, dim=1), smoothed_label) # b, n, h, w
+ loss = loss.sum(1)
+
+ sm_soft = F.softmax(soft / 1, 1) # n, c
+ entropy_mask = -1 * (sm_soft * torch.log(sm_soft + 1e-4)).sum(1)
+
+ # for class-wise entropy estimation
+ target = target.squeeze(-1)
+ unique_classes = list(target.unique())
+ if -1 in unique_classes:
+ unique_classes.remove(-1)
+ valid_mask = (target != -1).float()
+ entropy_mask = entropy_mask * valid_mask
+ loss_list = []
+ weight_list = []
+ for tmp_y in unique_classes:
+ tmp_mask = (target == tmp_y).float().squeeze()
+ tmp_entropy_mask = entropy_mask * tmp_mask
+ class_weight = 1
+ tmp_loss = (loss * tmp_entropy_mask).sum() / (tmp_entropy_mask.sum() + 1e-4)
+ loss_list.append(class_weight * tmp_loss)
+ weight_list.append(class_weight)
+
+ if len(weight_list) > 0:
+ loss = sum(loss_list) / (sum(weight_list) + 1e-4)
+ else:
+ loss = torch.zeros(1).cuda().mean()
+ return loss
+
+ def forward(self, data_dict):
+ offset = data_dict["offset"]
+ point = self.backbone(data_dict)
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ seg_logits = self.seg_head(feat)
+
+ if self.training:
+ target = data_dict["segment"]
+ pre_logits = seg_logits.clone()
+ refine_logits = (
+ self.post_refine_proto_batch(
+ feat=feat,
+ pred=seg_logits,
+ proto=self.seg_head.weight.squeeze(),
+ offset=offset,
+ )
+ * self.cos_temp
+ )
+
+ cac_pred = (
+ self.get_adaptive_perspective(
+ feat=feat,
+ target=target,
+ new_proto=self.seg_head.weight.detach().data.squeeze(),
+ proto=self.seg_head.weight.squeeze(),
+ )
+ * self.cos_temp
+ )
+
+ seg_loss = self.criteria(refine_logits, target) * self.main_weight
+ pre_loss = self.criteria(cac_pred, target) * self.pre_weight
+ pre_self_loss = self.criteria(pre_logits, target) * self.pre_self_weight
+ kl_loss = (
+ self.get_distill_loss(
+ pred=refine_logits, soft=cac_pred.detach(), target=target
+ )
+ * self.kl_weight
+ )
+ loss = seg_loss + pre_loss + pre_self_loss + kl_loss
+ return dict(
+ loss=loss,
+ seg_loss=seg_loss,
+ pre_loss=pre_loss,
+ pre_self_loss=pre_self_loss,
+ kl_loss=kl_loss,
+ )
+
+ elif "segment" in data_dict.keys():
+ refine_logits = (
+ self.post_refine_proto_batch(
+ feat=feat,
+ pred=seg_logits,
+ proto=self.seg_head.weight.squeeze(),
+ offset=offset,
+ )
+ * self.cos_temp
+ )
+
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss, seg_logits=refine_logits)
+
+ else:
+ refine_logits = (
+ self.post_refine_proto_batch(
+ feat=feat,
+ pred=seg_logits,
+ proto=self.seg_head.weight.squeeze(),
+ offset=offset,
+ )
+ * self.cos_temp
+ )
+ return dict(seg_logits=refine_logits)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/default.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..95aea1ff4405746dd0ed7f2d83c118e48e6283ec
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/default.py
@@ -0,0 +1,132 @@
+from contextlib import nullcontext
+
+import torch
+import torch.nn as nn
+import torch_scatter
+
+from pointcept.models.losses import build_criteria
+from pointcept.models.utils.structure import Point
+
+from .builder import MODELS, build_model
+
+
+@MODELS.register_module()
+class DefaultSegmentor(nn.Module):
+ def __init__(self, backbone=None, criteria=None):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.criteria = build_criteria(criteria)
+
+ def forward(self, input_dict):
+ if "condition" in input_dict.keys():
+ # PPT (https://arxiv.org/abs/2308.09718)
+ # currently, only support one batch one condition
+ input_dict["condition"] = input_dict["condition"][0]
+ seg_logits = self.backbone(input_dict)
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in input_dict.keys():
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
+
+
+@MODELS.register_module()
+class DefaultSegmentorV2(nn.Module):
+ def __init__(
+ self,
+ num_classes,
+ backbone_out_channels,
+ backbone=None,
+ criteria=None,
+ freeze_backbone=False,
+ ):
+ super().__init__()
+ self.seg_head = nn.Linear(backbone_out_channels, num_classes) if num_classes > 0 else nn.Identity()
+ self.backbone = build_model(backbone)
+ self.criteria = build_criteria(criteria)
+
+ if freeze_backbone:
+ self.optional_freeze = torch.no_grad
+ else:
+ self.optional_freeze = nullcontext
+
+ def forward(self, input_dict):
+ point = Point(input_dict)
+ with self.optional_freeze():
+ point = self.backbone(point)
+ # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ seg_logits = self.seg_head(feat)
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in input_dict.keys():
+ loss = self.criteria(seg_logits, input_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
+
+
+@MODELS.register_module()
+class DefaultClassifier(nn.Module):
+ def __init__(
+ self,
+ backbone=None,
+ criteria=None,
+ num_classes=40,
+ backbone_embed_dim=256,
+ ):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.criteria = build_criteria(criteria)
+ self.num_classes = num_classes
+ self.backbone_embed_dim = backbone_embed_dim
+ self.cls_head = nn.Sequential(
+ nn.Linear(backbone_embed_dim, 256),
+ nn.BatchNorm1d(256),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(256, 128),
+ nn.BatchNorm1d(128),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(128, num_classes),
+ )
+
+ def forward(self, input_dict):
+ point = Point(input_dict)
+ point = self.backbone(point)
+ # Backbone added after v1.5.0 return Point instead of feat
+ # And after v1.5.0 feature aggregation for classification operated in classifier
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ point.feat = torch_scatter.segment_csr(
+ src=point.feat,
+ indptr=nn.functional.pad(point.offset, (1, 0)),
+ reduce="mean",
+ )
+ feat = point.feat
+ else:
+ feat = point
+ cls_logits = self.cls_head(feat)
+ if self.training:
+ loss = self.criteria(cls_logits, input_dict["category"])
+ return dict(loss=loss)
+ elif "category" in input_dict.keys():
+ loss = self.criteria(cls_logits, input_dict["category"])
+ return dict(loss=loss, cls_logits=cls_logits)
+ else:
+ return dict(cls_logits=cls_logits)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee44096e13acdf7d6cf7ba9ff764b4b6af3aafe5
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/__init__.py
@@ -0,0 +1,4 @@
+from .builder import build_criteria
+
+from .misc import CrossEntropyLoss, SmoothCELoss, DiceLoss, FocalLoss, BinaryFocalLoss
+from .lovasz import LovaszLoss
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/builder.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef642d9830622d847eb2b7e7013252a32c2b6368
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/builder.py
@@ -0,0 +1,31 @@
+"""
+Criteria Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+LOSSES = Registry("losses")
+
+
+class Criteria(object):
+ def __init__(self, cfg=None):
+ self.cfg = cfg if cfg is not None else []
+ self.criteria = []
+ for loss_cfg in self.cfg:
+ self.criteria.append(LOSSES.build(cfg=loss_cfg))
+
+ def __call__(self, pred, target):
+ if len(self.criteria) == 0:
+ # loss computation occur in model
+ return pred
+ loss = 0
+ for c in self.criteria:
+ loss += c(pred, target)
+ return loss
+
+
+def build_criteria(cfg):
+ return Criteria(cfg)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/lovasz.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/lovasz.py
new file mode 100644
index 0000000000000000000000000000000000000000..de01a181bce59ba8288171bb58c0ddc728adcb5b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/lovasz.py
@@ -0,0 +1,257 @@
+"""
+Lovasz Loss
+refer https://arxiv.org/abs/1705.08790
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from typing import Optional
+from itertools import filterfalse
+import torch
+import torch.nn.functional as F
+from torch.nn.modules.loss import _Loss
+
+from .builder import LOSSES
+
+BINARY_MODE: str = "binary"
+MULTICLASS_MODE: str = "multiclass"
+MULTILABEL_MODE: str = "multilabel"
+
+
+def _lovasz_grad(gt_sorted):
+ """Compute gradient of the Lovasz extension w.r.t sorted errors
+ See Alg. 1 in paper
+ """
+ p = len(gt_sorted)
+ gts = gt_sorted.sum()
+ intersection = gts - gt_sorted.float().cumsum(0)
+ union = gts + (1 - gt_sorted).float().cumsum(0)
+ jaccard = 1.0 - intersection / union
+ if p > 1: # cover 1-pixel case
+ jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
+ return jaccard
+
+
+def _lovasz_hinge(logits, labels, per_image=True, ignore=None):
+ """
+ Binary Lovasz hinge loss
+ logits: [B, H, W] Logits at each pixel (between -infinity and +infinity)
+ labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
+ per_image: compute the loss per image instead of per batch
+ ignore: void class id
+ """
+ if per_image:
+ loss = mean(
+ _lovasz_hinge_flat(
+ *_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore)
+ )
+ for log, lab in zip(logits, labels)
+ )
+ else:
+ loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore))
+ return loss
+
+
+def _lovasz_hinge_flat(logits, labels):
+ """Binary Lovasz hinge loss
+ Args:
+ logits: [P] Logits at each prediction (between -infinity and +infinity)
+ labels: [P] Tensor, binary ground truth labels (0 or 1)
+ """
+ if len(labels) == 0:
+ # only void pixels, the gradients should be 0
+ return logits.sum() * 0.0
+ signs = 2.0 * labels.float() - 1.0
+ errors = 1.0 - logits * signs
+ errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
+ perm = perm.data
+ gt_sorted = labels[perm]
+ grad = _lovasz_grad(gt_sorted)
+ loss = torch.dot(F.relu(errors_sorted), grad)
+ return loss
+
+
+def _flatten_binary_scores(scores, labels, ignore=None):
+ """Flattens predictions in the batch (binary case)
+ Remove labels equal to 'ignore'
+ """
+ scores = scores.view(-1)
+ labels = labels.view(-1)
+ if ignore is None:
+ return scores, labels
+ valid = labels != ignore
+ vscores = scores[valid]
+ vlabels = labels[valid]
+ return vscores, vlabels
+
+
+def _lovasz_softmax(
+ probas, labels, classes="present", class_seen=None, per_image=False, ignore=None
+):
+ """Multi-class Lovasz-Softmax loss
+ Args:
+ @param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1).
+ Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
+ @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
+ @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
+ @param per_image: compute the loss per image instead of per batch
+ @param ignore: void class labels
+ """
+ if per_image:
+ loss = mean(
+ _lovasz_softmax_flat(
+ *_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore),
+ classes=classes
+ )
+ for prob, lab in zip(probas, labels)
+ )
+ else:
+ loss = _lovasz_softmax_flat(
+ *_flatten_probas(probas, labels, ignore),
+ classes=classes,
+ class_seen=class_seen
+ )
+ return loss
+
+
+def _lovasz_softmax_flat(probas, labels, classes="present", class_seen=None):
+ """Multi-class Lovasz-Softmax loss
+ Args:
+ @param probas: [P, C] Class probabilities at each prediction (between 0 and 1)
+ @param labels: [P] Tensor, ground truth labels (between 0 and C - 1)
+ @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
+ """
+ if probas.numel() == 0:
+ # only void pixels, the gradients should be 0
+ return probas.mean() * 0.0
+ C = probas.size(1)
+ losses = []
+ class_to_sum = list(range(C)) if classes in ["all", "present"] else classes
+ # for c in class_to_sum:
+ for c in labels.unique():
+ if class_seen is None:
+ fg = (labels == c).type_as(probas) # foreground for class c
+ if classes == "present" and fg.sum() == 0:
+ continue
+ if C == 1:
+ if len(classes) > 1:
+ raise ValueError("Sigmoid output possible only with 1 class")
+ class_pred = probas[:, 0]
+ else:
+ class_pred = probas[:, c]
+ errors = (fg - class_pred).abs()
+ errors_sorted, perm = torch.sort(errors, 0, descending=True)
+ perm = perm.data
+ fg_sorted = fg[perm]
+ losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted)))
+ else:
+ if c in class_seen:
+ fg = (labels == c).type_as(probas) # foreground for class c
+ if classes == "present" and fg.sum() == 0:
+ continue
+ if C == 1:
+ if len(classes) > 1:
+ raise ValueError("Sigmoid output possible only with 1 class")
+ class_pred = probas[:, 0]
+ else:
+ class_pred = probas[:, c]
+ errors = (fg - class_pred).abs()
+ errors_sorted, perm = torch.sort(errors, 0, descending=True)
+ perm = perm.data
+ fg_sorted = fg[perm]
+ losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted)))
+ return mean(losses)
+
+
+def _flatten_probas(probas, labels, ignore=None):
+ """Flattens predictions in the batch"""
+ if probas.dim() == 3:
+ # assumes output of a sigmoid layer
+ B, H, W = probas.size()
+ probas = probas.view(B, 1, H, W)
+
+ C = probas.size(1)
+ probas = torch.movedim(probas, 1, -1) # [B, C, Di, Dj, ...] -> [B, Di, Dj, ..., C]
+ probas = probas.contiguous().view(-1, C) # [P, C]
+
+ labels = labels.view(-1)
+ if ignore is None:
+ return probas, labels
+ valid = labels != ignore
+ vprobas = probas[valid]
+ vlabels = labels[valid]
+ return vprobas, vlabels
+
+
+def isnan(x):
+ return x != x
+
+
+def mean(values, ignore_nan=False, empty=0):
+ """Nan-mean compatible with generators."""
+ values = iter(values)
+ if ignore_nan:
+ values = filterfalse(isnan, values)
+ try:
+ n = 1
+ acc = next(values)
+ except StopIteration:
+ if empty == "raise":
+ raise ValueError("Empty mean")
+ return empty
+ for n, v in enumerate(values, 2):
+ acc += v
+ if n == 1:
+ return acc
+ return acc / n
+
+
+@LOSSES.register_module()
+class LovaszLoss(_Loss):
+ def __init__(
+ self,
+ mode: str,
+ class_seen: Optional[int] = None,
+ per_image: bool = False,
+ ignore_index: Optional[int] = None,
+ loss_weight: float = 1.0,
+ ):
+ """Lovasz loss for segmentation task.
+ It supports binary, multiclass and multilabel cases
+ Args:
+ mode: Loss mode 'binary', 'multiclass' or 'multilabel'
+ ignore_index: Label that indicates ignored pixels (does not contribute to loss)
+ per_image: If True loss computed per each image and then averaged, else computed per whole batch
+ Shape
+ - **y_pred** - torch.Tensor of shape (N, C, H, W)
+ - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
+ Reference
+ https://github.com/BloodAxe/pytorch-toolbelt
+ """
+ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
+ super().__init__()
+
+ self.mode = mode
+ self.ignore_index = ignore_index
+ self.per_image = per_image
+ self.class_seen = class_seen
+ self.loss_weight = loss_weight
+
+ def forward(self, y_pred, y_true):
+ if self.mode in {BINARY_MODE, MULTILABEL_MODE}:
+ loss = _lovasz_hinge(
+ y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index
+ )
+ elif self.mode == MULTICLASS_MODE:
+ y_pred = y_pred.softmax(dim=1)
+ loss = _lovasz_softmax(
+ y_pred,
+ y_true,
+ class_seen=self.class_seen,
+ per_image=self.per_image,
+ ignore=self.ignore_index,
+ )
+ else:
+ raise ValueError("Wrong mode {}.".format(self.mode))
+ return loss * self.loss_weight
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/misc.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec300a54b2d920d37882d25e8c7771bce01db97c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/losses/misc.py
@@ -0,0 +1,223 @@
+"""
+Misc Losses
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .builder import LOSSES
+
+
+@LOSSES.register_module()
+class CrossEntropyLoss(nn.Module):
+ def __init__(
+ self,
+ weight=None,
+ size_average=None,
+ reduce=None,
+ reduction="mean",
+ label_smoothing=0.0,
+ loss_weight=1.0,
+ ignore_index=-1,
+ ):
+ super(CrossEntropyLoss, self).__init__()
+ weight = torch.tensor(weight).cuda() if weight is not None else None
+ self.loss_weight = loss_weight
+ self.loss = nn.CrossEntropyLoss(
+ weight=weight,
+ size_average=size_average,
+ ignore_index=ignore_index,
+ reduce=reduce,
+ reduction=reduction,
+ label_smoothing=label_smoothing,
+ )
+
+ def forward(self, pred, target):
+ return self.loss(pred, target) * self.loss_weight
+
+
+@LOSSES.register_module()
+class SmoothCELoss(nn.Module):
+ def __init__(self, smoothing_ratio=0.1):
+ super(SmoothCELoss, self).__init__()
+ self.smoothing_ratio = smoothing_ratio
+
+ def forward(self, pred, target):
+ eps = self.smoothing_ratio
+ n_class = pred.size(1)
+ one_hot = torch.zeros_like(pred).scatter(1, target.view(-1, 1), 1)
+ one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
+ log_prb = F.log_softmax(pred, dim=1)
+ loss = -(one_hot * log_prb).total(dim=1)
+ loss = loss[torch.isfinite(loss)].mean()
+ return loss
+
+
+@LOSSES.register_module()
+class BinaryFocalLoss(nn.Module):
+ def __init__(self, gamma=2.0, alpha=0.5, logits=True, reduce=True, loss_weight=1.0):
+ """Binary Focal Loss
+ `
+ """
+ super(BinaryFocalLoss, self).__init__()
+ assert 0 < alpha < 1
+ self.gamma = gamma
+ self.alpha = alpha
+ self.logits = logits
+ self.reduce = reduce
+ self.loss_weight = loss_weight
+
+ def forward(self, pred, target, **kwargs):
+ """Forward function.
+ Args:
+ pred (torch.Tensor): The prediction with shape (N)
+ target (torch.Tensor): The ground truth. If containing class
+ indices, shape (N) where each value is 0≤targets[i]≤1, If containing class probabilities,
+ same shape as the input.
+ Returns:
+ torch.Tensor: The calculated loss
+ """
+ if self.logits:
+ bce = F.binary_cross_entropy_with_logits(pred, target, reduction="none")
+ else:
+ bce = F.binary_cross_entropy(pred, target, reduction="none")
+ pt = torch.exp(-bce)
+ alpha = self.alpha * target + (1 - self.alpha) * (1 - target)
+ focal_loss = alpha * (1 - pt) ** self.gamma * bce
+
+ if self.reduce:
+ focal_loss = torch.mean(focal_loss)
+ return focal_loss * self.loss_weight
+
+
+@LOSSES.register_module()
+class FocalLoss(nn.Module):
+ def __init__(
+ self, gamma=2.0, alpha=0.5, reduction="mean", loss_weight=1.0, ignore_index=-1
+ ):
+ """Focal Loss
+ `
+ """
+ super(FocalLoss, self).__init__()
+ assert reduction in (
+ "mean",
+ "sum",
+ ), "AssertionError: reduction should be 'mean' or 'sum'"
+ assert isinstance(
+ alpha, (float, list)
+ ), "AssertionError: alpha should be of type float"
+ assert isinstance(gamma, float), "AssertionError: gamma should be of type float"
+ assert isinstance(
+ loss_weight, float
+ ), "AssertionError: loss_weight should be of type float"
+ assert isinstance(ignore_index, int), "ignore_index must be of type int"
+ self.gamma = gamma
+ self.alpha = alpha
+ self.reduction = reduction
+ self.loss_weight = loss_weight
+ self.ignore_index = ignore_index
+
+ def forward(self, pred, target, **kwargs):
+ """Forward function.
+ Args:
+ pred (torch.Tensor): The prediction with shape (N, C) where C = number of classes.
+ target (torch.Tensor): The ground truth. If containing class
+ indices, shape (N) where each value is 0≤targets[i]≤C−1, If containing class probabilities,
+ same shape as the input.
+ Returns:
+ torch.Tensor: The calculated loss
+ """
+ # [B, C, d_1, d_2, ..., d_k] -> [C, B, d_1, d_2, ..., d_k]
+ pred = pred.transpose(0, 1)
+ # [C, B, d_1, d_2, ..., d_k] -> [C, N]
+ pred = pred.reshape(pred.size(0), -1)
+ # [C, N] -> [N, C]
+ pred = pred.transpose(0, 1).contiguous()
+ # (B, d_1, d_2, ..., d_k) --> (B * d_1 * d_2 * ... * d_k,)
+ target = target.view(-1).contiguous()
+ assert pred.size(0) == target.size(
+ 0
+ ), "The shape of pred doesn't match the shape of target"
+ valid_mask = target != self.ignore_index
+ target = target[valid_mask]
+ pred = pred[valid_mask]
+
+ if len(target) == 0:
+ return 0.0
+
+ num_classes = pred.size(1)
+ target = F.one_hot(target, num_classes=num_classes)
+
+ alpha = self.alpha
+ if isinstance(alpha, list):
+ alpha = pred.new_tensor(alpha)
+ pred_sigmoid = pred.sigmoid()
+ target = target.type_as(pred)
+ one_minus_pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
+ focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * one_minus_pt.pow(
+ self.gamma
+ )
+
+ loss = (
+ F.binary_cross_entropy_with_logits(pred, target, reduction="none")
+ * focal_weight
+ )
+ if self.reduction == "mean":
+ loss = loss.mean()
+ elif self.reduction == "sum":
+ loss = loss.total()
+ return self.loss_weight * loss
+
+
+@LOSSES.register_module()
+class DiceLoss(nn.Module):
+ def __init__(self, smooth=1, exponent=2, loss_weight=1.0, ignore_index=-1):
+ """DiceLoss.
+ This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
+ Volumetric Medical Image Segmentation `_.
+ """
+ super(DiceLoss, self).__init__()
+ self.smooth = smooth
+ self.exponent = exponent
+ self.loss_weight = loss_weight
+ self.ignore_index = ignore_index
+
+ def forward(self, pred, target, **kwargs):
+ # [B, C, d_1, d_2, ..., d_k] -> [C, B, d_1, d_2, ..., d_k]
+ pred = pred.transpose(0, 1)
+ # [C, B, d_1, d_2, ..., d_k] -> [C, N]
+ pred = pred.reshape(pred.size(0), -1)
+ # [C, N] -> [N, C]
+ pred = pred.transpose(0, 1).contiguous()
+ # (B, d_1, d_2, ..., d_k) --> (B * d_1 * d_2 * ... * d_k,)
+ target = target.view(-1).contiguous()
+ assert pred.size(0) == target.size(
+ 0
+ ), "The shape of pred doesn't match the shape of target"
+ valid_mask = target != self.ignore_index
+ target = target[valid_mask]
+ pred = pred[valid_mask]
+
+ pred = F.softmax(pred, dim=1)
+ num_classes = pred.shape[1]
+ target = F.one_hot(
+ torch.clamp(target.long(), 0, num_classes - 1), num_classes=num_classes
+ )
+
+ total_loss = 0
+ for i in range(num_classes):
+ if i != self.ignore_index:
+ num = torch.sum(torch.mul(pred[:, i], target[:, i])) * 2 + self.smooth
+ den = (
+ torch.sum(
+ pred[:, i].pow(self.exponent) + target[:, i].pow(self.exponent)
+ )
+ + self.smooth
+ )
+ dice_loss = 1 - num / den
+ total_loss += dice_loss
+ loss = total_loss / num_classes
+ return self.loss_weight * loss
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18733d94acc5c7ed0feb7c52c77cb040d53bee7e
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/__init__.py
@@ -0,0 +1,2 @@
+from .masked_scene_contrast_v1m1_base import MaskedSceneContrast
+from .masked_scene_contrast_v1m2_csc import MaskedSceneContrast
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b18bd563a36695eca4882a6620d4ddbe246ef80
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m1_base.py
@@ -0,0 +1,310 @@
+"""
+Masked Scene Contrast
+https://arxiv.org/abs/2303.14191
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+from itertools import chain
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch_geometric.nn.pool import voxel_grid
+
+from timm.models.layers import trunc_normal_
+import pointops
+
+from pointcept.models.builder import MODELS, build_model
+from pointcept.models.utils import offset2batch
+from pointcept.utils.comm import get_world_size
+
+
+@MODELS.register_module("MSC-v1m1")
+class MaskedSceneContrast(nn.Module):
+ def __init__(
+ self,
+ backbone,
+ backbone_in_channels,
+ backbone_out_channels,
+ mask_grid_size=0.1,
+ mask_rate=0.4,
+ view1_mix_prob=0,
+ view2_mix_prob=0,
+ matching_max_k=8,
+ matching_max_radius=0.03,
+ matching_max_pair=8192,
+ nce_t=0.4,
+ contrast_weight=1,
+ reconstruct_weight=1,
+ reconstruct_color=True,
+ reconstruct_normal=True,
+ ):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.mask_grid_size = mask_grid_size
+ self.mask_rate = mask_rate
+ self.view1_mix_prob = view1_mix_prob
+ self.view2_mix_prob = view2_mix_prob
+ self.matching_max_k = matching_max_k
+ self.matching_max_radius = matching_max_radius
+ self.matching_max_pair = matching_max_pair
+ self.nce_t = nce_t
+ self.contrast_weight = contrast_weight
+ self.reconstruct_weight = reconstruct_weight
+ self.reconstruct_color = reconstruct_color
+ self.reconstruct_normal = reconstruct_normal
+
+ self.mask_token = nn.Parameter(torch.zeros(1, backbone_in_channels))
+ trunc_normal_(self.mask_token, mean=0.0, std=0.02)
+ self.color_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_color else None
+ )
+ self.normal_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_normal else None
+ )
+ self.nce_criteria = torch.nn.CrossEntropyLoss(reduction="mean")
+
+ @torch.no_grad()
+ def generate_cross_masks(
+ self, view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ ):
+ # union origin coord
+ view1_batch = offset2batch(view1_offset)
+ view2_batch = offset2batch(view2_offset)
+
+ view1_batch_count = view1_batch.bincount()
+ view2_batch_count = view2_batch.bincount()
+ view1_origin_coord_split = view1_origin_coord.split(list(view1_batch_count))
+ view2_origin_coord_split = view2_origin_coord.split(list(view2_batch_count))
+ union_origin_coord = torch.cat(
+ list(
+ chain.from_iterable(
+ zip(view1_origin_coord_split, view2_origin_coord_split)
+ )
+ )
+ )
+ union_offset = torch.cat(
+ [view1_offset.unsqueeze(-1), view2_offset.unsqueeze(-1)], dim=-1
+ ).sum(-1)
+ union_batch = offset2batch(union_offset)
+
+ # grid partition
+ mask_patch_coord = union_origin_coord.div(self.mask_grid_size)
+ mask_patch_grid_coord = torch.floor(mask_patch_coord)
+ mask_patch_cluster = voxel_grid(
+ pos=mask_patch_grid_coord, size=1, batch=union_batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ mask_patch_cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ patch_num = unique.shape[0]
+ patch_max_point = counts.max().item()
+ patch2point_map = cluster.new_zeros(patch_num, patch_max_point)
+ patch2point_mask = torch.lt(
+ torch.arange(patch_max_point).cuda().unsqueeze(0), counts.unsqueeze(-1)
+ )
+ sorted_cluster_value, sorted_cluster_indices = torch.sort(cluster)
+ patch2point_map[patch2point_mask] = sorted_cluster_indices
+
+ # generate cross masks
+ assert self.mask_rate <= 0.5
+ patch_mask = torch.zeros(patch_num, device=union_origin_coord.device).int()
+ rand_perm = torch.randperm(patch_num)
+ mask_patch_num = int(patch_num * self.mask_rate)
+
+ # mask1 tag with 1, mask2 tag with 2
+ patch_mask[rand_perm[0:mask_patch_num]] = 1
+ patch_mask[rand_perm[mask_patch_num : mask_patch_num * 2]] = 2
+ point_mask = torch.zeros(
+ union_origin_coord.shape[0], device=union_origin_coord.device
+ ).int()
+ point_mask[
+ patch2point_map[patch_mask == 1][patch2point_mask[patch_mask == 1]]
+ ] = 1
+ point_mask[
+ patch2point_map[patch_mask == 2][patch2point_mask[patch_mask == 2]]
+ ] = 2
+
+ # separate mask to view1 and view2
+ point_mask_split = point_mask.split(
+ list(
+ torch.cat(
+ [view1_batch_count.unsqueeze(-1), view2_batch_count.unsqueeze(-1)],
+ dim=-1,
+ ).flatten()
+ )
+ )
+ view1_point_mask = torch.cat(point_mask_split[0::2]) == 1
+ view2_point_mask = torch.cat(point_mask_split[1::2]) == 2
+ return view1_point_mask, view2_point_mask
+
+ @torch.no_grad()
+ def match_contrastive_pair(
+ self, view1_coord, view1_offset, view2_coord, view2_offset, max_k, max_radius
+ ):
+ index, distance = pointops.knn_query(
+ max_k,
+ view2_coord.float(),
+ view2_offset.int(),
+ view1_coord.float(),
+ view1_offset.int(),
+ )
+ index = torch.cat(
+ [
+ torch.arange(index.shape[0], device=index.device, dtype=torch.long)
+ .view(-1, 1, 1)
+ .expand(-1, max_k, 1),
+ index.view(-1, max_k, 1),
+ ],
+ dim=-1,
+ )[distance.squeeze(-1) < max_radius]
+ unique, count = index[:, 0].unique(return_counts=True)
+ select = (
+ torch.cumsum(count, dim=0)
+ - torch.randint(count.max(), count.shape, device=count.device) % count
+ - 1
+ )
+ index = index[select]
+ if index.shape[0] > self.matching_max_pair:
+ index = index[torch.randperm(index.shape[0])[: self.matching_max_pair]]
+ return index
+
+ def compute_contrastive_loss(
+ self, view1_feat, view1_offset, view2_feat, view2_offset, match_index
+ ):
+ assert view1_offset.shape == view2_offset.shape
+
+ view1_feat = view1_feat[match_index[:, 0]]
+ view2_feat = view2_feat[match_index[:, 1]]
+ view1_feat = view1_feat / (
+ torch.norm(view1_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+ view2_feat = view2_feat / (
+ torch.norm(view2_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+ sim = torch.mm(view1_feat, view2_feat.transpose(1, 0))
+
+ with torch.no_grad():
+ pos_sim = torch.diagonal(sim).mean()
+ neg_sim = sim.mean(dim=-1).mean() - pos_sim / match_index.shape[0]
+ labels = torch.arange(sim.shape[0], device=view1_feat.device).long()
+ loss = self.nce_criteria(torch.div(sim, self.nce_t), labels)
+
+ if get_world_size() > 1:
+ dist.all_reduce(loss)
+ dist.all_reduce(pos_sim)
+ dist.all_reduce(neg_sim)
+ return (
+ loss / get_world_size(),
+ pos_sim / get_world_size(),
+ neg_sim / get_world_size(),
+ )
+
+ def forward(self, data_dict):
+ view1_origin_coord = data_dict["view1_origin_coord"]
+ view1_coord = data_dict["view1_coord"]
+ view1_feat = data_dict["view1_feat"]
+ view1_offset = data_dict["view1_offset"].int()
+
+ view2_origin_coord = data_dict["view2_origin_coord"]
+ view2_coord = data_dict["view2_coord"]
+ view2_feat = data_dict["view2_feat"]
+ view2_offset = data_dict["view2_offset"].int()
+
+ # mask generation by union original coord (without spatial augmentation)
+ view1_point_mask, view2_point_mask = self.generate_cross_masks(
+ view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ )
+
+ view1_mask_tokens = self.mask_token.expand(view1_coord.shape[0], -1)
+ view1_weight = view1_point_mask.unsqueeze(-1).type_as(view1_mask_tokens)
+ view1_feat = view1_feat * (1 - view1_weight) + view1_mask_tokens * view1_weight
+
+ view2_mask_tokens = self.mask_token.expand(view2_coord.shape[0], -1)
+ view2_weight = view2_point_mask.unsqueeze(-1).type_as(view2_mask_tokens)
+ view2_feat = view2_feat * (1 - view2_weight) + view2_mask_tokens * view2_weight
+
+ view1_data_dict = dict(
+ origin_coord=view1_origin_coord,
+ coord=view1_coord,
+ feat=view1_feat,
+ offset=view1_offset,
+ )
+ view2_data_dict = dict(
+ origin_coord=view2_origin_coord,
+ coord=view2_coord,
+ feat=view2_feat,
+ offset=view2_offset,
+ )
+
+ # SparseConv based method need grid coord
+ if "view1_grid_coord" in data_dict.keys():
+ view1_data_dict["grid_coord"] = data_dict["view1_grid_coord"]
+ if "view2_grid_coord" in data_dict.keys():
+ view2_data_dict["grid_coord"] = data_dict["view2_grid_coord"]
+
+ # view mixing strategy
+ if random.random() < self.view1_mix_prob:
+ view1_data_dict["offset"] = torch.cat(
+ [view1_offset[1:-1:2], view1_offset[-1].unsqueeze(0)], dim=0
+ )
+ if random.random() < self.view2_mix_prob:
+ view2_data_dict["offset"] = torch.cat(
+ [view2_offset[1:-1:2], view2_offset[-1].unsqueeze(0)], dim=0
+ )
+
+ view1_feat = self.backbone(view1_data_dict)
+ view2_feat = self.backbone(view2_data_dict)
+ match_index = self.match_contrastive_pair(
+ view1_origin_coord,
+ view1_offset,
+ view2_origin_coord,
+ view2_offset,
+ max_k=self.matching_max_k,
+ max_radius=self.matching_max_radius,
+ )
+ nce_loss, pos_sim, neg_sim = self.compute_contrastive_loss(
+ view1_feat, view1_offset, view2_feat, view2_offset, match_index
+ )
+ loss = nce_loss * self.contrast_weight
+ result_dict = dict(nce_loss=nce_loss, pos_sim=pos_sim, neg_sim=neg_sim)
+
+ if self.color_head is not None:
+ assert "view1_color" in data_dict.keys()
+ assert "view2_color" in data_dict.keys()
+ view1_color = data_dict["view1_color"]
+ view2_color = data_dict["view2_color"]
+ view1_color_pred = self.color_head(view1_feat[view1_point_mask])
+ view2_color_pred = self.color_head(view2_feat[view2_point_mask])
+ color_loss = (
+ torch.sum((view1_color_pred - view1_color[view1_point_mask]) ** 2)
+ + torch.sum((view2_color_pred - view2_color[view2_point_mask]) ** 2)
+ ) / (view1_color_pred.shape[0] + view2_color_pred.shape[0])
+ loss = loss + color_loss * self.reconstruct_weight
+ result_dict["color_loss"] = color_loss
+
+ if self.normal_head is not None:
+ assert "view1_normal" in data_dict.keys()
+ assert "view2_normal" in data_dict.keys()
+ view1_normal = data_dict["view1_normal"]
+ view2_normal = data_dict["view2_normal"]
+ view1_normal_pred = self.normal_head(view1_feat[view1_point_mask])
+ view2_normal_pred = self.normal_head(view2_feat[view2_point_mask])
+
+ view1_normal_pred = view1_normal_pred / (
+ torch.norm(view1_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ view2_normal_pred = view2_normal_pred / (
+ torch.norm(view2_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ normal_loss = (
+ torch.sum(view1_normal_pred * view1_normal[view1_point_mask])
+ + torch.sum(view2_normal_pred * view2_normal[view2_point_mask])
+ ) / (view1_normal_pred.shape[0] + view2_normal_pred.shape[0])
+ loss = loss + normal_loss * self.reconstruct_weight
+ result_dict["normal_loss"] = normal_loss
+
+ result_dict["loss"] = loss
+ return result_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m2_csc.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m2_csc.py
new file mode 100644
index 0000000000000000000000000000000000000000..139e26b89d88bbe8711fb9a162b5347d846fd399
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/masked_scene_contrast/masked_scene_contrast_v1m2_csc.py
@@ -0,0 +1,377 @@
+"""
+Masked Scene Contrast v1m2
+contrastive learning backend with CSC (https://arxiv.org/abs/2012.09165)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Chengyao Wang (cywang22@cse.cuhk.edu.hk)
+Please cite our work if the code is helpful to you.
+"""
+
+import random
+from itertools import chain
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch_geometric.nn.pool import voxel_grid
+
+from timm.models.layers import trunc_normal_
+import pointops
+
+from pointcept.models.builder import MODELS, build_model
+from pointcept.models.utils import offset2batch
+from pointcept.utils.comm import get_world_size
+
+
+@MODELS.register_module("MSC-v1m2")
+class MaskedSceneContrast(nn.Module):
+ def __init__(
+ self,
+ backbone,
+ backbone_in_channels,
+ backbone_out_channels,
+ mask_grid_size=0.1,
+ mask_rate=0.4,
+ view1_mix_prob=0,
+ view2_mix_prob=0,
+ matching_max_k=8,
+ matching_max_radius=0.03,
+ matching_max_pair=8192,
+ nce_t=0.4,
+ contrast_weight=1,
+ reconstruct_weight=1,
+ reconstruct_color=True,
+ reconstruct_normal=True,
+ partitions=4,
+ r1=0.125,
+ r2=2,
+ ):
+ super().__init__()
+ self.backbone = build_model(backbone)
+ self.mask_grid_size = mask_grid_size
+ self.mask_rate = mask_rate
+ self.view1_mix_prob = view1_mix_prob
+ self.view2_mix_prob = view2_mix_prob
+ self.matching_max_k = matching_max_k
+ self.matching_max_radius = matching_max_radius
+ self.matching_max_pair = matching_max_pair
+ self.nce_t = nce_t
+ self.contrast_weight = contrast_weight
+ self.reconstruct_weight = reconstruct_weight
+ self.reconstruct_color = reconstruct_color
+ self.reconstruct_normal = reconstruct_normal
+
+ # csc partition
+ self.partitions = partitions
+ self.r1 = r1
+ self.r2 = r2
+
+ self.mask_token = nn.Parameter(torch.zeros(1, backbone_in_channels))
+ trunc_normal_(self.mask_token, mean=0.0, std=0.02)
+ self.color_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_color else None
+ )
+ self.normal_head = (
+ nn.Linear(backbone_out_channels, 3) if reconstruct_normal else None
+ )
+ self.nce_criteria = torch.nn.CrossEntropyLoss(reduction="mean")
+
+ @torch.no_grad()
+ def generate_cross_masks(
+ self, view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ ):
+ # union origin coord
+ view1_batch = offset2batch(view1_offset)
+ view2_batch = offset2batch(view2_offset)
+
+ view1_batch_count = view1_batch.bincount()
+ view2_batch_count = view2_batch.bincount()
+ view1_origin_coord_split = view1_origin_coord.split(list(view1_batch_count))
+ view2_origin_coord_split = view2_origin_coord.split(list(view2_batch_count))
+ union_origin_coord = torch.cat(
+ list(
+ chain.from_iterable(
+ zip(view1_origin_coord_split, view2_origin_coord_split)
+ )
+ )
+ )
+ union_offset = torch.cat(
+ [view1_offset.unsqueeze(-1), view2_offset.unsqueeze(-1)], dim=-1
+ ).sum(-1)
+ union_batch = offset2batch(union_offset)
+
+ # grid partition
+ mask_patch_coord = union_origin_coord.div(self.mask_grid_size)
+ mask_patch_grid_coord = torch.floor(mask_patch_coord)
+ mask_patch_cluster = voxel_grid(
+ pos=mask_patch_grid_coord, size=1, batch=union_batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ mask_patch_cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ patch_num = unique.shape[0]
+ patch_max_point = counts.max().item()
+ patch2point_map = cluster.new_zeros(patch_num, patch_max_point)
+ patch2point_mask = torch.lt(
+ torch.arange(patch_max_point).cuda().unsqueeze(0), counts.unsqueeze(-1)
+ )
+ sorted_cluster_value, sorted_cluster_indices = torch.sort(cluster)
+ patch2point_map[patch2point_mask] = sorted_cluster_indices
+
+ # generate cross masks
+ assert self.mask_rate <= 0.5
+ patch_mask = torch.zeros(patch_num, device=union_origin_coord.device).int()
+ rand_perm = torch.randperm(patch_num)
+ mask_patch_num = int(patch_num * self.mask_rate)
+
+ # mask1 tag with 1, mask2 tag with 2
+ patch_mask[rand_perm[0:mask_patch_num]] = 1
+ patch_mask[rand_perm[mask_patch_num : mask_patch_num * 2]] = 2
+ point_mask = torch.zeros(
+ union_origin_coord.shape[0], device=union_origin_coord.device
+ ).int()
+ point_mask[
+ patch2point_map[patch_mask == 1][patch2point_mask[patch_mask == 1]]
+ ] = 1
+ point_mask[
+ patch2point_map[patch_mask == 2][patch2point_mask[patch_mask == 2]]
+ ] = 2
+
+ # separate mask to view1 and view2
+ point_mask_split = point_mask.split(
+ list(
+ torch.cat(
+ [view1_batch_count.unsqueeze(-1), view2_batch_count.unsqueeze(-1)],
+ dim=-1,
+ ).flatten()
+ )
+ )
+ view1_point_mask = torch.cat(point_mask_split[0::2]) == 1
+ view2_point_mask = torch.cat(point_mask_split[1::2]) == 2
+ return view1_point_mask, view2_point_mask
+
+ @torch.no_grad()
+ def match_contrastive_pair(
+ self, view1_coord, view1_offset, view2_coord, view2_offset, max_k, max_radius
+ ):
+ index, distance = pointops.knn_query(
+ max_k,
+ view2_coord.float(),
+ view2_offset.int(),
+ view1_coord.float(),
+ view1_offset.int(),
+ )
+ index = torch.cat(
+ [
+ torch.arange(index.shape[0], device=index.device, dtype=torch.long)
+ .view(-1, 1, 1)
+ .expand(-1, max_k, 1),
+ index.view(-1, max_k, 1),
+ ],
+ dim=-1,
+ )[distance.squeeze(-1) < max_radius]
+ unique, count = index[:, 0].unique(return_counts=True)
+ select = (
+ torch.cumsum(count, dim=0)
+ - torch.randint(count.max(), count.shape, device=count.device) % count
+ - 1
+ )
+ index = index[select]
+ if index.shape[0] > self.matching_max_pair:
+ index = index[torch.randperm(index.shape[0])[: self.matching_max_pair]]
+ return index
+
+ def compute_partitions(self, coord1, coord2):
+ partition_matrix = torch.zeros((coord1.shape[0], coord2.shape[0]))
+ partition_matrix = partition_matrix.cuda() - 1e7
+
+ rel_trans = coord1.unsqueeze(0) - coord2.unsqueeze(1)
+ mask_up = rel_trans[:, :, 2] > 0.0
+ mask_down = rel_trans[:, :, 2] < 0.0
+
+ distance_matrix = torch.sqrt(torch.sum(rel_trans.pow(2), 2).add(1e-7))
+
+ mask = (distance_matrix[:, :] > self.r1) & (distance_matrix[:, :] <= self.r2)
+ partition_matrix[mask & mask_up] = 0
+ partition_matrix[mask & mask_down] = 1
+
+ mask = distance_matrix[:, :] > self.r2
+ partition_matrix[mask & mask_up] = 2
+ partition_matrix[mask & mask_down] = 3
+
+ return partition_matrix
+
+ def compute_contrastive_loss(
+ self,
+ view1_feat,
+ view1_coord,
+ view1_offset,
+ view2_feat,
+ view2_coord,
+ view2_offset,
+ match_index,
+ ):
+ assert view1_offset.shape == view2_offset.shape
+ device = view1_feat.device
+ loss = torch.tensor(0.0, device=device)
+ pos_sim = torch.tensor(0.0, device=device)
+ neg_sim = torch.tensor(0.0, device=device)
+ large_num = 1e9
+
+ view1_feat = view1_feat[match_index[:, 0]]
+ view2_feat = view2_feat[match_index[:, 1]]
+ view1_feat = view1_feat / (
+ torch.norm(view1_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+ view2_feat = view2_feat / (
+ torch.norm(view2_feat, p=2, dim=1, keepdim=True) + 1e-7
+ )
+
+ view1_coord = view1_coord[match_index[:, 0]]
+ view2_coord = view2_coord[match_index[:, 1]]
+
+ batch = offset2batch(view1_offset)[match_index[:, 0]]
+ for batch_id in batch.unique():
+ batch_mask = batch == batch_id
+ sim = torch.mm(view1_feat[batch_mask], view2_feat[batch_mask].T)
+
+ with torch.no_grad():
+ pos_sim += torch.diagonal(sim).mean()
+ neg_sim += sim.mean(dim=-1).mean() - pos_sim / batch_mask.sum()
+
+ labels = torch.arange(sim.shape[0], device=view1_feat.device).long()
+ part = self.compute_partitions(
+ view1_coord[batch_mask], view2_coord[batch_mask]
+ )
+ for part_id in part.unique():
+ part_mask = part == part_id
+ part_mask.fill_diagonal_(True)
+ loss += self.nce_criteria(
+ torch.div(sim, self.nce_t) - large_num * (~part_mask).float(),
+ labels,
+ )
+
+ loss /= len(view1_offset) * self.partitions
+ pos_sim /= len(view1_offset)
+ neg_sim /= len(view1_offset)
+
+ if get_world_size() > 1:
+ dist.all_reduce(loss)
+ dist.all_reduce(pos_sim)
+ dist.all_reduce(neg_sim)
+ return (
+ loss / get_world_size(),
+ pos_sim / get_world_size(),
+ neg_sim / get_world_size(),
+ )
+
+ def forward(self, data_dict):
+ view1_origin_coord = data_dict["view1_origin_coord"]
+ view1_coord = data_dict["view1_coord"]
+ view1_feat = data_dict["view1_feat"]
+ view1_offset = data_dict["view1_offset"].int()
+
+ view2_origin_coord = data_dict["view2_origin_coord"]
+ view2_coord = data_dict["view2_coord"]
+ view2_feat = data_dict["view2_feat"]
+ view2_offset = data_dict["view2_offset"].int()
+
+ # mask generation by union original coord (without spatial augmentation)
+ view1_point_mask, view2_point_mask = self.generate_cross_masks(
+ view1_origin_coord, view1_offset, view2_origin_coord, view2_offset
+ )
+
+ view1_mask_tokens = self.mask_token.expand(view1_coord.shape[0], -1)
+ view1_weight = view1_point_mask.unsqueeze(-1).type_as(view1_mask_tokens)
+ view1_feat = view1_feat * (1 - view1_weight) + view1_mask_tokens * view1_weight
+
+ view2_mask_tokens = self.mask_token.expand(view2_coord.shape[0], -1)
+ view2_weight = view2_point_mask.unsqueeze(-1).type_as(view2_mask_tokens)
+ view2_feat = view2_feat * (1 - view2_weight) + view2_mask_tokens * view2_weight
+
+ view1_data_dict = dict(
+ origin_coord=view1_origin_coord,
+ coord=view1_coord,
+ feat=view1_feat,
+ offset=view1_offset,
+ )
+ view2_data_dict = dict(
+ origin_coord=view2_origin_coord,
+ coord=view2_coord,
+ feat=view2_feat,
+ offset=view2_offset,
+ )
+
+ # SparseConv based method need grid coord
+ if "view1_grid_coord" in data_dict.keys():
+ view1_data_dict["grid_coord"] = data_dict["view1_grid_coord"]
+ if "view2_grid_coord" in data_dict.keys():
+ view2_data_dict["grid_coord"] = data_dict["view2_grid_coord"]
+
+ # view mixing strategy
+ if random.random() < self.view1_mix_prob:
+ view1_data_dict["offset"] = torch.cat(
+ [view1_offset[1:-1:2], view1_offset[-1].unsqueeze(0)], dim=0
+ )
+ if random.random() < self.view2_mix_prob:
+ view2_data_dict["offset"] = torch.cat(
+ [view2_offset[1:-1:2], view2_offset[-1].unsqueeze(0)], dim=0
+ )
+
+ view1_feat = self.backbone(view1_data_dict)
+ view2_feat = self.backbone(view2_data_dict)
+ match_index = self.match_contrastive_pair(
+ view1_origin_coord,
+ view1_offset,
+ view2_origin_coord,
+ view2_offset,
+ max_k=self.matching_max_k,
+ max_radius=self.matching_max_radius,
+ )
+ nce_loss, pos_sim, neg_sim = self.compute_contrastive_loss(
+ view1_feat,
+ view1_origin_coord,
+ view1_offset,
+ view2_feat,
+ view2_origin_coord,
+ view2_offset,
+ match_index,
+ )
+ loss = nce_loss * self.contrast_weight
+ result_dict = dict(nce_loss=nce_loss, pos_sim=pos_sim, neg_sim=neg_sim)
+
+ if self.color_head is not None:
+ assert "view1_color" in data_dict.keys()
+ assert "view2_color" in data_dict.keys()
+ view1_color = data_dict["view1_color"]
+ view2_color = data_dict["view2_color"]
+ view1_color_pred = self.color_head(view1_feat[view1_point_mask])
+ view2_color_pred = self.color_head(view2_feat[view2_point_mask])
+ color_loss = (
+ torch.sum((view1_color_pred - view1_color[view1_point_mask]) ** 2)
+ + torch.sum((view2_color_pred - view2_color[view2_point_mask]) ** 2)
+ ) / (view1_color_pred.shape[0] + view2_color_pred.shape[0])
+ loss = loss + color_loss * self.reconstruct_weight
+ result_dict["color_loss"] = color_loss
+
+ if self.normal_head is not None:
+ assert "view1_normal" in data_dict.keys()
+ assert "view2_normal" in data_dict.keys()
+ view1_normal = data_dict["view1_normal"]
+ view2_normal = data_dict["view2_normal"]
+ view1_normal_pred = self.normal_head(view1_feat[view1_point_mask])
+ view2_normal_pred = self.normal_head(view2_feat[view2_point_mask])
+
+ view1_normal_pred = view1_normal_pred / (
+ torch.norm(view1_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ view2_normal_pred = view2_normal_pred / (
+ torch.norm(view2_normal_pred, p=2, dim=1, keepdim=True) + 1e-10
+ )
+ normal_loss = (
+ torch.sum(view1_normal_pred * view1_normal[view1_point_mask])
+ + torch.sum(view2_normal_pred * view2_normal[view2_point_mask])
+ ) / (view1_normal_pred.shape[0] + view2_normal_pred.shape[0])
+ loss = loss + normal_loss * self.reconstruct_weight
+ result_dict["normal_loss"] = normal_loss
+
+ result_dict["loss"] = loss
+ return result_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/modules.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a737ae9e52620ffc4eb139a81e0bdd46cded0ed
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/modules.py
@@ -0,0 +1,83 @@
+import sys
+import torch.nn as nn
+import spconv.pytorch as spconv
+from collections import OrderedDict
+from pointcept.models.utils.structure import Point
+
+
+class PointModule(nn.Module):
+ r"""PointModule
+ placeholder, all module subclass from this will take Point in PointSequential.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+
+class PointSequential(PointModule):
+ r"""A sequential container.
+ Modules will be added to it in the order they are passed in the constructor.
+ Alternatively, an ordered dict of modules can also be passed in.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__()
+ if len(args) == 1 and isinstance(args[0], OrderedDict):
+ for key, module in args[0].items():
+ self.add_module(key, module)
+ else:
+ for idx, module in enumerate(args):
+ self.add_module(str(idx), module)
+ for name, module in kwargs.items():
+ if sys.version_info < (3, 6):
+ raise ValueError("kwargs only supported in py36+")
+ if name in self._modules:
+ raise ValueError("name exists.")
+ self.add_module(name, module)
+
+ def __getitem__(self, idx):
+ if not (-len(self) <= idx < len(self)):
+ raise IndexError("index {} is out of range".format(idx))
+ if idx < 0:
+ idx += len(self)
+ it = iter(self._modules.values())
+ for i in range(idx):
+ next(it)
+ return next(it)
+
+ def __len__(self):
+ return len(self._modules)
+
+ def add(self, module, name=None):
+ if name is None:
+ name = str(len(self._modules))
+ if name in self._modules:
+ raise KeyError("name exists")
+ self.add_module(name, module)
+
+ def forward(self, input):
+ for k, module in self._modules.items():
+ # Point module
+ if isinstance(module, PointModule):
+ input = module(input)
+ # Spconv module
+ elif spconv.modules.is_spconv_module(module):
+ if isinstance(input, Point):
+ input.sparse_conv_feat = module(input.sparse_conv_feat)
+ input.feat = input.sparse_conv_feat.features
+ else:
+ input = module(input)
+ # PyTorch module
+ else:
+ if isinstance(input, Point):
+ input.feat = module(input.feat)
+ if "sparse_conv_feat" in input.keys():
+ input.sparse_conv_feat = input.sparse_conv_feat.replace_feature(
+ input.feat
+ )
+ elif isinstance(input, spconv.SparseConvTensor):
+ if input.indices.shape[0] != 0:
+ input = input.replace_feature(module(input.features))
+ else:
+ input = module(input)
+ return input
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/oacnns/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/oacnns/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..654b767080457f3814142159a121a2bef9c682b7
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/oacnns/__init__.py
@@ -0,0 +1 @@
+from .oacnns_v1m1_base import OACNNs
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/oacnns/oacnns_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/oacnns/oacnns_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd8ee6d25ed152f04ab8b3905bbd8c2bdf127d06
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/oacnns/oacnns_v1m1_base.py
@@ -0,0 +1,345 @@
+from functools import partial
+import torch
+import torch.nn as nn
+from einops import rearrange
+import spconv.pytorch as spconv
+from timm.models.layers import trunc_normal_
+from ..builder import MODELS
+from ..utils import offset2batch
+from torch_geometric.nn.pool import voxel_grid
+from torch_geometric.utils import scatter
+
+
+class BasicBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ norm_fn=None,
+ indice_key=None,
+ depth=4,
+ groups=None,
+ grid_size=None,
+ bias=False,
+ ):
+ super().__init__()
+ assert embed_channels % groups == 0
+ self.groups = groups
+ self.embed_channels = embed_channels
+ self.proj = nn.ModuleList()
+ self.grid_size = grid_size
+ self.weight = nn.ModuleList()
+ self.l_w = nn.ModuleList()
+ self.proj.append(
+ nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ )
+ for _ in range(depth - 1):
+ self.proj.append(
+ nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ )
+ self.l_w.append(
+ nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ )
+ self.weight.append(nn.Linear(embed_channels, embed_channels, bias=False))
+
+ self.adaptive = nn.Linear(embed_channels, depth - 1, bias=False)
+ self.fuse = nn.Sequential(
+ nn.Linear(embed_channels * 2, embed_channels, bias=False),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ self.voxel_block = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ indice_key=indice_key,
+ bias=bias,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ indice_key=indice_key,
+ bias=bias,
+ ),
+ norm_fn(embed_channels),
+ )
+ self.act = nn.ReLU()
+
+ def forward(self, x, clusters):
+ feat = x.features
+ feats = []
+ for i, cluster in enumerate(clusters):
+ pw = self.l_w[i](feat)
+ pw = pw - scatter(pw, cluster, reduce="mean")[cluster]
+ pw = self.weight[i](pw)
+ pw = torch.exp(pw - pw.max())
+ pw = pw / (scatter(pw, cluster, reduce="sum", dim=0)[cluster] + 1e-6)
+ pfeat = self.proj[i](feat) * pw
+ pfeat = scatter(pfeat, cluster, reduce="sum")[cluster]
+ feats.append(pfeat)
+ adp = self.adaptive(feat)
+ adp = torch.softmax(adp, dim=1)
+ feats = torch.stack(feats, dim=1)
+ feats = torch.einsum("l n, l n c -> l c", adp, feats)
+ feat = self.proj[-1](feat)
+ feat = torch.cat([feat, feats], dim=1)
+ feat = self.fuse(feat) + x.features
+ res = feat
+ x = x.replace_feature(feat)
+ x = self.voxel_block(x)
+ x = x.replace_feature(self.act(x.features + res))
+ return x
+
+
+class DonwBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ depth,
+ sp_indice_key,
+ point_grid_size,
+ num_ref=16,
+ groups=None,
+ norm_fn=None,
+ sub_indice_key=None,
+ ):
+ super().__init__()
+ self.num_ref = num_ref
+ self.depth = depth
+ self.point_grid_size = point_grid_size
+ self.down = spconv.SparseSequential(
+ spconv.SparseConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=2,
+ stride=2,
+ indice_key=sp_indice_key,
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ self.blocks = nn.ModuleList()
+ for _ in range(depth):
+ self.blocks.append(
+ BasicBlock(
+ in_channels=embed_channels,
+ embed_channels=embed_channels,
+ depth=len(point_grid_size) + 1,
+ groups=groups,
+ grid_size=point_grid_size,
+ norm_fn=norm_fn,
+ indice_key=sub_indice_key,
+ )
+ )
+
+ def forward(self, x):
+ x = self.down(x)
+ coord = x.indices[:, 1:].float()
+ batch = x.indices[:, 0]
+ clusters = []
+ for grid_size in self.point_grid_size:
+ cluster = voxel_grid(pos=coord, size=grid_size, batch=batch)
+ _, cluster = torch.unique(cluster, return_inverse=True)
+ clusters.append(cluster)
+ for block in self.blocks:
+ x = block(x, clusters)
+ return x
+
+
+class UpBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ depth,
+ sp_indice_key,
+ norm_fn=None,
+ down_ratio=2,
+ sub_indice_key=None,
+ ):
+ super().__init__()
+ assert depth > 0
+ self.up = spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=down_ratio,
+ indice_key=sp_indice_key,
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+ self.blocks = nn.ModuleList()
+ self.fuse = nn.Sequential(
+ nn.Linear(skip_channels + embed_channels, embed_channels),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ nn.Linear(embed_channels, embed_channels),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+
+ def forward(self, x, skip_x):
+ x = self.up(x)
+ x = x.replace_feature(
+ self.fuse(torch.cat([x.features, skip_x.features], dim=1)) + x.features
+ )
+ return x
+
+
+@MODELS.register_module()
+class OACNNs(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ embed_channels=64,
+ enc_num_ref=[16, 16, 16, 16],
+ enc_channels=[64, 64, 128, 256],
+ groups=[2, 4, 8, 16],
+ enc_depth=[2, 3, 6, 4],
+ down_ratio=[2, 2, 2, 2],
+ dec_channels=[96, 96, 128, 256],
+ point_grid_size=[[16, 32, 64], [8, 16, 24], [4, 8, 12], [2, 4, 6]],
+ dec_depth=[2, 2, 2, 2],
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_channels)
+ self.embed_channels = embed_channels
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+
+ self.stem = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ padding=1,
+ indice_key="stem",
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ padding=1,
+ indice_key="stem",
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ padding=1,
+ indice_key="stem",
+ bias=False,
+ ),
+ norm_fn(embed_channels),
+ nn.ReLU(),
+ )
+
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList()
+ for i in range(self.num_stages):
+ self.enc.append(
+ DonwBlock(
+ in_channels=embed_channels if i == 0 else enc_channels[i - 1],
+ embed_channels=enc_channels[i],
+ depth=enc_depth[i],
+ norm_fn=norm_fn,
+ groups=groups[i],
+ point_grid_size=point_grid_size[i],
+ num_ref=enc_num_ref[i],
+ sp_indice_key=f"spconv{i}",
+ sub_indice_key=f"subm{i + 1}",
+ )
+ )
+ self.dec.append(
+ UpBlock(
+ in_channels=(
+ enc_channels[-1]
+ if i == self.num_stages - 1
+ else dec_channels[i + 1]
+ ),
+ skip_channels=embed_channels if i == 0 else enc_channels[i - 1],
+ embed_channels=dec_channels[i],
+ depth=dec_depth[i],
+ norm_fn=norm_fn,
+ sp_indice_key=f"spconv{i}",
+ sub_indice_key=f"subm{i}",
+ )
+ )
+
+ self.final = spconv.SubMConv3d(dec_channels[0], num_classes, kernel_size=1)
+ self.apply(self._init_weights)
+
+ def forward(self, input_dict):
+ discrete_coord = input_dict["grid_coord"]
+ feat = input_dict["feat"]
+ offset = input_dict["offset"]
+ batch = offset2batch(offset)
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat([batch.unsqueeze(-1), discrete_coord], dim=1)
+ .int()
+ .contiguous(),
+ spatial_shape=torch.add(
+ torch.max(discrete_coord, dim=0).values, 1
+ ).tolist(),
+ batch_size=batch[-1].tolist() + 1,
+ )
+
+ x = self.stem(x)
+ skips = [x]
+ for i in range(self.num_stages):
+ x = self.enc[i](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ for i in reversed(range(self.num_stages)):
+ skip = skips.pop(-1)
+ x = self.dec[i](x, skip)
+ x = self.final(x)
+ return x.features
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/octformer/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/octformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06bea370d10808b0bad2b68189e957765fc46081
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/octformer/__init__.py
@@ -0,0 +1 @@
+from .octformer_v1m1_base import OctFormer
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/octformer/octformer_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/octformer/octformer_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c0faf700126b0d589867811e871025dc9782b76
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/octformer/octformer_v1m1_base.py
@@ -0,0 +1,629 @@
+"""
+Octree Transformer
+
+Modified from https://github.com/octree-nn/octformer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from typing import Optional, List, Dict
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+try:
+ import ocnn
+ from ocnn.octree import Octree, Points
+except ImportError:
+ from pointcept.utils.misc import DummyClass
+
+ ocnn = None
+ Octree = DummyClass
+ Points = DummyClass
+
+try:
+ import dwconv
+except ImportError:
+ dwconv = None
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch
+
+
+class OctreeT(Octree):
+ def __init__(
+ self,
+ octree: Octree,
+ patch_size: int = 24,
+ dilation: int = 4,
+ nempty: bool = True,
+ max_depth: Optional[int] = None,
+ start_depth: Optional[int] = None,
+ **kwargs
+ ):
+ super().__init__(octree.depth, octree.full_depth)
+ self.__dict__.update(octree.__dict__)
+
+ self.patch_size = patch_size
+ self.dilation = dilation
+ self.nempty = nempty
+ self.max_depth = max_depth or self.depth
+ self.start_depth = start_depth or self.full_depth
+ self.invalid_mask_value = -1e3
+ assert self.start_depth > 1
+
+ self.block_num = patch_size * dilation
+ self.nnum_t = self.nnum_nempty if nempty else self.nnum
+ self.nnum_a = ((self.nnum_t / self.block_num).ceil() * self.block_num).int()
+
+ num = self.max_depth + 1
+ self.batch_idx = [None] * num
+ self.patch_mask = [None] * num
+ self.dilate_mask = [None] * num
+ self.rel_pos = [None] * num
+ self.dilate_pos = [None] * num
+ self.build_t()
+
+ def build_t(self):
+ for d in range(self.start_depth, self.max_depth + 1):
+ self.build_batch_idx(d)
+ self.build_attn_mask(d)
+ self.build_rel_pos(d)
+
+ def build_batch_idx(self, depth: int):
+ batch = self.batch_id(depth, self.nempty)
+ self.batch_idx[depth] = self.patch_partition(batch, depth, self.batch_size)
+
+ def build_attn_mask(self, depth: int):
+ batch = self.batch_idx[depth]
+ mask = batch.view(-1, self.patch_size)
+ self.patch_mask[depth] = self._calc_attn_mask(mask)
+
+ mask = batch.view(-1, self.patch_size, self.dilation)
+ mask = mask.transpose(1, 2).reshape(-1, self.patch_size)
+ self.dilate_mask[depth] = self._calc_attn_mask(mask)
+
+ def _calc_attn_mask(self, mask: torch.Tensor):
+ attn_mask = mask.unsqueeze(2) - mask.unsqueeze(1)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, self.invalid_mask_value)
+ return attn_mask
+
+ def build_rel_pos(self, depth: int):
+ key = self.key(depth, self.nempty)
+ key = self.patch_partition(key, depth)
+ x, y, z, _ = ocnn.octree.key2xyz(key, depth)
+ xyz = torch.stack([x, y, z], dim=1)
+
+ xyz = xyz.view(-1, self.patch_size, 3)
+ self.rel_pos[depth] = xyz.unsqueeze(2) - xyz.unsqueeze(1)
+
+ xyz = xyz.view(-1, self.patch_size, self.dilation, 3)
+ xyz = xyz.transpose(1, 2).reshape(-1, self.patch_size, 3)
+ self.dilate_pos[depth] = xyz.unsqueeze(2) - xyz.unsqueeze(1)
+
+ def patch_partition(self, data: torch.Tensor, depth: int, fill_value=0):
+ num = self.nnum_a[depth] - self.nnum_t[depth]
+ tail = data.new_full((num,) + data.shape[1:], fill_value)
+ return torch.cat([data, tail], dim=0)
+
+ def patch_reverse(self, data: torch.Tensor, depth: int):
+ return data[: self.nnum_t[depth]]
+
+
+class MLP(torch.nn.Module):
+ def __init__(
+ self,
+ in_features: int,
+ hidden_features: Optional[int] = None,
+ out_features: Optional[int] = None,
+ activation=torch.nn.GELU,
+ drop: float = 0.0,
+ **kwargs
+ ):
+ super().__init__()
+ self.in_features = in_features
+ self.out_features = out_features or in_features
+ self.hidden_features = hidden_features or in_features
+
+ self.fc1 = torch.nn.Linear(self.in_features, self.hidden_features)
+ self.act = activation()
+ self.fc2 = torch.nn.Linear(self.hidden_features, self.out_features)
+ self.drop = torch.nn.Dropout(drop, inplace=True)
+
+ def forward(self, data: torch.Tensor):
+ data = self.fc1(data)
+ data = self.act(data)
+ data = self.drop(data)
+ data = self.fc2(data)
+ data = self.drop(data)
+ return data
+
+
+class OctreeDWConvBn(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ kernel_size: List[int] = [3],
+ stride: int = 1,
+ nempty: bool = False,
+ ):
+ super().__init__()
+ self.conv = dwconv.OctreeDWConv(
+ in_channels, kernel_size, nempty, use_bias=False
+ )
+ self.bn = torch.nn.BatchNorm1d(in_channels)
+
+ def forward(self, data: torch.Tensor, octree: Octree, depth: int):
+ out = self.conv(data, octree, depth)
+ out = self.bn(out)
+ return out
+
+
+class RPE(torch.nn.Module):
+ def __init__(self, patch_size: int, num_heads: int, dilation: int = 1):
+ super().__init__()
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.dilation = dilation
+ self.pos_bnd = self.get_pos_bnd(patch_size)
+ self.rpe_num = 2 * self.pos_bnd + 1
+ self.rpe_table = torch.nn.Parameter(torch.zeros(3 * self.rpe_num, num_heads))
+ torch.nn.init.trunc_normal_(self.rpe_table, std=0.02)
+
+ def get_pos_bnd(self, patch_size: int):
+ return int(0.8 * patch_size * self.dilation**0.5)
+
+ def xyz2idx(self, xyz: torch.Tensor):
+ mul = torch.arange(3, device=xyz.device) * self.rpe_num
+ xyz = xyz.clamp(-self.pos_bnd, self.pos_bnd)
+ idx = xyz + (self.pos_bnd + mul)
+ return idx
+
+ def forward(self, xyz):
+ idx = self.xyz2idx(xyz)
+ out = self.rpe_table.index_select(0, idx.reshape(-1))
+ out = out.view(idx.shape + (-1,)).sum(3)
+ out = out.permute(0, 3, 1, 2) # (N, K, K, H) -> (N, H, K, K)
+ return out
+
+ def extra_repr(self) -> str:
+ return "num_heads={}, pos_bnd={}, dilation={}".format(
+ self.num_heads, self.pos_bnd, self.dilation
+ ) # noqa
+
+
+class OctreeAttention(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ patch_size: int,
+ num_heads: int,
+ qkv_bias: bool = True,
+ qk_scale: Optional[float] = None,
+ attn_drop: float = 0.0,
+ proj_drop: float = 0.0,
+ dilation: int = 1,
+ use_rpe: bool = True,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.dilation = dilation
+ self.use_rpe = use_rpe
+ self.scale = qk_scale or (dim // num_heads) ** -0.5
+
+ self.qkv = torch.nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = torch.nn.Dropout(attn_drop)
+ self.proj = torch.nn.Linear(dim, dim)
+ self.proj_drop = torch.nn.Dropout(proj_drop)
+ self.softmax = torch.nn.Softmax(dim=-1)
+ self.rpe = RPE(patch_size, num_heads, dilation) if use_rpe else None
+
+ def forward(self, data: torch.Tensor, octree: OctreeT, depth: int):
+ H = self.num_heads
+ K = self.patch_size
+ C = self.dim
+ D = self.dilation
+
+ # patch partition
+ data = octree.patch_partition(data, depth)
+ if D > 1: # dilation
+ rel_pos = octree.dilate_pos[depth]
+ mask = octree.dilate_mask[depth]
+ data = data.view(-1, K, D, C).transpose(1, 2).reshape(-1, C)
+ else:
+ rel_pos = octree.rel_pos[depth]
+ mask = octree.patch_mask[depth]
+ data = data.view(-1, K, C)
+
+ # qkv
+ qkv = self.qkv(data).reshape(-1, K, 3, H, C // H).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # (N, H, K, C')
+ q = q * self.scale
+
+ # attn
+ attn = q @ k.transpose(-2, -1) # (N, H, K, K)
+ attn = self.apply_rpe(attn, rel_pos) # (N, H, K, K)
+ attn = attn + mask.unsqueeze(1)
+ attn = self.softmax(attn)
+ attn = self.attn_drop(attn)
+ data = (attn @ v).transpose(1, 2).reshape(-1, C)
+
+ # patch reverse
+ if D > 1: # dilation
+ data = data.view(-1, D, K, C).transpose(1, 2).reshape(-1, C)
+ data = octree.patch_reverse(data, depth)
+
+ # ffn
+ data = self.proj(data)
+ data = self.proj_drop(data)
+ return data
+
+ def apply_rpe(self, attn, rel_pos):
+ if self.use_rpe:
+ attn = attn + self.rpe(rel_pos)
+ return attn
+
+ def extra_repr(self) -> str:
+ return "dim={}, patch_size={}, num_heads={}, dilation={}".format(
+ self.dim, self.patch_size, self.num_heads, self.dilation
+ ) # noqa
+
+
+class OctFormerBlock(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ num_heads: int,
+ patch_size: int = 32,
+ dilation: int = 0,
+ mlp_ratio: float = 4.0,
+ qkv_bias: bool = True,
+ qk_scale: Optional[float] = None,
+ attn_drop: float = 0.0,
+ proj_drop: float = 0.0,
+ drop_path: float = 0.0,
+ nempty: bool = True,
+ activation: torch.nn.Module = torch.nn.GELU,
+ **kwargs
+ ):
+ super().__init__()
+ self.norm1 = torch.nn.LayerNorm(dim)
+ self.attention = OctreeAttention(
+ dim,
+ patch_size,
+ num_heads,
+ qkv_bias,
+ qk_scale,
+ attn_drop,
+ proj_drop,
+ dilation,
+ )
+ self.norm2 = torch.nn.LayerNorm(dim)
+ self.mlp = MLP(dim, int(dim * mlp_ratio), dim, activation, proj_drop)
+ self.drop_path = ocnn.nn.OctreeDropPath(drop_path, nempty)
+ self.cpe = OctreeDWConvBn(dim, nempty=nempty)
+
+ def forward(self, data: torch.Tensor, octree: OctreeT, depth: int):
+ data = self.cpe(data, octree, depth) + data
+ attn = self.attention(self.norm1(data), octree, depth)
+ data = data + self.drop_path(attn, octree, depth)
+ ffn = self.mlp(self.norm2(data))
+ data = data + self.drop_path(ffn, octree, depth)
+ return data
+
+
+class OctFormerStage(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ num_heads: int,
+ patch_size: int = 32,
+ dilation: int = 0,
+ mlp_ratio: float = 4.0,
+ qkv_bias: bool = True,
+ qk_scale: Optional[float] = None,
+ attn_drop: float = 0.0,
+ proj_drop: float = 0.0,
+ drop_path: float = 0.0,
+ nempty: bool = True,
+ activation: torch.nn.Module = torch.nn.GELU,
+ interval: int = 6,
+ use_checkpoint: bool = True,
+ num_blocks: int = 2,
+ octformer_block=OctFormerBlock,
+ **kwargs
+ ):
+ super().__init__()
+ self.num_blocks = num_blocks
+ self.use_checkpoint = use_checkpoint
+ self.interval = interval # normalization interval
+ self.num_norms = (num_blocks - 1) // self.interval
+
+ self.blocks = torch.nn.ModuleList(
+ [
+ octformer_block(
+ dim=dim,
+ num_heads=num_heads,
+ patch_size=patch_size,
+ dilation=1 if (i % 2 == 0) else dilation,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ drop_path=(
+ drop_path[i] if isinstance(drop_path, list) else drop_path
+ ),
+ nempty=nempty,
+ activation=activation,
+ )
+ for i in range(num_blocks)
+ ]
+ )
+ # self.norms = torch.nn.ModuleList([
+ # torch.nn.BatchNorm1d(dim) for _ in range(self.num_norms)])
+
+ def forward(self, data: torch.Tensor, octree: OctreeT, depth: int):
+ for i in range(self.num_blocks):
+ if self.use_checkpoint and self.training:
+ data = checkpoint(self.blocks[i], data, octree, depth)
+ else:
+ data = self.blocks[i](data, octree, depth)
+ # if i % self.interval == 0 and i != 0:
+ # data = self.norms[(i - 1) // self.interval](data)
+ return data
+
+
+class OctFormerDecoder(torch.nn.Module):
+ def __init__(
+ self, channels: List[int], fpn_channel: int, nempty: bool, head_up: int = 1
+ ):
+ super().__init__()
+ self.head_up = head_up
+ self.num_stages = len(channels)
+ self.conv1x1 = torch.nn.ModuleList(
+ [
+ torch.nn.Linear(channels[i], fpn_channel)
+ for i in range(self.num_stages - 1, -1, -1)
+ ]
+ )
+ self.upsample = ocnn.nn.OctreeUpsample("nearest", nempty)
+ self.conv3x3 = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeConvBnRelu(
+ fpn_channel, fpn_channel, kernel_size=[3], stride=1, nempty=nempty
+ )
+ for _ in range(self.num_stages)
+ ]
+ )
+ self.up_conv = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeDeconvBnRelu(
+ fpn_channel, fpn_channel, kernel_size=[3], stride=2, nempty=nempty
+ )
+ for _ in range(self.head_up)
+ ]
+ )
+
+ def forward(self, features: Dict[int, torch.Tensor], octree: Octree):
+ depth = min(features.keys())
+ depth_max = max(features.keys())
+ assert self.num_stages == len(features)
+
+ feature = self.conv1x1[0](features[depth])
+ conv_out = self.conv3x3[0](feature, octree, depth)
+ out = self.upsample(conv_out, octree, depth, depth_max)
+ for i in range(1, self.num_stages):
+ depth_i = depth + i
+ feature = self.upsample(feature, octree, depth_i - 1)
+ feature = self.conv1x1[i](features[depth_i]) + feature
+ conv_out = self.conv3x3[i](feature, octree, depth_i)
+ out = out + self.upsample(conv_out, octree, depth_i, depth_max)
+ for i in range(self.head_up):
+ out = self.up_conv[i](out, octree, depth_max + i)
+ return out
+
+
+class PatchEmbed(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels: int = 3,
+ dim: int = 96,
+ num_down: int = 2,
+ nempty: bool = True,
+ **kwargs
+ ):
+ super().__init__()
+ self.num_stages = num_down
+ self.delta_depth = -num_down
+ channels = [int(dim * 2**i) for i in range(-self.num_stages, 1)]
+
+ self.convs = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeConvBnRelu(
+ in_channels if i == 0 else channels[i],
+ channels[i],
+ kernel_size=[3],
+ stride=1,
+ nempty=nempty,
+ )
+ for i in range(self.num_stages)
+ ]
+ )
+ self.downsamples = torch.nn.ModuleList(
+ [
+ ocnn.modules.OctreeConvBnRelu(
+ channels[i],
+ channels[i + 1],
+ kernel_size=[2],
+ stride=2,
+ nempty=nempty,
+ )
+ for i in range(self.num_stages)
+ ]
+ )
+ self.proj = ocnn.modules.OctreeConvBnRelu(
+ channels[-1], dim, kernel_size=[3], stride=1, nempty=nempty
+ )
+
+ def forward(self, data: torch.Tensor, octree: Octree, depth: int):
+ # TODO: reduce to single input
+ for i in range(self.num_stages):
+ depth_i = depth - i
+ data = self.convs[i](data, octree, depth_i)
+ data = self.downsamples[i](data, octree, depth_i)
+ data = self.proj(data, octree, depth_i - 1)
+ return data
+
+
+class Downsample(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: List[int] = (2,),
+ nempty: bool = True,
+ ):
+ super().__init__()
+ self.norm = torch.nn.BatchNorm1d(out_channels)
+ self.conv = ocnn.nn.OctreeConv(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=2,
+ nempty=nempty,
+ use_bias=True,
+ )
+
+ def forward(self, data: torch.Tensor, octree: Octree, depth: int):
+ data = self.conv(data, octree, depth)
+ data = self.norm(data)
+ return data
+
+
+@MODELS.register_module("OctFormer-v1m1")
+class OctFormer(torch.nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ fpn_channels=168,
+ channels=(96, 192, 384, 384),
+ num_blocks=(2, 2, 18, 2),
+ num_heads=(6, 12, 24, 24),
+ patch_size=26,
+ stem_down=2,
+ head_up=2,
+ dilation=4,
+ drop_path=0.5,
+ nempty=True,
+ octree_scale_factor=10.24,
+ octree_depth=11,
+ octree_full_depth=2,
+ ):
+ super().__init__()
+ assert ocnn is not None, "Please follow `README.md` to install ocnn.`"
+ assert dwconv is not None, "Please follow `README.md` to install dwconv.`"
+
+ self.patch_size = patch_size
+ self.dilation = dilation
+ self.nempty = nempty
+ self.num_stages = len(num_blocks)
+ self.stem_down = stem_down
+ self.octree_scale_factor = octree_scale_factor
+ self.octree_depth = octree_depth
+ self.octree_full_depth = octree_full_depth
+ drop_ratio = torch.linspace(0, drop_path, sum(num_blocks)).tolist()
+
+ self.patch_embed = PatchEmbed(in_channels, channels[0], stem_down, nempty)
+ self.layers = torch.nn.ModuleList(
+ [
+ OctFormerStage(
+ dim=channels[i],
+ num_heads=num_heads[i],
+ patch_size=patch_size,
+ drop_path=drop_ratio[
+ sum(num_blocks[:i]) : sum(num_blocks[: i + 1])
+ ],
+ dilation=dilation,
+ nempty=nempty,
+ num_blocks=num_blocks[i],
+ )
+ for i in range(self.num_stages)
+ ]
+ )
+ self.downsamples = torch.nn.ModuleList(
+ [
+ Downsample(channels[i], channels[i + 1], kernel_size=[2], nempty=nempty)
+ for i in range(self.num_stages - 1)
+ ]
+ )
+ self.decoder = OctFormerDecoder(
+ channels=channels, fpn_channel=fpn_channels, nempty=nempty, head_up=head_up
+ )
+ self.interp = ocnn.nn.OctreeInterp("nearest", nempty)
+ self.seg_head = (
+ nn.Sequential(
+ nn.Linear(fpn_channels, fpn_channels),
+ torch.nn.BatchNorm1d(fpn_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(fpn_channels, num_classes),
+ )
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def points2octree(self, points):
+ octree = ocnn.octree.Octree(self.octree_depth, self.octree_full_depth)
+ octree.build_octree(points)
+ return octree
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ normal = data_dict["normal"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+
+ point = Points(
+ points=coord / self.octree_scale_factor,
+ normals=normal,
+ features=feat,
+ batch_id=batch.unsqueeze(-1),
+ batch_size=len(offset),
+ )
+ octree = ocnn.octree.Octree(
+ depth=self.octree_depth,
+ full_depth=self.octree_full_depth,
+ batch_size=len(offset),
+ device=coord.device,
+ )
+ octree.build_octree(point)
+ octree.construct_all_neigh()
+
+ feat = self.patch_embed(octree.features[octree.depth], octree, octree.depth)
+ depth = octree.depth - self.stem_down # current octree depth
+ octree = OctreeT(
+ octree,
+ self.patch_size,
+ self.dilation,
+ self.nempty,
+ max_depth=depth,
+ start_depth=depth - self.num_stages + 1,
+ )
+ features = {}
+ for i in range(self.num_stages):
+ depth_i = depth - i
+ feat = self.layers[i](feat, octree, depth_i)
+ features[depth_i] = feat
+ if i < self.num_stages - 1:
+ feat = self.downsamples[i](feat, octree, depth_i)
+ out = self.decoder(features, octree)
+ # interp representation to points before Octreeization
+ query_pts = torch.cat([point.points, point.batch_id], dim=1).contiguous()
+ out = self.interp(out, octree, octree.depth, query_pts)
+ out = self.seg_head(out)
+ return out
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d9f35f2a05f4a88043a45f2da64faf21f38f520
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/__init__.py
@@ -0,0 +1 @@
+from .point_group_v1m1_base import PointGroup
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/point_group_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/point_group_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c36d3fe009fa33136d92605f1c810cff8892959
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/point_group_v1m1_base.py
@@ -0,0 +1,174 @@
+"""
+PointGroup for instance segmentation
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Chengyao Wang
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+ from pointgroup_ops import ballquery_batch_p, bfs_cluster
+except ImportError:
+ ballquery_batch_p, bfs_cluster = None, None
+
+from pointcept.models.utils import offset2batch, batch2offset
+
+from pointcept.models.builder import MODELS, build_model
+
+
+@MODELS.register_module("PG-v1m1")
+class PointGroup(nn.Module):
+ def __init__(
+ self,
+ backbone,
+ backbone_out_channels=64,
+ semantic_num_classes=20,
+ semantic_ignore_index=-1,
+ segment_ignore_index=(-1, 0, 1),
+ instance_ignore_index=-1,
+ cluster_thresh=1.5,
+ cluster_closed_points=300,
+ cluster_propose_points=100,
+ cluster_min_points=50,
+ voxel_size=0.02,
+ ):
+ super().__init__()
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ self.semantic_num_classes = semantic_num_classes
+ self.segment_ignore_index = segment_ignore_index
+ self.semantic_ignore_index = semantic_ignore_index
+ self.instance_ignore_index = instance_ignore_index
+ self.cluster_thresh = cluster_thresh
+ self.cluster_closed_points = cluster_closed_points
+ self.cluster_propose_points = cluster_propose_points
+ self.cluster_min_points = cluster_min_points
+ self.voxel_size = voxel_size
+ self.backbone = build_model(backbone)
+ self.bias_head = nn.Sequential(
+ nn.Linear(backbone_out_channels, backbone_out_channels),
+ norm_fn(backbone_out_channels),
+ nn.ReLU(),
+ nn.Linear(backbone_out_channels, 3),
+ )
+ self.seg_head = nn.Linear(backbone_out_channels, semantic_num_classes)
+ self.ce_criteria = torch.nn.CrossEntropyLoss(ignore_index=semantic_ignore_index)
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ segment = data_dict["segment"]
+ instance = data_dict["instance"]
+ instance_centroid = data_dict["instance_centroid"]
+ offset = data_dict["offset"]
+
+ feat = self.backbone(data_dict)
+ bias_pred = self.bias_head(feat)
+ logit_pred = self.seg_head(feat)
+
+ # compute loss
+ seg_loss = self.ce_criteria(logit_pred, segment)
+
+ mask = (instance != self.instance_ignore_index).float()
+ bias_gt = instance_centroid - coord
+ bias_dist = torch.sum(torch.abs(bias_pred - bias_gt), dim=-1)
+ bias_l1_loss = torch.sum(bias_dist * mask) / (torch.sum(mask) + 1e-8)
+
+ bias_pred_norm = bias_pred / (
+ torch.norm(bias_pred, p=2, dim=1, keepdim=True) + 1e-8
+ )
+ bias_gt_norm = bias_gt / (torch.norm(bias_gt, p=2, dim=1, keepdim=True) + 1e-8)
+ cosine_similarity = -(bias_pred_norm * bias_gt_norm).sum(-1)
+ bias_cosine_loss = torch.sum(cosine_similarity * mask) / (
+ torch.sum(mask) + 1e-8
+ )
+
+ loss = seg_loss + bias_l1_loss + bias_cosine_loss
+ return_dict = dict(
+ loss=loss,
+ seg_loss=seg_loss,
+ bias_l1_loss=bias_l1_loss,
+ bias_cosine_loss=bias_cosine_loss,
+ )
+
+ if not self.training:
+ center_pred = coord + bias_pred
+ center_pred /= self.voxel_size
+ logit_pred = F.softmax(logit_pred, dim=-1)
+ segment_pred = torch.max(logit_pred, 1)[1] # [n]
+ # cluster
+ mask = (
+ ~torch.concat(
+ [
+ (segment_pred == index).unsqueeze(-1)
+ for index in self.segment_ignore_index
+ ],
+ dim=1,
+ )
+ .sum(-1)
+ .bool()
+ )
+
+ if mask.sum() == 0:
+ proposals_idx = torch.zeros(0).int()
+ proposals_offset = torch.zeros(1).int()
+ else:
+ center_pred_ = center_pred[mask]
+ segment_pred_ = segment_pred[mask]
+
+ batch_ = offset2batch(offset)[mask]
+ offset_ = nn.ConstantPad1d((1, 0), 0)(batch2offset(batch_))
+ idx, start_len = ballquery_batch_p(
+ center_pred_,
+ batch_.int(),
+ offset_.int(),
+ self.cluster_thresh,
+ self.cluster_closed_points,
+ )
+ proposals_idx, proposals_offset = bfs_cluster(
+ segment_pred_.int().cpu(),
+ idx.cpu(),
+ start_len.cpu(),
+ self.cluster_min_points,
+ )
+ proposals_idx[:, 1] = (
+ mask.nonzero().view(-1)[proposals_idx[:, 1].long()].int()
+ )
+
+ # get proposal
+ proposals_pred = torch.zeros(
+ (proposals_offset.shape[0] - 1, center_pred.shape[0]), dtype=torch.int
+ )
+ proposals_pred[proposals_idx[:, 0].long(), proposals_idx[:, 1].long()] = 1
+ instance_pred = segment_pred[
+ proposals_idx[:, 1][proposals_offset[:-1].long()].long()
+ ]
+ proposals_point_num = proposals_pred.sum(1)
+ proposals_mask = proposals_point_num > self.cluster_propose_points
+ proposals_pred = proposals_pred[proposals_mask]
+ instance_pred = instance_pred[proposals_mask]
+
+ pred_scores = []
+ pred_classes = []
+ pred_masks = proposals_pred.detach().cpu()
+ for proposal_id in range(len(proposals_pred)):
+ segment_ = proposals_pred[proposal_id]
+ confidence_ = logit_pred[
+ segment_.bool(), instance_pred[proposal_id]
+ ].mean()
+ object_ = instance_pred[proposal_id]
+ pred_scores.append(confidence_)
+ pred_classes.append(object_)
+ if len(pred_scores) > 0:
+ pred_scores = torch.stack(pred_scores).cpu()
+ pred_classes = torch.stack(pred_classes).cpu()
+ else:
+ pred_scores = torch.tensor([])
+ pred_classes = torch.tensor([])
+
+ return_dict["pred_scores"] = pred_scores
+ return_dict["pred_masks"] = pred_masks
+ return_dict["pred_classes"] = pred_classes
+ return return_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/utils.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d095b5bc89291ec30418c0aaf0eb9f672fe26ccc
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_group/utils.py
@@ -0,0 +1,176 @@
+import torch
+from torch.autograd import Function
+import pointgroup_ops
+
+
+class BallQueryBatchP(Function):
+ @staticmethod
+ def forward(ctx, coords, batch_idxs, batch_offsets, radius, meanActive):
+ """
+ :param ctx:
+ :param coords: (n, 3) float
+ :param batch_idxs: (n) int
+ :param batch_offsets: (B+1) int
+ :param radius: float
+ :param meanActive: int
+ :return: idx (nActive), int
+ :return: start_len (n, 2), int
+ """
+
+ n = coords.size(0)
+
+ assert coords.is_contiguous() and coords.is_cuda
+ assert batch_idxs.is_contiguous() and batch_idxs.is_cuda
+ assert batch_offsets.is_contiguous() and batch_offsets.is_cuda
+
+ while True:
+ idx = torch.cuda.IntTensor(n * meanActive).zero_()
+ start_len = torch.cuda.IntTensor(n, 2).zero_()
+ nActive = pointgroup_ops.ballquery_batch_p(
+ coords, batch_idxs, batch_offsets, idx, start_len, n, meanActive, radius
+ )
+ if nActive <= n * meanActive:
+ break
+ meanActive = int(nActive // n + 1)
+ idx = idx[:nActive]
+
+ return idx, start_len
+
+ @staticmethod
+ def backward(ctx, a=None, b=None):
+ return None, None, None
+
+
+ballquery_batch_p = BallQueryBatchP.apply
+
+
+class Clustering:
+ def __init__(
+ self,
+ ignored_labels,
+ class_mapping,
+ thresh=0.03,
+ closed_points=300,
+ min_points=50,
+ propose_points=100,
+ score_func=torch.max,
+ ) -> None:
+ self.ignored_labels = ignored_labels
+ self.thresh = thresh
+ self.closed_points = closed_points
+ self.min_points = min_points
+ self.class_mapping = class_mapping
+ self.propose_points = propose_points
+ self.score_func = score_func
+
+ def cluster(self, vertices, scores):
+ labels = torch.max(scores, 1)[1] # (N) long, cuda
+ proposals_idx, proposals_offset = self.cluster_(vertices, labels)
+
+ ## debug
+ # import ipdb; ipdb.set_trace()
+ # colors = np.array(create_color_palette())[labels.cpu()]
+ # write_triangle_mesh(vertices, colors, None, 'semantics.ply')
+
+ # scatter
+ proposals_pred = torch.zeros(
+ (proposals_offset.shape[0] - 1, vertices.shape[0]), dtype=torch.int
+ ) # (nProposal, N), int, cuda
+ proposals_pred[proposals_idx[:, 0].long(), proposals_idx[:, 1].long()] = 1
+ labels = labels[proposals_idx[:, 1][proposals_offset[:-1].long()].long()]
+
+ proposals_pointnum = proposals_pred.sum(1)
+ npoint_mask = proposals_pointnum > self.propose_points
+
+ proposals_pred = proposals_pred[npoint_mask]
+ labels = labels[npoint_mask]
+ return proposals_pred, labels
+
+ def cluster_(self, vertices, labels):
+ """
+ :param batch_idxs: (N), int, cuda
+ :labels: 0-19
+ """
+ batch_idxs = torch.zeros_like(labels)
+
+ mask_non_ignored = torch.ones_like(labels).bool()
+ for ignored_label in self.ignored_labels:
+ mask_non_ignored = mask_non_ignored & (
+ self.class_mapping[labels] != ignored_label
+ )
+ object_idxs = mask_non_ignored.nonzero().view(-1)
+
+ vertices_ = vertices[object_idxs].float()
+ labels_ = labels[object_idxs].int()
+
+ if vertices_.numel() == 0:
+ return torch.zeros((0, 2)).int(), torch.zeros(1).int()
+
+ batch_idxs_ = batch_idxs[object_idxs].int()
+ batch_offsets_ = torch.FloatTensor([0, object_idxs.shape[0]]).int().cuda()
+
+ idx, start_len = ballquery_batch_p(
+ vertices_, batch_idxs_, batch_offsets_, self.thresh, self.closed_points
+ )
+ proposals_idx, proposals_offset = bfs_cluster(
+ labels_.cpu(), idx.cpu(), start_len.cpu(), self.min_points
+ )
+ proposals_idx[:, 1] = object_idxs[proposals_idx[:, 1].long()].int()
+
+ return proposals_idx, proposals_offset
+
+ def get_instances(self, vertices, scores):
+ proposals_pred, labels = self.cluster(vertices, scores)
+ instances = {}
+ for proposal_id in range(len(proposals_pred)):
+ clusters_i = proposals_pred[proposal_id]
+ score = scores[clusters_i.bool(), labels[proposal_id]]
+ score = self.score_func(score)
+ instances[proposal_id] = {}
+ instances[proposal_id]["conf"] = score.cpu().numpy()
+ instances[proposal_id]["label_id"] = self.class_mapping.cpu()[
+ labels[proposal_id]
+ ]
+ instances[proposal_id]["pred_mask"] = clusters_i.cpu().numpy()
+ return instances
+
+
+class BFSCluster(Function):
+ @staticmethod
+ def forward(ctx, semantic_label, ball_query_idxs, start_len, threshold):
+ """
+ :param ctx:
+ :param semantic_label: (N), int
+ :param ball_query_idxs: (nActive), int
+ :param start_len: (N, 2), int
+ :return: cluster_idxs: int (sumNPoint, 2), dim 0 for cluster_id, dim 1 for corresponding point idxs in N
+ :return: cluster_offsets: int (nCluster + 1)
+ """
+
+ N = start_len.size(0)
+
+ assert semantic_label.is_contiguous()
+ assert ball_query_idxs.is_contiguous()
+ assert start_len.is_contiguous()
+
+ cluster_idxs = semantic_label.new()
+ cluster_offsets = semantic_label.new()
+
+ pointgroup_ops.bfs_cluster(
+ semantic_label,
+ ball_query_idxs,
+ start_len,
+ cluster_idxs,
+ cluster_offsets,
+ N,
+ threshold,
+ )
+
+ return cluster_idxs, cluster_offsets
+
+ @staticmethod
+ def backward(ctx, a=None):
+ return None
+
+
+bfs_cluster = BFSCluster.apply
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4c980b70b8c49dfc51625623071f21d6405d856
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/__init__.py
@@ -0,0 +1,4 @@
+from .point_prompt_training_v1m1_language_guided import *
+from .point_prompt_training_v1m2_decoupled import *
+
+from .prompt_driven_normalization import PDNorm
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/point_prompt_training_v1m1_language_guided.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/point_prompt_training_v1m1_language_guided.py
new file mode 100644
index 0000000000000000000000000000000000000000..10c09a5be7e437ecdc99b92507d17da0e7d369b4
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/point_prompt_training_v1m1_language_guided.py
@@ -0,0 +1,122 @@
+"""
+Point Prompt Training
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+from pointcept.models.utils.structure import Point
+from pointcept.models.builder import MODELS
+from pointcept.models.losses import build_criteria
+
+
+@MODELS.register_module("PPT-v1m1")
+class PointPromptTraining(nn.Module):
+ """
+ PointPromptTraining provides Data-driven Context and enables multi-dataset training with
+ Language-driven Categorical Alignment. PDNorm is supported by SpUNet-v1m3 to adapt the
+ backbone to a specific dataset with a given dataset condition and context.
+ """
+
+ def __init__(
+ self,
+ backbone=None,
+ criteria=None,
+ backbone_out_channels=96,
+ context_channels=256,
+ conditions=("Structured3D", "ScanNet", "S3DIS"),
+ template="[x]",
+ clip_model="ViT-B/16",
+ # fmt: off
+ class_name=(
+ "wall", "floor", "cabinet", "bed", "chair", "sofa", "table", "door",
+ "window", "bookshelf", "bookcase", "picture", "counter", "desk", "shelves", "curtain",
+ "dresser", "pillow", "mirror", "ceiling", "refrigerator", "television", "shower curtain", "nightstand",
+ "toilet", "sink", "lamp", "bathtub", "garbagebin", "board", "beam", "column",
+ "clutter", "otherstructure", "otherfurniture", "otherprop",
+ ),
+ valid_index=(
+ (0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 33, 34, 35),
+ (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 20, 22, 24, 25, 27, 34),
+ (0, 1, 4, 5, 6, 7, 8, 10, 19, 29, 30, 31, 32),
+ ),
+ # fmt: on
+ backbone_mode=False,
+ ):
+ super().__init__()
+ assert len(conditions) == len(valid_index)
+ assert backbone.type in [
+ "SpUNet-v1m3",
+ "PT-v2m3",
+ "PT-v3m1",
+ ] # SpUNet v1m3: Sparse UNet with PDNorm
+ self.backbone = MODELS.build(backbone)
+ self.criteria = build_criteria(criteria)
+ self.conditions = conditions
+ self.valid_index = valid_index
+ self.embedding_table = nn.Embedding(len(conditions), context_channels)
+ self.backbone_mode = backbone_mode
+ if not self.backbone_mode:
+ import clip
+
+ clip_model, _ = clip.load(
+ clip_model, device="cpu", download_root="./.cache/clip"
+ )
+ clip_model.requires_grad_(False)
+ class_prompt = [template.replace("[x]", name) for name in class_name]
+ class_token = clip.tokenize(class_prompt)
+ class_embedding = clip_model.encode_text(class_token)
+ class_embedding = class_embedding / class_embedding.norm(
+ dim=-1, keepdim=True
+ )
+ self.register_buffer("class_embedding", class_embedding)
+ self.proj_head = nn.Linear(
+ backbone_out_channels, clip_model.text_projection.shape[1]
+ )
+ self.logit_scale = clip_model.logit_scale
+
+ def forward(self, data_dict):
+ condition = data_dict["condition"][0]
+ assert condition in self.conditions
+ context = self.embedding_table(
+ torch.tensor(
+ [self.conditions.index(condition)], device=data_dict["coord"].device
+ )
+ )
+ data_dict["context"] = context
+ point = self.backbone(data_dict)
+ # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ if self.backbone_mode:
+ # PPT serve as a multi-dataset backbone when enable backbone mode
+ return feat
+ feat = self.proj_head(feat)
+ feat = feat / feat.norm(dim=-1, keepdim=True)
+ sim = (
+ feat
+ @ self.class_embedding[
+ self.valid_index[self.conditions.index(condition)], :
+ ].t()
+ )
+ logit_scale = self.logit_scale.exp()
+ seg_logits = logit_scale * sim
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in data_dict.keys():
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/point_prompt_training_v1m2_decoupled.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/point_prompt_training_v1m2_decoupled.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ad9c6bf1fcb5e17b139d6c06b44b589fcee816f
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/point_prompt_training_v1m2_decoupled.py
@@ -0,0 +1,79 @@
+"""
+Point Prompt Training with decoupled segmentation head
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+from pointcept.models.utils.structure import Point
+from pointcept.models.builder import MODELS
+from pointcept.models.losses import build_criteria
+
+
+@MODELS.register_module("PPT-v1m2")
+class PointPromptTraining(nn.Module):
+ """
+ PointPromptTraining v1m2 provides Data-driven Context and enables multi-dataset training with
+ Decoupled Segmentation Head. PDNorm is supported by SpUNet-v1m3 to adapt the
+ backbone to a specific dataset with a given dataset condition and context.
+ """
+
+ def __init__(
+ self,
+ backbone=None,
+ criteria=None,
+ backbone_out_channels=96,
+ context_channels=256,
+ conditions=("Structured3D", "ScanNet", "S3DIS"),
+ num_classes=(25, 20, 13),
+ backbone_mode=False,
+ ):
+ super().__init__()
+ assert len(conditions) == len(num_classes)
+ assert backbone.type in ["SpUNet-v1m3", "PT-v2m3", "PT-v3m1"]
+ self.backbone = MODELS.build(backbone)
+ self.criteria = build_criteria(criteria)
+ self.conditions = conditions
+ self.embedding_table = nn.Embedding(len(conditions), context_channels)
+ self.backbone_mode = backbone_mode
+ self.seg_heads = nn.ModuleList(
+ [nn.Linear(backbone_out_channels, num_cls) for num_cls in num_classes]
+ )
+
+ def forward(self, data_dict):
+ condition = data_dict["condition"][0]
+ assert condition in self.conditions
+ context = self.embedding_table(
+ torch.tensor(
+ [self.conditions.index(condition)], device=data_dict["coord"].device
+ )
+ )
+ data_dict["context"] = context
+ point = self.backbone(data_dict)
+ # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
+ # TODO: remove this part after make all backbone return Point only.
+ if isinstance(point, Point):
+ feat = point.feat
+ else:
+ feat = point
+ if self.backbone_mode:
+ # PPT serve as a multi-dataset backbone when enable backbone mode
+ return feat
+ seg_head = self.seg_heads[self.conditions.index(condition)]
+ seg_logits = seg_head(feat)
+ # train
+ if self.training:
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss)
+ # eval
+ elif "segment" in data_dict.keys():
+ loss = self.criteria(seg_logits, data_dict["segment"])
+ return dict(loss=loss, seg_logits=seg_logits)
+ # test
+ else:
+ return dict(seg_logits=seg_logits)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/prompt_driven_normalization.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/prompt_driven_normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d7d0d0c01dd4ccf939afeff870b5d72cab403a3
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_prompt_training/prompt_driven_normalization.py
@@ -0,0 +1,47 @@
+import torch.nn as nn
+
+from pointcept.models.modules import PointModule, PointSequential
+from pointcept.models.builder import MODULES
+
+
+@MODULES.register_module()
+class PDNorm(PointModule):
+ def __init__(
+ self,
+ num_features,
+ norm_layer,
+ context_channels=256,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ decouple=True,
+ adaptive=False,
+ ):
+ super().__init__()
+ self.conditions = conditions
+ self.decouple = decouple
+ self.adaptive = adaptive
+ if self.decouple:
+ self.norm = nn.ModuleList([norm_layer(num_features) for _ in conditions])
+ else:
+ self.norm = norm_layer
+ if self.adaptive:
+ self.modulation = nn.Sequential(
+ nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
+ )
+
+ def forward(self, point):
+ assert {"feat", "condition"}.issubset(point.keys())
+ if isinstance(point.condition, str):
+ condition = point.condition
+ else:
+ condition = point.condition[0]
+ if self.decouple:
+ assert condition in self.conditions
+ norm = self.norm[self.conditions.index(condition)]
+ else:
+ norm = self.norm
+ point.feat = norm(point.feat)
+ if self.adaptive:
+ assert "context" in point.keys()
+ shift, scale = self.modulation(point.context).chunk(2, dim=1)
+ point.feat = point.feat * (1.0 + scale) + shift
+ return point
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6493a312bfcf559642e6a2cc77d96c3770f0dd1
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/__init__.py
@@ -0,0 +1,3 @@
+from .point_transformer_seg import *
+from .point_transformer_partseg import *
+from .point_transformer_cls import *
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_cls.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_cls.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e12746fef73e9b3ee75b72942fbc8dc96e6e1bf
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_cls.py
@@ -0,0 +1,131 @@
+"""
+Point Transformer V1 for Object Classification
+
+Might be a bit different from the original paper
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+
+from .point_transformer_seg import TransitionDown, Bottleneck
+from pointcept.models.builder import MODELS
+
+
+class PointTransformerCls(nn.Module):
+ def __init__(self, block, blocks, in_channels=6, num_classes=40):
+ super().__init__()
+ self.in_channels = in_channels
+ self.in_planes, planes = in_channels, [32, 64, 128, 256, 512]
+ fpn_planes, fpnhead_planes, share_planes = 128, 64, 8
+ stride, nsample = [1, 4, 4, 4, 4], [8, 16, 16, 16, 16]
+ self.enc1 = self._make_enc(
+ block,
+ planes[0],
+ blocks[0],
+ share_planes,
+ stride=stride[0],
+ nsample=nsample[0],
+ ) # N/1
+ self.enc2 = self._make_enc(
+ block,
+ planes[1],
+ blocks[1],
+ share_planes,
+ stride=stride[1],
+ nsample=nsample[1],
+ ) # N/4
+ self.enc3 = self._make_enc(
+ block,
+ planes[2],
+ blocks[2],
+ share_planes,
+ stride=stride[2],
+ nsample=nsample[2],
+ ) # N/16
+ self.enc4 = self._make_enc(
+ block,
+ planes[3],
+ blocks[3],
+ share_planes,
+ stride=stride[3],
+ nsample=nsample[3],
+ ) # N/64
+ self.enc5 = self._make_enc(
+ block,
+ planes[4],
+ blocks[4],
+ share_planes,
+ stride=stride[4],
+ nsample=nsample[4],
+ ) # N/256
+ self.cls = nn.Sequential(
+ nn.Linear(planes[4], 256),
+ nn.BatchNorm1d(256),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(256, 128),
+ nn.BatchNorm1d(128),
+ nn.ReLU(inplace=True),
+ nn.Dropout(p=0.5),
+ nn.Linear(128, num_classes),
+ )
+
+ def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16):
+ layers = [
+ TransitionDown(self.in_planes, planes * block.expansion, stride, nsample)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(1, blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ p0 = data_dict["coord"]
+ x0 = data_dict["feat"]
+ o0 = data_dict["offset"].int()
+ x0 = p0 if self.in_channels == 3 else torch.cat((p0, x0), 1)
+ p1, x1, o1 = self.enc1([p0, x0, o0])
+ p2, x2, o2 = self.enc2([p1, x1, o1])
+ p3, x3, o3 = self.enc3([p2, x2, o2])
+ p4, x4, o4 = self.enc4([p3, x3, o3])
+ p5, x5, o5 = self.enc5([p4, x4, o4])
+ x = []
+ for i in range(o5.shape[0]):
+ if i == 0:
+ s_i, e_i, cnt = 0, o5[0], o5[0]
+ else:
+ s_i, e_i, cnt = o5[i - 1], o5[i], o5[i] - o5[i - 1]
+ x_b = x5[s_i:e_i, :].sum(0, True) / cnt
+ x.append(x_b)
+ x = torch.cat(x, 0)
+ x = self.cls(x)
+ return x
+
+
+@MODELS.register_module("PointTransformer-Cls26")
+class PointTransformerCls26(PointTransformerCls):
+ def __init__(self, **kwargs):
+ super(PointTransformerCls26, self).__init__(
+ Bottleneck, [1, 1, 1, 1, 1], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Cls38")
+class PointTransformerCls38(PointTransformerCls):
+ def __init__(self, **kwargs):
+ super(PointTransformerCls38, self).__init__(
+ Bottleneck, [1, 2, 2, 2, 2], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Cls50")
+class PointTransformerCls50(PointTransformerCls):
+ def __init__(self, **kwargs):
+ super(PointTransformerCls50, self).__init__(
+ Bottleneck, [1, 2, 3, 5, 2], **kwargs
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_partseg.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_partseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..3326a9f7d6fd62a9394e434135615339a3c679f8
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_partseg.py
@@ -0,0 +1,374 @@
+"""
+Point Transformer V1 for Part Segmentation
+
+Might be a bit different from the original paper
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import einops
+import pointops
+
+from pointcept.models.builder import MODELS
+from .utils import LayerNorm1d
+
+
+class PointTransformerLayer(nn.Module):
+ def __init__(self, in_planes, out_planes, share_planes=8, nsample=16):
+ super().__init__()
+ self.mid_planes = mid_planes = out_planes // 1
+ self.out_planes = out_planes
+ self.share_planes = share_planes
+ self.nsample = nsample
+ self.linear_q = nn.Linear(in_planes, mid_planes)
+ self.linear_k = nn.Linear(in_planes, mid_planes)
+ self.linear_v = nn.Linear(in_planes, out_planes)
+ self.linear_p = nn.Sequential(
+ nn.Linear(3, 3),
+ LayerNorm1d(3),
+ nn.ReLU(inplace=True),
+ nn.Linear(3, out_planes),
+ )
+ self.linear_w = nn.Sequential(
+ LayerNorm1d(mid_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(mid_planes, out_planes // share_planes),
+ LayerNorm1d(out_planes // share_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(out_planes // share_planes, out_planes // share_planes),
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ def forward(self, pxo) -> torch.Tensor:
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ x_q, x_k, x_v = self.linear_q(x), self.linear_k(x), self.linear_v(x)
+ x_k, idx = pointops.knn_query_and_group(
+ x_k, p, o, new_xyz=p, new_offset=o, nsample=self.nsample, with_xyz=True
+ )
+ x_v, _ = pointops.knn_query_and_group(
+ x_v,
+ p,
+ o,
+ new_xyz=p,
+ new_offset=o,
+ idx=idx,
+ nsample=self.nsample,
+ with_xyz=False,
+ )
+ p_r, x_k = x_k[:, :, 0:3], x_k[:, :, 3:]
+ p_r = self.linear_p(p_r)
+ r_qk = (
+ x_k
+ - x_q.unsqueeze(1)
+ + einops.reduce(
+ p_r, "n ns (i j) -> n ns j", reduction="sum", j=self.mid_planes
+ )
+ )
+ w = self.linear_w(r_qk) # (n, nsample, c)
+ w = self.softmax(w)
+ x = torch.einsum(
+ "n t s i, n t i -> n s i",
+ einops.rearrange(x_v + p_r, "n ns (s i) -> n ns s i", s=self.share_planes),
+ w,
+ )
+ x = einops.rearrange(x, "n s i -> n (s i)")
+ return x
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_planes, out_planes, stride=1, nsample=16):
+ super().__init__()
+ self.stride, self.nsample = stride, nsample
+ if stride != 1:
+ self.linear = nn.Linear(3 + in_planes, out_planes, bias=False)
+ self.pool = nn.MaxPool1d(nsample)
+ else:
+ self.linear = nn.Linear(in_planes, out_planes, bias=False)
+ self.bn = nn.BatchNorm1d(out_planes)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ if self.stride != 1:
+ n_o, count = [o[0].item() // self.stride], o[0].item() // self.stride
+ for i in range(1, o.shape[0]):
+ count += (o[i].item() - o[i - 1].item()) // self.stride
+ n_o.append(count)
+ n_o = torch.cuda.IntTensor(n_o)
+ idx = pointops.farthest_point_sampling(p, o, n_o) # (m)
+ n_p = p[idx.long(), :] # (m, 3)
+ x, _ = pointops.knn_query_and_group(
+ x,
+ p,
+ offset=o,
+ new_xyz=n_p,
+ new_offset=n_o,
+ nsample=self.nsample,
+ with_xyz=True,
+ )
+ x = self.relu(
+ self.bn(self.linear(x).transpose(1, 2).contiguous())
+ ) # (m, c, nsample)
+ x = self.pool(x).squeeze(-1) # (m, c)
+ p, o = n_p, n_o
+ else:
+ x = self.relu(self.bn(self.linear(x))) # (n, c)
+ return [p, x, o]
+
+
+class TransitionUp(nn.Module):
+ def __init__(self, in_planes, out_planes=None, num_shape_class=None):
+ super().__init__()
+ if out_planes is None:
+ self.num_shape_class = num_shape_class
+ if num_shape_class is not None:
+ self.linear1 = nn.Sequential(
+ nn.Linear(2 * in_planes + 1024, in_planes),
+ nn.BatchNorm1d(in_planes),
+ nn.ReLU(inplace=True),
+ )
+ else:
+ self.linear1 = nn.Sequential(
+ nn.Linear(2 * in_planes, in_planes),
+ nn.BatchNorm1d(in_planes),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, in_planes), nn.ReLU(inplace=True)
+ )
+ if num_shape_class is not None:
+ self.linear3 = nn.Sequential(
+ nn.Linear(num_shape_class, 1024), nn.ReLU(inplace=True)
+ )
+ else:
+ self.linear1 = nn.Sequential(
+ nn.Linear(out_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, pxo1, pxo2=None, y=None):
+ if pxo2 is None:
+ _, x, o = pxo1 # (n, 3), (n, c), (b)
+ x_tmp = []
+ for i in range(o.shape[0]):
+ if i == 0:
+ s_i, e_i, cnt = 0, o[0], o[0]
+ else:
+ s_i, e_i, cnt = o[i - 1], o[i], o[i] - o[i - 1]
+ x_b = x[s_i:e_i, :]
+ y_b = y[i].unsqueeze(-1).unsqueeze(-1).long()
+ y_onehot = torch.zeros(1, self.num_shape_class).cuda() # (1, l)
+ y_onehot.scatter_(1, y_b, 1) # (1, l)
+ x_b = torch.cat(
+ (
+ x_b,
+ self.linear2(x_b.sum(0, True) / cnt).repeat(cnt, 1),
+ self.linear3(y_onehot).repeat(cnt, 1),
+ ),
+ dim=1,
+ )
+ x_tmp.append(x_b)
+ x = torch.cat(x_tmp, 0)
+ x = self.linear1(x)
+ else:
+ p1, x1, o1 = pxo1
+ p2, x2, o2 = pxo2
+ x = self.linear1(x1) + pointops.interpolation(
+ p2, p1, self.linear2(x2), o2, o1
+ )
+ return x
+
+
+class Bottleneck(nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, share_planes=8, nsample=16):
+ super(Bottleneck, self).__init__()
+ self.linear1 = nn.Linear(in_planes, planes, bias=False)
+ self.bn1 = nn.BatchNorm1d(planes)
+ self.transformer = PointTransformerLayer(planes, planes, share_planes, nsample)
+ self.bn2 = nn.BatchNorm1d(planes)
+ self.linear3 = nn.Linear(planes, planes * self.expansion, bias=False)
+ self.bn3 = nn.BatchNorm1d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ identity = x
+ x = self.relu(self.bn1(self.linear1(x)))
+ x = self.relu(self.bn2(self.transformer([p, x, o])))
+ x = self.bn3(self.linear3(x))
+ x += identity
+ x = self.relu(x)
+ return [p, x, o]
+
+
+class PointTransformerSeg(nn.Module):
+ def __init__(
+ self, block, blocks, in_channels=6, num_classes=50, num_shape_classes=None
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_shape_classes = num_shape_classes
+ self.in_planes, planes = in_channels, [32, 64, 128, 256, 512]
+ fpn_planes, fpnhead_planes, share_planes = 128, 64, 8
+ stride, nsample = [1, 4, 4, 4, 4], [8, 16, 16, 16, 16]
+ self.enc1 = self._make_enc(
+ block,
+ planes[0],
+ blocks[0],
+ share_planes,
+ stride=stride[0],
+ nsample=nsample[0],
+ ) # N/1
+ self.enc2 = self._make_enc(
+ block,
+ planes[1],
+ blocks[1],
+ share_planes,
+ stride=stride[1],
+ nsample=nsample[1],
+ ) # N/4
+ self.enc3 = self._make_enc(
+ block,
+ planes[2],
+ blocks[2],
+ share_planes,
+ stride=stride[2],
+ nsample=nsample[2],
+ ) # N/16
+ self.enc4 = self._make_enc(
+ block,
+ planes[3],
+ blocks[3],
+ share_planes,
+ stride=stride[3],
+ nsample=nsample[3],
+ ) # N/64
+ self.enc5 = self._make_enc(
+ block,
+ planes[4],
+ blocks[4],
+ share_planes,
+ stride=stride[4],
+ nsample=nsample[4],
+ ) # N/256
+ self.dec5 = self._make_dec(
+ block,
+ planes[4],
+ 1,
+ share_planes,
+ num_shape_classes=num_shape_classes,
+ nsample=nsample[4],
+ is_head=True,
+ ) # transform p5
+ self.dec4 = self._make_dec(
+ block, planes[3], 1, share_planes, nsample=nsample[3]
+ ) # fusion p5 and p4
+ self.dec3 = self._make_dec(
+ block, planes[2], 1, share_planes, nsample=nsample[2]
+ ) # fusion p4 and p3
+ self.dec2 = self._make_dec(
+ block, planes[1], 1, share_planes, nsample=nsample[1]
+ ) # fusion p3 and p2
+ self.dec1 = self._make_dec(
+ block, planes[0], 1, share_planes, nsample=nsample[0]
+ ) # fusion p2 and p1
+ self.cls = nn.Sequential(
+ nn.Linear(planes[0], planes[0]),
+ nn.BatchNorm1d(planes[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(planes[0], num_classes),
+ )
+
+ def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16):
+ layers = [
+ TransitionDown(self.in_planes, planes * block.expansion, stride, nsample)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def _make_dec(
+ self,
+ block,
+ planes,
+ blocks,
+ share_planes=8,
+ num_shape_classes=None,
+ nsample=16,
+ is_head=False,
+ ):
+ layers = [
+ TransitionUp(
+ self.in_planes,
+ None if is_head else planes * block.expansion,
+ num_shape_classes,
+ )
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ p0 = data_dict["coord"]
+ x0 = data_dict["feat"]
+ o0 = data_dict["offset"].int()
+ if self.num_shape_classes is not None:
+ y = data_dict["cls_token"]
+ p1, x1, o1 = self.enc1([p0, x0, o0])
+ p2, x2, o2 = self.enc2([p1, x1, o1])
+ p3, x3, o3 = self.enc3([p2, x2, o2])
+ p4, x4, o4 = self.enc4([p3, x3, o3])
+ p5, x5, o5 = self.enc5([p4, x4, o4])
+ if self.num_shape_classes is not None:
+ x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5], y=y), o5])[1]
+ else:
+ x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5]), o5])[1]
+ x4 = self.dec4[1:]([p4, self.dec4[0]([p4, x4, o4], [p5, x5, o5]), o4])[1]
+ x3 = self.dec3[1:]([p3, self.dec3[0]([p3, x3, o3], [p4, x4, o4]), o3])[1]
+ x2 = self.dec2[1:]([p2, self.dec2[0]([p2, x2, o2], [p3, x3, o3]), o2])[1]
+ x1 = self.dec1[1:]([p1, self.dec1[0]([p1, x1, o1], [p2, x2, o2]), o1])[1]
+ x = self.cls(x1)
+ return x
+
+
+@MODELS.register_module("PointTransformer-PartSeg26")
+class PointTransformerSeg26(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg26, self).__init__(
+ Bottleneck, [1, 1, 1, 1, 1], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-PartSeg38")
+class PointTransformerSeg38(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg38, self).__init__(
+ Bottleneck, [1, 2, 2, 2, 2], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-PartSeg50")
+class PointTransformerSeg50(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg50, self).__init__(
+ Bottleneck, [1, 2, 3, 5, 2], **kwargs
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_seg.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..248cacad1ade65e48fa4686560fb40617a0ea449
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/point_transformer_seg.py
@@ -0,0 +1,327 @@
+"""
+Point Transformer V1 for Semantic Segmentation
+
+Might be a bit different from the original paper
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+import einops
+import pointops
+
+from pointcept.models.builder import MODELS
+from .utils import LayerNorm1d
+
+
+class PointTransformerLayer(nn.Module):
+ def __init__(self, in_planes, out_planes, share_planes=8, nsample=16):
+ super().__init__()
+ self.mid_planes = mid_planes = out_planes // 1
+ self.out_planes = out_planes
+ self.share_planes = share_planes
+ self.nsample = nsample
+ self.linear_q = nn.Linear(in_planes, mid_planes)
+ self.linear_k = nn.Linear(in_planes, mid_planes)
+ self.linear_v = nn.Linear(in_planes, out_planes)
+ self.linear_p = nn.Sequential(
+ nn.Linear(3, 3),
+ LayerNorm1d(3),
+ nn.ReLU(inplace=True),
+ nn.Linear(3, out_planes),
+ )
+ self.linear_w = nn.Sequential(
+ LayerNorm1d(mid_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(mid_planes, out_planes // share_planes),
+ LayerNorm1d(out_planes // share_planes),
+ nn.ReLU(inplace=True),
+ nn.Linear(out_planes // share_planes, out_planes // share_planes),
+ )
+ self.softmax = nn.Softmax(dim=1)
+
+ def forward(self, pxo) -> torch.Tensor:
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ x_q, x_k, x_v = self.linear_q(x), self.linear_k(x), self.linear_v(x)
+ x_k, idx = pointops.knn_query_and_group(
+ x_k, p, o, new_xyz=p, new_offset=o, nsample=self.nsample, with_xyz=True
+ )
+ x_v, _ = pointops.knn_query_and_group(
+ x_v,
+ p,
+ o,
+ new_xyz=p,
+ new_offset=o,
+ idx=idx,
+ nsample=self.nsample,
+ with_xyz=False,
+ )
+ p_r, x_k = x_k[:, :, 0:3], x_k[:, :, 3:]
+ p_r = self.linear_p(p_r)
+ r_qk = (
+ x_k
+ - x_q.unsqueeze(1)
+ + einops.reduce(
+ p_r, "n ns (i j) -> n ns j", reduction="sum", j=self.mid_planes
+ )
+ )
+ w = self.linear_w(r_qk) # (n, nsample, c)
+ w = self.softmax(w)
+ x = torch.einsum(
+ "n t s i, n t i -> n s i",
+ einops.rearrange(x_v + p_r, "n ns (s i) -> n ns s i", s=self.share_planes),
+ w,
+ )
+ x = einops.rearrange(x, "n s i -> n (s i)")
+ return x
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_planes, out_planes, stride=1, nsample=16):
+ super().__init__()
+ self.stride, self.nsample = stride, nsample
+ if stride != 1:
+ self.linear = nn.Linear(3 + in_planes, out_planes, bias=False)
+ self.pool = nn.MaxPool1d(nsample)
+ else:
+ self.linear = nn.Linear(in_planes, out_planes, bias=False)
+ self.bn = nn.BatchNorm1d(out_planes)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ if self.stride != 1:
+ n_o, count = [o[0].item() // self.stride], o[0].item() // self.stride
+ for i in range(1, o.shape[0]):
+ count += (o[i].item() - o[i - 1].item()) // self.stride
+ n_o.append(count)
+ n_o = torch.cuda.IntTensor(n_o)
+ idx = pointops.farthest_point_sampling(p, o, n_o) # (m)
+ n_p = p[idx.long(), :] # (m, 3)
+ x, _ = pointops.knn_query_and_group(
+ x,
+ p,
+ offset=o,
+ new_xyz=n_p,
+ new_offset=n_o,
+ nsample=self.nsample,
+ with_xyz=True,
+ )
+ x = self.relu(
+ self.bn(self.linear(x).transpose(1, 2).contiguous())
+ ) # (m, c, nsample)
+ x = self.pool(x).squeeze(-1) # (m, c)
+ p, o = n_p, n_o
+ else:
+ x = self.relu(self.bn(self.linear(x))) # (n, c)
+ return [p, x, o]
+
+
+class TransitionUp(nn.Module):
+ def __init__(self, in_planes, out_planes=None):
+ super().__init__()
+ if out_planes is None:
+ self.linear1 = nn.Sequential(
+ nn.Linear(2 * in_planes, in_planes),
+ nn.BatchNorm1d(in_planes),
+ nn.ReLU(inplace=True),
+ )
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, in_planes), nn.ReLU(inplace=True)
+ )
+ else:
+ self.linear1 = nn.Sequential(
+ nn.Linear(out_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+ self.linear2 = nn.Sequential(
+ nn.Linear(in_planes, out_planes),
+ nn.BatchNorm1d(out_planes),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, pxo1, pxo2=None):
+ if pxo2 is None:
+ _, x, o = pxo1 # (n, 3), (n, c), (b)
+ x_tmp = []
+ for i in range(o.shape[0]):
+ if i == 0:
+ s_i, e_i, cnt = 0, o[0], o[0]
+ else:
+ s_i, e_i, cnt = o[i - 1], o[i], o[i] - o[i - 1]
+ x_b = x[s_i:e_i, :]
+ x_b = torch.cat(
+ (x_b, self.linear2(x_b.sum(0, True) / cnt).repeat(cnt, 1)), 1
+ )
+ x_tmp.append(x_b)
+ x = torch.cat(x_tmp, 0)
+ x = self.linear1(x)
+ else:
+ p1, x1, o1 = pxo1
+ p2, x2, o2 = pxo2
+ x = self.linear1(x1) + pointops.interpolation(
+ p2, p1, self.linear2(x2), o2, o1
+ )
+ return x
+
+
+class Bottleneck(nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, share_planes=8, nsample=16):
+ super(Bottleneck, self).__init__()
+ self.linear1 = nn.Linear(in_planes, planes, bias=False)
+ self.bn1 = nn.BatchNorm1d(planes)
+ self.transformer = PointTransformerLayer(planes, planes, share_planes, nsample)
+ self.bn2 = nn.BatchNorm1d(planes)
+ self.linear3 = nn.Linear(planes, planes * self.expansion, bias=False)
+ self.bn3 = nn.BatchNorm1d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, pxo):
+ p, x, o = pxo # (n, 3), (n, c), (b)
+ identity = x
+ x = self.relu(self.bn1(self.linear1(x)))
+ x = self.relu(self.bn2(self.transformer([p, x, o])))
+ x = self.bn3(self.linear3(x))
+ x += identity
+ x = self.relu(x)
+ return [p, x, o]
+
+
+class PointTransformerSeg(nn.Module):
+ def __init__(self, block, blocks, in_channels=6, num_classes=13):
+ super().__init__()
+ self.in_channels = in_channels
+ self.in_planes, planes = in_channels, [32, 64, 128, 256, 512]
+ fpn_planes, fpnhead_planes, share_planes = 128, 64, 8
+ stride, nsample = [1, 4, 4, 4, 4], [8, 16, 16, 16, 16]
+ self.enc1 = self._make_enc(
+ block,
+ planes[0],
+ blocks[0],
+ share_planes,
+ stride=stride[0],
+ nsample=nsample[0],
+ ) # N/1
+ self.enc2 = self._make_enc(
+ block,
+ planes[1],
+ blocks[1],
+ share_planes,
+ stride=stride[1],
+ nsample=nsample[1],
+ ) # N/4
+ self.enc3 = self._make_enc(
+ block,
+ planes[2],
+ blocks[2],
+ share_planes,
+ stride=stride[2],
+ nsample=nsample[2],
+ ) # N/16
+ self.enc4 = self._make_enc(
+ block,
+ planes[3],
+ blocks[3],
+ share_planes,
+ stride=stride[3],
+ nsample=nsample[3],
+ ) # N/64
+ self.enc5 = self._make_enc(
+ block,
+ planes[4],
+ blocks[4],
+ share_planes,
+ stride=stride[4],
+ nsample=nsample[4],
+ ) # N/256
+ self.dec5 = self._make_dec(
+ block, planes[4], 1, share_planes, nsample=nsample[4], is_head=True
+ ) # transform p5
+ self.dec4 = self._make_dec(
+ block, planes[3], 1, share_planes, nsample=nsample[3]
+ ) # fusion p5 and p4
+ self.dec3 = self._make_dec(
+ block, planes[2], 1, share_planes, nsample=nsample[2]
+ ) # fusion p4 and p3
+ self.dec2 = self._make_dec(
+ block, planes[1], 1, share_planes, nsample=nsample[1]
+ ) # fusion p3 and p2
+ self.dec1 = self._make_dec(
+ block, planes[0], 1, share_planes, nsample=nsample[0]
+ ) # fusion p2 and p1
+ self.cls = nn.Sequential(
+ nn.Linear(planes[0], planes[0]),
+ nn.BatchNorm1d(planes[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(planes[0], num_classes),
+ )
+
+ def _make_enc(self, block, planes, blocks, share_planes=8, stride=1, nsample=16):
+ layers = [
+ TransitionDown(self.in_planes, planes * block.expansion, stride, nsample)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def _make_dec(
+ self, block, planes, blocks, share_planes=8, nsample=16, is_head=False
+ ):
+ layers = [
+ TransitionUp(self.in_planes, None if is_head else planes * block.expansion)
+ ]
+ self.in_planes = planes * block.expansion
+ for _ in range(blocks):
+ layers.append(
+ block(self.in_planes, self.in_planes, share_planes, nsample=nsample)
+ )
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ p0 = data_dict["coord"]
+ x0 = data_dict["feat"]
+ o0 = data_dict["offset"].int()
+ p1, x1, o1 = self.enc1([p0, x0, o0])
+ p2, x2, o2 = self.enc2([p1, x1, o1])
+ p3, x3, o3 = self.enc3([p2, x2, o2])
+ p4, x4, o4 = self.enc4([p3, x3, o3])
+ p5, x5, o5 = self.enc5([p4, x4, o4])
+ x5 = self.dec5[1:]([p5, self.dec5[0]([p5, x5, o5]), o5])[1]
+ x4 = self.dec4[1:]([p4, self.dec4[0]([p4, x4, o4], [p5, x5, o5]), o4])[1]
+ x3 = self.dec3[1:]([p3, self.dec3[0]([p3, x3, o3], [p4, x4, o4]), o3])[1]
+ x2 = self.dec2[1:]([p2, self.dec2[0]([p2, x2, o2], [p3, x3, o3]), o2])[1]
+ x1 = self.dec1[1:]([p1, self.dec1[0]([p1, x1, o1], [p2, x2, o2]), o1])[1]
+ x = self.cls(x1)
+ return x
+
+
+@MODELS.register_module("PointTransformer-Seg26")
+class PointTransformerSeg26(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg26, self).__init__(
+ Bottleneck, [1, 1, 1, 1, 1], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Seg38")
+class PointTransformerSeg38(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg38, self).__init__(
+ Bottleneck, [1, 2, 2, 2, 2], **kwargs
+ )
+
+
+@MODELS.register_module("PointTransformer-Seg50")
+class PointTransformerSeg50(PointTransformerSeg):
+ def __init__(self, **kwargs):
+ super(PointTransformerSeg50, self).__init__(
+ Bottleneck, [1, 2, 3, 5, 2], **kwargs
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/utils.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5687701835bb1f8a8936ea5ae5d52285567dc77
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer/utils.py
@@ -0,0 +1,14 @@
+import torch
+import torch.nn as nn
+
+torch.nn.LayerNorm
+
+
+class LayerNorm1d(nn.BatchNorm1d):
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ return (
+ super()
+ .forward(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9689fa2518b599bc6f94e6f8d0ea461859b8909
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/__init__.py
@@ -0,0 +1,10 @@
+"""
+Point Transformer V2
+
+Copyright (c) Xiaoyang Wu (xiaoyang.wu@connect.hku.hk). All Rights Reserved.
+Please cite our work if you use any part of the code.
+"""
+
+from .point_transformer_v2m1_origin import *
+from .point_transformer_v2m2_base import *
+from .point_transformer_v2m3_pdnorm import *
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m1_origin.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m1_origin.py
new file mode 100644
index 0000000000000000000000000000000000000000..b325d9eb7d5e1507ce62d5cbf60bb000cf83acbc
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m1_origin.py
@@ -0,0 +1,614 @@
+"""
+Point Transformer V2 mode 1
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from copy import deepcopy
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from torch_geometric.nn.pool import voxel_grid
+from torch_scatter import segment_csr
+
+import einops
+from timm.models.layers import DropPath
+import pointops
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class GroupedLinear(nn.Module):
+ __constants__ = ["in_features", "out_features", "groups"]
+ in_features: int
+ out_features: int
+ groups: int
+ weight: torch.Tensor
+
+ def __init__(
+ self, in_features: int, out_features: int, groups: int, device=None, dtype=None
+ ) -> None:
+ factory_kwargs = {"device": device, "dtype": dtype}
+ super(GroupedLinear, self).__init__()
+ self.in_features = in_features
+ self.out_features = out_features
+ self.groups = groups
+ assert in_features & groups == 0
+ assert out_features % groups == 0
+ # for convenient, currently only support out_features == groups, one output
+ assert out_features == groups
+ self.weight = nn.Parameter(torch.empty((1, in_features), **factory_kwargs))
+ self.reset_parameters()
+
+ def reset_parameters(self) -> None:
+ nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ return (
+ (input * self.weight)
+ .reshape(
+ list(input.shape[:-1]) + [self.groups, input.shape[-1] // self.groups]
+ )
+ .sum(-1)
+ )
+
+ def extra_repr(self) -> str:
+ return "in_features={}, out_features={}, bias={}".format(
+ self.in_features, self.out_features, self.bias is not None
+ )
+
+
+class PointBatchNorm(nn.Module):
+ """
+ Batch Normalization for Point Clouds data in shape of [B*N, C], [B*N, L, C]
+ """
+
+ def __init__(self, embed_channels):
+ super().__init__()
+ self.norm = nn.BatchNorm1d(embed_channels)
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ if input.dim() == 3:
+ return (
+ self.norm(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
+ elif input.dim() == 2:
+ return self.norm(input)
+ else:
+ raise NotImplementedError
+
+
+class GroupedVectorAttention(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ attn_drop_rate=0.0,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ ):
+ super(GroupedVectorAttention, self).__init__()
+ self.embed_channels = embed_channels
+ self.groups = groups
+ assert embed_channels % groups == 0
+ self.attn_drop_rate = attn_drop_rate
+ self.qkv_bias = qkv_bias
+ self.pe_multiplier = pe_multiplier
+ self.pe_bias = pe_bias
+
+ self.linear_q = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.linear_k = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear_v = nn.Linear(embed_channels, embed_channels, bias=qkv_bias)
+
+ if self.pe_multiplier:
+ self.linear_p_multiplier = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ if self.pe_bias:
+ self.linear_p_bias = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ self.weight_encoding = nn.Sequential(
+ GroupedLinear(embed_channels, groups, groups),
+ PointBatchNorm(groups),
+ nn.ReLU(inplace=True),
+ nn.Linear(groups, groups),
+ )
+ self.softmax = nn.Softmax(dim=1)
+ self.attn_drop = nn.Dropout(attn_drop_rate)
+
+ def forward(self, feat, coord, reference_index):
+ query, key, value = (
+ self.linear_q(feat),
+ self.linear_k(feat),
+ self.linear_v(feat),
+ )
+ key = pointops.grouping(reference_index, key, coord, with_xyz=True)
+ value = pointops.grouping(reference_index, value, coord, with_xyz=False)
+ pos, key = key[:, :, 0:3], key[:, :, 3:]
+ relation_qk = key - query.unsqueeze(1)
+ if self.pe_multiplier:
+ pem = self.linear_p_multiplier(pos)
+ relation_qk = relation_qk * pem
+ if self.pe_bias:
+ peb = self.linear_p_bias(pos)
+ relation_qk = relation_qk + peb
+ value = value + peb
+
+ weight = self.weight_encoding(relation_qk)
+ weight = self.attn_drop(self.softmax(weight))
+
+ mask = torch.sign(reference_index + 1)
+ weight = torch.einsum("n s g, n s -> n s g", weight, mask)
+ value = einops.rearrange(value, "n ns (g i) -> n ns g i", g=self.groups)
+ feat = torch.einsum("n s g i, n s g -> n g i", value, weight)
+ feat = einops.rearrange(feat, "n g i -> n (g i)")
+ return feat
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(Block, self).__init__()
+ self.attn = GroupedVectorAttention(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ attn_drop_rate=attn_drop_rate,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ )
+ self.fc1 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.fc3 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.norm1 = PointBatchNorm(embed_channels)
+ self.norm2 = PointBatchNorm(embed_channels)
+ self.norm3 = PointBatchNorm(embed_channels)
+ self.act = nn.ReLU(inplace=True)
+ self.enable_checkpoint = enable_checkpoint
+ self.drop_path = (
+ DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ )
+
+ def forward(self, points, reference_index):
+ coord, feat, offset = points
+ identity = feat
+ feat = self.act(self.norm1(self.fc1(feat)))
+ feat = (
+ self.attn(feat, coord, reference_index)
+ if not self.enable_checkpoint
+ else checkpoint(self.attn, feat, coord, reference_index)
+ )
+ feat = self.act(self.norm2(feat))
+ feat = self.norm3(self.fc3(feat))
+ feat = identity + self.drop_path(feat)
+ feat = self.act(feat)
+ return [coord, feat, offset]
+
+
+class BlockSequence(nn.Module):
+ def __init__(
+ self,
+ depth,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(BlockSequence, self).__init__()
+
+ if isinstance(drop_path_rate, list):
+ drop_path_rates = drop_path_rate
+ assert len(drop_path_rates) == depth
+ elif isinstance(drop_path_rate, float):
+ drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+ else:
+ drop_path_rates = [0.0 for _ in range(depth)]
+
+ self.neighbours = neighbours
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rates[i],
+ enable_checkpoint=enable_checkpoint,
+ )
+ self.blocks.append(block)
+
+ def forward(self, points):
+ coord, feat, offset = points
+ # reference index query of neighbourhood attention
+ # for windows attention, modify reference index query method
+ reference_index, _ = pointops.knn_query(self.neighbours, coord, offset)
+ for block in self.blocks:
+ points = block(points, reference_index)
+ return points
+
+
+class GridPool(nn.Module):
+ """
+ Partition-based Pooling (Grid Pooling)
+ """
+
+ def __init__(self, in_channels, out_channels, grid_size, bias=False):
+ super(GridPool, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.grid_size = grid_size
+
+ self.fc = nn.Linear(in_channels, out_channels, bias=bias)
+ self.norm = PointBatchNorm(out_channels)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, points, start=None):
+ coord, feat, offset = points
+ batch = offset2batch(offset)
+ feat = self.act(self.norm(self.fc(feat)))
+ start = (
+ segment_csr(
+ coord,
+ torch.cat([batch.new_zeros(1), torch.cumsum(batch.bincount(), dim=0)]),
+ reduce="min",
+ )
+ if start is None
+ else start
+ )
+ cluster = voxel_grid(
+ pos=coord - start[batch], size=self.grid_size, batch=batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ _, sorted_cluster_indices = torch.sort(cluster)
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ coord = segment_csr(coord[sorted_cluster_indices], idx_ptr, reduce="mean")
+ feat = segment_csr(feat[sorted_cluster_indices], idx_ptr, reduce="max")
+ batch = batch[idx_ptr[:-1]]
+ offset = batch2offset(batch)
+ return [coord, feat, offset], cluster
+
+
+class UnpoolWithSkip(nn.Module):
+ """
+ Map Unpooling with skip connection
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ bias=True,
+ skip=True,
+ backend="map",
+ ):
+ super(UnpoolWithSkip, self).__init__()
+ self.in_channels = in_channels
+ self.skip_channels = skip_channels
+ self.out_channels = out_channels
+ self.skip = skip
+ self.backend = backend
+ assert self.backend in ["map", "interp"]
+
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.proj_skip = nn.Sequential(
+ nn.Linear(skip_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, points, skip_points, cluster=None):
+ coord, feat, offset = points
+ skip_coord, skip_feat, skip_offset = skip_points
+ if self.backend == "map" and cluster is not None:
+ feat = self.proj(feat)[cluster]
+ else:
+ feat = pointops.interpolation(
+ coord, skip_coord, self.proj(feat), offset, skip_offset
+ )
+ if self.skip:
+ feat = feat + self.proj_skip(skip_feat)
+ return [skip_coord, feat, skip_offset]
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ grid_size=None,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ ):
+ super(Encoder, self).__init__()
+
+ self.down = GridPool(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ grid_size=grid_size,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ points, cluster = self.down(points)
+ return self.blocks(points), cluster
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ groups,
+ depth,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(Decoder, self).__init__()
+
+ self.up = UnpoolWithSkip(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ skip_channels=skip_channels,
+ backend=unpool_backend,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points, skip_points, cluster):
+ points = self.up(points, skip_points, cluster)
+ return self.blocks(points)
+
+
+class GVAPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(GVAPatchEmbed, self).__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, embed_channels, bias=False),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ coord, feat, offset = points
+ feat = self.proj(feat)
+ return self.blocks([coord, feat, offset])
+
+
+@MODELS.register_module("PT-v2m1")
+class PointTransformerV2(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ patch_embed_depth=1,
+ patch_embed_channels=48,
+ patch_embed_groups=6,
+ patch_embed_neighbours=8,
+ enc_depths=(2, 2, 6, 2),
+ enc_channels=(96, 192, 384, 512),
+ enc_groups=(12, 24, 48, 64),
+ enc_neighbours=(16, 16, 16, 16),
+ dec_depths=(1, 1, 1, 1),
+ dec_channels=(48, 96, 192, 384),
+ dec_groups=(6, 12, 24, 48),
+ dec_neighbours=(16, 16, 16, 16),
+ grid_sizes=(0.06, 0.12, 0.24, 0.48),
+ attn_qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(PointTransformerV2, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_depths)
+ assert self.num_stages == len(dec_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(dec_channels)
+ assert self.num_stages == len(enc_groups)
+ assert self.num_stages == len(dec_groups)
+ assert self.num_stages == len(enc_neighbours)
+ assert self.num_stages == len(dec_neighbours)
+ assert self.num_stages == len(grid_sizes)
+ self.patch_embed = GVAPatchEmbed(
+ in_channels=in_channels,
+ embed_channels=patch_embed_channels,
+ groups=patch_embed_groups,
+ depth=patch_embed_depth,
+ neighbours=patch_embed_neighbours,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ enc_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(enc_depths))
+ ]
+ dec_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(dec_depths))
+ ]
+ enc_channels = [patch_embed_channels] + list(enc_channels)
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ self.enc_stages = nn.ModuleList()
+ self.dec_stages = nn.ModuleList()
+ for i in range(self.num_stages):
+ enc = Encoder(
+ depth=enc_depths[i],
+ in_channels=enc_channels[i],
+ embed_channels=enc_channels[i + 1],
+ groups=enc_groups[i],
+ grid_size=grid_sizes[i],
+ neighbours=enc_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=enc_dp_rates[
+ sum(enc_depths[:i]) : sum(enc_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ )
+ dec = Decoder(
+ depth=dec_depths[i],
+ in_channels=dec_channels[i + 1],
+ skip_channels=enc_channels[i],
+ embed_channels=dec_channels[i],
+ groups=dec_groups[i],
+ neighbours=dec_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dec_dp_rates[
+ sum(dec_depths[:i]) : sum(dec_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ unpool_backend=unpool_backend,
+ )
+ self.enc_stages.append(enc)
+ self.dec_stages.append(dec)
+ self.seg_head = (
+ nn.Sequential(
+ nn.Linear(dec_channels[0], dec_channels[0]),
+ PointBatchNorm(dec_channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(dec_channels[0], num_classes),
+ )
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"].int()
+
+ # a batch of point cloud is a list of coord, feat and offset
+ points = [coord, feat, offset]
+ points = self.patch_embed(points)
+ skips = [[points]]
+ for i in range(self.num_stages):
+ points, cluster = self.enc_stages[i](points)
+ skips[-1].append(cluster) # record grid cluster of pooling
+ skips.append([points]) # record points info of current stage
+
+ points = skips.pop(-1)[0] # unpooling points info in the last enc stage
+ for i in reversed(range(self.num_stages)):
+ skip_points, cluster = skips.pop(-1)
+ points = self.dec_stages[i](points, skip_points, cluster)
+ coord, feat, offset = points
+ seg_logits = self.seg_head(feat)
+ return seg_logits
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m2_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m2_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..dec45ff92504cf184505d0cad96d30346613df46
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m2_base.py
@@ -0,0 +1,576 @@
+"""
+Point Transformer V2 Mode 2 (recommend)
+
+Disable Grouped Linear
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from copy import deepcopy
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from torch_geometric.nn.pool import voxel_grid
+from torch_scatter import segment_csr
+
+import einops
+from timm.models.layers import DropPath
+import pointops
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class PointBatchNorm(nn.Module):
+ """
+ Batch Normalization for Point Clouds data in shape of [B*N, C], [B*N, L, C]
+ """
+
+ def __init__(self, embed_channels):
+ super().__init__()
+ self.norm = nn.BatchNorm1d(embed_channels)
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ if input.dim() == 3:
+ return (
+ self.norm(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
+ elif input.dim() == 2:
+ return self.norm(input)
+ else:
+ raise NotImplementedError
+
+
+class GroupedVectorAttention(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ attn_drop_rate=0.0,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ ):
+ super(GroupedVectorAttention, self).__init__()
+ self.embed_channels = embed_channels
+ self.groups = groups
+ assert embed_channels % groups == 0
+ self.attn_drop_rate = attn_drop_rate
+ self.qkv_bias = qkv_bias
+ self.pe_multiplier = pe_multiplier
+ self.pe_bias = pe_bias
+
+ self.linear_q = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.linear_k = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear_v = nn.Linear(embed_channels, embed_channels, bias=qkv_bias)
+
+ if self.pe_multiplier:
+ self.linear_p_multiplier = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ if self.pe_bias:
+ self.linear_p_bias = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ self.weight_encoding = nn.Sequential(
+ nn.Linear(embed_channels, groups),
+ PointBatchNorm(groups),
+ nn.ReLU(inplace=True),
+ nn.Linear(groups, groups),
+ )
+ self.softmax = nn.Softmax(dim=1)
+ self.attn_drop = nn.Dropout(attn_drop_rate)
+
+ def forward(self, feat, coord, reference_index):
+ query, key, value = (
+ self.linear_q(feat),
+ self.linear_k(feat),
+ self.linear_v(feat),
+ )
+ key = pointops.grouping(reference_index, key, coord, with_xyz=True)
+ value = pointops.grouping(reference_index, value, coord, with_xyz=False)
+ pos, key = key[:, :, 0:3], key[:, :, 3:]
+ relation_qk = key - query.unsqueeze(1)
+ if self.pe_multiplier:
+ pem = self.linear_p_multiplier(pos)
+ relation_qk = relation_qk * pem
+ if self.pe_bias:
+ peb = self.linear_p_bias(pos)
+ relation_qk = relation_qk + peb
+ value = value + peb
+
+ weight = self.weight_encoding(relation_qk)
+ weight = self.attn_drop(self.softmax(weight))
+
+ mask = torch.sign(reference_index + 1)
+ weight = torch.einsum("n s g, n s -> n s g", weight, mask)
+ value = einops.rearrange(value, "n ns (g i) -> n ns g i", g=self.groups)
+ feat = torch.einsum("n s g i, n s g -> n g i", value, weight)
+ feat = einops.rearrange(feat, "n g i -> n (g i)")
+ return feat
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(Block, self).__init__()
+ self.attn = GroupedVectorAttention(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ attn_drop_rate=attn_drop_rate,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ )
+ self.fc1 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.fc3 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.norm1 = PointBatchNorm(embed_channels)
+ self.norm2 = PointBatchNorm(embed_channels)
+ self.norm3 = PointBatchNorm(embed_channels)
+ self.act = nn.ReLU(inplace=True)
+ self.enable_checkpoint = enable_checkpoint
+ self.drop_path = (
+ DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ )
+
+ def forward(self, points, reference_index):
+ coord, feat, offset = points
+ identity = feat
+ feat = self.act(self.norm1(self.fc1(feat)))
+ feat = (
+ self.attn(feat, coord, reference_index)
+ if not self.enable_checkpoint
+ else checkpoint(self.attn, feat, coord, reference_index)
+ )
+ feat = self.act(self.norm2(feat))
+ feat = self.norm3(self.fc3(feat))
+ feat = identity + self.drop_path(feat)
+ feat = self.act(feat)
+ return [coord, feat, offset]
+
+
+class BlockSequence(nn.Module):
+ def __init__(
+ self,
+ depth,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(BlockSequence, self).__init__()
+
+ if isinstance(drop_path_rate, list):
+ drop_path_rates = drop_path_rate
+ assert len(drop_path_rates) == depth
+ elif isinstance(drop_path_rate, float):
+ drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+ else:
+ drop_path_rates = [0.0 for _ in range(depth)]
+
+ self.neighbours = neighbours
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rates[i],
+ enable_checkpoint=enable_checkpoint,
+ )
+ self.blocks.append(block)
+
+ def forward(self, points):
+ coord, feat, offset = points
+ # reference index query of neighbourhood attention
+ # for windows attention, modify reference index query method
+ reference_index, _ = pointops.knn_query(self.neighbours, coord, offset)
+ for block in self.blocks:
+ points = block(points, reference_index)
+ return points
+
+
+class GridPool(nn.Module):
+ """
+ Partition-based Pooling (Grid Pooling)
+ """
+
+ def __init__(self, in_channels, out_channels, grid_size, bias=False):
+ super(GridPool, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.grid_size = grid_size
+
+ self.fc = nn.Linear(in_channels, out_channels, bias=bias)
+ self.norm = PointBatchNorm(out_channels)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, points, start=None):
+ coord, feat, offset = points
+ batch = offset2batch(offset)
+ feat = self.act(self.norm(self.fc(feat)))
+ start = (
+ segment_csr(
+ coord,
+ torch.cat([batch.new_zeros(1), torch.cumsum(batch.bincount(), dim=0)]),
+ reduce="min",
+ )
+ if start is None
+ else start
+ )
+ cluster = voxel_grid(
+ pos=coord - start[batch], size=self.grid_size, batch=batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ _, sorted_cluster_indices = torch.sort(cluster)
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ coord = segment_csr(coord[sorted_cluster_indices], idx_ptr, reduce="mean")
+ feat = segment_csr(feat[sorted_cluster_indices], idx_ptr, reduce="max")
+ batch = batch[idx_ptr[:-1]]
+ offset = batch2offset(batch)
+ return [coord, feat, offset], cluster
+
+
+class UnpoolWithSkip(nn.Module):
+ """
+ Map Unpooling with skip connection
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ bias=True,
+ skip=True,
+ backend="map",
+ ):
+ super(UnpoolWithSkip, self).__init__()
+ self.in_channels = in_channels
+ self.skip_channels = skip_channels
+ self.out_channels = out_channels
+ self.skip = skip
+ self.backend = backend
+ assert self.backend in ["map", "interp"]
+
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.proj_skip = nn.Sequential(
+ nn.Linear(skip_channels, out_channels, bias=bias),
+ PointBatchNorm(out_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ def forward(self, points, skip_points, cluster=None):
+ coord, feat, offset = points
+ skip_coord, skip_feat, skip_offset = skip_points
+ if self.backend == "map" and cluster is not None:
+ feat = self.proj(feat)[cluster]
+ else:
+ feat = pointops.interpolation(
+ coord, skip_coord, self.proj(feat), offset, skip_offset
+ )
+ if self.skip:
+ feat = feat + self.proj_skip(skip_feat)
+ return [skip_coord, feat, skip_offset]
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ grid_size=None,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ ):
+ super(Encoder, self).__init__()
+
+ self.down = GridPool(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ grid_size=grid_size,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ points, cluster = self.down(points)
+ return self.blocks(points), cluster
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ groups,
+ depth,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(Decoder, self).__init__()
+
+ self.up = UnpoolWithSkip(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ skip_channels=skip_channels,
+ backend=unpool_backend,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points, skip_points, cluster):
+ points = self.up(points, skip_points, cluster)
+ return self.blocks(points)
+
+
+class GVAPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(GVAPatchEmbed, self).__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ self.proj = nn.Sequential(
+ nn.Linear(in_channels, embed_channels, bias=False),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ coord, feat, offset = points
+ feat = self.proj(feat)
+ return self.blocks([coord, feat, offset])
+
+
+@MODELS.register_module("PT-v2m2")
+class PointTransformerV2(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ patch_embed_depth=1,
+ patch_embed_channels=48,
+ patch_embed_groups=6,
+ patch_embed_neighbours=8,
+ enc_depths=(2, 2, 6, 2),
+ enc_channels=(96, 192, 384, 512),
+ enc_groups=(12, 24, 48, 64),
+ enc_neighbours=(16, 16, 16, 16),
+ dec_depths=(1, 1, 1, 1),
+ dec_channels=(48, 96, 192, 384),
+ dec_groups=(6, 12, 24, 48),
+ dec_neighbours=(16, 16, 16, 16),
+ grid_sizes=(0.06, 0.12, 0.24, 0.48),
+ attn_qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(PointTransformerV2, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_depths)
+ assert self.num_stages == len(dec_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(dec_channels)
+ assert self.num_stages == len(enc_groups)
+ assert self.num_stages == len(dec_groups)
+ assert self.num_stages == len(enc_neighbours)
+ assert self.num_stages == len(dec_neighbours)
+ assert self.num_stages == len(grid_sizes)
+ self.patch_embed = GVAPatchEmbed(
+ in_channels=in_channels,
+ embed_channels=patch_embed_channels,
+ groups=patch_embed_groups,
+ depth=patch_embed_depth,
+ neighbours=patch_embed_neighbours,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ enc_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(enc_depths))
+ ]
+ dec_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(dec_depths))
+ ]
+ enc_channels = [patch_embed_channels] + list(enc_channels)
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ self.enc_stages = nn.ModuleList()
+ self.dec_stages = nn.ModuleList()
+ for i in range(self.num_stages):
+ enc = Encoder(
+ depth=enc_depths[i],
+ in_channels=enc_channels[i],
+ embed_channels=enc_channels[i + 1],
+ groups=enc_groups[i],
+ grid_size=grid_sizes[i],
+ neighbours=enc_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=enc_dp_rates[
+ sum(enc_depths[:i]) : sum(enc_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ )
+ dec = Decoder(
+ depth=dec_depths[i],
+ in_channels=dec_channels[i + 1],
+ skip_channels=enc_channels[i],
+ embed_channels=dec_channels[i],
+ groups=dec_groups[i],
+ neighbours=dec_neighbours[i],
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dec_dp_rates[
+ sum(dec_depths[:i]) : sum(dec_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ unpool_backend=unpool_backend,
+ )
+ self.enc_stages.append(enc)
+ self.dec_stages.append(dec)
+ self.seg_head = (
+ nn.Sequential(
+ nn.Linear(dec_channels[0], dec_channels[0]),
+ PointBatchNorm(dec_channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(dec_channels[0], num_classes),
+ )
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"].int()
+
+ # a batch of point cloud is a list of coord, feat and offset
+ points = [coord, feat, offset]
+ points = self.patch_embed(points)
+ skips = [[points]]
+ for i in range(self.num_stages):
+ points, cluster = self.enc_stages[i](points)
+ skips[-1].append(cluster) # record grid cluster of pooling
+ skips.append([points]) # record points info of current stage
+
+ points = skips.pop(-1)[0] # unpooling points info in the last enc stage
+ for i in reversed(range(self.num_stages)):
+ skip_points, cluster = skips.pop(-1)
+ points = self.dec_stages[i](points, skip_points, cluster)
+ coord, feat, offset = points
+ seg_logits = self.seg_head(feat)
+ return seg_logits
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m3_pdnorm.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m3_pdnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b944f19f2b13a73ae01bad4c094c51e4213896c4
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v2/point_transformer_v2m3_pdnorm.py
@@ -0,0 +1,659 @@
+"""
+Point Transformer V2M3
+
+Enable Prompt-Driven Normalization for Point Prompt Training
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from copy import deepcopy
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+from torch_geometric.nn.pool import voxel_grid
+from torch_scatter import segment_csr
+
+import einops
+from timm.models.layers import DropPath
+import pointops
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class PDBatchNorm(torch.nn.Module):
+ def __init__(
+ self,
+ num_features,
+ context_channels=256,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ decouple=True,
+ adaptive=False,
+ affine=True,
+ ):
+ super().__init__()
+ self.conditions = conditions
+ self.decouple = decouple
+ self.adaptive = adaptive
+ self.affine = affine
+ if self.decouple:
+ self.bns = nn.ModuleList(
+ [
+ nn.BatchNorm1d(
+ num_features=num_features,
+ eps=eps,
+ momentum=momentum,
+ affine=affine,
+ )
+ for _ in conditions
+ ]
+ )
+ else:
+ self.bn = nn.BatchNorm1d(
+ num_features=num_features, eps=eps, momentum=momentum, affine=affine
+ )
+ if self.adaptive:
+ self.modulation = nn.Sequential(
+ nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
+ )
+
+ def forward(self, feat, condition=None, context=None):
+ if self.decouple:
+ assert condition in self.conditions
+ bn = self.bns[self.conditions.index(condition)]
+ else:
+ bn = self.bn
+ feat = bn(feat)
+ if self.adaptive:
+ assert context is not None
+ shift, scale = self.modulation(context).chunk(2, dim=1)
+ feat = feat * (1.0 + scale) + shift
+ return feat
+
+
+class PointBatchNorm(nn.Module):
+ """
+ Batch Normalization for Point Clouds data in shape of [B*N, C], [B*N, L, C]
+ """
+
+ def __init__(self, embed_channels):
+ super().__init__()
+ self.norm = nn.BatchNorm1d(embed_channels)
+
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
+ if input.dim() == 3:
+ return (
+ self.norm(input.transpose(1, 2).contiguous())
+ .transpose(1, 2)
+ .contiguous()
+ )
+ elif input.dim() == 2:
+ return self.norm(input)
+ else:
+ raise NotImplementedError
+
+
+class GroupedVectorAttention(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ attn_drop_rate=0.0,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ ):
+ super(GroupedVectorAttention, self).__init__()
+ self.embed_channels = embed_channels
+ self.groups = groups
+ assert embed_channels % groups == 0
+ self.attn_drop_rate = attn_drop_rate
+ self.qkv_bias = qkv_bias
+ self.pe_multiplier = pe_multiplier
+ self.pe_bias = pe_bias
+
+ self.linear_q = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.linear_k = nn.Sequential(
+ nn.Linear(embed_channels, embed_channels, bias=qkv_bias),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ )
+
+ self.linear_v = nn.Linear(embed_channels, embed_channels, bias=qkv_bias)
+
+ if self.pe_multiplier:
+ self.linear_p_multiplier = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ if self.pe_bias:
+ self.linear_p_bias = nn.Sequential(
+ nn.Linear(3, embed_channels),
+ PointBatchNorm(embed_channels),
+ nn.ReLU(inplace=True),
+ nn.Linear(embed_channels, embed_channels),
+ )
+ self.weight_encoding = nn.Sequential(
+ nn.Linear(embed_channels, groups),
+ PointBatchNorm(groups),
+ nn.ReLU(inplace=True),
+ nn.Linear(groups, groups),
+ )
+ self.softmax = nn.Softmax(dim=1)
+ self.attn_drop = nn.Dropout(attn_drop_rate)
+
+ def forward(self, feat, coord, reference_index):
+ query, key, value = (
+ self.linear_q(feat),
+ self.linear_k(feat),
+ self.linear_v(feat),
+ )
+ key = pointops.grouping(reference_index, key, coord, with_xyz=True)
+ value = pointops.grouping(reference_index, value, coord, with_xyz=False)
+ pos, key = key[:, :, 0:3], key[:, :, 3:]
+ relation_qk = key - query.unsqueeze(1)
+ if self.pe_multiplier:
+ pem = self.linear_p_multiplier(pos)
+ relation_qk = relation_qk * pem
+ if self.pe_bias:
+ peb = self.linear_p_bias(pos)
+ relation_qk = relation_qk + peb
+ value = value + peb
+
+ weight = self.weight_encoding(relation_qk)
+ weight = self.attn_drop(self.softmax(weight))
+
+ mask = torch.sign(reference_index + 1)
+ weight = torch.einsum("n s g, n s -> n s g", weight, mask)
+ value = einops.rearrange(value, "n ns (g i) -> n ns g i", g=self.groups)
+ feat = torch.einsum("n s g i, n s g -> n g i", value, weight)
+ feat = einops.rearrange(feat, "n g i -> n (g i)")
+ return feat
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ groups,
+ norm_fn=None,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(Block, self).__init__()
+ self.attn = GroupedVectorAttention(
+ embed_channels=embed_channels,
+ groups=groups,
+ qkv_bias=qkv_bias,
+ attn_drop_rate=attn_drop_rate,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ )
+
+ assert norm_fn is not None
+
+ self.fc1 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.fc3 = nn.Linear(embed_channels, embed_channels, bias=False)
+ self.norm1 = norm_fn(embed_channels)
+ self.norm2 = norm_fn(embed_channels)
+ self.norm3 = norm_fn(embed_channels)
+ self.act = nn.ReLU(inplace=True)
+ self.enable_checkpoint = enable_checkpoint
+ self.drop_path = (
+ DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ )
+
+ def forward(self, points, reference_index):
+ coord, feat, offset, condition, context = points
+ identity = feat
+ feat = self.act(self.norm1(self.fc1(feat), condition, context))
+ feat = (
+ self.attn(feat, coord, reference_index)
+ if not self.enable_checkpoint
+ else checkpoint(self.attn, feat, coord, reference_index)
+ )
+ feat = self.act(self.norm2(feat, condition, context))
+ feat = self.norm3(self.fc3(feat), condition, context)
+ feat = identity + self.drop_path(feat)
+ feat = self.act(feat)
+ return [coord, feat, offset, condition, context]
+
+
+class BlockSequence(nn.Module):
+ def __init__(
+ self,
+ depth,
+ embed_channels,
+ groups,
+ neighbours=16,
+ norm_fn=None,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(BlockSequence, self).__init__()
+
+ if isinstance(drop_path_rate, list):
+ drop_path_rates = drop_path_rate
+ assert len(drop_path_rates) == depth
+ elif isinstance(drop_path_rate, float):
+ drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+ else:
+ drop_path_rates = [0.0 for _ in range(depth)]
+
+ self.neighbours = neighbours
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels=embed_channels,
+ groups=groups,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rates[i],
+ enable_checkpoint=enable_checkpoint,
+ )
+ self.blocks.append(block)
+
+ def forward(self, points):
+ coord, feat, offset, condition, context = points
+ # reference index query of neighbourhood attention
+ # for windows attention, modify reference index query method
+ reference_index, _ = pointops.knn_query(self.neighbours, coord, offset)
+ for block in self.blocks:
+ points = block(points, reference_index)
+ return points
+
+
+class GridPool(nn.Module):
+ """
+ Partition-based Pooling (Grid Pooling)
+ """
+
+ def __init__(self, in_channels, out_channels, grid_size, norm_fn, bias=False):
+ super(GridPool, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.grid_size = grid_size
+
+ self.fc = nn.Linear(in_channels, out_channels, bias=bias)
+ self.norm = norm_fn(out_channels)
+ self.act = nn.ReLU(inplace=True)
+
+ def forward(self, points, start=None):
+ coord, feat, offset, condition, context = points
+ batch = offset2batch(offset)
+ feat = self.act(self.norm(self.fc(feat), condition, context))
+ start = (
+ segment_csr(
+ coord,
+ torch.cat([batch.new_zeros(1), torch.cumsum(batch.bincount(), dim=0)]),
+ reduce="min",
+ )
+ if start is None
+ else start
+ )
+ cluster = voxel_grid(
+ pos=coord - start[batch], size=self.grid_size, batch=batch, start=0
+ )
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+ _, sorted_cluster_indices = torch.sort(cluster)
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ coord = segment_csr(coord[sorted_cluster_indices], idx_ptr, reduce="mean")
+ feat = segment_csr(feat[sorted_cluster_indices], idx_ptr, reduce="max")
+ batch = batch[idx_ptr[:-1]]
+ offset = batch2offset(batch)
+ return [coord, feat, offset, condition, context], cluster
+
+
+class UnpoolWithSkip(nn.Module):
+ """
+ Map Unpooling with skip connection
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ norm_fn,
+ bias=True,
+ skip=True,
+ backend="map",
+ ):
+ super(UnpoolWithSkip, self).__init__()
+ self.in_channels = in_channels
+ self.skip_channels = skip_channels
+ self.out_channels = out_channels
+ self.skip = skip
+ self.backend = backend
+ assert self.backend in ["map", "interp"]
+
+ self.proj_linear = nn.Linear(in_channels, out_channels, bias=bias)
+ self.proj_norm = norm_fn(out_channels)
+ self.proj_act = nn.ReLU(inplace=True)
+
+ self.proj_skip_linear = nn.Linear(skip_channels, out_channels, bias=bias)
+ self.proj_skip_norm = norm_fn(out_channels)
+ self.proj_skip_act = nn.ReLU(inplace=True)
+
+ def forward(self, points, skip_points, cluster=None):
+ coord, feat, offset, condition, context = points
+ skip_coord, skip_feat, skip_offset, _, _ = skip_points
+ feat = self.proj_act(self.proj_norm(self.proj_linear(feat), condition, context))
+ if self.backend == "map" and cluster is not None:
+ feat = feat[cluster]
+ else:
+ feat = pointops.interpolation(coord, skip_coord, feat, offset, skip_offset)
+ if self.skip:
+ feat = feat + self.proj_skip_act(
+ self.proj_skip_norm(
+ self.proj_skip_linear(skip_feat), condition, context
+ )
+ )
+ return [skip_coord, feat, skip_offset, condition, context]
+
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ norm_fn,
+ grid_size=None,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ ):
+ super(Encoder, self).__init__()
+
+ self.down = GridPool(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ grid_size=grid_size,
+ norm_fn=norm_fn,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ points, cluster = self.down(points)
+ return self.blocks(points), cluster
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ embed_channels,
+ groups,
+ depth,
+ norm_fn,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=None,
+ drop_path_rate=None,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ ):
+ super(Decoder, self).__init__()
+
+ self.up = UnpoolWithSkip(
+ in_channels=in_channels,
+ out_channels=embed_channels,
+ skip_channels=skip_channels,
+ backend=unpool_backend,
+ norm_fn=norm_fn,
+ )
+
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate if attn_drop_rate is not None else 0.0,
+ drop_path_rate=drop_path_rate if drop_path_rate is not None else 0.0,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points, skip_points, cluster):
+ points = self.up(points, skip_points, cluster)
+ return self.blocks(points)
+
+
+class GVAPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ depth,
+ in_channels,
+ embed_channels,
+ groups,
+ norm_fn,
+ neighbours=16,
+ qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ enable_checkpoint=False,
+ ):
+ super(GVAPatchEmbed, self).__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ self.proj_linear = nn.Linear(in_channels, embed_channels, bias=False)
+ self.proj_norm = norm_fn(embed_channels)
+ self.proj_act = nn.ReLU(inplace=True)
+ self.blocks = BlockSequence(
+ depth=depth,
+ embed_channels=embed_channels,
+ groups=groups,
+ neighbours=neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=drop_path_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ def forward(self, points):
+ coord, feat, offset, condition, context = points
+ feat = self.proj_act(self.proj_norm(self.proj_linear(feat), condition, context))
+ return self.blocks([coord, feat, offset, condition, context])
+
+
+@MODELS.register_module("PT-v2m3")
+class PointTransformerV2(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ patch_embed_depth=1,
+ patch_embed_channels=48,
+ patch_embed_groups=6,
+ patch_embed_neighbours=8,
+ enc_depths=(2, 2, 6, 2),
+ enc_channels=(96, 192, 384, 512),
+ enc_groups=(12, 24, 48, 64),
+ enc_neighbours=(16, 16, 16, 16),
+ dec_depths=(1, 1, 1, 1),
+ dec_channels=(48, 96, 192, 384),
+ dec_groups=(6, 12, 24, 48),
+ dec_neighbours=(16, 16, 16, 16),
+ grid_sizes=(0.06, 0.12, 0.24, 0.48),
+ attn_qkv_bias=True,
+ pe_multiplier=False,
+ pe_bias=True,
+ attn_drop_rate=0.0,
+ drop_path_rate=0,
+ enable_checkpoint=False,
+ unpool_backend="map",
+ context_channels=256,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ norm_decouple=True,
+ norm_adaptive=True,
+ norm_affine=False,
+ ):
+ super(PointTransformerV2, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.num_stages = len(enc_depths)
+ assert self.num_stages == len(dec_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(dec_channels)
+ assert self.num_stages == len(enc_groups)
+ assert self.num_stages == len(dec_groups)
+ assert self.num_stages == len(enc_neighbours)
+ assert self.num_stages == len(dec_neighbours)
+ assert self.num_stages == len(grid_sizes)
+
+ norm_fn = partial(
+ PDBatchNorm,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=conditions,
+ context_channels=context_channels,
+ decouple=norm_decouple,
+ adaptive=norm_adaptive,
+ affine=norm_affine,
+ )
+
+ self.patch_embed = GVAPatchEmbed(
+ in_channels=in_channels,
+ embed_channels=patch_embed_channels,
+ groups=patch_embed_groups,
+ depth=patch_embed_depth,
+ neighbours=patch_embed_neighbours,
+ norm_fn=norm_fn,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ enable_checkpoint=enable_checkpoint,
+ )
+
+ enc_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(enc_depths))
+ ]
+ dec_dp_rates = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(dec_depths))
+ ]
+ enc_channels = [patch_embed_channels] + list(enc_channels)
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ self.enc_stages = nn.ModuleList()
+ self.dec_stages = nn.ModuleList()
+ for i in range(self.num_stages):
+ enc = Encoder(
+ depth=enc_depths[i],
+ in_channels=enc_channels[i],
+ embed_channels=enc_channels[i + 1],
+ groups=enc_groups[i],
+ grid_size=grid_sizes[i],
+ neighbours=enc_neighbours[i],
+ norm_fn=norm_fn,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=enc_dp_rates[
+ sum(enc_depths[:i]) : sum(enc_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ )
+ dec = Decoder(
+ depth=dec_depths[i],
+ in_channels=dec_channels[i + 1],
+ skip_channels=enc_channels[i],
+ embed_channels=dec_channels[i],
+ groups=dec_groups[i],
+ neighbours=dec_neighbours[i],
+ norm_fn=norm_fn,
+ qkv_bias=attn_qkv_bias,
+ pe_multiplier=pe_multiplier,
+ pe_bias=pe_bias,
+ attn_drop_rate=attn_drop_rate,
+ drop_path_rate=dec_dp_rates[
+ sum(dec_depths[:i]) : sum(dec_depths[: i + 1])
+ ],
+ enable_checkpoint=enable_checkpoint,
+ unpool_backend=unpool_backend,
+ )
+ self.enc_stages.append(enc)
+ self.dec_stages.append(dec)
+ self.seg_head = (
+ nn.Sequential(nn.Linear(dec_channels[0], num_classes))
+ if num_classes > 0
+ else nn.Identity()
+ )
+
+ def forward(self, data_dict):
+ coord = data_dict["coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"].int()
+ condition = data_dict["condition"][0]
+ context = data_dict["context"] if "context" in data_dict.keys() else None
+
+ # a batch of point cloud is a list of coord, feat and offset
+ points = [coord, feat, offset, condition, context]
+ points = self.patch_embed(points)
+ skips = [[points]]
+ for i in range(self.num_stages):
+ points, cluster = self.enc_stages[i](points)
+ skips[-1].append(cluster) # record grid cluster of pooling
+ skips.append([points]) # record points info of current stage
+
+ points = skips.pop(-1)[0] # unpooling points info in the last enc stage
+ for i in reversed(range(self.num_stages)):
+ skip_points, cluster = skips.pop(-1)
+ points = self.dec_stages[i](points, skip_points, cluster)
+ coord, feat, offset, _, _ = points
+ seg_logits = self.seg_head(feat)
+ return seg_logits
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v3/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fe25f32abaf4d60241dcf21507f85a47e46f070
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v3/__init__.py
@@ -0,0 +1 @@
+from .point_transformer_v3m1_base import *
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v3/point_transformer_v3m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v3/point_transformer_v3m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9f567162dc424324ee5c30a0803fe3ea465f9b1
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/point_transformer_v3/point_transformer_v3m1_base.py
@@ -0,0 +1,714 @@
+"""
+Point Transformer - V3 Mode1
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from addict import Dict
+import math
+import torch
+import torch.nn as nn
+import spconv.pytorch as spconv
+import torch_scatter
+from timm.models.layers import DropPath
+
+try:
+ import flash_attn
+except ImportError:
+ flash_attn = None
+
+from pointcept.models.point_prompt_training import PDNorm
+from pointcept.models.builder import MODELS
+from pointcept.models.utils.misc import offset2bincount
+from pointcept.models.utils.structure import Point
+from pointcept.models.modules import PointModule, PointSequential
+
+
+class RPE(torch.nn.Module):
+ def __init__(self, patch_size, num_heads):
+ super().__init__()
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.pos_bnd = int((4 * patch_size) ** (1 / 3) * 2)
+ self.rpe_num = 2 * self.pos_bnd + 1
+ self.rpe_table = torch.nn.Parameter(torch.zeros(3 * self.rpe_num, num_heads))
+ torch.nn.init.trunc_normal_(self.rpe_table, std=0.02)
+
+ def forward(self, coord):
+ idx = (
+ coord.clamp(-self.pos_bnd, self.pos_bnd) # clamp into bnd
+ + self.pos_bnd # relative position to positive index
+ + torch.arange(3, device=coord.device) * self.rpe_num # x, y, z stride
+ )
+ out = self.rpe_table.index_select(0, idx.reshape(-1))
+ out = out.view(idx.shape + (-1,)).sum(3)
+ out = out.permute(0, 3, 1, 2) # (N, K, K, H) -> (N, H, K, K)
+ return out
+
+
+class SerializedAttention(PointModule):
+ def __init__(
+ self,
+ channels,
+ num_heads,
+ patch_size,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ order_index=0,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=True,
+ upcast_softmax=True,
+ ):
+ super().__init__()
+ assert channels % num_heads == 0
+ self.channels = channels
+ self.num_heads = num_heads
+ self.scale = qk_scale or (channels // num_heads) ** -0.5
+ self.order_index = order_index
+ self.upcast_attention = upcast_attention
+ self.upcast_softmax = upcast_softmax
+ self.enable_rpe = enable_rpe
+ self.enable_flash = enable_flash
+ if enable_flash:
+ assert (
+ enable_rpe is False
+ ), "Set enable_rpe to False when enable Flash Attention"
+ assert (
+ upcast_attention is False
+ ), "Set upcast_attention to False when enable Flash Attention"
+ assert (
+ upcast_softmax is False
+ ), "Set upcast_softmax to False when enable Flash Attention"
+ assert flash_attn is not None, "Make sure flash_attn is installed."
+ self.patch_size = patch_size
+ self.attn_drop = attn_drop
+ else:
+ # when disable flash attention, we still don't want to use mask
+ # consequently, patch size will auto set to the
+ # min number of patch_size_max and number of points
+ self.patch_size_max = patch_size
+ self.patch_size = 0
+ self.attn_drop = torch.nn.Dropout(attn_drop)
+
+ self.qkv = torch.nn.Linear(channels, channels * 3, bias=qkv_bias)
+ self.proj = torch.nn.Linear(channels, channels)
+ self.proj_drop = torch.nn.Dropout(proj_drop)
+ self.softmax = torch.nn.Softmax(dim=-1)
+ self.rpe = RPE(patch_size, num_heads) if self.enable_rpe else None
+
+ @torch.no_grad()
+ def get_rel_pos(self, point, order):
+ K = self.patch_size
+ rel_pos_key = f"rel_pos_{self.order_index}"
+ if rel_pos_key not in point.keys():
+ grid_coord = point.grid_coord[order]
+ grid_coord = grid_coord.reshape(-1, K, 3)
+ point[rel_pos_key] = grid_coord.unsqueeze(2) - grid_coord.unsqueeze(1)
+ return point[rel_pos_key]
+
+ @torch.no_grad()
+ def get_padding_and_inverse(self, point):
+ pad_key = "pad"
+ unpad_key = "unpad"
+ cu_seqlens_key = "cu_seqlens_key"
+ if (
+ pad_key not in point.keys()
+ or unpad_key not in point.keys()
+ or cu_seqlens_key not in point.keys()
+ ):
+ offset = point.offset
+ bincount = offset2bincount(offset)
+ bincount_pad = (
+ torch.div(
+ bincount + self.patch_size - 1,
+ self.patch_size,
+ rounding_mode="trunc",
+ )
+ * self.patch_size
+ )
+ # only pad point when num of points larger than patch_size
+ mask_pad = bincount > self.patch_size
+ bincount_pad = ~mask_pad * bincount + mask_pad * bincount_pad
+ _offset = nn.functional.pad(offset, (1, 0))
+ _offset_pad = nn.functional.pad(torch.cumsum(bincount_pad, dim=0), (1, 0))
+ pad = torch.arange(_offset_pad[-1], device=offset.device)
+ unpad = torch.arange(_offset[-1], device=offset.device)
+ cu_seqlens = []
+ for i in range(len(offset)):
+ unpad[_offset[i] : _offset[i + 1]] += _offset_pad[i] - _offset[i]
+ if bincount[i] != bincount_pad[i]:
+ pad[
+ _offset_pad[i + 1]
+ - self.patch_size
+ + (bincount[i] % self.patch_size) : _offset_pad[i + 1]
+ ] = pad[
+ _offset_pad[i + 1]
+ - 2 * self.patch_size
+ + (bincount[i] % self.patch_size) : _offset_pad[i + 1]
+ - self.patch_size
+ ]
+ pad[_offset_pad[i] : _offset_pad[i + 1]] -= _offset_pad[i] - _offset[i]
+ cu_seqlens.append(
+ torch.arange(
+ _offset_pad[i],
+ _offset_pad[i + 1],
+ step=self.patch_size,
+ dtype=torch.int32,
+ device=offset.device,
+ )
+ )
+ point[pad_key] = pad
+ point[unpad_key] = unpad
+ point[cu_seqlens_key] = nn.functional.pad(
+ torch.concat(cu_seqlens), (0, 1), value=_offset_pad[-1]
+ )
+ return point[pad_key], point[unpad_key], point[cu_seqlens_key]
+
+ def forward(self, point):
+ if not self.enable_flash:
+ self.patch_size = min(
+ offset2bincount(point.offset).min().tolist(), self.patch_size_max
+ )
+
+ H = self.num_heads
+ K = self.patch_size
+ C = self.channels
+
+ pad, unpad, cu_seqlens = self.get_padding_and_inverse(point)
+
+ order = point.serialized_order[self.order_index][pad]
+ inverse = unpad[point.serialized_inverse[self.order_index]]
+
+ # padding and reshape feat and batch for serialized point patch
+ qkv = self.qkv(point.feat)[order]
+
+ if not self.enable_flash:
+ # encode and reshape qkv: (N', K, 3, H, C') => (3, N', H, K, C')
+ q, k, v = (
+ qkv.reshape(-1, K, 3, H, C // H).permute(2, 0, 3, 1, 4).unbind(dim=0)
+ )
+ # attn
+ if self.upcast_attention:
+ q = q.float()
+ k = k.float()
+ attn = (q * self.scale) @ k.transpose(-2, -1) # (N', H, K, K)
+ if self.enable_rpe:
+ attn = attn + self.rpe(self.get_rel_pos(point, order))
+ if self.upcast_softmax:
+ attn = attn.float()
+ attn = self.softmax(attn)
+ attn = self.attn_drop(attn).to(qkv.dtype)
+ feat = (attn @ v).transpose(1, 2).reshape(-1, C)
+ else:
+ feat = flash_attn.flash_attn_varlen_qkvpacked_func(
+ qkv.half().reshape(-1, 3, H, C // H),
+ cu_seqlens,
+ max_seqlen=self.patch_size,
+ dropout_p=self.attn_drop if self.training else 0,
+ softmax_scale=self.scale,
+ ).reshape(-1, C)
+ feat = feat.to(qkv.dtype)
+ feat = feat[inverse]
+
+ # ffn
+ feat = self.proj(feat)
+ feat = self.proj_drop(feat)
+ point.feat = feat
+ return point
+
+
+class MLP(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ hidden_channels=None,
+ out_channels=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_channels = out_channels or in_channels
+ hidden_channels = hidden_channels or in_channels
+ self.fc1 = nn.Linear(in_channels, hidden_channels)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_channels, out_channels)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class Block(PointModule):
+ def __init__(
+ self,
+ channels,
+ num_heads,
+ patch_size=48,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ drop_path=0.0,
+ norm_layer=nn.LayerNorm,
+ act_layer=nn.GELU,
+ pre_norm=True,
+ order_index=0,
+ cpe_indice_key=None,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=True,
+ upcast_softmax=True,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.pre_norm = pre_norm
+
+ self.cpe = PointSequential(
+ spconv.SubMConv3d(
+ channels,
+ channels,
+ kernel_size=3,
+ bias=True,
+ indice_key=cpe_indice_key,
+ ),
+ nn.Linear(channels, channels),
+ norm_layer(channels),
+ )
+
+ self.norm1 = PointSequential(norm_layer(channels))
+ self.attn = SerializedAttention(
+ channels=channels,
+ patch_size=patch_size,
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ order_index=order_index,
+ enable_rpe=enable_rpe,
+ enable_flash=enable_flash,
+ upcast_attention=upcast_attention,
+ upcast_softmax=upcast_softmax,
+ )
+ self.norm2 = PointSequential(norm_layer(channels))
+ self.mlp = PointSequential(
+ MLP(
+ in_channels=channels,
+ hidden_channels=int(channels * mlp_ratio),
+ out_channels=channels,
+ act_layer=act_layer,
+ drop=proj_drop,
+ )
+ )
+ self.drop_path = PointSequential(
+ DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ )
+
+ def forward(self, point: Point):
+ shortcut = point.feat
+ point = self.cpe(point)
+ point.feat = shortcut + point.feat
+ shortcut = point.feat
+ if self.pre_norm:
+ point = self.norm1(point)
+ point = self.drop_path(self.attn(point))
+ point.feat = shortcut + point.feat
+ if not self.pre_norm:
+ point = self.norm1(point)
+
+ shortcut = point.feat
+ if self.pre_norm:
+ point = self.norm2(point)
+ point = self.drop_path(self.mlp(point))
+ point.feat = shortcut + point.feat
+ if not self.pre_norm:
+ point = self.norm2(point)
+ point.sparse_conv_feat = point.sparse_conv_feat.replace_feature(point.feat)
+ return point
+
+
+class SerializedPooling(PointModule):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ stride=2,
+ norm_layer=None,
+ act_layer=None,
+ reduce="max",
+ shuffle_orders=True,
+ traceable=True, # record parent and cluster
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ assert stride == 2 ** (math.ceil(stride) - 1).bit_length() # 2, 4, 8
+ # TODO: add support to grid pool (any stride)
+ self.stride = stride
+ assert reduce in ["sum", "mean", "min", "max"]
+ self.reduce = reduce
+ self.shuffle_orders = shuffle_orders
+ self.traceable = traceable
+
+ self.proj = nn.Linear(in_channels, out_channels)
+ if norm_layer is not None:
+ self.norm = PointSequential(norm_layer(out_channels))
+ if act_layer is not None:
+ self.act = PointSequential(act_layer())
+
+ def forward(self, point: Point):
+ pooling_depth = (math.ceil(self.stride) - 1).bit_length()
+ if pooling_depth > point.serialized_depth:
+ pooling_depth = 0
+ assert {
+ "serialized_code",
+ "serialized_order",
+ "serialized_inverse",
+ "serialized_depth",
+ }.issubset(
+ point.keys()
+ ), "Run point.serialization() point cloud before SerializedPooling"
+
+ code = point.serialized_code >> pooling_depth * 3
+ code_, cluster, counts = torch.unique(
+ code[0],
+ sorted=True,
+ return_inverse=True,
+ return_counts=True,
+ )
+ # indices of point sorted by cluster, for torch_scatter.segment_csr
+ _, indices = torch.sort(cluster)
+ # index pointer for sorted point, for torch_scatter.segment_csr
+ idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
+ # head_indices of each cluster, for reduce attr e.g. code, batch
+ head_indices = indices[idx_ptr[:-1]]
+ # generate down code, order, inverse
+ code = code[:, head_indices]
+ order = torch.argsort(code)
+ inverse = torch.zeros_like(order).scatter_(
+ dim=1,
+ index=order,
+ src=torch.arange(0, code.shape[1], device=order.device).repeat(
+ code.shape[0], 1
+ ),
+ )
+
+ if self.shuffle_orders:
+ perm = torch.randperm(code.shape[0])
+ code = code[perm]
+ order = order[perm]
+ inverse = inverse[perm]
+
+ # collect information
+ point_dict = Dict(
+ feat=torch_scatter.segment_csr(
+ self.proj(point.feat)[indices], idx_ptr, reduce=self.reduce
+ ),
+ coord=torch_scatter.segment_csr(
+ point.coord[indices], idx_ptr, reduce="mean"
+ ),
+ grid_coord=point.grid_coord[head_indices] >> pooling_depth,
+ serialized_code=code,
+ serialized_order=order,
+ serialized_inverse=inverse,
+ serialized_depth=point.serialized_depth - pooling_depth,
+ batch=point.batch[head_indices],
+ )
+
+ if "condition" in point.keys():
+ point_dict["condition"] = point.condition
+ if "context" in point.keys():
+ point_dict["context"] = point.context
+
+ if self.traceable:
+ point_dict["pooling_inverse"] = cluster
+ point_dict["pooling_parent"] = point
+ point = Point(point_dict)
+ if self.norm is not None:
+ point = self.norm(point)
+ if self.act is not None:
+ point = self.act(point)
+ point.sparsify()
+ return point
+
+
+class SerializedUnpooling(PointModule):
+ def __init__(
+ self,
+ in_channels,
+ skip_channels,
+ out_channels,
+ norm_layer=None,
+ act_layer=None,
+ traceable=False, # record parent and cluster
+ ):
+ super().__init__()
+ self.proj = PointSequential(nn.Linear(in_channels, out_channels))
+ self.proj_skip = PointSequential(nn.Linear(skip_channels, out_channels))
+
+ if norm_layer is not None:
+ self.proj.add(norm_layer(out_channels))
+ self.proj_skip.add(norm_layer(out_channels))
+
+ if act_layer is not None:
+ self.proj.add(act_layer())
+ self.proj_skip.add(act_layer())
+
+ self.traceable = traceable
+
+ def forward(self, point):
+ assert "pooling_parent" in point.keys()
+ assert "pooling_inverse" in point.keys()
+ parent = point.pop("pooling_parent")
+ inverse = point.pop("pooling_inverse")
+ point = self.proj(point)
+ parent = self.proj_skip(parent)
+ parent.feat = parent.feat + point.feat[inverse]
+
+ if self.traceable:
+ parent["unpooling_parent"] = point
+ return parent
+
+
+class Embedding(PointModule):
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ norm_layer=None,
+ act_layer=None,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+
+ # TODO: check remove spconv
+ self.stem = PointSequential(
+ conv=spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ )
+ )
+ if norm_layer is not None:
+ self.stem.add(norm_layer(embed_channels), name="norm")
+ if act_layer is not None:
+ self.stem.add(act_layer(), name="act")
+
+ def forward(self, point: Point):
+ point = self.stem(point)
+ return point
+
+
+@MODELS.register_module("PT-v3m1")
+class PointTransformerV3(PointModule):
+ def __init__(
+ self,
+ in_channels=6,
+ order=("z", "z-trans"),
+ stride=(2, 2, 2, 2),
+ enc_depths=(2, 2, 2, 6, 2),
+ enc_channels=(32, 64, 128, 256, 512),
+ enc_num_head=(2, 4, 8, 16, 32),
+ enc_patch_size=(48, 48, 48, 48, 48),
+ dec_depths=(2, 2, 2, 2),
+ dec_channels=(64, 64, 128, 256),
+ dec_num_head=(4, 4, 8, 16),
+ dec_patch_size=(48, 48, 48, 48),
+ mlp_ratio=4,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ drop_path=0.3,
+ pre_norm=True,
+ shuffle_orders=True,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=False,
+ upcast_softmax=False,
+ cls_mode=False,
+ pdnorm_bn=False,
+ pdnorm_ln=False,
+ pdnorm_decouple=True,
+ pdnorm_adaptive=False,
+ pdnorm_affine=True,
+ pdnorm_conditions=("ScanNet", "S3DIS", "Structured3D"),
+ ):
+ super().__init__()
+ self.num_stages = len(enc_depths)
+ self.order = [order] if isinstance(order, str) else order
+ self.cls_mode = cls_mode
+ self.shuffle_orders = shuffle_orders
+
+ assert self.num_stages == len(stride) + 1
+ assert self.num_stages == len(enc_depths)
+ assert self.num_stages == len(enc_channels)
+ assert self.num_stages == len(enc_num_head)
+ assert self.num_stages == len(enc_patch_size)
+ assert self.cls_mode or self.num_stages == len(dec_depths) + 1
+ assert self.cls_mode or self.num_stages == len(dec_channels) + 1
+ assert self.cls_mode or self.num_stages == len(dec_num_head) + 1
+ assert self.cls_mode or self.num_stages == len(dec_patch_size) + 1
+
+ # norm layers
+ if pdnorm_bn:
+ bn_layer = partial(
+ PDNorm,
+ norm_layer=partial(
+ nn.BatchNorm1d, eps=1e-3, momentum=0.01, affine=pdnorm_affine
+ ),
+ conditions=pdnorm_conditions,
+ decouple=pdnorm_decouple,
+ adaptive=pdnorm_adaptive,
+ )
+ else:
+ bn_layer = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ if pdnorm_ln:
+ ln_layer = partial(
+ PDNorm,
+ norm_layer=partial(nn.LayerNorm, elementwise_affine=pdnorm_affine),
+ conditions=pdnorm_conditions,
+ decouple=pdnorm_decouple,
+ adaptive=pdnorm_adaptive,
+ )
+ else:
+ ln_layer = nn.LayerNorm
+ # activation layers
+ act_layer = nn.GELU
+
+ self.embedding = Embedding(
+ in_channels=in_channels,
+ embed_channels=enc_channels[0],
+ norm_layer=bn_layer,
+ act_layer=act_layer,
+ )
+
+ # encoder
+ enc_drop_path = [
+ x.item() for x in torch.linspace(0, drop_path, sum(enc_depths))
+ ]
+ self.enc = PointSequential()
+ for s in range(self.num_stages):
+ enc_drop_path_ = enc_drop_path[
+ sum(enc_depths[:s]) : sum(enc_depths[: s + 1])
+ ]
+ enc = PointSequential()
+ if s > 0:
+ enc.add(
+ SerializedPooling(
+ in_channels=enc_channels[s - 1],
+ out_channels=enc_channels[s],
+ stride=stride[s - 1],
+ norm_layer=bn_layer,
+ act_layer=act_layer,
+ ),
+ name="down",
+ )
+ for i in range(enc_depths[s]):
+ enc.add(
+ Block(
+ channels=enc_channels[s],
+ num_heads=enc_num_head[s],
+ patch_size=enc_patch_size[s],
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ drop_path=enc_drop_path_[i],
+ norm_layer=ln_layer,
+ act_layer=act_layer,
+ pre_norm=pre_norm,
+ order_index=i % len(self.order),
+ cpe_indice_key=f"stage{s}",
+ enable_rpe=enable_rpe,
+ enable_flash=enable_flash,
+ upcast_attention=upcast_attention,
+ upcast_softmax=upcast_softmax,
+ ),
+ name=f"block{i}",
+ )
+ if len(enc) != 0:
+ self.enc.add(module=enc, name=f"enc{s}")
+
+ # decoder
+ if not self.cls_mode:
+ dec_drop_path = [
+ x.item() for x in torch.linspace(0, drop_path, sum(dec_depths))
+ ]
+ self.dec = PointSequential()
+ dec_channels = list(dec_channels) + [enc_channels[-1]]
+ for s in reversed(range(self.num_stages - 1)):
+ dec_drop_path_ = dec_drop_path[
+ sum(dec_depths[:s]) : sum(dec_depths[: s + 1])
+ ]
+ dec_drop_path_.reverse()
+ dec = PointSequential()
+ dec.add(
+ SerializedUnpooling(
+ in_channels=dec_channels[s + 1],
+ skip_channels=enc_channels[s],
+ out_channels=dec_channels[s],
+ norm_layer=bn_layer,
+ act_layer=act_layer,
+ ),
+ name="up",
+ )
+ for i in range(dec_depths[s]):
+ dec.add(
+ Block(
+ channels=dec_channels[s],
+ num_heads=dec_num_head[s],
+ patch_size=dec_patch_size[s],
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=proj_drop,
+ drop_path=dec_drop_path_[i],
+ norm_layer=ln_layer,
+ act_layer=act_layer,
+ pre_norm=pre_norm,
+ order_index=i % len(self.order),
+ cpe_indice_key=f"stage{s}",
+ enable_rpe=enable_rpe,
+ enable_flash=enable_flash,
+ upcast_attention=upcast_attention,
+ upcast_softmax=upcast_softmax,
+ ),
+ name=f"block{i}",
+ )
+ self.dec.add(module=dec, name=f"dec{s}")
+
+ def forward(self, data_dict):
+ point = Point(data_dict)
+ point.serialization(order=self.order, shuffle_orders=self.shuffle_orders)
+ point.sparsify()
+
+ point = self.embedding(point)
+ point = self.enc(point)
+ if not self.cls_mode:
+ point = self.dec(point)
+ # else:
+ # point.feat = torch_scatter.segment_csr(
+ # src=point.feat,
+ # indptr=nn.functional.pad(point.offset, (1, 0)),
+ # reduce="mean",
+ # )
+ return point
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aafc3859f43bd47da8cd966a57d4307cc771525f
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/__init__.py
@@ -0,0 +1,4 @@
+from .mink_unet import *
+from .spconv_unet_v1m1_base import *
+from .spconv_unet_v1m2_bn_momentum import *
+from .spconv_unet_v1m3_pdnorm import *
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/mink_unet.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/mink_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ff8a01d0500abc207b82a8252a8131b5e671469
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/mink_unet.py
@@ -0,0 +1,442 @@
+"""
+SparseUNet Driven by MinkowskiEngine
+
+Modified from chrischoy/SpatioTemporalSegmentation
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+
+try:
+ import MinkowskiEngine as ME
+except ImportError:
+ ME = None
+
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+class BasicBlock(nn.Module):
+ expansion = 1
+
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ bn_momentum=0.1,
+ dimension=-1,
+ ):
+ super(BasicBlock, self).__init__()
+ assert dimension > 0
+
+ self.conv1 = ME.MinkowskiConvolution(
+ inplanes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ dimension=dimension,
+ )
+ self.norm1 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+ self.conv2 = ME.MinkowskiConvolution(
+ planes,
+ planes,
+ kernel_size=3,
+ stride=1,
+ dilation=dilation,
+ dimension=dimension,
+ )
+ self.norm2 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+ self.relu = ME.MinkowskiReLU(inplace=True)
+ self.downsample = downsample
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.norm1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.norm2(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(
+ self,
+ inplanes,
+ planes,
+ stride=1,
+ dilation=1,
+ downsample=None,
+ bn_momentum=0.1,
+ dimension=-1,
+ ):
+ super(Bottleneck, self).__init__()
+ assert dimension > 0
+
+ self.conv1 = ME.MinkowskiConvolution(
+ inplanes, planes, kernel_size=1, dimension=dimension
+ )
+ self.norm1 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+
+ self.conv2 = ME.MinkowskiConvolution(
+ planes,
+ planes,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ dimension=dimension,
+ )
+ self.norm2 = ME.MinkowskiBatchNorm(planes, momentum=bn_momentum)
+
+ self.conv3 = ME.MinkowskiConvolution(
+ planes, planes * self.expansion, kernel_size=1, dimension=dimension
+ )
+ self.norm3 = ME.MinkowskiBatchNorm(
+ planes * self.expansion, momentum=bn_momentum
+ )
+
+ self.relu = ME.MinkowskiReLU(inplace=True)
+ self.downsample = downsample
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.norm1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.norm2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.norm3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class MinkUNetBase(nn.Module):
+ BLOCK = None
+ PLANES = None
+ DILATIONS = (1, 1, 1, 1, 1, 1, 1, 1)
+ LAYERS = (2, 2, 2, 2, 2, 2, 2, 2)
+ PLANES = (32, 64, 128, 256, 256, 128, 96, 96)
+ INIT_DIM = 32
+ OUT_TENSOR_STRIDE = 1
+
+ def __init__(self, in_channels, out_channels, dimension=3):
+ super().__init__()
+ assert ME is not None, "Please follow `README.md` to install MinkowskiEngine.`"
+ self.D = dimension
+ assert self.BLOCK is not None
+ # Output of the first conv concated to conv6
+ self.inplanes = self.INIT_DIM
+ self.conv0p1s1 = ME.MinkowskiConvolution(
+ in_channels, self.inplanes, kernel_size=5, dimension=self.D
+ )
+
+ self.bn0 = ME.MinkowskiBatchNorm(self.inplanes)
+
+ self.conv1p1s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bn1 = ME.MinkowskiBatchNorm(self.inplanes)
+
+ self.block1 = self._make_layer(self.BLOCK, self.PLANES[0], self.LAYERS[0])
+
+ self.conv2p2s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bn2 = ME.MinkowskiBatchNorm(self.inplanes)
+
+ self.block2 = self._make_layer(self.BLOCK, self.PLANES[1], self.LAYERS[1])
+
+ self.conv3p4s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+
+ self.bn3 = ME.MinkowskiBatchNorm(self.inplanes)
+ self.block3 = self._make_layer(self.BLOCK, self.PLANES[2], self.LAYERS[2])
+
+ self.conv4p8s2 = ME.MinkowskiConvolution(
+ self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bn4 = ME.MinkowskiBatchNorm(self.inplanes)
+ self.block4 = self._make_layer(self.BLOCK, self.PLANES[3], self.LAYERS[3])
+
+ self.convtr4p16s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[4], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr4 = ME.MinkowskiBatchNorm(self.PLANES[4])
+
+ self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion
+ self.block5 = self._make_layer(self.BLOCK, self.PLANES[4], self.LAYERS[4])
+ self.convtr5p8s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[5], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr5 = ME.MinkowskiBatchNorm(self.PLANES[5])
+
+ self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion
+ self.block6 = self._make_layer(self.BLOCK, self.PLANES[5], self.LAYERS[5])
+ self.convtr6p4s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[6], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr6 = ME.MinkowskiBatchNorm(self.PLANES[6])
+
+ self.inplanes = self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion
+ self.block7 = self._make_layer(self.BLOCK, self.PLANES[6], self.LAYERS[6])
+ self.convtr7p2s2 = ME.MinkowskiConvolutionTranspose(
+ self.inplanes, self.PLANES[7], kernel_size=2, stride=2, dimension=self.D
+ )
+ self.bntr7 = ME.MinkowskiBatchNorm(self.PLANES[7])
+
+ self.inplanes = self.PLANES[7] + self.INIT_DIM
+ self.block8 = self._make_layer(self.BLOCK, self.PLANES[7], self.LAYERS[7])
+
+ self.final = ME.MinkowskiConvolution(
+ self.PLANES[7] * self.BLOCK.expansion,
+ out_channels,
+ kernel_size=1,
+ bias=True,
+ dimension=self.D,
+ )
+ self.relu = ME.MinkowskiReLU(inplace=True)
+
+ self.weight_initialization()
+
+ def weight_initialization(self):
+ for m in self.modules():
+ if isinstance(m, ME.MinkowskiConvolution):
+ ME.utils.kaiming_normal_(m.kernel, mode="fan_out", nonlinearity="relu")
+
+ if isinstance(m, ME.MinkowskiBatchNorm):
+ nn.init.constant_(m.bn.weight, 1)
+ nn.init.constant_(m.bn.bias, 0)
+
+ def _make_layer(self, block, planes, blocks, stride=1, dilation=1, bn_momentum=0.1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ ME.MinkowskiConvolution(
+ self.inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=stride,
+ dimension=self.D,
+ ),
+ ME.MinkowskiBatchNorm(planes * block.expansion),
+ )
+ layers = []
+ layers.append(
+ block(
+ self.inplanes,
+ planes,
+ stride=stride,
+ dilation=dilation,
+ downsample=downsample,
+ dimension=self.D,
+ )
+ )
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(
+ block(
+ self.inplanes, planes, stride=1, dilation=dilation, dimension=self.D
+ )
+ )
+
+ return nn.Sequential(*layers)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+ in_field = ME.TensorField(
+ feat,
+ coordinates=torch.cat([batch.unsqueeze(-1).int(), grid_coord.int()], dim=1),
+ quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
+ minkowski_algorithm=ME.MinkowskiAlgorithm.SPEED_OPTIMIZED,
+ device=feat.device,
+ )
+ x = in_field.sparse()
+
+ out = self.conv0p1s1(x)
+ out = self.bn0(out)
+ out_p1 = self.relu(out)
+
+ out = self.conv1p1s2(out_p1)
+ out = self.bn1(out)
+ out = self.relu(out)
+ out_b1p2 = self.block1(out)
+
+ out = self.conv2p2s2(out_b1p2)
+ out = self.bn2(out)
+ out = self.relu(out)
+ out_b2p4 = self.block2(out)
+
+ out = self.conv3p4s2(out_b2p4)
+ out = self.bn3(out)
+ out = self.relu(out)
+ out_b3p8 = self.block3(out)
+
+ # tensor_stride=16
+ out = self.conv4p8s2(out_b3p8)
+ out = self.bn4(out)
+ out = self.relu(out)
+ out = self.block4(out)
+
+ # tensor_stride=8
+ out = self.convtr4p16s2(out)
+ out = self.bntr4(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_b3p8)
+ out = self.block5(out)
+
+ # tensor_stride=4
+ out = self.convtr5p8s2(out)
+ out = self.bntr5(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_b2p4)
+ out = self.block6(out)
+
+ # tensor_stride=2
+ out = self.convtr6p4s2(out)
+ out = self.bntr6(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_b1p2)
+ out = self.block7(out)
+
+ # tensor_stride=1
+ out = self.convtr7p2s2(out)
+ out = self.bntr7(out)
+ out = self.relu(out)
+
+ out = ME.cat(out, out_p1)
+ out = self.block8(out)
+
+ return self.final(out).slice(in_field).F
+
+
+@MODELS.register_module()
+class MinkUNet14(MinkUNetBase):
+ BLOCK = BasicBlock
+ LAYERS = (1, 1, 1, 1, 1, 1, 1, 1)
+
+
+@MODELS.register_module()
+class MinkUNet18(MinkUNetBase):
+ BLOCK = BasicBlock
+ LAYERS = (2, 2, 2, 2, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet34(MinkUNetBase):
+ BLOCK = BasicBlock
+ LAYERS = (2, 3, 4, 6, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet50(MinkUNetBase):
+ BLOCK = Bottleneck
+ LAYERS = (2, 3, 4, 6, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet101(MinkUNetBase):
+ BLOCK = Bottleneck
+ LAYERS = (2, 3, 4, 23, 2, 2, 2, 2)
+
+
+@MODELS.register_module()
+class MinkUNet14A(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 128, 128, 96, 96)
+
+
+@MODELS.register_module()
+class MinkUNet14B(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 128, 128, 128, 128)
+
+
+@MODELS.register_module()
+class MinkUNet14C(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 192, 192, 128, 128)
+
+
+@MODELS.register_module()
+class MinkUNet14D(MinkUNet14):
+ PLANES = (32, 64, 128, 256, 384, 384, 384, 384)
+
+
+@MODELS.register_module()
+class MinkUNet18A(MinkUNet18):
+ PLANES = (32, 64, 128, 256, 128, 128, 96, 96)
+
+
+@MODELS.register_module()
+class MinkUNet18B(MinkUNet18):
+ PLANES = (32, 64, 128, 256, 128, 128, 128, 128)
+
+
+@MODELS.register_module()
+class MinkUNet18D(MinkUNet18):
+ PLANES = (32, 64, 128, 256, 384, 384, 384, 384)
+
+
+@MODELS.register_module()
+class MinkUNet34A(MinkUNet34):
+ PLANES = (32, 64, 128, 256, 256, 128, 96, 96)
+
+
+@MODELS.register_module()
+class MinkUNet34B(MinkUNet34):
+ PLANES = (32, 64, 128, 256, 256, 128, 64, 32)
+
+
+@MODELS.register_module()
+class MinkUNet34C(MinkUNet34):
+ PLANES = (32, 64, 128, 256, 256, 128, 96, 96)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfcacb00b8dfb8a38aa9ab6968b0c9c63a63301c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m1_base.py
@@ -0,0 +1,463 @@
+"""
+SparseUNet Driven by SpConv (recommend)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+import spconv.pytorch as spconv
+from torch_geometric.utils import scatter
+
+from timm.models.layers import trunc_normal_
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch
+
+
+class BasicBlock(spconv.SparseModule):
+ expansion = 1
+
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ stride=1,
+ norm_fn=None,
+ indice_key=None,
+ bias=False,
+ ):
+ super().__init__()
+
+ assert norm_fn is not None
+
+ if in_channels == embed_channels:
+ self.proj = spconv.SparseSequential(nn.Identity())
+ else:
+ self.proj = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels, embed_channels, kernel_size=1, bias=False
+ ),
+ norm_fn(embed_channels),
+ )
+
+ self.conv1 = spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn1 = norm_fn(embed_channels)
+ self.relu = nn.ReLU()
+ self.conv2 = spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn2 = norm_fn(embed_channels)
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = out.replace_feature(self.bn1(out.features))
+ out = out.replace_feature(self.relu(out.features))
+
+ out = self.conv2(out)
+ out = out.replace_feature(self.bn2(out.features))
+
+ out = out.replace_feature(out.features + self.proj(residual).features)
+ out = out.replace_feature(self.relu(out.features))
+
+ return out
+
+
+@MODELS.register_module("SpUNet-v1m1")
+class SpUNetBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ cls_mode=False,
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+ self.cls_mode = cls_mode
+
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ block = BasicBlock
+
+ self.conv_input = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ base_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ ),
+ norm_fn(base_channels),
+ nn.ReLU(),
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList() if not self.cls_mode else None
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ spconv.SparseSequential(
+ spconv.SparseConv3d(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(channels[s]),
+ nn.ReLU(),
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+ if not self.cls_mode:
+ # decode num_stages
+ self.up.append(
+ spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(dec_channels),
+ nn.ReLU(),
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels + enc_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ final_in_channels = (
+ channels[-1] if not self.cls_mode else channels[self.num_stages - 1]
+ )
+ self.final = (
+ spconv.SubMConv3d(
+ final_in_channels, num_classes, kernel_size=1, padding=1, bias=True
+ )
+ if num_classes > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, input_dict):
+ grid_coord = input_dict["grid_coord"]
+ feat = input_dict["feat"]
+ offset = input_dict["offset"]
+
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 96).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input(x)
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s](x)
+ x = self.enc[s](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ if not self.cls_mode:
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s](x)
+ skip = skips.pop(-1)
+ x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x = self.dec[s](x)
+
+ x = self.final(x)
+ if self.cls_mode:
+ x = x.replace_feature(
+ scatter(x.features, x.indices[:, 0].long(), reduce="mean", dim=0)
+ )
+ return x.features
+
+
+@MODELS.register_module()
+class SpUNetNoSkipBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+ block = BasicBlock
+
+ self.conv_input = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ base_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ ),
+ norm_fn(base_channels),
+ nn.ReLU(),
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList()
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ spconv.SparseSequential(
+ spconv.SparseConv3d(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(channels[s]),
+ nn.ReLU(),
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+
+ # decode num_stages
+ self.up.append(
+ spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(dec_channels),
+ nn.ReLU(),
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ self.final = (
+ spconv.SubMConv3d(
+ channels[-1], out_channels, kernel_size=1, padding=1, bias=True
+ )
+ if out_channels > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 1).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input(x)
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s](x)
+ x = self.enc[s](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s](x)
+ # skip = skips.pop(-1)
+ # x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x = self.dec[s](x)
+
+ x = self.final(x)
+ return x.features
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m2_bn_momentum.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m2_bn_momentum.py
new file mode 100644
index 0000000000000000000000000000000000000000..979b1b8b5488d6c55bbc20ad0cede5002c8a5c67
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m2_bn_momentum.py
@@ -0,0 +1,290 @@
+"""
+SparseUNet Driven by SpConv (recommend)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+try:
+ import spconv.pytorch as spconv
+except ImportError:
+ import warnings
+
+ warnings.warn("Please follow `README.md` to install spconv2.`")
+
+from timm.models.layers import trunc_normal_
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+class BasicBlock(spconv.SparseModule):
+ expansion = 1
+
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ stride=1,
+ norm_fn=None,
+ indice_key=None,
+ bias=False,
+ ):
+ super().__init__()
+
+ assert norm_fn is not None
+
+ if in_channels == embed_channels:
+ self.proj = spconv.SparseSequential(nn.Identity())
+ else:
+ self.proj = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels, embed_channels, kernel_size=1, bias=False
+ ),
+ norm_fn(embed_channels, momentum=0.02),
+ )
+
+ self.conv1 = spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn1 = norm_fn(embed_channels)
+ self.relu = nn.ReLU()
+ self.conv2 = spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn2 = norm_fn(embed_channels)
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = out.replace_feature(self.bn1(out.features))
+ out = out.replace_feature(self.relu(out.features))
+
+ out = self.conv2(out)
+ out = out.replace_feature(self.bn2(out.features))
+
+ out = out.replace_feature(out.features + self.proj(residual).features)
+ out = out.replace_feature(self.relu(out.features))
+
+ return out
+
+
+@MODELS.register_module("SpUNet-v1m2")
+class SpUNetBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ bn_momentum=0.1,
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+
+ norm_fn = partial(nn.BatchNorm1d, eps=1e-5, momentum=bn_momentum)
+ block = BasicBlock
+
+ self.conv_input = spconv.SparseSequential(
+ spconv.SubMConv3d(
+ in_channels,
+ base_channels,
+ kernel_size=5,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ ),
+ norm_fn(base_channels, momentum=0.02),
+ nn.ReLU(),
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList()
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ spconv.SparseSequential(
+ spconv.SparseConv3d(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(channels[s], momentum=0.02),
+ nn.ReLU(),
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+
+ # decode num_stages
+ self.up.append(
+ spconv.SparseSequential(
+ spconv.SparseInverseConv3d(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ ),
+ norm_fn(dec_channels, momentum=0.02),
+ nn.ReLU(),
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels + enc_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ self.final = (
+ spconv.SubMConv3d(
+ channels[-1], num_classes, kernel_size=1, padding=1, bias=True
+ )
+ if num_classes > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 1).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input(x)
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s](x)
+ x = self.enc[s](x)
+ skips.append(x)
+ x = skips.pop(-1)
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s](x)
+ skip = skips.pop(-1)
+ x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x = self.dec[s](x)
+
+ x = self.final(x)
+ return x.features
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m3_pdnorm.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m3_pdnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..968f8f2c5a19cf016f9427812a8071ca83d612d5
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/sparse_unet/spconv_unet_v1m3_pdnorm.py
@@ -0,0 +1,429 @@
+"""
+SparseUNet V1M3
+
+Enable Prompt-Driven Normalization for Point Prompt Training
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from functools import partial
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+import spconv.pytorch as spconv
+from torch_geometric.utils import scatter
+
+from timm.models.layers import trunc_normal_
+
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch
+
+
+class PDBatchNorm(torch.nn.Module):
+ def __init__(
+ self,
+ num_features,
+ context_channels=256,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ decouple=True,
+ adaptive=False,
+ affine=True,
+ ):
+ super().__init__()
+ self.conditions = conditions
+ self.decouple = decouple
+ self.adaptive = adaptive
+ self.affine = affine
+ if self.decouple:
+ self.bns = nn.ModuleList(
+ [
+ nn.BatchNorm1d(
+ num_features=num_features,
+ eps=eps,
+ momentum=momentum,
+ affine=affine,
+ )
+ for _ in conditions
+ ]
+ )
+ else:
+ self.bn = nn.BatchNorm1d(
+ num_features=num_features, eps=eps, momentum=momentum, affine=affine
+ )
+ if self.adaptive:
+ self.modulation = nn.Sequential(
+ nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
+ )
+
+ def forward(self, feat, condition=None, context=None):
+ if self.decouple:
+ assert condition in self.conditions
+ bn = self.bns[self.conditions.index(condition)]
+ else:
+ bn = self.bn
+ feat = bn(feat)
+ if self.adaptive:
+ assert context is not None
+ shift, scale = self.modulation(context).chunk(2, dim=1)
+ feat = feat * (1.0 + scale) + shift
+ return feat
+
+
+class BasicBlock(spconv.SparseModule):
+ expansion = 1
+
+ def __init__(
+ self,
+ in_channels,
+ embed_channels,
+ stride=1,
+ norm_fn=None,
+ indice_key=None,
+ bias=False,
+ ):
+ super().__init__()
+
+ assert norm_fn is not None
+
+ self.in_channels = in_channels
+ self.embed_channels = embed_channels
+ if in_channels == embed_channels:
+ self.proj = spconv.SparseSequential(nn.Identity())
+ else:
+ # TODO remove norm after project
+ self.proj_conv = spconv.SubMConv3d(
+ in_channels, embed_channels, kernel_size=1, bias=False
+ )
+ self.proj_norm = norm_fn(embed_channels)
+
+ self.conv1 = spconv.SubMConv3d(
+ in_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn1 = norm_fn(embed_channels)
+ self.relu = nn.ReLU()
+ self.conv2 = spconv.SubMConv3d(
+ embed_channels,
+ embed_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn2 = norm_fn(embed_channels)
+ self.stride = stride
+
+ def forward(self, x):
+ x, condition, context = x
+ residual = x
+
+ out = self.conv1(x)
+ out = out.replace_feature(self.bn1(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+
+ out = self.conv2(out)
+ out = out.replace_feature(self.bn2(out.features, condition, context))
+
+ if self.in_channels == self.embed_channels:
+ residual = self.proj(residual)
+ else:
+ residual = residual.replace_feature(
+ self.proj_norm(self.proj_conv(residual).features, condition, context)
+ )
+ out = out.replace_feature(out.features + residual.features)
+ out = out.replace_feature(self.relu(out.features))
+ return out, condition, context
+
+
+class SPConvDown(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ indice_key,
+ kernel_size=2,
+ bias=False,
+ norm_fn=None,
+ ):
+ super().__init__()
+ self.conv = spconv.SparseConv3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=kernel_size,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn = norm_fn(out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x, condition, context = x
+ out = self.conv(x)
+ out = out.replace_feature(self.bn(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+ return out
+
+
+class SPConvUp(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ indice_key,
+ kernel_size=2,
+ bias=False,
+ norm_fn=None,
+ ):
+ super().__init__()
+ self.conv = spconv.SparseInverseConv3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ bias=bias,
+ indice_key=indice_key,
+ )
+ self.bn = norm_fn(out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x, condition, context = x
+ out = self.conv(x)
+ out = out.replace_feature(self.bn(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+ return out
+
+
+class SPConvPatchEmbedding(nn.Module):
+ def __init__(self, in_channels, out_channels, kernel_size=5, norm_fn=None):
+ super().__init__()
+ self.conv = spconv.SubMConv3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ padding=1,
+ bias=False,
+ indice_key="stem",
+ )
+ self.bn = norm_fn(out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x, condition, context = x
+ out = self.conv(x)
+ out = out.replace_feature(self.bn(out.features, condition, context))
+ out = out.replace_feature(self.relu(out.features))
+ return out
+
+
+@MODELS.register_module("SpUNet-v1m3")
+class SpUNetBase(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes=0,
+ base_channels=32,
+ context_channels=256,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 3, 4, 6, 2, 2, 2, 2),
+ cls_mode=False,
+ conditions=("ScanNet", "S3DIS", "Structured3D"),
+ zero_init=True,
+ norm_decouple=True,
+ norm_adaptive=True,
+ norm_affine=False,
+ ):
+ super().__init__()
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+ self.cls_mode = cls_mode
+ self.conditions = conditions
+ self.zero_init = zero_init
+
+ norm_fn = partial(
+ PDBatchNorm,
+ eps=1e-3,
+ momentum=0.01,
+ conditions=conditions,
+ context_channels=context_channels,
+ decouple=norm_decouple,
+ adaptive=norm_adaptive,
+ affine=norm_affine,
+ )
+ block = BasicBlock
+
+ self.conv_input = SPConvPatchEmbedding(
+ in_channels, base_channels, kernel_size=5, norm_fn=norm_fn
+ )
+
+ enc_channels = base_channels
+ dec_channels = channels[-1]
+ self.down = nn.ModuleList()
+ self.up = nn.ModuleList()
+ self.enc = nn.ModuleList()
+ self.dec = nn.ModuleList() if not self.cls_mode else None
+
+ for s in range(self.num_stages):
+ # encode num_stages
+ self.down.append(
+ SPConvDown(
+ enc_channels,
+ channels[s],
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ norm_fn=norm_fn,
+ )
+ )
+ self.enc.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ # (f"block{i}", block(enc_channels, channels[s], norm_fn=norm_fn, indice_key=f"subm{s + 1}"))
+ # if i == 0 else
+ (
+ f"block{i}",
+ block(
+ channels[s],
+ channels[s],
+ norm_fn=norm_fn,
+ indice_key=f"subm{s + 1}",
+ ),
+ )
+ for i in range(layers[s])
+ ]
+ )
+ )
+ )
+ if not self.cls_mode:
+ # decode num_stages
+ self.up.append(
+ SPConvUp(
+ channels[len(channels) - s - 2],
+ dec_channels,
+ kernel_size=2,
+ bias=False,
+ indice_key=f"spconv{s + 1}",
+ norm_fn=norm_fn,
+ )
+ )
+ self.dec.append(
+ spconv.SparseSequential(
+ OrderedDict(
+ [
+ (
+ (
+ f"block{i}",
+ block(
+ dec_channels + enc_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ if i == 0
+ else (
+ f"block{i}",
+ block(
+ dec_channels,
+ dec_channels,
+ norm_fn=norm_fn,
+ indice_key=f"subm{s}",
+ ),
+ )
+ )
+ for i in range(layers[len(channels) - s - 1])
+ ]
+ )
+ )
+ )
+
+ enc_channels = channels[s]
+ dec_channels = channels[len(channels) - s - 2]
+
+ final_in_channels = (
+ channels[-1] if not self.cls_mode else channels[self.num_stages - 1]
+ )
+ self.final = (
+ spconv.SubMConv3d(
+ final_in_channels, num_classes, kernel_size=1, padding=1, bias=True
+ )
+ if num_classes > 0
+ else spconv.Identity()
+ )
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, spconv.SubMConv3d):
+ trunc_normal_(m.weight, std=0.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.BatchNorm1d):
+ if m.affine:
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+ elif isinstance(m, PDBatchNorm):
+ if self.zero_init:
+ nn.init.constant_(m.modulation[-1].weight, 0)
+ nn.init.constant_(m.modulation[-1].bias, 0)
+
+ def forward(self, input_dict):
+ grid_coord = input_dict["grid_coord"]
+ feat = input_dict["feat"]
+ offset = input_dict["offset"]
+ condition = input_dict["condition"][0]
+ context = input_dict["context"] if "context" in input_dict.keys() else None
+
+ batch = offset2batch(offset)
+ sparse_shape = torch.add(torch.max(grid_coord, dim=0).values, 96).tolist()
+ x = spconv.SparseConvTensor(
+ features=feat,
+ indices=torch.cat(
+ [batch.unsqueeze(-1).int(), grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=batch[-1].tolist() + 1,
+ )
+ x = self.conv_input([x, condition, context])
+ skips = [x]
+ # enc forward
+ for s in range(self.num_stages):
+ x = self.down[s]([x, condition, context])
+ x, _, _ = self.enc[s]([x, condition, context])
+ skips.append(x)
+ x = skips.pop(-1)
+ if not self.cls_mode:
+ # dec forward
+ for s in reversed(range(self.num_stages)):
+ x = self.up[s]([x, condition, context])
+ skip = skips.pop(-1)
+ x = x.replace_feature(torch.cat((x.features, skip.features), dim=1))
+ x, _, _ = self.dec[s]([x, condition, context])
+
+ x = self.final(x)
+ if self.cls_mode:
+ x = x.replace_feature(
+ scatter(x.features, x.indices[:, 0].long(), reduce="mean", dim=0)
+ )
+ return x.features
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/spvcnn/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/spvcnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecdc75a6878026437124c187323ca9676bf35c76
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/spvcnn/__init__.py
@@ -0,0 +1 @@
+from .ts_spvcnn import *
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/spvcnn/ts_spvcnn.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/spvcnn/ts_spvcnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..c26f1ea8d41a8edfbd1542b0f9e607e660441b38
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/spvcnn/ts_spvcnn.py
@@ -0,0 +1,438 @@
+"""
+SPVCNN
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+import torch.nn as nn
+
+try:
+ import torchsparse
+ import torchsparse.nn as spnn
+ import torchsparse.nn.functional as F
+ from torchsparse.nn.utils import get_kernel_offsets
+ from torchsparse import PointTensor, SparseTensor
+except ImportError:
+ torchsparse = None
+
+
+from pointcept.models.utils import offset2batch
+from pointcept.models.builder import MODELS
+
+
+def initial_voxelize(z):
+ pc_hash = F.sphash(torch.floor(z.C).int())
+ sparse_hash = torch.unique(pc_hash)
+ idx_query = F.sphashquery(pc_hash, sparse_hash)
+ counts = F.spcount(idx_query.int(), len(sparse_hash))
+
+ inserted_coords = F.spvoxelize(torch.floor(z.C), idx_query, counts)
+ inserted_coords = torch.round(inserted_coords).int()
+ inserted_feat = F.spvoxelize(z.F, idx_query, counts)
+
+ new_tensor = SparseTensor(inserted_feat, inserted_coords, 1)
+ new_tensor.cmaps.setdefault(new_tensor.stride, new_tensor.coords)
+ z.additional_features["idx_query"][1] = idx_query
+ z.additional_features["counts"][1] = counts
+ return new_tensor
+
+
+# x: SparseTensor, z: PointTensor
+# return: SparseTensor
+def point_to_voxel(x, z):
+ if (
+ z.additional_features is None
+ or z.additional_features.get("idx_query") is None
+ or z.additional_features["idx_query"].get(x.s) is None
+ ):
+ pc_hash = F.sphash(
+ torch.cat(
+ [
+ torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],
+ z.C[:, -1].int().view(-1, 1),
+ ],
+ 1,
+ )
+ )
+ sparse_hash = F.sphash(x.C)
+ idx_query = F.sphashquery(pc_hash, sparse_hash)
+ counts = F.spcount(idx_query.int(), x.C.shape[0])
+ z.additional_features["idx_query"][x.s] = idx_query
+ z.additional_features["counts"][x.s] = counts
+ else:
+ idx_query = z.additional_features["idx_query"][x.s]
+ counts = z.additional_features["counts"][x.s]
+
+ inserted_feat = F.spvoxelize(z.F, idx_query, counts)
+ new_tensor = SparseTensor(inserted_feat, x.C, x.s)
+ new_tensor.cmaps = x.cmaps
+ new_tensor.kmaps = x.kmaps
+
+ return new_tensor
+
+
+# x: SparseTensor, z: PointTensor
+# return: PointTensor
+def voxel_to_point(x, z, nearest=False):
+ if (
+ z.idx_query is None
+ or z.weights is None
+ or z.idx_query.get(x.s) is None
+ or z.weights.get(x.s) is None
+ ):
+ off = spnn.utils.get_kernel_offsets(2, x.s, 1, device=z.F.device)
+ old_hash = F.sphash(
+ torch.cat(
+ [
+ torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],
+ z.C[:, -1].int().view(-1, 1),
+ ],
+ 1,
+ ),
+ off,
+ )
+ pc_hash = F.sphash(x.C.to(z.F.device))
+ idx_query = F.sphashquery(old_hash, pc_hash)
+ weights = (
+ F.calc_ti_weights(z.C, idx_query, scale=x.s[0]).transpose(0, 1).contiguous()
+ )
+ idx_query = idx_query.transpose(0, 1).contiguous()
+ if nearest:
+ weights[:, 1:] = 0.0
+ idx_query[:, 1:] = -1
+ new_feat = F.spdevoxelize(x.F, idx_query, weights)
+ new_tensor = PointTensor(
+ new_feat, z.C, idx_query=z.idx_query, weights=z.weights
+ )
+ new_tensor.additional_features = z.additional_features
+ new_tensor.idx_query[x.s] = idx_query
+ new_tensor.weights[x.s] = weights
+ z.idx_query[x.s] = idx_query
+ z.weights[x.s] = weights
+
+ else:
+ new_feat = F.spdevoxelize(x.F, z.idx_query.get(x.s), z.weights.get(x.s))
+ new_tensor = PointTensor(
+ new_feat, z.C, idx_query=z.idx_query, weights=z.weights
+ )
+ new_tensor.additional_features = z.additional_features
+
+ return new_tensor
+
+
+class BasicConvolutionBlock(nn.Module):
+ def __init__(self, inc, outc, ks=3, stride=1, dilation=1):
+ super().__init__()
+ self.net = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=ks, dilation=dilation, stride=stride),
+ spnn.BatchNorm(outc),
+ spnn.ReLU(True),
+ )
+
+ def forward(self, x):
+ out = self.net(x)
+ return out
+
+
+class BasicDeconvolutionBlock(nn.Module):
+ def __init__(self, inc, outc, ks=3, stride=1):
+ super().__init__()
+ self.net = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=ks, stride=stride, transposed=True),
+ spnn.BatchNorm(outc),
+ spnn.ReLU(True),
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+class ResidualBlock(nn.Module):
+ def __init__(self, inc, outc, ks=3, stride=1, dilation=1):
+ super().__init__()
+ self.net = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=ks, dilation=dilation, stride=stride),
+ spnn.BatchNorm(outc),
+ spnn.ReLU(True),
+ spnn.Conv3d(outc, outc, kernel_size=ks, dilation=dilation, stride=1),
+ spnn.BatchNorm(outc),
+ )
+
+ if inc == outc and stride == 1:
+ self.downsample = nn.Identity()
+ else:
+ self.downsample = nn.Sequential(
+ spnn.Conv3d(inc, outc, kernel_size=1, dilation=1, stride=stride),
+ spnn.BatchNorm(outc),
+ )
+
+ self.relu = spnn.ReLU(True)
+
+ def forward(self, x):
+ out = self.relu(self.net(x) + self.downsample(x))
+ return out
+
+
+@MODELS.register_module()
+class SPVCNN(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ base_channels=32,
+ channels=(32, 64, 128, 256, 256, 128, 96, 96),
+ layers=(2, 2, 2, 2, 2, 2, 2, 2),
+ ): # not implement
+ super().__init__()
+
+ assert (
+ torchsparse is not None
+ ), "Please follow `README.md` to install torchsparse.`"
+ assert len(layers) % 2 == 0
+ assert len(layers) == len(channels)
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.base_channels = base_channels
+ self.channels = channels
+ self.layers = layers
+ self.num_stages = len(layers) // 2
+
+ self.stem = nn.Sequential(
+ spnn.Conv3d(in_channels, base_channels, kernel_size=3, stride=1),
+ spnn.BatchNorm(base_channels),
+ spnn.ReLU(True),
+ spnn.Conv3d(base_channels, base_channels, kernel_size=3, stride=1),
+ spnn.BatchNorm(base_channels),
+ spnn.ReLU(True),
+ )
+
+ self.stage1 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ base_channels, base_channels, ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(base_channels, channels[0], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[0], channels[0], ks=3, stride=1, dilation=1)
+ for _ in range(layers[0] - 1)
+ ]
+ )
+
+ self.stage2 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ channels[0], channels[0], ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(channels[0], channels[1], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[1], channels[1], ks=3, stride=1, dilation=1)
+ for _ in range(layers[1] - 1)
+ ]
+ )
+
+ self.stage3 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ channels[1], channels[1], ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(channels[1], channels[2], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[2], channels[2], ks=3, stride=1, dilation=1)
+ for _ in range(layers[2] - 1)
+ ]
+ )
+
+ self.stage4 = nn.Sequential(
+ *[
+ BasicConvolutionBlock(
+ channels[2], channels[2], ks=2, stride=2, dilation=1
+ ),
+ ResidualBlock(channels[2], channels[3], ks=3, stride=1, dilation=1),
+ ]
+ + [
+ ResidualBlock(channels[3], channels[3], ks=3, stride=1, dilation=1)
+ for _ in range(layers[3] - 1)
+ ]
+ )
+
+ self.up1 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[3], channels[4], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[4] + channels[2],
+ channels[4],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[4], channels[4], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[4] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.up2 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[4], channels[5], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[5] + channels[1],
+ channels[5],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[5], channels[5], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[5] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.up3 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[5], channels[6], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[6] + channels[0],
+ channels[6],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[6], channels[6], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[6] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.up4 = nn.ModuleList(
+ [
+ BasicDeconvolutionBlock(channels[6], channels[7], ks=2, stride=2),
+ nn.Sequential(
+ *[
+ ResidualBlock(
+ channels[7] + base_channels,
+ channels[7],
+ ks=3,
+ stride=1,
+ dilation=1,
+ )
+ ]
+ + [
+ ResidualBlock(
+ channels[7], channels[7], ks=3, stride=1, dilation=1
+ )
+ for _ in range(layers[7] - 1)
+ ]
+ ),
+ ]
+ )
+
+ self.classifier = nn.Sequential(nn.Linear(channels[7], out_channels))
+
+ self.point_transforms = nn.ModuleList(
+ [
+ nn.Sequential(
+ nn.Linear(base_channels, channels[3]),
+ nn.BatchNorm1d(channels[3]),
+ nn.ReLU(True),
+ ),
+ nn.Sequential(
+ nn.Linear(channels[3], channels[5]),
+ nn.BatchNorm1d(channels[5]),
+ nn.ReLU(True),
+ ),
+ nn.Sequential(
+ nn.Linear(channels[5], channels[7]),
+ nn.BatchNorm1d(channels[7]),
+ nn.ReLU(True),
+ ),
+ ]
+ )
+
+ self.weight_initialization()
+ self.dropout = nn.Dropout(0.3, True)
+
+ def weight_initialization(self):
+ for m in self.modules():
+ if isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+
+ # x: SparseTensor z: PointTensor
+ z = PointTensor(
+ feat,
+ torch.cat(
+ [grid_coord.float(), batch.unsqueeze(-1).float()], dim=1
+ ).contiguous(),
+ )
+ x0 = initial_voxelize(z)
+
+ x0 = self.stem(x0)
+ z0 = voxel_to_point(x0, z, nearest=False)
+ z0.F = z0.F
+
+ x1 = point_to_voxel(x0, z0)
+ x1 = self.stage1(x1)
+ x2 = self.stage2(x1)
+ x3 = self.stage3(x2)
+ x4 = self.stage4(x3)
+ z1 = voxel_to_point(x4, z0)
+ z1.F = z1.F + self.point_transforms[0](z0.F)
+
+ y1 = point_to_voxel(x4, z1)
+ y1.F = self.dropout(y1.F)
+ y1 = self.up1[0](y1)
+ y1 = torchsparse.cat([y1, x3])
+ y1 = self.up1[1](y1)
+
+ y2 = self.up2[0](y1)
+ y2 = torchsparse.cat([y2, x2])
+ y2 = self.up2[1](y2)
+ z2 = voxel_to_point(y2, z1)
+ z2.F = z2.F + self.point_transforms[1](z1.F)
+
+ y3 = point_to_voxel(y2, z2)
+ y3.F = self.dropout(y3.F)
+ y3 = self.up3[0](y3)
+ y3 = torchsparse.cat([y3, x1])
+ y3 = self.up3[1](y3)
+
+ y4 = self.up4[0](y3)
+ y4 = torchsparse.cat([y4, x0])
+ y4 = self.up4[1](y4)
+ z3 = voxel_to_point(y4, z2)
+ z3.F = z3.F + self.point_transforms[2](z2.F)
+
+ out = self.classifier(z3.F)
+ return out
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..24712d2bc0fe76a52a273172fe9c4f37c807a6c3
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/__init__.py
@@ -0,0 +1,2 @@
+from .stratified_transformer_v1m1_origin import StratifiedTransformer
+from .stratified_transformer_v1m2_refine import StratifiedTransformer
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/stratified_transformer_v1m1_origin.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/stratified_transformer_v1m1_origin.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bf18f71298efa1e96f0697c96e26d2127140c36
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/stratified_transformer_v1m1_origin.py
@@ -0,0 +1,830 @@
+import torch
+import torch.nn as nn
+
+try:
+ import torch_points_kernels as tp
+except ImportError:
+ tp = None
+
+try:
+ from torch_points3d.modules.KPConv.kernels import KPConvLayer
+ from torch_points3d.core.common_modules import FastBatchNorm1d
+except ImportError:
+ KPConvLayer = None
+ FastBatchNorm1d = None
+
+from torch_scatter import scatter_softmax
+from timm.models.layers import DropPath, trunc_normal_
+from torch_geometric.nn.pool import voxel_grid
+
+try:
+ import pointops2.pointops as pointops
+except ImportError:
+ pointops = None
+
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+def get_indice_pairs(
+ p2v_map, counts, new_p2v_map, new_counts, downsample_idx, batch, xyz, window_size, i
+):
+ # p2v_map: [n, k]
+ # counts: [n, ]
+
+ n, k = p2v_map.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < counts.unsqueeze(-1) # [n, k]
+ mask_mat = mask.unsqueeze(-1) & mask.unsqueeze(-2) # [n, k, k]
+ index_0 = p2v_map.unsqueeze(-1).expand(-1, -1, k)[mask_mat] # [M, ]
+ index_1 = p2v_map.unsqueeze(1).expand(-1, k, -1)[mask_mat] # [M, ]
+
+ downsample_mask = torch.zeros_like(batch).bool() # [N, ]
+ downsample_mask[downsample_idx.long()] = True
+
+ downsample_mask = downsample_mask[new_p2v_map] # [n, k]
+ n, k = new_p2v_map.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < new_counts.unsqueeze(-1) # [n, k]
+ downsample_mask = downsample_mask & mask
+ mask_mat = mask.unsqueeze(-1) & downsample_mask.unsqueeze(-2) # [n, k, k]
+ xyz_min = xyz.min(0)[0]
+ if i % 2 == 0:
+ window_coord = (xyz[new_p2v_map] - xyz_min) // window_size # [n, k, 3]
+ else:
+ window_coord = (
+ xyz[new_p2v_map] + 1 / 2 * window_size - xyz_min
+ ) // window_size # [n, k, 3]
+
+ mask_mat_prev = (window_coord.unsqueeze(2) != window_coord.unsqueeze(1)).any(
+ -1
+ ) # [n, k, k]
+ mask_mat = mask_mat & mask_mat_prev # [n, k, k]
+
+ new_index_0 = new_p2v_map.unsqueeze(-1).expand(-1, -1, k)[mask_mat] # [M, ]
+ new_index_1 = new_p2v_map.unsqueeze(1).expand(-1, k, -1)[mask_mat] # [M, ]
+
+ index_0 = torch.cat([index_0, new_index_0], 0)
+ index_1 = torch.cat([index_1, new_index_1], 0)
+ return index_0, index_1
+
+
+def grid_sample(pos, batch, size, start, return_p2v=True):
+ # pos: float [N, 3]
+ # batch: long [N]
+ # size: float [3, ]
+ # start: float [3, ] / None
+
+ cluster = voxel_grid(pos, batch, size, start=start) # [N, ]
+
+ if return_p2v == False:
+ unique, cluster = torch.unique(cluster, sorted=True, return_inverse=True)
+ return cluster
+
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+
+ # obtain p2v_map
+ n = unique.shape[0]
+ k = counts.max().item()
+ p2v_map = cluster.new_zeros(n, k) # [n, k]
+ mask = torch.arange(k).cuda().unsqueeze(0) < counts.unsqueeze(-1) # [n, k]
+ p2v_map[mask] = torch.argsort(cluster)
+
+ return cluster, p2v_map, counts
+
+
+class Mlp(nn.Module):
+ """Multilayer perceptron."""
+
+ def __init__(
+ self,
+ in_features,
+ hidden_features=None,
+ out_features=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop, inplace=True)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_channels, out_channels, ratio, k, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.ratio = ratio
+ self.k = k
+ self.norm = norm_layer(in_channels) if norm_layer else None
+ self.linear = nn.Linear(in_channels, out_channels, bias=False)
+ self.pool = nn.MaxPool1d(k)
+
+ def forward(self, feats, xyz, offset):
+ n_offset, count = [int(offset[0].item() * self.ratio) + 1], int(
+ offset[0].item() * self.ratio
+ ) + 1
+ for i in range(1, offset.shape[0]):
+ count += ((offset[i].item() - offset[i - 1].item()) * self.ratio) + 1
+ n_offset.append(count)
+ n_offset = torch.cuda.IntTensor(n_offset)
+ idx = pointops.furthestsampling(xyz, offset, n_offset) # (m)
+ n_xyz = xyz[idx.long(), :] # (m, 3)
+
+ feats = pointops.queryandgroup(
+ self.k, xyz, n_xyz, feats, None, offset, n_offset, use_xyz=False
+ ) # (m, nsample, 3+c)
+ m, k, c = feats.shape
+ feats = (
+ self.linear(self.norm(feats.view(m * k, c)).view(m, k, c))
+ .transpose(1, 2)
+ .contiguous()
+ )
+ feats = self.pool(feats).squeeze(-1) # (m, c)
+
+ return feats, n_xyz, n_offset
+
+
+class WindowAttention(nn.Module):
+ """Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(
+ self,
+ dim,
+ window_size,
+ num_heads,
+ quant_size,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim**-0.5
+ self.window_size = window_size
+
+ self.quant_size = quant_size
+ self.rel_query = rel_query
+ self.rel_key = rel_key
+ self.rel_value = rel_value
+
+ quant_grid_length = int((2 * window_size + 1e-4) // quant_size)
+
+ if rel_query:
+ self.relative_pos_query_table = nn.Parameter(
+ torch.zeros(2 * quant_grid_length, num_heads, head_dim, 3)
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+ if rel_key:
+ self.relative_pos_key_table = nn.Parameter(
+ torch.zeros(2 * quant_grid_length, num_heads, head_dim, 3)
+ )
+ trunc_normal_(self.relative_pos_key_table, std=0.02)
+ if rel_value:
+ self.relative_pos_value_table = nn.Parameter(
+ torch.zeros(2 * quant_grid_length, num_heads, head_dim, 3)
+ )
+ trunc_normal_(self.relative_pos_value_table, std=0.02)
+
+ self.quant_grid_length = quant_grid_length
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop, inplace=True)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop, inplace=True)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ # def forward(self, feats, xyz, index_0, index_1):
+ def forward(self, feats, xyz, index_0, index_1, index_0_offsets, n_max):
+ """Forward function.
+
+ Args:
+ feats: N, C
+ xyz: N, 3
+ index_0: M,
+ index_1: M,
+ """
+
+ N, C = feats.shape
+ M = index_0.shape[0]
+
+ assert index_0.shape[0] == index_1.shape[0]
+
+ # Query, Key, Value
+ qkv = (
+ self.qkv(feats)
+ .reshape(N, 3, self.num_heads, C // self.num_heads)
+ .permute(1, 0, 2, 3)
+ .contiguous()
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2] # [N, num_heads, C//num_heads]
+ query = query * self.scale
+ attn_flat = pointops.attention_step1_v2(
+ query.float(), key.float(), index_1.int(), index_0_offsets.int(), n_max
+ )
+
+ # # Position embedding
+ relative_position = xyz[index_0] - xyz[index_1]
+ relative_position = torch.round(relative_position * 100000) / 100000
+ relative_position_index = (
+ relative_position + 2 * self.window_size - 0.0001
+ ) // self.quant_size
+ assert (relative_position_index >= 0).all()
+ assert (relative_position_index <= 2 * self.quant_grid_length - 1).all()
+
+ assert self.rel_query and self.rel_key
+ if self.rel_query and self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx_v3(
+ query.float(),
+ index_0_offsets.int(),
+ n_max,
+ key.float(),
+ index_1.int(),
+ self.relative_pos_query_table.float(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ )
+ elif self.rel_query:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ query.float(),
+ index_0.int(),
+ self.relative_pos_query_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ elif self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ key.float(),
+ index_1.int(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ else:
+ relative_position_bias = 0.0
+
+ attn_flat = attn_flat + relative_position_bias # [M, num_heads]
+
+ softmax_attn_flat = scatter_softmax(
+ src=attn_flat, index=index_0, dim=0
+ ) # [M, num_heads]
+
+ if self.rel_value:
+ x = pointops.attention_step2_with_rel_pos_value_v2(
+ softmax_attn_flat.float(),
+ value.float(),
+ index_0_offsets.int(),
+ n_max,
+ index_1.int(),
+ self.relative_pos_value_table.float(),
+ relative_position_index.int(),
+ )
+ else:
+ x = pointops.attention_step2(
+ softmax_attn_flat.float(), value.float(), index_0.int(), index_1.int()
+ )
+
+ x = x.view(N, C)
+
+ x = self.proj(x)
+ x = self.proj_drop(x) # [N, C]
+
+ return x
+
+
+class SwinTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_heads,
+ window_size,
+ quant_size,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ drop_path=0.0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ act_layer=nn.GELU,
+ norm_layer=nn.LayerNorm,
+ mode=4,
+ ): # mode=4:mean
+ super().__init__()
+ self.mode = mode
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim,
+ window_size,
+ num_heads=num_heads,
+ quant_size=quant_size,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ )
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(
+ in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer
+ )
+
+ def forward(self, feats, xyz, index_0, index_1, index_0_offsets, n_max):
+ # feats: [N, c]
+ # pos: [N, 3]
+
+ short_cut = feats
+
+ feats = self.norm1(feats)
+
+ feats = self.attn(
+ feats, xyz, index_0, index_1, index_0_offsets, n_max
+ ) # index_0 MUST be in ascending order
+
+ feats = short_cut + self.drop_path(feats)
+ feats = feats + self.drop_path(self.mlp(self.norm2(feats)))
+
+ return feats
+
+
+class BasicLayer(nn.Module):
+ def __init__(
+ self,
+ downsample_scale,
+ depth,
+ channel,
+ num_heads,
+ window_size,
+ grid_size,
+ quant_size,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ drop_path=0.0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ ratio=0.25,
+ k=16,
+ out_channels=None,
+ ):
+ super().__init__()
+ self.depth = depth
+ self.grid_size = grid_size
+ self.max_window_counts = 64
+ self.window_size = window_size
+ self.downsample_scale = downsample_scale
+
+ self.blocks = nn.ModuleList(
+ [
+ SwinTransformerBlock(
+ channel,
+ num_heads,
+ window_size,
+ quant_size,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ drop_path=(
+ drop_path[i] if isinstance(drop_path, list) else drop_path
+ ),
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ norm_layer=norm_layer,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ self.downsample = (
+ downsample(channel, out_channels, ratio, k) if downsample else None
+ )
+
+ def forward(self, feats, xyz, offset):
+ # feats: N, C
+ # xyz: N, 3
+
+ window_size = torch.tensor([self.window_size] * 3).type_as(xyz).to(xyz.device)
+
+ offset_ = offset.clone()
+ offset_[1:] = offset_[1:] - offset_[:-1]
+ batch = (
+ torch.cat([torch.tensor([ii] * o) for ii, o in enumerate(offset_)], 0)
+ .long()
+ .cuda()
+ )
+
+ v2p_map, p2v_map, counts = grid_sample(xyz, batch, window_size, start=None)
+
+ shift_size = 1 / 2 * window_size
+ shift_v2p_map, shift_p2v_map, shift_counts = grid_sample(
+ xyz + shift_size, batch, window_size, start=xyz.min(0)[0]
+ )
+
+ downsample_scale = self.downsample_scale
+ new_offset, count = [offset[0].item() // downsample_scale + 1], offset[
+ 0
+ ].item() // downsample_scale + 1
+ for i in range(1, offset.shape[0]):
+ count += (offset[i].item() - offset[i - 1].item()) // downsample_scale + 1
+ new_offset.append(count)
+
+ new_offset = torch.cuda.IntTensor(new_offset)
+ downsample_idx = pointops.furthestsampling(
+ xyz, offset.int(), new_offset.int()
+ ) # [N/16,]
+
+ new_window_size = 2 * torch.tensor([self.window_size] * 3).type_as(xyz).to(
+ xyz.device
+ )
+
+ # offset_ = new_offset.clone()
+ # offset_[1:] = offset_[1:] - offset_[:-1]
+ # new_batch = torch.cat([torch.tensor([ii]*o) for ii,o in enumerate(offset_)], 0).long().cuda()
+
+ new_v2p_map, new_p2v_map, new_counts = grid_sample(
+ xyz, batch, new_window_size, start=None
+ )
+
+ shift_size = 1 / 2 * new_window_size
+ shift_new_v2p_map, shift_new_p2v_map, shift_new_counts = grid_sample(
+ xyz + shift_size, batch, new_window_size, start=xyz.min(0)[0]
+ )
+
+ for i, blk in enumerate(self.blocks):
+ p2v_map_blk = p2v_map if i % 2 == 0 else shift_p2v_map
+ counts_blk = counts if i % 2 == 0 else shift_counts
+
+ new_p2v_map_blk = new_p2v_map if i % 2 == 0 else shift_new_p2v_map
+ new_counts_blk = new_counts if i % 2 == 0 else shift_new_counts
+
+ index_0, index_1 = get_indice_pairs(
+ p2v_map_blk,
+ counts_blk,
+ new_p2v_map_blk,
+ new_counts_blk,
+ downsample_idx,
+ batch,
+ xyz,
+ window_size,
+ i,
+ )
+
+ # rearrange index for acceleration
+ index_0, indices = torch.sort(index_0) # [M,]
+ index_1 = index_1[indices] # [M,]
+ index_0_counts = index_0.bincount()
+ n_max = index_0_counts.max()
+ index_0_offsets = index_0_counts.cumsum(dim=-1) # [N]
+ index_0_offsets = torch.cat(
+ [torch.zeros(1, dtype=torch.long).cuda(), index_0_offsets], 0
+ ) # [N+1]
+
+ feats = blk(feats, xyz, index_0, index_1, index_0_offsets, n_max)
+
+ if self.downsample:
+ feats_down, xyz_down, offset_down = self.downsample(feats, xyz, offset)
+ else:
+ feats_down, xyz_down, offset_down = None, None, None
+
+ return feats, xyz, offset, feats_down, xyz_down, offset_down
+
+
+class Upsample(nn.Module):
+ def __init__(self, k, in_channels, out_channels, bn_momentum=0.02):
+ super().__init__()
+ self.k = k
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.linear1 = nn.Sequential(
+ nn.LayerNorm(out_channels), nn.Linear(out_channels, out_channels)
+ )
+ self.linear2 = nn.Sequential(
+ nn.LayerNorm(in_channels), nn.Linear(in_channels, out_channels)
+ )
+
+ def forward(
+ self, feats, xyz, support_xyz, offset, support_offset, support_feats=None
+ ):
+ feats = self.linear1(support_feats) + pointops.interpolation(
+ xyz, support_xyz, self.linear2(feats), offset, support_offset
+ )
+ return feats, support_xyz, support_offset
+
+
+class KPConvSimpleBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ self.kpconv = KPConvLayer(
+ in_channels,
+ out_channels,
+ point_influence=prev_grid_size * sigma,
+ add_one=False,
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = nn.LeakyReLU(negative_slope=negative_slope)
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # xyz: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.activation(self.bn(feats))
+ return feats
+
+
+class KPConvResBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ d_2 = out_channels // 4
+ activation = nn.LeakyReLU(negative_slope=negative_slope)
+ self.unary_1 = torch.nn.Sequential(
+ nn.Linear(in_channels, d_2, bias=False),
+ FastBatchNorm1d(d_2, momentum=bn_momentum),
+ activation,
+ )
+ self.unary_2 = torch.nn.Sequential(
+ nn.Linear(d_2, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ activation,
+ )
+ self.kpconv = KPConvLayer(
+ d_2, d_2, point_influence=prev_grid_size * sigma, add_one=False
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = activation
+
+ if in_channels != out_channels:
+ self.shortcut_op = torch.nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ )
+ else:
+ self.shortcut_op = nn.Identity()
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # xyz: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ shortcut = feats
+ feats = self.unary_1(feats)
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.unary_2(feats)
+ shortcut = self.shortcut_op(shortcut)
+ feats += shortcut
+ return feats
+
+
+@MODELS.register_module("ST-v1m1")
+class StratifiedTransformer(nn.Module):
+ def __init__(
+ self,
+ downsample_scale,
+ depths,
+ channels,
+ num_heads,
+ window_size,
+ up_k,
+ grid_sizes,
+ quant_sizes,
+ rel_query=True,
+ rel_key=False,
+ rel_value=False,
+ drop_path_rate=0.2,
+ num_layers=4,
+ concat_xyz=False,
+ num_classes=13,
+ ratio=0.25,
+ k=16,
+ prev_grid_size=0.04,
+ sigma=1.0,
+ stem_transformer=False,
+ kp_ball_radius=0.02 * 2.5,
+ kp_max_neighbor=34,
+ ):
+ super().__init__()
+ assert (
+ KPConvLayer is not None and FastBatchNorm1d is not None
+ ), "Please make sure torch_points3d is installed"
+ assert tp is not None, "Please make sure torch_points_kernels is installed"
+ assert pointops is not None, "Please make sure pointops2 is installed"
+
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+ ] # stochastic depth decay rule
+
+ self.kp_ball_radius = kp_ball_radius
+ self.kp_max_neighbor = kp_max_neighbor
+ if stem_transformer:
+ self.stem_layer = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ 3 if not concat_xyz else 6,
+ channels[0],
+ prev_grid_size,
+ sigma=sigma,
+ )
+ ]
+ )
+ self.layer_start = 0
+ else:
+ self.stem_layer = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ 3 if not concat_xyz else 6,
+ channels[0],
+ prev_grid_size,
+ sigma=sigma,
+ ),
+ KPConvResBlock(
+ channels[0], channels[0], prev_grid_size, sigma=sigma
+ ),
+ ]
+ )
+ self.downsample = TransitionDown(channels[0], channels[1], ratio, k)
+ self.layer_start = 1
+
+ self.layers = nn.ModuleList(
+ [
+ BasicLayer(
+ downsample_scale,
+ depths[i],
+ channels[i],
+ num_heads[i],
+ window_size[i],
+ grid_sizes[i],
+ quant_sizes[i],
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+ downsample=TransitionDown if i < num_layers - 1 else None,
+ ratio=ratio,
+ k=k,
+ out_channels=channels[i + 1] if i < num_layers - 1 else None,
+ )
+ for i in range(self.layer_start, num_layers)
+ ]
+ )
+
+ self.upsamples = nn.ModuleList(
+ [
+ Upsample(up_k, channels[i], channels[i - 1])
+ for i in range(num_layers - 1, 0, -1)
+ ]
+ )
+
+ self.classifier = nn.Sequential(
+ nn.Linear(channels[0], channels[0]),
+ nn.BatchNorm1d(channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(channels[0], num_classes),
+ )
+
+ self.init_weights()
+
+ def forward(self, data_dict):
+ feats = data_dict["feat"]
+ xyz = data_dict["coord"]
+ offset = data_dict["offset"].int()
+ batch = offset2batch(offset)
+ neighbor_idx = tp.ball_query(
+ self.kp_ball_radius,
+ self.kp_max_neighbor,
+ xyz,
+ xyz,
+ mode="partial_dense",
+ batch_x=batch,
+ batch_y=batch,
+ )[0]
+
+ feats_stack = []
+ xyz_stack = []
+ offset_stack = []
+
+ for i, layer in enumerate(self.stem_layer):
+ feats = layer(feats, xyz, batch, neighbor_idx)
+
+ feats = feats.contiguous()
+
+ if self.layer_start == 1:
+ feats_stack.append(feats)
+ xyz_stack.append(xyz)
+ offset_stack.append(offset)
+ feats, xyz, offset = self.downsample(feats, xyz, offset)
+
+ for i, layer in enumerate(self.layers):
+ feats, xyz, offset, feats_down, xyz_down, offset_down = layer(
+ feats, xyz, offset
+ )
+
+ feats_stack.append(feats)
+ xyz_stack.append(xyz)
+ offset_stack.append(offset)
+
+ feats = feats_down
+ xyz = xyz_down
+ offset = offset_down
+
+ feats = feats_stack.pop()
+ xyz = xyz_stack.pop()
+ offset = offset_stack.pop()
+
+ for i, upsample in enumerate(self.upsamples):
+ feats, xyz, offset = upsample(
+ feats,
+ xyz,
+ xyz_stack.pop(),
+ offset,
+ offset_stack.pop(),
+ support_feats=feats_stack.pop(),
+ )
+
+ out = self.classifier(feats)
+
+ return out
+
+ def init_weights(self):
+ """Initialize the weights in backbone."""
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ self.apply(_init_weights)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/stratified_transformer_v1m2_refine.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/stratified_transformer_v1m2_refine.py
new file mode 100644
index 0000000000000000000000000000000000000000..234afc12a7be6ea1feb87259c8c77e1bf0a8b3d3
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/stratified_transformer/stratified_transformer_v1m2_refine.py
@@ -0,0 +1,763 @@
+"""
+Stratified Transformer
+
+Modified from https://github.com/dvlab-research/Stratified-Transformer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from copy import deepcopy
+import torch
+import torch.nn as nn
+
+try:
+ import torch_points_kernels as tp
+except ImportError:
+ tp = None
+
+try:
+ from torch_points3d.modules.KPConv.kernels import KPConvLayer
+ from torch_points3d.core.common_modules import FastBatchNorm1d
+except ImportError:
+ KPConvLayer = None
+ FastBatchNorm1d = None
+
+from torch_scatter import scatter_softmax
+from timm.models.layers import DropPath, trunc_normal_
+from torch_geometric.nn.pool import voxel_grid
+
+try:
+ import pointops2.pointops as pointops
+except ImportError:
+ pointops = None
+
+from pointcept.models.builder import MODELS
+
+
+def offset2batch(offset):
+ return (
+ torch.cat(
+ [
+ (
+ torch.tensor([i] * (o - offset[i - 1]))
+ if i > 0
+ else torch.tensor([i] * o)
+ )
+ for i, o in enumerate(offset)
+ ],
+ dim=0,
+ )
+ .long()
+ .to(offset.device)
+ )
+
+
+def grid_sample(coords, batch, size, start, return_p2v=True):
+ cluster = voxel_grid(coords, batch, size, start=start)
+
+ if not return_p2v:
+ unique, cluster = torch.unique(cluster, sorted=True, return_inverse=True)
+ return cluster
+ else:
+ unique, cluster, counts = torch.unique(
+ cluster, sorted=True, return_inverse=True, return_counts=True
+ )
+
+ # obtain p2v_map
+ n = unique.shape[0]
+ k = counts.max().item()
+ p2v_map = cluster.new_zeros(n, k)
+ mask = torch.arange(k).cuda().unsqueeze(0) < counts.unsqueeze(-1)
+ p2v_map[mask] = torch.argsort(cluster)
+ return cluster, p2v_map, counts
+
+
+class WindowAttention(nn.Module):
+ """Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ """
+
+ def __init__(
+ self,
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ scale=None,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ ):
+ super().__init__()
+ self.embed_channels = embed_channels
+ self.head_channels = embed_channels // num_heads
+ self.num_heads = num_heads
+ self.scale = scale or self.head_channels**-0.5
+
+ self.window_size = window_size
+ self.quant_size = quant_size
+
+ self.rel_query = rel_query
+ self.rel_key = rel_key
+ self.rel_value = rel_value
+
+ self.quant_grid_length = int((2 * window_size + 1e-4) // quant_size)
+
+ assert self.rel_query and self.rel_key
+ if rel_query:
+ self.relative_pos_query_table = nn.Parameter(
+ torch.zeros(
+ 2 * self.quant_grid_length, self.num_heads, self.head_channels, 3
+ )
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+
+ if rel_key:
+ self.relative_pos_key_table = nn.Parameter(
+ torch.zeros(
+ 2 * self.quant_grid_length, self.num_heads, self.head_channels, 3
+ )
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+
+ if rel_value:
+ self.relative_pos_value_table = nn.Parameter(
+ torch.zeros(
+ 2 * self.quant_grid_length, self.num_heads, self.head_channels, 3
+ )
+ )
+ trunc_normal_(self.relative_pos_query_table, std=0.02)
+
+ self.qkv = nn.Linear(embed_channels, embed_channels * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop, inplace=True)
+ self.proj = nn.Linear(embed_channels, embed_channels)
+ self.proj_drop = nn.Dropout(proj_drop, inplace=True)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, feats, coords, index_0, index_1, index_0_offsets, n_max):
+ n, c = feats.shape
+ m = index_0.shape[0]
+
+ assert index_0.shape[0] == index_1.shape[0]
+
+ qkv = (
+ self.qkv(feats)
+ .reshape(n, 3, self.num_heads, c // self.num_heads)
+ .permute(1, 0, 2, 3)
+ .contiguous()
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2]
+ query = query * self.scale
+ attn_flat = pointops.attention_step1_v2(
+ query.float(), key.float(), index_1.int(), index_0_offsets.int(), n_max
+ )
+
+ # Position embedding
+ relative_position = coords[index_0] - coords[index_1]
+ relative_position = torch.round(relative_position * 100000) / 100000
+ relative_position_index = torch.div(
+ relative_position + 2 * self.window_size - 1e-4,
+ self.quant_size,
+ rounding_mode="trunc",
+ )
+ # relative_position_index = (relative_position + 2 * self.window_size - 1e-4) // self.quant_size
+ assert (relative_position_index >= 0).all()
+ assert (relative_position_index <= 2 * self.quant_grid_length - 1).all()
+
+ if self.rel_query and self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx_v3(
+ query.float(),
+ index_0_offsets.int(),
+ n_max,
+ key.float(),
+ index_1.int(),
+ self.relative_pos_query_table.float(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ )
+ elif self.rel_query:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ query.float(),
+ index_0.int(),
+ self.relative_pos_query_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ elif self.rel_key:
+ relative_position_bias = pointops.dot_prod_with_idx(
+ key.float(),
+ index_1.int(),
+ self.relative_pos_key_table.float(),
+ relative_position_index.int(),
+ ) # [M, num_heads]
+ else:
+ relative_position_bias = 0.0
+
+ attn_flat += relative_position_bias
+ softmax_attn_flat = scatter_softmax(src=attn_flat, index=index_0, dim=0)
+
+ if self.rel_value:
+ x = pointops.attention_step2_with_rel_pos_value_v2(
+ softmax_attn_flat.float(),
+ value.float(),
+ index_0_offsets.int(),
+ n_max,
+ index_1.int(),
+ self.relative_pos_value_table.float(),
+ relative_position_index.int(),
+ )
+ else:
+ x = pointops.attention_step2(
+ softmax_attn_flat.float(), value.float(), index_0.int(), index_1.int()
+ )
+
+ x = x.view(n, c)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+
+class MLP(nn.Module):
+ def __init__(self, in_channels, hidden_channels=None, out_channels=None, drop=0.0):
+ super().__init__()
+ out_channels = out_channels or in_channels
+ hidden_channels = hidden_channels or in_channels
+ self.fc1 = nn.Linear(in_channels, hidden_channels)
+ self.act = nn.GELU()
+ self.fc2 = nn.Linear(hidden_channels, out_channels)
+ self.drop = nn.Dropout(drop, inplace=True)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class Block(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ mlp_expend_ratio=4.0,
+ drop_path=0.0,
+ qk_scale=None,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ ):
+ super().__init__()
+ self.norm1 = nn.LayerNorm(embed_channels)
+ self.attn = WindowAttention(
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ scale=qk_scale,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ )
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = nn.LayerNorm(embed_channels)
+ self.mlp = MLP(
+ in_channels=embed_channels,
+ hidden_channels=int(embed_channels * mlp_expend_ratio),
+ )
+
+ def forward(self, feats, coords, index_0, index_1, index_0_offsets, n_max):
+ short_cut = feats
+ feats = self.norm1(feats)
+ feats = self.attn(feats, coords, index_0, index_1, index_0_offsets, n_max)
+
+ feats = short_cut + self.drop_path(feats)
+ feats += self.drop_path(self.mlp(self.norm2(feats)))
+ return feats
+
+
+class BasicLayer(nn.Module):
+ def __init__(
+ self,
+ embed_channels,
+ out_channels,
+ depth,
+ num_heads,
+ window_size,
+ quant_size,
+ mlp_expend_ratio=4.0,
+ down_ratio=0.25,
+ down_num_sample=16,
+ drop_path=None,
+ qk_scale=None,
+ down=True,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ ):
+ super().__init__()
+ self.depth = depth
+ self.window_size = window_size
+ self.quant_size = quant_size
+ self.down_ratio = down_ratio
+
+ if isinstance(drop_path, list):
+ drop_path = drop_path
+ assert len(drop_path) == depth
+ elif isinstance(drop_path, float):
+ drop_path = [deepcopy(drop_path) for _ in range(depth)]
+ else:
+ drop_path = [0.0 for _ in range(depth)]
+
+ self.blocks = nn.ModuleList()
+ for i in range(depth):
+ block = Block(
+ embed_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ mlp_expend_ratio=mlp_expend_ratio,
+ drop_path=drop_path[i],
+ qk_scale=qk_scale,
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ )
+ self.blocks.append(block)
+
+ self.down = (
+ TransitionDown(embed_channels, out_channels, down_ratio, down_num_sample)
+ if down
+ else None
+ )
+
+ def forward(self, feats, coords, offset):
+ # window_size -> [window_size, window_size, window_size]
+ window_size = torch.tensor(
+ [self.window_size] * 3, dtype=coords.dtype, device=coords.device
+ )
+ new_window_size = 2 * torch.tensor(
+ [self.window_size] * 3, dtype=coords.dtype, device=coords.device
+ )
+ batch = offset2batch(offset)
+
+ # compute new offset
+ new_offset = [int(offset[0].item() * self.down_ratio) + 1]
+ count = int(offset[0].item() * self.down_ratio) + 1
+ for i in range(1, offset.shape[0]):
+ count += (
+ int((offset[i].item() - offset[i - 1].item()) * self.down_ratio) + 1
+ )
+ new_offset.append(count)
+ new_offset = torch.cuda.IntTensor(new_offset)
+ down_idx = pointops.furthestsampling(coords, offset.int(), new_offset.int())
+
+ # compute window mapping
+ coords_min = coords.min(0).values
+ v2p_map, p2v_map, counts = grid_sample(coords, batch, window_size, start=None)
+ shift_size = window_size * 1 / 2
+ shift_v2p_map, shift_p2v_map, shift_counts = grid_sample(
+ coords + shift_size, batch, window_size, start=coords_min
+ )
+
+ new_v2p_map, new_p2v_map, new_counts = grid_sample(
+ coords, batch, new_window_size, start=None
+ )
+ shift_size = new_window_size * 1 / 2
+ shift_new_v2p_map, shift_new_p2v_map, shift_new_counts = grid_sample(
+ coords + shift_size, batch, new_window_size, start=coords_min
+ )
+
+ # stratified attention
+ for i, blk in enumerate(self.blocks):
+ p2v_map_blk = p2v_map if i % 2 == 0 else shift_p2v_map
+ counts_blk = counts if i % 2 == 0 else shift_counts
+
+ new_p2v_map_blk = new_p2v_map if i % 2 == 0 else shift_new_p2v_map
+ new_counts_blk = new_counts if i % 2 == 0 else shift_new_counts
+
+ n, k = p2v_map_blk.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < counts_blk.unsqueeze(-1)
+ mask_mat = mask.unsqueeze(-1) & mask.unsqueeze(-2)
+ index_0 = p2v_map_blk.unsqueeze(-1).expand(-1, -1, k)[mask_mat]
+ index_1 = p2v_map_blk.unsqueeze(1).expand(-1, k, -1)[mask_mat]
+
+ down_mask = torch.zeros_like(batch).bool()
+ down_mask[down_idx.long()] = True
+ down_mask = down_mask[new_p2v_map_blk] # [n, k], down sample mask
+ n, k = new_p2v_map_blk.shape
+ mask = torch.arange(k).unsqueeze(0).cuda() < new_counts_blk.unsqueeze(
+ -1
+ ) # [n, k]
+ down_mask = down_mask & mask # down sample and window mask
+ # [n, k, k] query: dense point in large windows; key: sparse point in large windows
+ mask_mat = mask.unsqueeze(-1) & down_mask.unsqueeze(-2)
+
+ if i % 2 == 0:
+ # [n, k, 3]
+ # window_coord = (coords[new_p2v_map_blk] - coords_min) // window_size
+ window_coord = torch.div(
+ coords[new_p2v_map_blk] - coords_min,
+ window_size,
+ rounding_mode="trunc",
+ )
+ else:
+ # [n, k, 3]
+ # window_coord = (coords[new_p2v_map_blk] - coords_min + 1/2 * window_size) // window_size
+ window_coord = torch.div(
+ coords[new_p2v_map_blk] - coords_min + 1 / 2 * window_size,
+ window_size,
+ rounding_mode="trunc",
+ )
+ # [n, k, k], whether pair points are in same small windows
+ mask_mat_prev = (
+ window_coord.unsqueeze(2) != window_coord.unsqueeze(1)
+ ).any(-1)
+ mask_mat = mask_mat & mask_mat_prev
+
+ new_index_0 = new_p2v_map_blk.unsqueeze(-1).expand(-1, -1, k)[mask_mat]
+ new_index_1 = new_p2v_map_blk.unsqueeze(1).expand(-1, k, -1)[mask_mat]
+
+ index_0 = torch.cat([index_0, new_index_0], 0)
+ index_1 = torch.cat([index_1, new_index_1], 0)
+
+ # rearrange index for acceleration
+ index_0, indices = torch.sort(index_0)
+ index_1 = index_1[indices]
+ index_0_counts = index_0.bincount()
+ n_max = index_0_counts.max()
+ index_0_offsets = index_0_counts.cumsum(dim=-1)
+ index_0_offsets = torch.cat(
+ [torch.zeros(1, dtype=torch.long).cuda(), index_0_offsets], 0
+ )
+
+ feats = blk(feats, coords, index_0, index_1, index_0_offsets, n_max)
+
+ if self.down:
+ feats_down, coords_down, offset_down = self.down(feats, coords, offset)
+ else:
+ feats_down, coords_down, offset_down = None, None, None
+
+ return feats, coords, offset, feats_down, coords_down, offset_down
+
+
+class TransitionDown(nn.Module):
+ def __init__(self, in_channels, out_channels, ratio, k, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.ratio = ratio
+ self.k = k
+ self.norm = norm_layer(in_channels) if norm_layer else None
+ self.linear = nn.Linear(in_channels, out_channels, bias=False)
+ self.pool = nn.MaxPool1d(k)
+
+ def forward(self, feats, coords, offset):
+ new_offset, count = [int(offset[0].item() * self.ratio) + 1], int(
+ offset[0].item() * self.ratio
+ ) + 1
+ for i in range(1, offset.shape[0]):
+ count += ((offset[i].item() - offset[i - 1].item()) * self.ratio) + 1
+ new_offset.append(count)
+ new_offset = torch.cuda.IntTensor(new_offset)
+ idx = pointops.furthestsampling(coords, offset, new_offset) # (m)
+ new_coords = coords[idx.long(), :] # (m, 3)
+
+ feats = pointops.queryandgroup(
+ self.k, coords, new_coords, feats, None, offset, new_offset, use_xyz=False
+ ) # (m, nsample, 3+c)
+ m, k, c = feats.shape
+ feats = (
+ self.linear(self.norm(feats.view(m * k, c)).view(m, k, c))
+ .transpose(1, 2)
+ .contiguous()
+ )
+ feats = self.pool(feats).squeeze(-1) # (m, c)
+ return feats, new_coords, new_offset
+
+
+class TransitionUp(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.linear1 = nn.Sequential(
+ nn.LayerNorm(out_channels), nn.Linear(out_channels, out_channels)
+ )
+
+ self.linear2 = nn.Sequential(
+ nn.LayerNorm(in_channels), nn.Linear(in_channels, out_channels)
+ )
+
+ def forward(self, feats, coords, offset, skip_feats, skip_coords, skip_offset):
+ feats = self.linear1(skip_feats) + pointops.interpolation(
+ coords, skip_coords, self.linear2(feats), offset, skip_offset
+ )
+ return feats, skip_coords, skip_offset
+
+
+class KPConvSimpleBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ self.kpconv = KPConvLayer(
+ in_channels,
+ out_channels,
+ point_influence=prev_grid_size * sigma,
+ add_one=False,
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = nn.LeakyReLU(negative_slope=negative_slope)
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # coords: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.activation(self.bn(feats))
+ return feats
+
+
+class KPConvResBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ prev_grid_size,
+ sigma=1.0,
+ negative_slope=0.2,
+ bn_momentum=0.02,
+ ):
+ super().__init__()
+ d_2 = out_channels // 4
+ activation = nn.LeakyReLU(negative_slope=negative_slope)
+ self.unary_1 = torch.nn.Sequential(
+ nn.Linear(in_channels, d_2, bias=False),
+ FastBatchNorm1d(d_2, momentum=bn_momentum),
+ activation,
+ )
+ self.unary_2 = torch.nn.Sequential(
+ nn.Linear(d_2, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ activation,
+ )
+ self.kpconv = KPConvLayer(
+ d_2, d_2, point_influence=prev_grid_size * sigma, add_one=False
+ )
+ self.bn = FastBatchNorm1d(out_channels, momentum=bn_momentum)
+ self.activation = activation
+
+ if in_channels != out_channels:
+ self.shortcut_op = torch.nn.Sequential(
+ nn.Linear(in_channels, out_channels, bias=False),
+ FastBatchNorm1d(out_channels, momentum=bn_momentum),
+ )
+ else:
+ self.shortcut_op = nn.Identity()
+
+ def forward(self, feats, xyz, batch, neighbor_idx):
+ # feats: [N, C]
+ # coords: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+
+ shortcut = feats
+ feats = self.unary_1(feats)
+ feats = self.kpconv(xyz, xyz, neighbor_idx, feats)
+ feats = self.unary_2(feats)
+ shortcut = self.shortcut_op(shortcut)
+ feats += shortcut
+ return feats
+
+
+@MODELS.register_module("ST-v1m2")
+class StratifiedTransformer(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ channels=(48, 96, 192, 384, 384),
+ num_heads=(6, 12, 24, 24),
+ depths=(3, 9, 3, 3),
+ window_size=(0.2, 0.4, 0.8, 1.6),
+ quant_size=(0.01, 0.02, 0.04, 0.08),
+ mlp_expend_ratio=4.0,
+ down_ratio=0.25,
+ down_num_sample=16,
+ kp_ball_radius=2.5 * 0.02,
+ kp_max_neighbor=34,
+ kp_grid_size=0.02,
+ kp_sigma=1.0,
+ drop_path_rate=0.2,
+ rel_query=True,
+ rel_key=True,
+ rel_value=True,
+ qkv_bias=True,
+ stem=True,
+ ):
+ super().__init__()
+ assert (
+ KPConvLayer is not None and FastBatchNorm1d is not None
+ ), "Please make sure torch_points3d is installed"
+ assert tp is not None, "Please make sure torch_points_kernels is installed"
+ assert pointops is not None, "Please make sure pointops2 is installed"
+ # stochastic depth decay rule
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+ self.kp_ball_radius = kp_ball_radius
+ self.kp_max_neighbor = kp_max_neighbor
+ self.stem = stem
+ if stem:
+ self.point_embed = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ in_channels, channels[0], kp_grid_size, sigma=kp_sigma
+ ),
+ KPConvResBlock(
+ channels[0], channels[0], kp_grid_size, sigma=kp_sigma
+ ),
+ ]
+ )
+ self.down = TransitionDown(
+ channels[0], channels[1], down_ratio, down_num_sample
+ )
+ else:
+ assert channels[0] == channels[1]
+ self.point_embed = nn.ModuleList(
+ [
+ KPConvSimpleBlock(
+ in_channels, channels[1], kp_grid_size, sigma=kp_sigma
+ ),
+ ]
+ )
+
+ num_layers = len(depths)
+ self.layers = nn.ModuleList()
+ for i in range(num_layers):
+ layer = BasicLayer(
+ embed_channels=channels[i + 1],
+ out_channels=channels[i + 2] if i < num_layers - 1 else channels[i + 1],
+ depth=depths[i],
+ num_heads=num_heads[i],
+ window_size=window_size[i],
+ quant_size=quant_size[i],
+ mlp_expend_ratio=mlp_expend_ratio,
+ down_ratio=down_ratio,
+ down_num_sample=down_num_sample,
+ drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+ rel_query=rel_query,
+ rel_key=rel_key,
+ rel_value=rel_value,
+ qkv_bias=qkv_bias,
+ down=True if i < num_layers - 1 else False,
+ )
+ self.layers.append(layer)
+
+ self.up = nn.ModuleList(
+ [
+ TransitionUp(channels[i + 1], channels[i])
+ for i in reversed(range(1, num_layers))
+ ]
+ )
+ if self.stem:
+ self.up.append(TransitionUp(channels[1], channels[0]))
+
+ self.classifier = nn.Sequential(
+ nn.Linear(channels[0], channels[0]),
+ nn.BatchNorm1d(channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(channels[0], num_classes),
+ )
+
+ self.init_weights()
+
+ def forward(self, data_dict):
+ feats = data_dict["feat"]
+ coords = data_dict["coord"]
+ offset = data_dict["offset"].int()
+ batch = offset2batch(offset)
+ neighbor_idx = tp.ball_query(
+ self.kp_ball_radius,
+ self.kp_max_neighbor,
+ coords,
+ coords,
+ mode="partial_dense",
+ batch_x=batch,
+ batch_y=batch,
+ )[0]
+
+ feats_stack = []
+ coords_stack = []
+ offset_stack = []
+
+ for i, layer in enumerate(self.point_embed):
+ feats = layer(feats, coords, batch, neighbor_idx)
+
+ feats = feats.contiguous()
+ if self.stem:
+ feats_stack.append(feats)
+ coords_stack.append(coords)
+ offset_stack.append(offset)
+ feats, coords, offset = self.down(feats, coords, offset)
+
+ for i, layer in enumerate(self.layers):
+ feats, coords, offset, feats_down, coords_down, offset_down = layer(
+ feats, coords, offset
+ )
+
+ feats_stack.append(feats)
+ coords_stack.append(coords)
+ offset_stack.append(offset)
+
+ feats = feats_down
+ coords = coords_down
+ offset = offset_down
+
+ feats = feats_stack.pop()
+ coords = coords_stack.pop()
+ offset = offset_stack.pop()
+
+ for i, up in enumerate(self.up):
+ feats, coords, offset = up(
+ feats,
+ coords,
+ offset,
+ feats_stack.pop(),
+ coords_stack.pop(),
+ offset_stack.pop(),
+ )
+
+ out = self.classifier(feats)
+ return out
+
+ def init_weights(self):
+ """Initialize the weights in backbone."""
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ self.apply(_init_weights)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36050969d9abb027778008e4d6d8f77710f52392
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/__init__.py
@@ -0,0 +1 @@
+from .swin3d_v1m1_base import Swin3DUNet
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/mink_layers.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/mink_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee3e8cfc002e8311ac196335592c337644659612
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/mink_layers.py
@@ -0,0 +1,249 @@
+"""
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import MinkowskiEngine as ME
+import numpy as np
+
+
+def assign_feats(sp, x):
+ return ME.SparseTensor(
+ features=x.float(),
+ coordinate_map_key=sp.coordinate_map_key,
+ coordinate_manager=sp.coordinate_manager,
+ )
+
+
+class MinkConvBN(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ ):
+ super().__init__()
+ self.conv_layers = nn.Sequential(
+ ME.MinkowskiConvolution(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ dimension=dimension,
+ ),
+ ME.MinkowskiBatchNorm(out_channels),
+ )
+
+ def forward(self, x):
+ x = self.conv_layers(x)
+ return x
+
+
+class MinkConvBNRelu(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ ):
+ super().__init__()
+ self.conv_layers = nn.Sequential(
+ ME.MinkowskiConvolution(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ dimension=dimension,
+ ),
+ ME.MinkowskiBatchNorm(out_channels),
+ ME.MinkowskiReLU(inplace=True),
+ )
+
+ def forward(self, x):
+ x = self.conv_layers(x)
+ if x.F.dtype == torch.float16:
+ x = assign_feats(x, x.F.float())
+ return x
+
+
+class MinkDeConvBNRelu(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ ):
+ super().__init__()
+ self.conv_layers = nn.Sequential(
+ ME.MinkowskiConvolutionTranspose(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=dilation,
+ bias=bias,
+ dimension=dimension,
+ ),
+ ME.MinkowskiBatchNorm(out_channels),
+ ME.MinkowskiReLU(),
+ )
+
+ def forward(self, x):
+ x = self.conv_layers(x)
+ return x
+
+
+class MinkResBlock(nn.Module):
+ def __init__(self, in_channels, out_channels, stride=1, dilation=1):
+ super(MinkResBlock, self).__init__()
+
+ self.conv1 = ME.MinkowskiConvolution(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ stride=stride,
+ dilation=dilation,
+ bias=False,
+ dimension=3,
+ )
+ self.norm1 = ME.MinkowskiBatchNorm(out_channels)
+ self.conv2 = ME.MinkowskiConvolution(
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ stride=1,
+ dilation=dilation,
+ bias=False,
+ dimension=3,
+ )
+
+ self.norm2 = ME.MinkowskiBatchNorm(out_channels)
+ self.relu = ME.MinkowskiReLU(inplace=True)
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.norm1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.norm2(out)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+
+class SparseTensorLinear(nn.Module):
+ def __init__(self, in_channels, out_channels, bias=False):
+ super().__init__()
+ self.linear = nn.Linear(in_channels, out_channels, bias=bias)
+
+ def forward(self, sp):
+ x = self.linear(sp.F)
+ return assign_feats(sp, x.float())
+
+
+class SparseTensorLayerNorm(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.norm = nn.LayerNorm(dim)
+
+ def forward(self, sp):
+ x = self.norm(sp.F)
+ return assign_feats(sp, x.float())
+
+
+class MinkResBlock_v2(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ d_2 = out_channels // 4
+ self.conv1 = torch.nn.Sequential(
+ SparseTensorLinear(in_channels, d_2, bias=False),
+ ME.MinkowskiBatchNorm(d_2),
+ ME.MinkowskiReLU(),
+ )
+ self.unary_2 = torch.nn.Sequential(
+ SparseTensorLinear(d_2, out_channels, bias=False),
+ ME.MinkowskiBatchNorm(out_channels),
+ ME.MinkowskiReLU(),
+ )
+ self.spconv = ME.MinkowskiConvolution(
+ in_channels=d_2,
+ out_channels=d_2,
+ kernel_size=5,
+ stride=1,
+ dilation=1,
+ bias=False,
+ dimension=3,
+ )
+ if in_channels != out_channels:
+ self.shortcut_op = torch.nn.Sequential(
+ SparseTensorLinear(in_channels, out_channels, bias=False),
+ ME.MinkowskiBatchNorm(out_channels),
+ )
+ else:
+ self.shortcut_op = nn.Identity()
+
+ def forward(self, x):
+ # feats: [N, C]
+ # xyz: [N, 3]
+ # batch: [N,]
+ # neighbor_idx: [N, M]
+ shortcut = x
+ x = self.unary_1(x)
+ x = self.spconv(x)
+ x = self.unary_2(x)
+ shortcut = self.shortcut_op(shortcut)
+ x += shortcut
+ return x
+
+
+class MinkResBlock_BottleNeck(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super(MinkResBlock_BottleNeck, self).__init__()
+ bottle_neck = out_channels // 4
+ self.conv1x1a = MinkConvBNRelu(
+ in_channels, bottle_neck, kernel_size=1, stride=1
+ )
+ self.conv3x3 = MinkConvBNRelu(bottle_neck, bottle_neck, kernel_size=3, stride=1)
+ self.conv1x1b = MinkConvBN(bottle_neck, out_channels, kernel_size=1, stride=1)
+ if in_channels != out_channels:
+ self.conv1x1c = MinkConvBN(
+ in_channels, out_channels, kernel_size=1, stride=1
+ )
+ else:
+ self.conv1x1c = None
+ self.relu = ME.MinkowskiReLU(inplace=True)
+
+ def forward(self, x):
+ residual = x
+ out = self.conv1x1a(x)
+ out = self.conv3x3(out)
+ out = self.conv1x1b(out)
+ if self.conv1x1c is not None:
+ residual = self.conv1x1c(residual)
+ out = self.relu(out + residual)
+
+ return out
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/swin3d_layers.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/swin3d_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e737e9677ae93f8f5f9188ba774fcd1d0fa42443
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/swin3d_layers.py
@@ -0,0 +1,876 @@
+"""
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+"""
+
+import numpy as np
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath, trunc_normal_
+import MinkowskiEngine as ME
+from MinkowskiEngine import SparseTensor
+from Swin3D.sparse_dl.attn.attn_coff import (
+ SelfAttnAIOFunction,
+ PosEmb,
+ TableDims,
+ IndexMode,
+ PrecisionMode,
+)
+import Swin3D.sparse_dl.knn
+from Swin3D.sparse_dl.knn import KNN
+
+from .mink_layers import (
+ assign_feats,
+ SparseTensorLayerNorm,
+ SparseTensorLinear,
+)
+
+
+def query_knn_feature(
+ K, src_xyz, query_xyz, src_feat, src_offset, query_offset, return_idx=False
+):
+ """
+ gather feature in the KNN neighborhood
+ """
+ assert (
+ src_xyz.is_contiguous()
+ and query_xyz.is_contiguous()
+ and src_feat.is_contiguous()
+ )
+ if query_xyz is None:
+ query_xyz = src_xyz
+ query_offset = src_offset
+
+ idx, _ = KNN.apply(K, src_xyz, query_xyz, src_offset, query_offset)
+
+ n, m, c = src_xyz.shape[0], query_xyz.shape[0], src_feat.shape[1]
+ grouped_feat = src_feat[idx.view(-1).long(), :].view(m, K, c)
+
+ if return_idx:
+ return grouped_feat, idx
+ else:
+ return grouped_feat
+
+
+def knn_linear_interpolation(
+ src_xyz, query_xyz, src_feat, src_offset, query_offset, K=3
+):
+ """
+ interpolation feature using distance in KNN neighborhood
+ """
+ N, C = query_xyz.shape[0], src_feat.shape[1]
+ assert (
+ src_xyz.is_contiguous()
+ and query_xyz.is_contiguous()
+ and src_feat.is_contiguous()
+ )
+ # (N, K)
+ idx, dist = KNN.apply(K, src_xyz, query_xyz, src_offset, query_offset)
+ weight = 1.0 / (dist + 1e-8)
+ norm = torch.sum(weight, dim=1, keepdim=True)
+ weight = weight / norm
+ query_feat = torch.zeros((N, C), dtype=src_feat.dtype, device=src_feat.device)
+ for i in range(K):
+ query_feat += src_feat[idx[:, i].long(), :] * weight[:, i].unsqueeze(-1)
+ return query_feat
+
+
+def sparse_self_attention(
+ w_w_id: torch.Tensor, w_sizes: torch.Tensor, protocol: str = "v1"
+):
+ """
+ Args:
+ indices [torch.Tensor]: sparse window index with shape [N, 2], N is the total
+ number of non-empty voxels with indices (window_id, within_window_id). window_id
+ is ordered and starts from 0; within_window_id is a sparse index to indicate the
+ offset of kernel_size ** 3.
+ feats [torch.Tensor]: sprase features of each non-empty voxel with shape [N, C]
+ Outputs:
+ [M, 3]: sparse indices of cofficient matrix (window_id, att_a_id, att_b_id). att_a_id
+ and att_b_id are the within_window_id
+ [M, 1]: the sparse coffient matrix
+
+ Spaces:
+ W: total number of windows
+ N: total number of input voxels
+ M: total number of output cofficients
+ """
+ w_sizes_2 = w_sizes**2
+
+ # w2n_indices - [W], mapping window index to window global offset in input
+ # space
+ w_cumsum = torch.cumsum(w_sizes, dim=-1)
+ w2n_indices = torch.cat(
+ [torch.zeros(1, dtype=w_cumsum.dtype, device=w_cumsum.device), w_cumsum[:-1]]
+ )
+
+ # w2m indices - [W], mapping window index to window global offset in output
+ # space
+ w2_cumsum = torch.cumsum(w_sizes_2, dim=-1)
+ w2m_indices = torch.cat(
+ [torch.zeros(1, dtype=w2_cumsum.dtype, device=w2_cumsum.device), w2_cumsum[:-1]]
+ )
+
+ # m2w indices - [M], mapping element global offset to the window index
+ m2w_indices = torch.zeros(
+ [w2_cumsum[-1]], dtype=w_sizes.dtype, device=w_sizes.device
+ )
+ m2w_offset = torch.zeros(
+ [w2_cumsum[-1]], dtype=w_sizes.dtype, device=w_sizes.device
+ )
+ m2w_indices[w2m_indices[1:]] = 1
+ m2w_offset[w2m_indices[1:]] = w_sizes_2[:-1]
+ m2w_indices = torch.cumsum(m2w_indices, dim=-1)
+ m2w_offset = torch.cumsum(m2w_offset, dim=-1)
+
+ # m_indices = [M], element global offset in output space
+ m_indices = torch.arange(
+ 0, w2_cumsum[-1], dtype=w_sizes.dtype, device=w_sizes.device
+ )
+
+ # m2n_indices - [M], mapping element global offset to the window global offset
+ # in input space
+ m2n_indices = w2n_indices[m2w_indices]
+
+ m_offset = m_indices - m2w_offset
+ m2w_sizes = w_sizes[m2w_indices]
+
+ # print_log_main("m_offset:", m_offset, m_offset.shape)
+ # print_log_main("m2n_indices:", m2n_indices, m2n_indices.shape)
+
+ y_offset = m2n_indices + m_offset % m2w_sizes
+ x_offset = m2n_indices + torch.div(m_offset, m2w_sizes, rounding_mode="floor")
+
+ # print_log_main("=================================")
+ # print_log_main(w_sizes[:5])
+ # print_log_main(x_offset[:50])
+ # print_log_main(y_offset[:50])
+ # coord = torch.stack([m2w_indices, w_w_id[x_offset], w_w_id[y_offset]], axis=-1)
+ if protocol == "v1":
+ return x_offset, y_offset
+ elif protocol == "v2":
+ return x_offset, y_offset, m2w_indices, w_sizes, w2n_indices, w2m_indices
+
+
+class Mlp(nn.Module):
+ def __init__(
+ self,
+ in_features,
+ hidden_features=None,
+ out_features=None,
+ act_layer=nn.GELU,
+ drop=0.0,
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class GridCoordsDown(nn.Module):
+ """
+ downsample the grid coordinates
+ keep the nearest point to the average point of the downsampled grid
+ """
+
+ def __init__(self, stride):
+ super().__init__()
+ self.stride = stride
+ self.avg_pool = ME.MinkowskiAvgPooling(
+ kernel_size=self.stride, stride=self.stride, dimension=3
+ )
+ self.unpool = ME.MinkowskiPoolingTranspose(
+ kernel_size=stride, stride=stride, dimension=3
+ )
+ self.max_pool = ME.MinkowskiMaxPooling(
+ kernel_size=self.stride, stride=self.stride, dimension=3
+ )
+
+ def forward(self, coords_sp, sp, return_map=False):
+ device = sp.C.device
+ # is_pool = True means pooling map
+ # is_pool = False means conv map (query as center)
+
+ N = sp.shape[0]
+ avg_coords_sp = self.avg_pool(coords_sp)
+ dist_sp = self.unpool(avg_coords_sp) - coords_sp
+ dist = dist_sp.F
+ dist = -torch.sqrt((dist**2).sum(dim=1)).unsqueeze(1)
+ dist_sp = assign_feats(dist_sp, dist)
+ min_dist_sp = self.max_pool(dist_sp)
+ map_pair = sp.coordinate_manager.kernel_map(
+ dist_sp.coordinate_map_key,
+ min_dist_sp.coordinate_map_key,
+ stride=self.stride,
+ kernel_size=self.stride,
+ is_pool=True,
+ )[0]
+ in_map, out_map = map_pair
+ broad_min_dist_sp = self.unpool(min_dist_sp)
+ mask = (broad_min_dist_sp.F == dist_sp.F).squeeze(1)
+ in_map = in_map[mask].long()
+ out_map = out_map[mask].long()
+ downsample_map = torch.zeros(N, dtype=torch.long, device=device) - 1
+ downsample_map[out_map] = in_map
+ assert (downsample_map >= 0).all()
+ assert (dist_sp.F[downsample_map] == min_dist_sp.F).all()
+ new_coords = coords_sp.F[downsample_map]
+ new_coords_sp = assign_feats(sp, new_coords)
+ if return_map:
+ return new_coords_sp, downsample_map
+ else:
+ return new_coords_sp
+
+
+def get_offset(batch):
+ offset = []
+ bs = batch.max() + 1
+ for i in range(bs):
+ offset.append(torch.sum(batch == i))
+ offset = torch.cuda.IntTensor(offset)
+ offset = offset.cumsum(dim=0).int()
+ return offset
+
+
+class GridDownsample(nn.Module):
+ """
+ use stride to downsample voxel
+ use grid maxpooling with kernel_size
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
+ super().__init__()
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.sp_pool = ME.MinkowskiMaxPooling(
+ kernel_size=kernel_size, stride=stride, dimension=3
+ )
+ self.coords_pool = GridCoordsDown(stride=stride)
+ self.norm = SparseTensorLayerNorm(in_channels)
+ self.linear = SparseTensorLinear(in_channels, out_channels)
+
+ def forward(self, sp, coords_sp):
+ sp_down = self.sp_pool(self.linear(self.norm(sp)))
+ coords_sp_down = self.coords_pool(coords_sp, sp_down)
+ return sp_down, coords_sp_down
+
+ def extra_repr(self) -> str:
+ return f"kernel_size={self.kernel_size}, stride={self.stride}, in_channels={self.in_channels}, out_channels={self.out_channels}"
+
+
+class GridKNNDownsample(nn.Module):
+ """
+ use stride to downsample voxel
+ use KNN to do maxpooling
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
+ super().__init__()
+ self.stride = stride
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.k = 16
+ self.sp_pool = ME.MinkowskiMaxPooling(
+ kernel_size=stride, stride=stride, dimension=3
+ )
+ self.coords_pool = GridCoordsDown(stride=stride)
+ self.norm = nn.LayerNorm(in_channels)
+ self.linear = nn.Linear(in_channels, out_channels, bias=False)
+ self.pool = nn.MaxPool1d(self.k)
+
+ def forward(self, sp, coords_sp):
+ # calculate the voxel
+ sp_down = self.sp_pool(sp)
+ # for downsampled cRSE
+ coords_sp_down = self.coords_pool(coords_sp, sp_down)
+ offset = get_offset(sp.C[:, 0])
+ n_offset = get_offset(sp_down.C[:, 0])
+
+ xyz = coords_sp.F[:, 1:4].detach().contiguous()
+ n_xyz = coords_sp_down.F[:, 1:4].detach().contiguous()
+ feats = query_knn_feature(self.k, xyz, n_xyz, sp.F, offset, n_offset)
+ m, k, c = feats.shape
+ feats = (
+ self.linear(self.norm(feats.view(m * k, c)).view(m, k, c))
+ .transpose(1, 2)
+ .contiguous()
+ )
+ feats = self.pool(feats).squeeze(-1)
+ sp = assign_feats(sp_down, feats.float())
+ coords_sp = coords_sp_down
+ return sp, coords_sp
+
+ def extra_repr(self) -> str:
+ return f"kernel_size={self.k}, stride={self.stride}, in_channels={self.in_channels}, out_channels={self.out_channels}"
+
+
+class Upsample(nn.Module):
+ """
+ upsample using trilinear interpolation
+ follower by attn block according to self.attn
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ num_heads,
+ window_size,
+ quant_size,
+ attn=True,
+ up_k=3,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ self.linear1 = nn.Sequential(
+ nn.LayerNorm(out_channels), nn.Linear(out_channels, out_channels)
+ )
+ self.linear2 = nn.Sequential(
+ nn.LayerNorm(in_channels), nn.Linear(in_channels, out_channels)
+ )
+ self.up_k = up_k
+ self.attn = attn and window_size > 0
+ if self.attn:
+ self.block = BasicLayer(
+ dim=out_channels,
+ depth=1,
+ num_heads=num_heads,
+ window_size=window_size,
+ quant_size=quant_size,
+ drop_path=0.1,
+ downsample=None,
+ out_channels=None,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+
+ def forward(self, sp, coords_sp, sp_up, coords_sp_up):
+ feats = sp.F
+ support_feats = sp_up.F
+ xyz = coords_sp.F[:, 1:4].detach().contiguous()
+ support_xyz = coords_sp_up.F[:, 1:4].detach().contiguous()
+ offset = get_offset(sp.C[:, 0])
+ support_offset = get_offset(sp_up.C[:, 0])
+
+ feats = self.linear1(support_feats) + knn_linear_interpolation(
+ xyz, support_xyz, self.linear2(feats), offset, support_offset, K=self.up_k
+ )
+ sp_up = assign_feats(sp_up, feats)
+ if self.attn:
+ sp_up, _, _ = self.block(sp_up, coords_sp_up)
+ return sp_up
+
+ def extra_repr(self) -> str:
+ return f"up_k={self.up_k}, in_channels={self.in_channels}, out_channels={self.out_channels}, attn={self.attn}"
+
+
+class WindowAttention(nn.Module):
+ """
+ Window based multi-head self attention (W-MSA) module with cRSE.
+ Designed for sparse structure
+ It supports both of shifted and non-shifted window.
+
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ quant_size (int): quant_size for for finer cRSE table
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ cRSE (str | 'XYZ', 'XYZ_RGB', 'XYZ_RGB_NORM'): cRSE mode. Default: 'XYZ_RGB'
+ fp16_mode (int | 0, 1, 2): fp16 mode for attention module, Default: 0
+ 0: fp32 forward and fp32 backward
+ 1: fp16 forward and fp32 backward
+ 2: fp16 forward and fp16 backward
+ """
+
+ def __init__(
+ self,
+ dim,
+ window_size,
+ quant_size,
+ num_heads,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim**-0.5
+
+ # color in [-1, 1], color_windowsize = 2
+ # normal in [-1, 1], normal_windowsize = 2
+ self.color_windowsize = 2
+ self.normal_windowsize = 2
+
+ self.fp16_mode = fp16_mode
+
+ table_offsets = []
+ self.cRSE = cRSE
+ if "XYZ" in cRSE:
+ self.xyz_quant_size = quant_size
+ quant_grid_length_xyz = window_size * self.xyz_quant_size
+ table_shape_xyz = (3, 2 * quant_grid_length_xyz, num_heads, head_dim)
+ self.query_xyz_table = nn.Parameter(torch.zeros(table_shape_xyz))
+ trunc_normal_(self.query_xyz_table, std=0.02)
+ self.key_xyz_table = nn.Parameter(torch.zeros(table_shape_xyz))
+ trunc_normal_(self.key_xyz_table, std=0.02)
+ self.value_xyz_table = nn.Parameter(torch.zeros(table_shape_xyz))
+ trunc_normal_(self.value_xyz_table, std=0.02)
+ table_offsets += [np.prod(table_shape_xyz[1:])] * 3
+
+ if "RGB" in cRSE:
+ self.color_quant_size = quant_size * 2
+ quant_grid_length_rgb = self.color_windowsize * self.color_quant_size
+ table_shape_rgb = (3, 2 * quant_grid_length_rgb, num_heads, head_dim)
+ self.query_rgb_table = nn.Parameter(torch.zeros(table_shape_rgb))
+ trunc_normal_(self.query_rgb_table, std=0.02)
+ self.key_rgb_table = nn.Parameter(torch.zeros(table_shape_rgb))
+ trunc_normal_(self.key_rgb_table, std=0.02)
+ self.value_rgb_table = nn.Parameter(torch.zeros(table_shape_rgb))
+ trunc_normal_(self.value_rgb_table, std=0.02)
+ table_offsets += [np.prod(table_shape_rgb[1:])] * 3
+
+ if "NORM" in cRSE:
+ self.normal_quant_size = quant_size * 2
+ quant_grid_length_norm = self.normal_windowsize * self.normal_quant_size
+ table_shape_norm = (3, 2 * quant_grid_length_norm, num_heads, head_dim)
+ self.query_norm_table = nn.Parameter(torch.zeros(table_shape_norm))
+ trunc_normal_(self.query_norm_table, std=0.02)
+ self.key_norm_table = nn.Parameter(torch.zeros(table_shape_norm))
+ trunc_normal_(self.key_norm_table, std=0.02)
+ self.value_norm_table = nn.Parameter(torch.zeros(table_shape_norm))
+ trunc_normal_(self.value_norm_table, std=0.02)
+ table_offsets += [np.prod(table_shape_norm[1:])] * 3
+
+ self.table_offsets = table_offsets
+
+ self.quant_size = quant_size
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop, inplace=True)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop, inplace=True)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, feats: torch.Tensor, attn_args):
+ """Forward function.
+
+ Args:
+ feats: N, C
+ attn_args: arguments for computing attention
+ """
+ num_v, _ = feats.shape
+ num_sc = self.dim // self.num_heads
+
+ (
+ x_offset,
+ y_offset,
+ m2w_indices,
+ w_sizes,
+ w2n_indices,
+ n2n_indices,
+ w2m_indices,
+ n_coords,
+ ) = attn_args
+
+ # Query, Key, Value
+ qkv = self.qkv(feats)
+ qkv = (
+ qkv.reshape(num_v, 3, self.num_heads, num_sc)
+ .permute(1, 0, 2, 3)
+ .contiguous()
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2] # [N, num_heads, C//num_heads]
+ query = query * self.scale
+
+ table_offsets = torch.IntTensor(self.table_offsets).cuda()
+ query_table, key_table, value_table = [], [], []
+ n_cRSE = []
+ if "XYZ" in self.cRSE:
+ n_xyz = n_coords[:, 0:3]
+ n_xyz = n_xyz * self.quant_size
+ n_cRSE.append(n_xyz)
+ query_table.append(self.query_xyz_table.view(-1))
+ key_table.append(self.key_xyz_table.view(-1))
+ value_table.append(self.value_xyz_table.view(-1))
+ if "RGB" in self.cRSE:
+ n_rgb = n_coords[:, 3:6]
+ n_rgb = n_rgb * self.color_quant_size
+ n_cRSE.append(n_rgb)
+ query_table.append(self.query_rgb_table.view(-1))
+ key_table.append(self.key_rgb_table.view(-1))
+ value_table.append(self.value_rgb_table.view(-1))
+ if "NORM" in self.cRSE:
+ n_norm = n_coords[:, 6:9]
+ n_norm = n_norm * self.normal_quant_size
+ n_cRSE.append(n_norm)
+ query_table.append(self.query_norm_table.view(-1))
+ key_table.append(self.key_norm_table.view(-1))
+ value_table.append(self.value_norm_table.view(-1))
+
+ n_cRSE = torch.cat(n_cRSE, dim=1)
+
+ indices = [m2w_indices, w_sizes, w2m_indices, w2n_indices, n2n_indices, n_cRSE]
+ query_table = torch.cat(query_table)
+ key_table = torch.cat(key_table)
+ value_table = torch.cat(value_table)
+
+ if self.fp16_mode == 0:
+ # do not use fp16
+ # cast q,k,v to fp32 in forward and backward
+ fp16_mode = PrecisionMode.HALF_NONE
+ elif self.fp16_mode == 1:
+ # use fp16 only in forward
+ fp16_mode = PrecisionMode.HALF_FORWARD
+ elif self.fp16_mode == 2:
+ # use fp16 both in forward and backward
+ fp16_mode = PrecisionMode.HALF_ALL
+
+ updated_values = SelfAttnAIOFunction.apply(
+ query,
+ key,
+ value,
+ query_table,
+ key_table,
+ value_table,
+ table_offsets,
+ indices,
+ PosEmb.SEPARATE,
+ TableDims.D0,
+ IndexMode.INDIRECT,
+ fp16_mode,
+ )
+
+ updated_values = updated_values.flatten(1)
+ updated_feats = updated_values.view(num_v, self.dim)
+
+ updated_feats = self.proj(updated_feats)
+ updated_feats = self.proj_drop(updated_feats) # [N, C]
+
+ return updated_feats
+
+
+class SwinTransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ num_heads,
+ window_size,
+ quant_size,
+ drop_path=0.0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ act_layer=nn.GELU,
+ norm_layer=nn.LayerNorm,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.window_size = window_size
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim,
+ window_size=self.window_size,
+ quant_size=quant_size,
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(
+ in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer
+ )
+
+ def forward(self, feats, attn_args):
+ # feats: [N, c]
+ short_cut = feats
+ feats = self.norm1(feats)
+ feats = self.attn(feats, attn_args) # [N, c]
+
+ feats = short_cut + self.drop_path(feats)
+ feats = feats + self.drop_path(self.mlp(self.norm2(feats)))
+
+ return feats
+
+
+class BasicLayer(nn.Module):
+ """A basic Swin3D layer for one stage.
+
+ Args:
+ dim (int): Number of input channels.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ quant_size (int): quant_size for for finer cRSE table
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ cRSE (str | 'XYZ', 'XYZ_RGB', 'XYZ_RGB_NORM'): cRSE mode. Default: 'XYZ_RGB'
+ fp16_mode (int | 0, 1, 2): fp16 mode for attention module, Default: 0
+ 0: fp32 forward and fp32 backward
+ 1: fp16 forward and fp32 backward
+ 2: fp16 forward and fp16 backward
+ """
+
+ def __init__(
+ self,
+ dim,
+ depth,
+ num_heads,
+ window_size,
+ quant_size,
+ out_channels=None,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_path=0.0,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ down_stride=2,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ self.window_size = window_size
+ self.depth = depth
+ self.dim = dim
+ self.num_heads = num_heads
+ self.quant_size = quant_size
+ self.cRSE = cRSE
+ self.fp16_mode = fp16_mode
+
+ self.shift_size = window_size // 2
+ # build blocks
+ self.blocks = nn.ModuleList(
+ [
+ SwinTransformerBlock(
+ dim,
+ num_heads,
+ window_size,
+ quant_size,
+ drop_path=(
+ drop_path[i] if isinstance(drop_path, list) else drop_path
+ ),
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ norm_layer=norm_layer,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ self.pool = ME.MinkowskiMaxPooling(
+ kernel_size=self.window_size, stride=self.window_size, dimension=3
+ )
+
+ if downsample is not None:
+ if out_channels is None:
+ out_channels = dim * 2
+ self.downsample = downsample(
+ dim, out_channels, kernel_size=down_stride, stride=down_stride
+ )
+ else:
+ self.downsample = None
+
+ def get_map_pair(self, sp):
+ """
+ use minkowski pool to calculate windows
+ get the mapping from voxel to window
+ """
+ window_size = [self.window_size] * 3
+ pool_sp = self.pool(sp)
+ windows = pool_sp.C
+ window_N = windows.shape[0]
+
+ stride_in = sp.coordinate_map_key.get_tensor_stride()
+ x, y, z = [
+ torch.arange(window_size[i], device=self.device) * stride_in[i]
+ for i in range(3)
+ ]
+ x, y, z = torch.meshgrid(x, y, z)
+ i = torch.zeros_like(x, device=self.device)
+ local_window = torch.stack([i, x, y, z], dim=-1).flatten(0, -2)
+ all_windows = windows.unsqueeze(1) + local_window.unsqueeze(0)
+ all_windows = all_windows.flatten(0, -2).int()
+ cm = sp.coordinate_manager
+ query_key, (map, inverse_map) = cm.insert_and_map(
+ all_windows, tensor_stride=stride_in
+ )
+ map_pair = cm.kernel_map(query_key, sp.coordinate_map_key, kernel_size=1)[0]
+ return map_pair, window_N
+
+ def get_window_mapping(self, sp):
+ """
+ calculate the relationshape in the window:
+ w_w_id: non-empty idx inside the window(sorted by window)
+ w_w_xyz: xyz inside the window(sorted by window)
+ nempty_num: non-empty voxel number in each window
+ sort_idx: sort voxel according to window_id, to gather the point inside the same window
+ inv_sort_idx: inverse sort index
+ """
+ map_pair, window_N = self.get_map_pair(sp)
+ window_size = self.window_size
+ nW = window_size**3
+ in_map, out_map = map_pair
+ in_map, sort_idx = torch.sort(in_map)
+ # assert out_map == arange(out_map.shape[0])
+ out_map = out_map[sort_idx]
+ sort_idx = out_map.long()
+ inv_sort_idx = torch.zeros_like(sort_idx)
+ inv_sort_idx[sort_idx] = torch.arange(
+ sort_idx.shape[0], dtype=sort_idx.dtype, device=self.device
+ )
+ N = window_N * nW
+ v2w_mask = torch.zeros(N, dtype=torch.bool, device=self.device)
+ w_id = (
+ torch.arange(window_N, dtype=torch.long, device=self.device)
+ .unsqueeze(1)
+ .repeat(1, nW)
+ .view(-1)
+ )
+ w_w_id = (
+ torch.arange(nW, dtype=torch.long, device=self.device)
+ .unsqueeze(0)
+ .repeat(window_N, 1)
+ .view(-1)
+ )
+ v2w_mask[in_map.long()] = True
+ nempty_num = v2w_mask.view(-1, nW).sum(dim=-1)
+ w_id = w_id[in_map.long()]
+ w_w_id = w_w_id[in_map.long()]
+ w_w_xyz = torch.stack(
+ [
+ w_w_id // window_size // window_size,
+ w_w_id // window_size % window_size,
+ w_w_id % window_size,
+ ],
+ dim=-1,
+ )
+ return w_w_id, w_w_xyz, nempty_num, sort_idx, inv_sort_idx
+
+ def get_index01(self, sp, local_xyz, colors):
+ """
+ calculate the arguments for sparse attention
+ """
+ (
+ w_w_id,
+ w_w_xyz,
+ nempty_num,
+ n2n_indices,
+ inv_sort_idx,
+ ) = self.get_window_mapping(sp)
+ local_xyz = local_xyz[n2n_indices]
+ colors = colors[n2n_indices]
+ # recover the relative pos in the voxel
+ n_coords = w_w_xyz + local_xyz
+ n_coords = torch.cat([n_coords, colors], dim=1)
+ (
+ x_offset,
+ y_offset,
+ m2w_indices,
+ w_sizes,
+ w2n_indices,
+ w2m_indices,
+ ) = sparse_self_attention(w_w_id, nempty_num, protocol="v2")
+ return (
+ x_offset,
+ y_offset,
+ m2w_indices,
+ w_sizes,
+ w2n_indices,
+ n2n_indices,
+ w2m_indices,
+ n_coords,
+ )
+
+ def get_shifted_sp(self, sp):
+ """
+ get the shifted sparse tensor for shift-window
+ """
+ stride_in = sp.coordinate_map_key.get_tensor_stride()
+ shift_size = self.shift_size * stride_in[0]
+ shifted_C = sp.C.clone()
+ shifted_C[:, 1:] += shift_size
+ shifted_sp = SparseTensor(
+ features=sp.F,
+ coordinates=shifted_C,
+ device=self.device,
+ tensor_stride=stride_in,
+ )
+ return shifted_sp
+
+ def get_window_pos(self, sp):
+ stride_in = sp.coordinate_map_key.get_tensor_stride()
+ return (sp.C[:, 1:] / stride_in[0]) % self.window_size
+
+ def forward(self, sp, coords_sp):
+ """
+ xyz: position of point inside voxel
+ colors: other signal for cRSE, include colors and normals
+ local_xyz: relative position of point indide voxel(using for finer cRSE table)
+ """
+ colors = coords_sp.F[:, 4:]
+ xyz = coords_sp.F[:, :4]
+ local_xyz = (xyz - coords_sp.C)[
+ :, 1:
+ ] / coords_sp.coordinate_map_key.get_tensor_stride()[0]
+ self.device = sp.device
+ sp_shift = self.get_shifted_sp(sp)
+
+ attn_args = self.get_index01(sp, local_xyz, colors)
+ attn_args_shift = self.get_index01(sp_shift, local_xyz, colors)
+
+ feats = sp.F
+ for i, blk in enumerate(self.blocks):
+ attn_args_blk = attn_args if i % 2 == 0 else attn_args_shift
+ feats = blk(feats, attn_args_blk) # [N, C]
+
+ sp = assign_feats(sp, feats)
+ if self.downsample is not None:
+ sp_down, coords_sp = self.downsample(sp, coords_sp)
+ return sp, sp_down, coords_sp
+ else:
+ return sp, sp, coords_sp
+
+ def extra_repr(self) -> str:
+ return f"window_size={self.window_size}, depth={self.depth}, channel={self.dim}, num_heads={self.num_heads}, quant_size={self.quant_size}, cRSE={self.cRSE}, fp16_mode={self.fp16_mode}"
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/swin3d_v1m1_base.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/swin3d_v1m1_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..1295e5d791e8ac33d3d4c43be03d4f08ade1345f
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/swin3d/swin3d_v1m1_base.py
@@ -0,0 +1,190 @@
+import torch
+import torch.nn as nn
+import MinkowskiEngine as ME
+from MinkowskiEngine import SparseTensor
+from timm.models.layers import trunc_normal_
+
+from .mink_layers import MinkConvBNRelu, MinkResBlock
+from .swin3d_layers import GridDownsample, GridKNNDownsample, BasicLayer, Upsample
+from pointcept.models.builder import MODELS
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+@MODELS.register_module("Swin3D-v1m1")
+class Swin3DUNet(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ num_classes,
+ base_grid_size,
+ depths,
+ channels,
+ num_heads,
+ window_sizes,
+ quant_size,
+ drop_path_rate=0.2,
+ up_k=3,
+ num_layers=5,
+ stem_transformer=True,
+ down_stride=2,
+ upsample="linear",
+ knn_down=True,
+ cRSE="XYZ_RGB",
+ fp16_mode=0,
+ ):
+ super().__init__()
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+ ] # stochastic depth decay rule
+ if knn_down:
+ downsample = GridKNNDownsample
+ else:
+ downsample = GridDownsample
+
+ self.cRSE = cRSE
+ if stem_transformer:
+ self.stem_layer = MinkConvBNRelu(
+ in_channels=in_channels,
+ out_channels=channels[0],
+ kernel_size=3,
+ stride=1,
+ )
+ self.layer_start = 0
+ else:
+ self.stem_layer = nn.Sequential(
+ MinkConvBNRelu(
+ in_channels=in_channels,
+ out_channels=channels[0],
+ kernel_size=3,
+ stride=1,
+ ),
+ MinkResBlock(in_channels=channels[0], out_channels=channels[0]),
+ )
+ self.downsample = downsample(
+ channels[0], channels[1], kernel_size=down_stride, stride=down_stride
+ )
+ self.layer_start = 1
+ self.layers = nn.ModuleList(
+ [
+ BasicLayer(
+ dim=channels[i],
+ depth=depths[i],
+ num_heads=num_heads[i],
+ window_size=window_sizes[i],
+ quant_size=quant_size,
+ drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
+ downsample=downsample if i < num_layers - 1 else None,
+ down_stride=down_stride if i == 0 else 2,
+ out_channels=channels[i + 1] if i < num_layers - 1 else None,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+ for i in range(self.layer_start, num_layers)
+ ]
+ )
+
+ if "attn" in upsample:
+ up_attn = True
+ else:
+ up_attn = False
+
+ self.upsamples = nn.ModuleList(
+ [
+ Upsample(
+ channels[i],
+ channels[i - 1],
+ num_heads[i - 1],
+ window_sizes[i - 1],
+ quant_size,
+ attn=up_attn,
+ up_k=up_k,
+ cRSE=cRSE,
+ fp16_mode=fp16_mode,
+ )
+ for i in range(num_layers - 1, 0, -1)
+ ]
+ )
+
+ self.classifier = nn.Sequential(
+ nn.Linear(channels[0], channels[0]),
+ nn.BatchNorm1d(channels[0]),
+ nn.ReLU(inplace=True),
+ nn.Linear(channels[0], num_classes),
+ )
+ self.num_classes = num_classes
+ self.base_grid_size = base_grid_size
+ self.init_weights()
+
+ def forward(self, data_dict):
+ grid_coord = data_dict["grid_coord"]
+ feat = data_dict["feat"]
+ coord_feat = data_dict["coord_feat"]
+ coord = data_dict["coord"]
+ offset = data_dict["offset"]
+ batch = offset2batch(offset)
+ in_field = ME.TensorField(
+ features=torch.cat(
+ [
+ batch.unsqueeze(-1),
+ coord / self.base_grid_size,
+ coord_feat / 1.001,
+ feat,
+ ],
+ dim=1,
+ ),
+ coordinates=torch.cat([batch.unsqueeze(-1).int(), grid_coord.int()], dim=1),
+ quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
+ minkowski_algorithm=ME.MinkowskiAlgorithm.SPEED_OPTIMIZED,
+ device=feat.device,
+ )
+
+ sp = in_field.sparse()
+ coords_sp = SparseTensor(
+ features=sp.F[:, : coord_feat.shape[-1] + 4],
+ coordinate_map_key=sp.coordinate_map_key,
+ coordinate_manager=sp.coordinate_manager,
+ )
+ sp = SparseTensor(
+ features=sp.F[:, coord_feat.shape[-1] + 4 :],
+ coordinate_map_key=sp.coordinate_map_key,
+ coordinate_manager=sp.coordinate_manager,
+ )
+ sp_stack = []
+ coords_sp_stack = []
+ sp = self.stem_layer(sp)
+ if self.layer_start > 0:
+ sp_stack.append(sp)
+ coords_sp_stack.append(coords_sp)
+ sp, coords_sp = self.downsample(sp, coords_sp)
+
+ for i, layer in enumerate(self.layers):
+ coords_sp_stack.append(coords_sp)
+ sp, sp_down, coords_sp = layer(sp, coords_sp)
+ sp_stack.append(sp)
+ assert (coords_sp.C == sp_down.C).all()
+ sp = sp_down
+
+ sp = sp_stack.pop()
+ coords_sp = coords_sp_stack.pop()
+ for i, upsample in enumerate(self.upsamples):
+ sp_i = sp_stack.pop()
+ coords_sp_i = coords_sp_stack.pop()
+ sp = upsample(sp, coords_sp, sp_i, coords_sp_i)
+ coords_sp = coords_sp_i
+
+ output = self.classifier(sp.slice(in_field).F)
+ return output
+
+ def init_weights(self):
+ """Initialize the weights in backbone."""
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ self.apply(_init_weights)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..66e6bc0f62993abb3625a9598f54e7775aeb0008
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/__init__.py
@@ -0,0 +1,4 @@
+from .misc import offset2batch, offset2bincount, batch2offset, off_diagonal
+from .checkpoint import checkpoint
+from .serialization import encode, decode
+from .structure import Point
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/checkpoint.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..58820352bd5d1b37b3905b038816323253ffd3de
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/checkpoint.py
@@ -0,0 +1,57 @@
+"""
+Checkpoint Utils for Models
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+
+
+class CheckpointFunction(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+
+ with torch.no_grad():
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ with torch.enable_grad():
+ # Fixes a bug where the first op in run_function modifies the
+ # Tensor storage in place, which is not allowed for detach()'d
+ # Tensors.
+ shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+ input_grads = torch.autograd.grad(
+ output_tensors,
+ ctx.input_tensors + ctx.input_params,
+ output_grads,
+ allow_unused=True,
+ )
+ del ctx.input_tensors
+ del ctx.input_params
+ del output_tensors
+ return (None, None) + input_grads
+
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ args = tuple(inputs) + tuple(params)
+ return CheckpointFunction.apply(func, len(inputs), *args)
+ else:
+ return func(*inputs)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/misc.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..61dfdfb44a82fc0ef585ca5732518fe85e466889
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/misc.py
@@ -0,0 +1,35 @@
+"""
+General Utils for Models
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+
+
+@torch.inference_mode()
+def offset2bincount(offset):
+ return torch.diff(
+ offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
+ )
+
+
+@torch.inference_mode()
+def offset2batch(offset):
+ bincount = offset2bincount(offset)
+ return torch.arange(
+ len(bincount), device=offset.device, dtype=torch.long
+ ).repeat_interleave(bincount)
+
+
+@torch.inference_mode()
+def batch2offset(batch):
+ return torch.cumsum(batch.bincount(), dim=0).long()
+
+
+def off_diagonal(x):
+ # return a flattened view of the off-diagonal elements of a square matrix
+ n, m = x.shape
+ assert n == m
+ return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..058c5e1001c76d9c7014bf0bbb824eec4f54f476
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/__init__.py
@@ -0,0 +1,8 @@
+from .default import (
+ encode,
+ decode,
+ z_order_encode,
+ z_order_decode,
+ hilbert_encode,
+ hilbert_decode,
+)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/default.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..15898b55625fc0e1125db9b713e900892f04176c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/default.py
@@ -0,0 +1,59 @@
+import torch
+from .z_order import xyz2key as z_order_encode_
+from .z_order import key2xyz as z_order_decode_
+from .hilbert import encode as hilbert_encode_
+from .hilbert import decode as hilbert_decode_
+
+
+@torch.inference_mode()
+def encode(grid_coord, batch=None, depth=16, order="z"):
+ assert order in {"z", "z-trans", "hilbert", "hilbert-trans"}
+ if order == "z":
+ code = z_order_encode(grid_coord, depth=depth)
+ elif order == "z-trans":
+ code = z_order_encode(grid_coord[:, [1, 0, 2]], depth=depth)
+ elif order == "hilbert":
+ code = hilbert_encode(grid_coord, depth=depth)
+ elif order == "hilbert-trans":
+ code = hilbert_encode(grid_coord[:, [1, 0, 2]], depth=depth)
+ else:
+ raise NotImplementedError
+ if batch is not None:
+ batch = batch.long()
+ code = batch << depth * 3 | code
+ return code
+
+
+@torch.inference_mode()
+def decode(code, depth=16, order="z"):
+ assert order in {"z", "hilbert"}
+ batch = code >> depth * 3
+ code = code & ((1 << depth * 3) - 1)
+ if order == "z":
+ grid_coord = z_order_decode(code, depth=depth)
+ elif order == "hilbert":
+ grid_coord = hilbert_decode(code, depth=depth)
+ else:
+ raise NotImplementedError
+ return grid_coord, batch
+
+
+def z_order_encode(grid_coord: torch.Tensor, depth: int = 16):
+ x, y, z = grid_coord[:, 0].long(), grid_coord[:, 1].long(), grid_coord[:, 2].long()
+ # we block the support to batch, maintain batched code in Point class
+ code = z_order_encode_(x, y, z, b=None, depth=depth)
+ return code
+
+
+def z_order_decode(code: torch.Tensor, depth):
+ x, y, z = z_order_decode_(code, depth=depth)
+ grid_coord = torch.stack([x, y, z], dim=-1) # (N, 3)
+ return grid_coord
+
+
+def hilbert_encode(grid_coord: torch.Tensor, depth: int = 16):
+ return hilbert_encode_(grid_coord, num_dims=3, num_bits=depth)
+
+
+def hilbert_decode(code: torch.Tensor, depth: int = 16):
+ return hilbert_decode_(code, num_dims=3, num_bits=depth)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/hilbert.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/hilbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..c96a3a9e15be64059811eb86139f28c6016ad0fe
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/hilbert.py
@@ -0,0 +1,303 @@
+"""
+Hilbert Order
+Modified from https://github.com/PrincetonLIPS/numpy-hilbert-curve
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Kaixin Xu
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+
+
+def right_shift(binary, k=1, axis=-1):
+ """Right shift an array of binary values.
+
+ Parameters:
+ -----------
+ binary: An ndarray of binary values.
+
+ k: The number of bits to shift. Default 1.
+
+ axis: The axis along which to shift. Default -1.
+
+ Returns:
+ --------
+ Returns an ndarray with zero prepended and the ends truncated, along
+ whatever axis was specified."""
+
+ # If we're shifting the whole thing, just return zeros.
+ if binary.shape[axis] <= k:
+ return torch.zeros_like(binary)
+
+ # Determine the padding pattern.
+ # padding = [(0,0)] * len(binary.shape)
+ # padding[axis] = (k,0)
+
+ # Determine the slicing pattern to eliminate just the last one.
+ slicing = [slice(None)] * len(binary.shape)
+ slicing[axis] = slice(None, -k)
+ shifted = torch.nn.functional.pad(
+ binary[tuple(slicing)], (k, 0), mode="constant", value=0
+ )
+
+ return shifted
+
+
+def binary2gray(binary, axis=-1):
+ """Convert an array of binary values into Gray codes.
+
+ This uses the classic X ^ (X >> 1) trick to compute the Gray code.
+
+ Parameters:
+ -----------
+ binary: An ndarray of binary values.
+
+ axis: The axis along which to compute the gray code. Default=-1.
+
+ Returns:
+ --------
+ Returns an ndarray of Gray codes.
+ """
+ shifted = right_shift(binary, axis=axis)
+
+ # Do the X ^ (X >> 1) trick.
+ gray = torch.logical_xor(binary, shifted)
+
+ return gray
+
+
+def gray2binary(gray, axis=-1):
+ """Convert an array of Gray codes back into binary values.
+
+ Parameters:
+ -----------
+ gray: An ndarray of gray codes.
+
+ axis: The axis along which to perform Gray decoding. Default=-1.
+
+ Returns:
+ --------
+ Returns an ndarray of binary values.
+ """
+
+ # Loop the log2(bits) number of times necessary, with shift and xor.
+ shift = 2 ** (torch.Tensor([gray.shape[axis]]).log2().ceil().int() - 1)
+ while shift > 0:
+ gray = torch.logical_xor(gray, right_shift(gray, shift))
+ shift = torch.div(shift, 2, rounding_mode="floor")
+ return gray
+
+
+def encode(locs, num_dims, num_bits):
+ """Decode an array of locations in a hypercube into a Hilbert integer.
+
+ This is a vectorized-ish version of the Hilbert curve implementation by John
+ Skilling as described in:
+
+ Skilling, J. (2004, April). Programming the Hilbert curve. In AIP Conference
+ Proceedings (Vol. 707, No. 1, pp. 381-387). American Institute of Physics.
+
+ Params:
+ -------
+ locs - An ndarray of locations in a hypercube of num_dims dimensions, in
+ which each dimension runs from 0 to 2**num_bits-1. The shape can
+ be arbitrary, as long as the last dimension of the same has size
+ num_dims.
+
+ num_dims - The dimensionality of the hypercube. Integer.
+
+ num_bits - The number of bits for each dimension. Integer.
+
+ Returns:
+ --------
+ The output is an ndarray of uint64 integers with the same shape as the
+ input, excluding the last dimension, which needs to be num_dims.
+ """
+
+ # Keep around the original shape for later.
+ orig_shape = locs.shape
+ bitpack_mask = 1 << torch.arange(0, 8).to(locs.device)
+ bitpack_mask_rev = bitpack_mask.flip(-1)
+
+ if orig_shape[-1] != num_dims:
+ raise ValueError(
+ """
+ The shape of locs was surprising in that the last dimension was of size
+ %d, but num_dims=%d. These need to be equal.
+ """
+ % (orig_shape[-1], num_dims)
+ )
+
+ if num_dims * num_bits > 63:
+ raise ValueError(
+ """
+ num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
+ into a int64. Are you sure you need that many points on your Hilbert
+ curve?
+ """
+ % (num_dims, num_bits, num_dims * num_bits)
+ )
+
+ # Treat the location integers as 64-bit unsigned and then split them up into
+ # a sequence of uint8s. Preserve the association by dimension.
+ locs_uint8 = locs.long().view(torch.uint8).reshape((-1, num_dims, 8)).flip(-1)
+
+ # Now turn these into bits and truncate to num_bits.
+ gray = (
+ locs_uint8.unsqueeze(-1)
+ .bitwise_and(bitpack_mask_rev)
+ .ne(0)
+ .byte()
+ .flatten(-2, -1)[..., -num_bits:]
+ )
+
+ # Run the decoding process the other way.
+ # Iterate forwards through the bits.
+ for bit in range(0, num_bits):
+ # Iterate forwards through the dimensions.
+ for dim in range(0, num_dims):
+ # Identify which ones have this bit active.
+ mask = gray[:, dim, bit]
+
+ # Where this bit is on, invert the 0 dimension for lower bits.
+ gray[:, 0, bit + 1 :] = torch.logical_xor(
+ gray[:, 0, bit + 1 :], mask[:, None]
+ )
+
+ # Where the bit is off, exchange the lower bits with the 0 dimension.
+ to_flip = torch.logical_and(
+ torch.logical_not(mask[:, None]).repeat(1, gray.shape[2] - bit - 1),
+ torch.logical_xor(gray[:, 0, bit + 1 :], gray[:, dim, bit + 1 :]),
+ )
+ gray[:, dim, bit + 1 :] = torch.logical_xor(
+ gray[:, dim, bit + 1 :], to_flip
+ )
+ gray[:, 0, bit + 1 :] = torch.logical_xor(gray[:, 0, bit + 1 :], to_flip)
+
+ # Now flatten out.
+ gray = gray.swapaxes(1, 2).reshape((-1, num_bits * num_dims))
+
+ # Convert Gray back to binary.
+ hh_bin = gray2binary(gray)
+
+ # Pad back out to 64 bits.
+ extra_dims = 64 - num_bits * num_dims
+ padded = torch.nn.functional.pad(hh_bin, (extra_dims, 0), "constant", 0)
+
+ # Convert binary values into uint8s.
+ hh_uint8 = (
+ (padded.flip(-1).reshape((-1, 8, 8)) * bitpack_mask)
+ .sum(2)
+ .squeeze()
+ .type(torch.uint8)
+ )
+
+ # Convert uint8s into uint64s.
+ hh_uint64 = hh_uint8.view(torch.int64).squeeze()
+
+ return hh_uint64
+
+
+def decode(hilberts, num_dims, num_bits):
+ """Decode an array of Hilbert integers into locations in a hypercube.
+
+ This is a vectorized-ish version of the Hilbert curve implementation by John
+ Skilling as described in:
+
+ Skilling, J. (2004, April). Programming the Hilbert curve. In AIP Conference
+ Proceedings (Vol. 707, No. 1, pp. 381-387). American Institute of Physics.
+
+ Params:
+ -------
+ hilberts - An ndarray of Hilbert integers. Must be an integer dtype and
+ cannot have fewer bits than num_dims * num_bits.
+
+ num_dims - The dimensionality of the hypercube. Integer.
+
+ num_bits - The number of bits for each dimension. Integer.
+
+ Returns:
+ --------
+ The output is an ndarray of unsigned integers with the same shape as hilberts
+ but with an additional dimension of size num_dims.
+ """
+
+ if num_dims * num_bits > 64:
+ raise ValueError(
+ """
+ num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
+ into a uint64. Are you sure you need that many points on your Hilbert
+ curve?
+ """
+ % (num_dims, num_bits)
+ )
+
+ # Handle the case where we got handed a naked integer.
+ hilberts = torch.atleast_1d(hilberts)
+
+ # Keep around the shape for later.
+ orig_shape = hilberts.shape
+ bitpack_mask = 2 ** torch.arange(0, 8).to(hilberts.device)
+ bitpack_mask_rev = bitpack_mask.flip(-1)
+
+ # Treat each of the hilberts as a s equence of eight uint8.
+ # This treats all of the inputs as uint64 and makes things uniform.
+ hh_uint8 = (
+ hilberts.ravel().type(torch.int64).view(torch.uint8).reshape((-1, 8)).flip(-1)
+ )
+
+ # Turn these lists of uints into lists of bits and then truncate to the size
+ # we actually need for using Skilling's procedure.
+ hh_bits = (
+ hh_uint8.unsqueeze(-1)
+ .bitwise_and(bitpack_mask_rev)
+ .ne(0)
+ .byte()
+ .flatten(-2, -1)[:, -num_dims * num_bits :]
+ )
+
+ # Take the sequence of bits and Gray-code it.
+ gray = binary2gray(hh_bits)
+
+ # There has got to be a better way to do this.
+ # I could index them differently, but the eventual packbits likes it this way.
+ gray = gray.reshape((-1, num_bits, num_dims)).swapaxes(1, 2)
+
+ # Iterate backwards through the bits.
+ for bit in range(num_bits - 1, -1, -1):
+ # Iterate backwards through the dimensions.
+ for dim in range(num_dims - 1, -1, -1):
+ # Identify which ones have this bit active.
+ mask = gray[:, dim, bit]
+
+ # Where this bit is on, invert the 0 dimension for lower bits.
+ gray[:, 0, bit + 1 :] = torch.logical_xor(
+ gray[:, 0, bit + 1 :], mask[:, None]
+ )
+
+ # Where the bit is off, exchange the lower bits with the 0 dimension.
+ to_flip = torch.logical_and(
+ torch.logical_not(mask[:, None]),
+ torch.logical_xor(gray[:, 0, bit + 1 :], gray[:, dim, bit + 1 :]),
+ )
+ gray[:, dim, bit + 1 :] = torch.logical_xor(
+ gray[:, dim, bit + 1 :], to_flip
+ )
+ gray[:, 0, bit + 1 :] = torch.logical_xor(gray[:, 0, bit + 1 :], to_flip)
+
+ # Pad back out to 64 bits.
+ extra_dims = 64 - num_bits
+ padded = torch.nn.functional.pad(gray, (extra_dims, 0), "constant", 0)
+
+ # Now chop these up into blocks of 8.
+ locs_chopped = padded.flip(-1).reshape((-1, num_dims, 8, 8))
+
+ # Take those blocks and turn them unto uint8s.
+ # from IPython import embed; embed()
+ locs_uint8 = (locs_chopped * bitpack_mask).sum(3).squeeze().type(torch.uint8)
+
+ # Finally, treat these as uint64s.
+ flat_locs = locs_uint8.view(torch.int64)
+
+ # Return them in the expected shape.
+ return flat_locs.reshape((*orig_shape, num_dims))
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/z_order.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/z_order.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fd01a5bcf4b6c76c5d75db4999326e174409ee3
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/serialization/z_order.py
@@ -0,0 +1,126 @@
+# --------------------------------------------------------
+# Octree-based Sparse Convolutional Neural Networks
+# Copyright (c) 2022 Peng-Shuai Wang
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Peng-Shuai Wang
+# --------------------------------------------------------
+
+import torch
+from typing import Optional, Union
+
+
+class KeyLUT:
+ def __init__(self):
+ r256 = torch.arange(256, dtype=torch.int64)
+ r512 = torch.arange(512, dtype=torch.int64)
+ zero = torch.zeros(256, dtype=torch.int64)
+ device = torch.device("cpu")
+
+ self._encode = {
+ device: (
+ self.xyz2key(r256, zero, zero, 8),
+ self.xyz2key(zero, r256, zero, 8),
+ self.xyz2key(zero, zero, r256, 8),
+ )
+ }
+ self._decode = {device: self.key2xyz(r512, 9)}
+
+ def encode_lut(self, device=torch.device("cpu")):
+ if device not in self._encode:
+ cpu = torch.device("cpu")
+ self._encode[device] = tuple(e.to(device) for e in self._encode[cpu])
+ return self._encode[device]
+
+ def decode_lut(self, device=torch.device("cpu")):
+ if device not in self._decode:
+ cpu = torch.device("cpu")
+ self._decode[device] = tuple(e.to(device) for e in self._decode[cpu])
+ return self._decode[device]
+
+ def xyz2key(self, x, y, z, depth):
+ key = torch.zeros_like(x)
+ for i in range(depth):
+ mask = 1 << i
+ key = (
+ key
+ | ((x & mask) << (2 * i + 2))
+ | ((y & mask) << (2 * i + 1))
+ | ((z & mask) << (2 * i + 0))
+ )
+ return key
+
+ def key2xyz(self, key, depth):
+ x = torch.zeros_like(key)
+ y = torch.zeros_like(key)
+ z = torch.zeros_like(key)
+ for i in range(depth):
+ x = x | ((key & (1 << (3 * i + 2))) >> (2 * i + 2))
+ y = y | ((key & (1 << (3 * i + 1))) >> (2 * i + 1))
+ z = z | ((key & (1 << (3 * i + 0))) >> (2 * i + 0))
+ return x, y, z
+
+
+_key_lut = KeyLUT()
+
+
+def xyz2key(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ z: torch.Tensor,
+ b: Optional[Union[torch.Tensor, int]] = None,
+ depth: int = 16,
+):
+ r"""Encodes :attr:`x`, :attr:`y`, :attr:`z` coordinates to the shuffled keys
+ based on pre-computed look up tables. The speed of this function is much
+ faster than the method based on for-loop.
+
+ Args:
+ x (torch.Tensor): The x coordinate.
+ y (torch.Tensor): The y coordinate.
+ z (torch.Tensor): The z coordinate.
+ b (torch.Tensor or int): The batch index of the coordinates, and should be
+ smaller than 32768. If :attr:`b` is :obj:`torch.Tensor`, the size of
+ :attr:`b` must be the same as :attr:`x`, :attr:`y`, and :attr:`z`.
+ depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
+ """
+
+ EX, EY, EZ = _key_lut.encode_lut(x.device)
+ x, y, z = x.long(), y.long(), z.long()
+
+ mask = 255 if depth > 8 else (1 << depth) - 1
+ key = EX[x & mask] | EY[y & mask] | EZ[z & mask]
+ if depth > 8:
+ mask = (1 << (depth - 8)) - 1
+ key16 = EX[(x >> 8) & mask] | EY[(y >> 8) & mask] | EZ[(z >> 8) & mask]
+ key = key16 << 24 | key
+
+ if b is not None:
+ b = b.long()
+ key = b << 48 | key
+
+ return key
+
+
+def key2xyz(key: torch.Tensor, depth: int = 16):
+ r"""Decodes the shuffled key to :attr:`x`, :attr:`y`, :attr:`z` coordinates
+ and the batch index based on pre-computed look up tables.
+
+ Args:
+ key (torch.Tensor): The shuffled key.
+ depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
+ """
+
+ DX, DY, DZ = _key_lut.decode_lut(key.device)
+ x, y, z = torch.zeros_like(key), torch.zeros_like(key), torch.zeros_like(key)
+
+ b = key >> 48
+ key = key & ((1 << 48) - 1)
+
+ n = (depth + 2) // 3
+ for i in range(n):
+ k = key >> (i * 9) & 511
+ x = x | (DX[k] << (i * 3))
+ y = y | (DY[k] << (i * 3))
+ z = z | (DZ[k] << (i * 3))
+
+ return x, y, z, b
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/structure.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/structure.py
new file mode 100644
index 0000000000000000000000000000000000000000..47fcd054067967f1ce5953d32df288ecc41c7aae
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/models/utils/structure.py
@@ -0,0 +1,180 @@
+import torch
+import spconv.pytorch as spconv
+
+try:
+ import ocnn
+except ImportError:
+ ocnn = None
+from addict import Dict
+
+from pointcept.models.utils.serialization import encode, decode
+from pointcept.models.utils import offset2batch, batch2offset
+
+
+class Point(Dict):
+ """
+ Point Structure of Pointcept
+
+ A Point (point cloud) in Pointcept is a dictionary that contains various properties of
+ a batched point cloud. The property with the following names have a specific definition
+ as follows:
+
+ - "coord": original coordinate of point cloud;
+ - "grid_coord": grid coordinate for specific grid size (related to GridSampling);
+ Point also support the following optional attributes:
+ - "offset": if not exist, initialized as batch size is 1;
+ - "batch": if not exist, initialized as batch size is 1;
+ - "feat": feature of point cloud, default input of model;
+ - "grid_size": Grid size of point cloud (related to GridSampling);
+ (related to Serialization)
+ - "serialized_depth": depth of serialization, 2 ** depth * grid_size describe the maximum of point cloud range;
+ - "serialized_code": a list of serialization codes;
+ - "serialized_order": a list of serialization order determined by code;
+ - "serialized_inverse": a list of inverse mapping determined by code;
+ (related to Sparsify: SpConv)
+ - "sparse_shape": Sparse shape for Sparse Conv Tensor;
+ - "sparse_conv_feat": SparseConvTensor init with information provide by Point;
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ # If one of "offset" or "batch" do not exist, generate by the existing one
+ if "batch" not in self.keys() and "offset" in self.keys():
+ self["batch"] = offset2batch(self.offset)
+ elif "offset" not in self.keys() and "batch" in self.keys():
+ self["offset"] = batch2offset(self.batch)
+
+ def serialization(self, order="z", depth=None, shuffle_orders=False):
+ """
+ Point Cloud Serialization
+
+ relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
+ """
+ assert "batch" in self.keys()
+ if "grid_coord" not in self.keys():
+ # if you don't want to operate GridSampling in data augmentation,
+ # please add the following augmentation into your pipline:
+ # dict(type="Copy", keys_dict={"grid_size": 0.01}),
+ # (adjust `grid_size` to what your want)
+ assert {"grid_size", "coord"}.issubset(self.keys())
+ self["grid_coord"] = torch.div(
+ self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
+ ).int()
+
+ if depth is None:
+ # Adaptive measure the depth of serialization cube (length = 2 ^ depth)
+ depth = int(self.grid_coord.max()).bit_length()
+ self["serialized_depth"] = depth
+ # Maximum bit length for serialization code is 63 (int64)
+ assert depth * 3 + len(self.offset).bit_length() <= 63
+ # Here we follow OCNN and set the depth limitation to 16 (48bit) for the point position.
+ # Although depth is limited to less than 16, we can encode a 655.36^3 (2^16 * 0.01) meter^3
+ # cube with a grid size of 0.01 meter. We consider it is enough for the current stage.
+ # We can unlock the limitation by optimizing the z-order encoding function if necessary.
+ assert depth <= 16
+
+ # The serialization codes are arranged as following structures:
+ # [Order1 ([n]),
+ # Order2 ([n]),
+ # ...
+ # OrderN ([n])] (k, n)
+ code = [
+ encode(self.grid_coord, self.batch, depth, order=order_) for order_ in order
+ ]
+ code = torch.stack(code)
+ order = torch.argsort(code)
+ inverse = torch.zeros_like(order).scatter_(
+ dim=1,
+ index=order,
+ src=torch.arange(0, code.shape[1], device=order.device).repeat(
+ code.shape[0], 1
+ ),
+ )
+
+ if shuffle_orders:
+ perm = torch.randperm(code.shape[0])
+ code = code[perm]
+ order = order[perm]
+ inverse = inverse[perm]
+
+ self["serialized_code"] = code
+ self["serialized_order"] = order
+ self["serialized_inverse"] = inverse
+
+ def sparsify(self, pad=96):
+ """
+ Point Cloud Serialization
+
+ Point cloud is sparse, here we use "sparsify" to specifically refer to
+ preparing "spconv.SparseConvTensor" for SpConv.
+
+ relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
+
+ pad: padding sparse for sparse shape.
+ """
+ assert {"feat", "batch"}.issubset(self.keys())
+ if "grid_coord" not in self.keys():
+ # if you don't want to operate GridSampling in data augmentation,
+ # please add the following augmentation into your pipline:
+ # dict(type="Copy", keys_dict={"grid_size": 0.01}),
+ # (adjust `grid_size` to what your want)
+ assert {"grid_size", "coord"}.issubset(self.keys())
+ self["grid_coord"] = torch.div(
+ self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
+ ).int()
+ if "sparse_shape" in self.keys():
+ sparse_shape = self.sparse_shape
+ else:
+ sparse_shape = torch.add(
+ torch.max(self.grid_coord, dim=0).values, pad
+ ).tolist()
+ sparse_conv_feat = spconv.SparseConvTensor(
+ features=self.feat,
+ indices=torch.cat(
+ [self.batch.unsqueeze(-1).int(), self.grid_coord.int()], dim=1
+ ).contiguous(),
+ spatial_shape=sparse_shape,
+ batch_size=self.batch[-1].tolist() + 1,
+ )
+ self["sparse_shape"] = sparse_shape
+ self["sparse_conv_feat"] = sparse_conv_feat
+
+ def octreetization(self, depth=None, full_depth=None):
+ """
+ Point Cloud Octreelization
+
+ Generate octree with OCNN
+ relay on ["grid_coord", "batch", "feat"]
+ """
+ assert (
+ ocnn is not None
+ ), "Please follow https://github.com/octree-nn/ocnn-pytorch install ocnn."
+ assert {"grid_coord", "feat", "batch"}.issubset(self.keys())
+ # add 1 to make grid space support shift order
+ if depth is None:
+ if "depth" in self.keys():
+ depth = self.depth
+ else:
+ depth = int(self.grid_coord.max() + 1).bit_length()
+ if full_depth is None:
+ full_depth = 2
+ self["depth"] = depth
+ assert depth <= 16 # maximum in ocnn
+
+ # [0, 2**depth] -> [0, 2] -> [-1, 1]
+ coord = self.grid_coord / 2 ** (self.depth - 1) - 1.0
+ point = ocnn.octree.Points(
+ points=coord,
+ features=self.feat,
+ batch_id=self.batch.unsqueeze(-1),
+ batch_size=self.batch[-1] + 1,
+ )
+ octree = ocnn.octree.Octree(
+ depth=depth,
+ full_depth=full_depth,
+ batch_size=self.batch[-1] + 1,
+ device=coord.device,
+ )
+ octree.build_octree(point)
+ octree.construct_all_neigh()
+ self["octree"] = octree
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/__init__.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/cache.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..623897e42a7a4256a65a1a0e9a7b5c0c46ce5a3e
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/cache.py
@@ -0,0 +1,56 @@
+"""
+Data Cache Utils
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import SharedArray
+
+try:
+ from multiprocessing.shared_memory import ShareableList
+except ImportError:
+ import warnings
+
+ warnings.warn("Please update python version >= 3.8 to enable shared_memory")
+import numpy as np
+
+
+def shared_array(name, var=None):
+ if var is not None:
+ # check exist
+ if os.path.exists(f"/dev/shm/{name}"):
+ return SharedArray.attach(f"shm://{name}")
+ # create shared_array
+ data = SharedArray.create(f"shm://{name}", var.shape, dtype=var.dtype)
+ data[...] = var[...]
+ data.flags.writeable = False
+ else:
+ data = SharedArray.attach(f"shm://{name}").copy()
+ return data
+
+
+def shared_dict(name, var=None):
+ name = str(name)
+ assert "." not in name # '.' is used as sep flag
+ data = {}
+ if var is not None:
+ assert isinstance(var, dict)
+ keys = var.keys()
+ # current version only cache np.array
+ keys_valid = []
+ for key in keys:
+ if isinstance(var[key], np.ndarray):
+ keys_valid.append(key)
+ keys = keys_valid
+
+ ShareableList(sequence=keys, name=name + ".keys")
+ for key in keys:
+ if isinstance(var[key], np.ndarray):
+ data[key] = shared_array(name=f"{name}.{key}", var=var[key])
+ else:
+ keys = list(ShareableList(name=name + ".keys"))
+ for key in keys:
+ data[key] = shared_array(name=f"{name}.{key}")
+ return data
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/comm.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/comm.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e29e7c690fe0500d3d9a84b6a8749e2f4f4655
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/comm.py
@@ -0,0 +1,198 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+This file contains primitives for multi-gpu communication.
+This is useful when doing distributed training.
+Modified from detectron2(https://github.com/facebookresearch/detectron2)
+
+Copyright (c) Xiaoyang Wu (xiaoyang.wu@connect.hku.hk). All Rights Reserved.
+Please cite our work if you use any part of the code.
+"""
+
+import functools
+import numpy as np
+import torch
+import torch.distributed as dist
+
+_LOCAL_PROCESS_GROUP = None
+"""
+A torch process group which only includes processes that on the same machine as the current process.
+This variable is set when processes are spawned by `launch()` in "engine/launch.py".
+"""
+
+
+def get_world_size() -> int:
+ if not dist.is_available():
+ return 1
+ if not dist.is_initialized():
+ return 1
+ return dist.get_world_size()
+
+
+def get_rank() -> int:
+ if not dist.is_available():
+ return 0
+ if not dist.is_initialized():
+ return 0
+ return dist.get_rank()
+
+
+def get_local_rank() -> int:
+ """
+ Returns:
+ The rank of the current process within the local (per-machine) process group.
+ """
+ if not dist.is_available():
+ return 0
+ if not dist.is_initialized():
+ return 0
+ assert (
+ _LOCAL_PROCESS_GROUP is not None
+ ), "Local process group is not created! Please use launch() to spawn processes!"
+ return dist.get_rank(group=_LOCAL_PROCESS_GROUP)
+
+
+def get_local_size() -> int:
+ """
+ Returns:
+ The size of the per-machine process group,
+ i.e. the number of processes per machine.
+ """
+ if not dist.is_available():
+ return 1
+ if not dist.is_initialized():
+ return 1
+ return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)
+
+
+def is_main_process() -> bool:
+ return get_rank() == 0
+
+
+def synchronize():
+ """
+ Helper function to synchronize (barrier) among all processes when
+ using distributed training
+ """
+ if not dist.is_available():
+ return
+ if not dist.is_initialized():
+ return
+ world_size = dist.get_world_size()
+ if world_size == 1:
+ return
+ if dist.get_backend() == dist.Backend.NCCL:
+ # This argument is needed to avoid warnings.
+ # It's valid only for NCCL backend.
+ dist.barrier(device_ids=[torch.cuda.current_device()])
+ else:
+ dist.barrier()
+
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+ """
+ Return a process group based on gloo backend, containing all the ranks
+ The result is cached.
+ """
+ if dist.get_backend() == "nccl":
+ return dist.new_group(backend="gloo")
+ else:
+ return dist.group.WORLD
+
+
+def all_gather(data, group=None):
+ """
+ Run all_gather on arbitrary picklable data (not necessarily tensors).
+ Args:
+ data: any picklable object
+ group: a torch process group. By default, will use a group which
+ contains all ranks on gloo backend.
+ Returns:
+ list[data]: list of data gathered from each rank
+ """
+ if get_world_size() == 1:
+ return [data]
+ if group is None:
+ group = (
+ _get_global_gloo_group()
+ ) # use CPU group by default, to reduce GPU RAM usage.
+ world_size = dist.get_world_size(group)
+ if world_size == 1:
+ return [data]
+
+ output = [None for _ in range(world_size)]
+ dist.all_gather_object(output, data, group=group)
+ return output
+
+
+def gather(data, dst=0, group=None):
+ """
+ Run gather on arbitrary picklable data (not necessarily tensors).
+ Args:
+ data: any picklable object
+ dst (int): destination rank
+ group: a torch process group. By default, will use a group which
+ contains all ranks on gloo backend.
+ Returns:
+ list[data]: on dst, a list of data gathered from each rank. Otherwise,
+ an empty list.
+ """
+ if get_world_size() == 1:
+ return [data]
+ if group is None:
+ group = _get_global_gloo_group()
+ world_size = dist.get_world_size(group=group)
+ if world_size == 1:
+ return [data]
+ rank = dist.get_rank(group=group)
+
+ if rank == dst:
+ output = [None for _ in range(world_size)]
+ dist.gather_object(data, output, dst=dst, group=group)
+ return output
+ else:
+ dist.gather_object(data, None, dst=dst, group=group)
+ return []
+
+
+def shared_random_seed():
+ """
+ Returns:
+ int: a random number that is the same across all workers.
+ If workers need a shared RNG, they can use this shared seed to
+ create one.
+ All workers must call this function, otherwise it will deadlock.
+ """
+ ints = np.random.randint(2**31)
+ all_ints = all_gather(ints)
+ return all_ints[0]
+
+
+def reduce_dict(input_dict, average=True):
+ """
+ Reduce the values in the dictionary from all processes so that process with rank
+ 0 has the reduced results.
+ Args:
+ input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor.
+ average (bool): whether to do average or sum
+ Returns:
+ a dict with the same keys as input_dict, after reduction.
+ """
+ world_size = get_world_size()
+ if world_size < 2:
+ return input_dict
+ with torch.no_grad():
+ names = []
+ values = []
+ # sort the keys so that they are consistent across processes
+ for k in sorted(input_dict.keys()):
+ names.append(k)
+ values.append(input_dict[k])
+ values = torch.stack(values, dim=0)
+ dist.reduce(values, dst=0)
+ if dist.get_rank() == 0 and average:
+ # only main process gets accumulated, so only divide by
+ # world_size in this case
+ values /= world_size
+ reduced_dict = {k: v for k, v in zip(names, values)}
+ return reduced_dict
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/config.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..316dd458b3760b38feeb33d941ad9ad060364a61
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/config.py
@@ -0,0 +1,694 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import ast
+import copy
+import os
+import os.path as osp
+import platform
+import shutil
+import sys
+import tempfile
+import uuid
+import warnings
+from argparse import Action, ArgumentParser
+from collections import abc
+from importlib import import_module
+
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+from .misc import import_modules_from_strings
+from .path import check_file_exist
+
+if platform.system() == "Windows":
+ import regex as re
+else:
+ import re
+
+BASE_KEY = "_base_"
+DELETE_KEY = "_delete_"
+DEPRECATION_KEY = "_deprecation_"
+RESERVED_KEYS = ["filename", "text", "pretty_text"]
+
+
+class ConfigDict(Dict):
+ def __missing__(self, name):
+ raise KeyError(name)
+
+ def __getattr__(self, name):
+ try:
+ value = super(ConfigDict, self).__getattr__(name)
+ except KeyError:
+ ex = AttributeError(
+ f"'{self.__class__.__name__}' object has no " f"attribute '{name}'"
+ )
+ except Exception as e:
+ ex = e
+ else:
+ return value
+ raise ex
+
+
+def add_args(parser, cfg, prefix=""):
+ for k, v in cfg.items():
+ if isinstance(v, str):
+ parser.add_argument("--" + prefix + k)
+ elif isinstance(v, int):
+ parser.add_argument("--" + prefix + k, type=int)
+ elif isinstance(v, float):
+ parser.add_argument("--" + prefix + k, type=float)
+ elif isinstance(v, bool):
+ parser.add_argument("--" + prefix + k, action="store_true")
+ elif isinstance(v, dict):
+ add_args(parser, v, prefix + k + ".")
+ elif isinstance(v, abc.Iterable):
+ parser.add_argument("--" + prefix + k, type=type(v[0]), nargs="+")
+ else:
+ print(f"cannot parse key {prefix + k} of type {type(v)}")
+ return parser
+
+
+class Config:
+ """A facility for config and config files.
+
+ It supports common file formats as configs: python/json/yaml. The interface
+ is the same as a dict object and also allows access config values as
+ attributes.
+
+ Example:
+ >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+ >>> cfg.a
+ 1
+ >>> cfg.b
+ {'b1': [0, 1]}
+ >>> cfg.b.b1
+ [0, 1]
+ >>> cfg = Config.fromfile('tests/data/config/a.py')
+ >>> cfg.filename
+ "/home/kchen/projects/mmcv/tests/data/config/a.py"
+ >>> cfg.item4
+ 'test'
+ >>> cfg
+ "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
+ "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
+ """
+
+ @staticmethod
+ def _validate_py_syntax(filename):
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ content = f.read()
+ try:
+ ast.parse(content)
+ except SyntaxError as e:
+ raise SyntaxError(
+ "There are syntax errors in config " f"file {filename}: {e}"
+ )
+
+ @staticmethod
+ def _substitute_predefined_vars(filename, temp_config_name):
+ file_dirname = osp.dirname(filename)
+ file_basename = osp.basename(filename)
+ file_basename_no_extension = osp.splitext(file_basename)[0]
+ file_extname = osp.splitext(filename)[1]
+ support_templates = dict(
+ fileDirname=file_dirname,
+ fileBasename=file_basename,
+ fileBasenameNoExtension=file_basename_no_extension,
+ fileExtname=file_extname,
+ )
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ config_file = f.read()
+ for key, value in support_templates.items():
+ regexp = r"\{\{\s*" + str(key) + r"\s*\}\}"
+ value = value.replace("\\", "/")
+ config_file = re.sub(regexp, value, config_file)
+ with open(temp_config_name, "w", encoding="utf-8") as tmp_config_file:
+ tmp_config_file.write(config_file)
+
+ @staticmethod
+ def _pre_substitute_base_vars(filename, temp_config_name):
+ """Substitute base variable placehoders to string, so that parsing
+ would work."""
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ config_file = f.read()
+ base_var_dict = {}
+ regexp = r"\{\{\s*" + BASE_KEY + r"\.([\w\.]+)\s*\}\}"
+ base_vars = set(re.findall(regexp, config_file))
+ for base_var in base_vars:
+ randstr = f"_{base_var}_{uuid.uuid4().hex.lower()[:6]}"
+ base_var_dict[randstr] = base_var
+ regexp = r"\{\{\s*" + BASE_KEY + r"\." + base_var + r"\s*\}\}"
+ config_file = re.sub(regexp, f'"{randstr}"', config_file)
+ with open(temp_config_name, "w", encoding="utf-8") as tmp_config_file:
+ tmp_config_file.write(config_file)
+ return base_var_dict
+
+ @staticmethod
+ def _substitute_base_vars(cfg, base_var_dict, base_cfg):
+ """Substitute variable strings to their actual values."""
+ cfg = copy.deepcopy(cfg)
+
+ if isinstance(cfg, dict):
+ for k, v in cfg.items():
+ if isinstance(v, str) and v in base_var_dict:
+ new_v = base_cfg
+ for new_k in base_var_dict[v].split("."):
+ new_v = new_v[new_k]
+ cfg[k] = new_v
+ elif isinstance(v, (list, tuple, dict)):
+ cfg[k] = Config._substitute_base_vars(v, base_var_dict, base_cfg)
+ elif isinstance(cfg, tuple):
+ cfg = tuple(
+ Config._substitute_base_vars(c, base_var_dict, base_cfg) for c in cfg
+ )
+ elif isinstance(cfg, list):
+ cfg = [
+ Config._substitute_base_vars(c, base_var_dict, base_cfg) for c in cfg
+ ]
+ elif isinstance(cfg, str) and cfg in base_var_dict:
+ new_v = base_cfg
+ for new_k in base_var_dict[cfg].split("."):
+ new_v = new_v[new_k]
+ cfg = new_v
+
+ return cfg
+
+ @staticmethod
+ def _file2dict(filename, use_predefined_variables=True):
+ filename = osp.abspath(osp.expanduser(filename))
+ check_file_exist(filename)
+ fileExtname = osp.splitext(filename)[1]
+ if fileExtname not in [".py", ".json", ".yaml", ".yml"]:
+ raise IOError("Only py/yml/yaml/json type are supported now!")
+
+ with tempfile.TemporaryDirectory() as temp_config_dir:
+ temp_config_file = tempfile.NamedTemporaryFile(
+ dir=temp_config_dir, suffix=fileExtname
+ )
+ if platform.system() == "Windows":
+ temp_config_file.close()
+ temp_config_name = osp.basename(temp_config_file.name)
+ # Substitute predefined variables
+ if use_predefined_variables:
+ Config._substitute_predefined_vars(filename, temp_config_file.name)
+ else:
+ shutil.copyfile(filename, temp_config_file.name)
+ # Substitute base variables from placeholders to strings
+ base_var_dict = Config._pre_substitute_base_vars(
+ temp_config_file.name, temp_config_file.name
+ )
+
+ if filename.endswith(".py"):
+ temp_module_name = osp.splitext(temp_config_name)[0]
+ sys.path.insert(0, temp_config_dir)
+ Config._validate_py_syntax(filename)
+ mod = import_module(temp_module_name)
+ sys.path.pop(0)
+ cfg_dict = {
+ name: value
+ for name, value in mod.__dict__.items()
+ if not name.startswith("__")
+ }
+ # delete imported module
+ del sys.modules[temp_module_name]
+ elif filename.endswith((".yml", ".yaml", ".json")):
+ raise NotImplementedError
+ # close temp file
+ temp_config_file.close()
+
+ # check deprecation information
+ if DEPRECATION_KEY in cfg_dict:
+ deprecation_info = cfg_dict.pop(DEPRECATION_KEY)
+ warning_msg = (
+ f"The config file {filename} will be deprecated " "in the future."
+ )
+ if "expected" in deprecation_info:
+ warning_msg += f' Please use {deprecation_info["expected"]} ' "instead."
+ if "reference" in deprecation_info:
+ warning_msg += (
+ " More information can be found at "
+ f'{deprecation_info["reference"]}'
+ )
+ warnings.warn(warning_msg)
+
+ cfg_text = filename + "\n"
+ with open(filename, "r", encoding="utf-8") as f:
+ # Setting encoding explicitly to resolve coding issue on windows
+ cfg_text += f.read()
+
+ if BASE_KEY in cfg_dict:
+ cfg_dir = osp.dirname(filename)
+ base_filename = cfg_dict.pop(BASE_KEY)
+ base_filename = (
+ base_filename if isinstance(base_filename, list) else [base_filename]
+ )
+
+ cfg_dict_list = list()
+ cfg_text_list = list()
+ for f in base_filename:
+ _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f))
+ cfg_dict_list.append(_cfg_dict)
+ cfg_text_list.append(_cfg_text)
+
+ base_cfg_dict = dict()
+ for c in cfg_dict_list:
+ duplicate_keys = base_cfg_dict.keys() & c.keys()
+ if len(duplicate_keys) > 0:
+ raise KeyError(
+ "Duplicate key is not allowed among bases. "
+ f"Duplicate keys: {duplicate_keys}"
+ )
+ base_cfg_dict.update(c)
+
+ # Substitute base variables from strings to their actual values
+ cfg_dict = Config._substitute_base_vars(
+ cfg_dict, base_var_dict, base_cfg_dict
+ )
+
+ base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
+ cfg_dict = base_cfg_dict
+
+ # merge cfg_text
+ cfg_text_list.append(cfg_text)
+ cfg_text = "\n".join(cfg_text_list)
+
+ return cfg_dict, cfg_text
+
+ @staticmethod
+ def _merge_a_into_b(a, b, allow_list_keys=False):
+ """merge dict ``a`` into dict ``b`` (non-inplace).
+
+ Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
+ in-place modifications.
+
+ Args:
+ a (dict): The source dict to be merged into ``b``.
+ b (dict): The origin dict to be fetch keys from ``a``.
+ allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
+ are allowed in source ``a`` and will replace the element of the
+ corresponding index in b if b is a list. Default: False.
+
+ Returns:
+ dict: The modified dict of ``b`` using ``a``.
+
+ Examples:
+ # Normally merge a into b.
+ >>> Config._merge_a_into_b(
+ ... dict(obj=dict(a=2)), dict(obj=dict(a=1)))
+ {'obj': {'a': 2}}
+
+ # Delete b first and merge a into b.
+ >>> Config._merge_a_into_b(
+ ... dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1)))
+ {'obj': {'a': 2}}
+
+ # b is a list
+ >>> Config._merge_a_into_b(
+ ... {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True)
+ [{'a': 2}, {'b': 2}]
+ """
+ b = b.copy()
+ for k, v in a.items():
+ if allow_list_keys and k.isdigit() and isinstance(b, list):
+ k = int(k)
+ if len(b) <= k:
+ raise KeyError(f"Index {k} exceeds the length of list {b}")
+ b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
+ elif isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
+ allowed_types = (dict, list) if allow_list_keys else dict
+ if not isinstance(b[k], allowed_types):
+ raise TypeError(
+ f"{k}={v} in child config cannot inherit from base "
+ f"because {k} is a dict in the child config but is of "
+ f"type {type(b[k])} in base config. You may set "
+ f"`{DELETE_KEY}=True` to ignore the base config"
+ )
+ b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
+ else:
+ b[k] = v
+ return b
+
+ @staticmethod
+ def fromfile(filename, use_predefined_variables=True, import_custom_modules=True):
+ cfg_dict, cfg_text = Config._file2dict(filename, use_predefined_variables)
+ if import_custom_modules and cfg_dict.get("custom_imports", None):
+ import_modules_from_strings(**cfg_dict["custom_imports"])
+ return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
+
+ @staticmethod
+ def fromstring(cfg_str, file_format):
+ """Generate config from config str.
+
+ Args:
+ cfg_str (str): Config str.
+ file_format (str): Config file format corresponding to the
+ config str. Only py/yml/yaml/json type are supported now!
+
+ Returns:
+ obj:`Config`: Config obj.
+ """
+ if file_format not in [".py", ".json", ".yaml", ".yml"]:
+ raise IOError("Only py/yml/yaml/json type are supported now!")
+ if file_format != ".py" and "dict(" in cfg_str:
+ # check if users specify a wrong suffix for python
+ warnings.warn('Please check "file_format", the file format may be .py')
+ with tempfile.NamedTemporaryFile(
+ "w", encoding="utf-8", suffix=file_format, delete=False
+ ) as temp_file:
+ temp_file.write(cfg_str)
+ # on windows, previous implementation cause error
+ # see PR 1077 for details
+ cfg = Config.fromfile(temp_file.name)
+ os.remove(temp_file.name)
+ return cfg
+
+ @staticmethod
+ def auto_argparser(description=None):
+ """Generate argparser from config file automatically (experimental)"""
+ partial_parser = ArgumentParser(description=description)
+ partial_parser.add_argument("config", help="config file path")
+ cfg_file = partial_parser.parse_known_args()[0].config
+ cfg = Config.fromfile(cfg_file)
+ parser = ArgumentParser(description=description)
+ parser.add_argument("config", help="config file path")
+ add_args(parser, cfg)
+ return parser, cfg
+
+ def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
+ if cfg_dict is None:
+ cfg_dict = dict()
+ elif not isinstance(cfg_dict, dict):
+ raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
+ for key in cfg_dict:
+ if key in RESERVED_KEYS:
+ raise KeyError(f"{key} is reserved for config file")
+
+ super(Config, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
+ super(Config, self).__setattr__("_filename", filename)
+ if cfg_text:
+ text = cfg_text
+ elif filename:
+ with open(filename, "r") as f:
+ text = f.read()
+ else:
+ text = ""
+ super(Config, self).__setattr__("_text", text)
+
+ @property
+ def filename(self):
+ return self._filename
+
+ @property
+ def text(self):
+ return self._text
+
+ @property
+ def pretty_text(self):
+ indent = 4
+
+ def _indent(s_, num_spaces):
+ s = s_.split("\n")
+ if len(s) == 1:
+ return s_
+ first = s.pop(0)
+ s = [(num_spaces * " ") + line for line in s]
+ s = "\n".join(s)
+ s = first + "\n" + s
+ return s
+
+ def _format_basic_types(k, v, use_mapping=False):
+ if isinstance(v, str):
+ v_str = f"'{v}'"
+ else:
+ v_str = str(v)
+
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: {v_str}"
+ else:
+ attr_str = f"{str(k)}={v_str}"
+ attr_str = _indent(attr_str, indent)
+
+ return attr_str
+
+ def _format_list(k, v, use_mapping=False):
+ # check if all items in the list are dict
+ if all(isinstance(_, dict) for _ in v):
+ v_str = "[\n"
+ v_str += "\n".join(
+ f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
+ ).rstrip(",")
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: {v_str}"
+ else:
+ attr_str = f"{str(k)}={v_str}"
+ attr_str = _indent(attr_str, indent) + "]"
+ else:
+ attr_str = _format_basic_types(k, v, use_mapping)
+ return attr_str
+
+ def _contain_invalid_identifier(dict_str):
+ contain_invalid_identifier = False
+ for key_name in dict_str:
+ contain_invalid_identifier |= not str(key_name).isidentifier()
+ return contain_invalid_identifier
+
+ def _format_dict(input_dict, outest_level=False):
+ r = ""
+ s = []
+
+ use_mapping = _contain_invalid_identifier(input_dict)
+ if use_mapping:
+ r += "{"
+ for idx, (k, v) in enumerate(input_dict.items()):
+ is_last = idx >= len(input_dict) - 1
+ end = "" if outest_level or is_last else ","
+ if isinstance(v, dict):
+ v_str = "\n" + _format_dict(v)
+ if use_mapping:
+ k_str = f"'{k}'" if isinstance(k, str) else str(k)
+ attr_str = f"{k_str}: dict({v_str}"
+ else:
+ attr_str = f"{str(k)}=dict({v_str}"
+ attr_str = _indent(attr_str, indent) + ")" + end
+ elif isinstance(v, list):
+ attr_str = _format_list(k, v, use_mapping) + end
+ else:
+ attr_str = _format_basic_types(k, v, use_mapping) + end
+
+ s.append(attr_str)
+ r += "\n".join(s)
+ if use_mapping:
+ r += "}"
+ return r
+
+ cfg_dict = self._cfg_dict.to_dict()
+ text = _format_dict(cfg_dict, outest_level=True)
+ # copied from setup.cfg
+ yapf_style = dict(
+ based_on_style="pep8",
+ blank_line_before_nested_class_or_def=True,
+ split_before_expression_after_opening_paren=True,
+ )
+ text, _ = FormatCode(text, style_config=yapf_style, verify=True)
+
+ return text
+
+ def __repr__(self):
+ return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
+
+ def __len__(self):
+ return len(self._cfg_dict)
+
+ def __getattr__(self, name):
+ return getattr(self._cfg_dict, name)
+
+ def __getitem__(self, name):
+ return self._cfg_dict.__getitem__(name)
+
+ def __setattr__(self, name, value):
+ if isinstance(value, dict):
+ value = ConfigDict(value)
+ self._cfg_dict.__setattr__(name, value)
+
+ def __setitem__(self, name, value):
+ if isinstance(value, dict):
+ value = ConfigDict(value)
+ self._cfg_dict.__setitem__(name, value)
+
+ def __iter__(self):
+ return iter(self._cfg_dict)
+
+ def __getstate__(self):
+ return (self._cfg_dict, self._filename, self._text)
+
+ def __setstate__(self, state):
+ _cfg_dict, _filename, _text = state
+ super(Config, self).__setattr__("_cfg_dict", _cfg_dict)
+ super(Config, self).__setattr__("_filename", _filename)
+ super(Config, self).__setattr__("_text", _text)
+
+ def dump(self, file=None):
+ cfg_dict = super(Config, self).__getattribute__("_cfg_dict").to_dict()
+ if self.filename.endswith(".py"):
+ if file is None:
+ return self.pretty_text
+ else:
+ with open(file, "w", encoding="utf-8") as f:
+ f.write(self.pretty_text)
+ else:
+ import mmcv
+
+ if file is None:
+ file_format = self.filename.split(".")[-1]
+ return mmcv.dump(cfg_dict, file_format=file_format)
+ else:
+ mmcv.dump(cfg_dict, file)
+
+ def merge_from_dict(self, options, allow_list_keys=True):
+ """Merge list into cfg_dict.
+
+ Merge the dict parsed by MultipleKVAction into this cfg.
+
+ Examples:
+ >>> options = {'models.backbone.depth': 50,
+ ... 'models.backbone.with_cp':True}
+ >>> cfg = Config(dict(models=dict(backbone=dict(type='ResNet'))))
+ >>> cfg.merge_from_dict(options)
+ >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+ >>> assert cfg_dict == dict(
+ ... models=dict(backbone=dict(depth=50, with_cp=True)))
+
+ # Merge list element
+ >>> cfg = Config(dict(pipeline=[
+ ... dict(type='LoadImage'), dict(type='LoadAnnotations')]))
+ >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})
+ >>> cfg.merge_from_dict(options, allow_list_keys=True)
+ >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+ >>> assert cfg_dict == dict(pipeline=[
+ ... dict(type='SelfLoadImage'), dict(type='LoadAnnotations')])
+
+ Args:
+ options (dict): dict of configs to merge from.
+ allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
+ are allowed in ``options`` and will replace the element of the
+ corresponding index in the config if the config is a list.
+ Default: True.
+ """
+ option_cfg_dict = {}
+ for full_key, v in options.items():
+ d = option_cfg_dict
+ key_list = full_key.split(".")
+ for subkey in key_list[:-1]:
+ d.setdefault(subkey, ConfigDict())
+ d = d[subkey]
+ subkey = key_list[-1]
+ d[subkey] = v
+
+ cfg_dict = super(Config, self).__getattribute__("_cfg_dict")
+ super(Config, self).__setattr__(
+ "_cfg_dict",
+ Config._merge_a_into_b(
+ option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys
+ ),
+ )
+
+
+class DictAction(Action):
+ """
+ argparse action to split an argument into KEY=VALUE form
+ on the first = and append to a dictionary. List options can
+ be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
+ brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
+ list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
+ """
+
+ @staticmethod
+ def _parse_int_float_bool(val):
+ try:
+ return int(val)
+ except ValueError:
+ pass
+ try:
+ return float(val)
+ except ValueError:
+ pass
+ if val.lower() in ["true", "false"]:
+ return True if val.lower() == "true" else False
+ return val
+
+ @staticmethod
+ def _parse_iterable(val):
+ """Parse iterable values in the string.
+
+ All elements inside '()' or '[]' are treated as iterable values.
+
+ Args:
+ val (str): Value string.
+
+ Returns:
+ list | tuple: The expanded list or tuple from the string.
+
+ Examples:
+ >>> DictAction._parse_iterable('1,2,3')
+ [1, 2, 3]
+ >>> DictAction._parse_iterable('[a, b, c]')
+ ['a', 'b', 'c']
+ >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]')
+ [(1, 2, 3), ['a', 'b'], 'c']
+ """
+
+ def find_next_comma(string):
+ """Find the position of next comma in the string.
+
+ If no ',' is found in the string, return the string length. All
+ chars inside '()' and '[]' are treated as one element and thus ','
+ inside these brackets are ignored.
+ """
+ assert (string.count("(") == string.count(")")) and (
+ string.count("[") == string.count("]")
+ ), f"Imbalanced brackets exist in {string}"
+ end = len(string)
+ for idx, char in enumerate(string):
+ pre = string[:idx]
+ # The string before this ',' is balanced
+ if (
+ (char == ",")
+ and (pre.count("(") == pre.count(")"))
+ and (pre.count("[") == pre.count("]"))
+ ):
+ end = idx
+ break
+ return end
+
+ # Strip ' and " characters and replace whitespace.
+ val = val.strip("'\"").replace(" ", "")
+ is_tuple = False
+ if val.startswith("(") and val.endswith(")"):
+ is_tuple = True
+ val = val[1:-1]
+ elif val.startswith("[") and val.endswith("]"):
+ val = val[1:-1]
+ elif "," not in val:
+ # val is a single value
+ return DictAction._parse_int_float_bool(val)
+
+ values = []
+ while len(val) > 0:
+ comma_idx = find_next_comma(val)
+ element = DictAction._parse_iterable(val[:comma_idx])
+ values.append(element)
+ val = val[comma_idx + 1 :]
+ if is_tuple:
+ values = tuple(values)
+ return values
+
+ def __call__(self, parser, namespace, values, option_string=None):
+ options = {}
+ for kv in values:
+ key, val = kv.split("=", maxsplit=1)
+ options[key] = self._parse_iterable(val)
+ setattr(namespace, self.dest, options)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/env.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/env.py
new file mode 100644
index 0000000000000000000000000000000000000000..653f007dde5c4a7564e732da88dd47e7d37adf97
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/env.py
@@ -0,0 +1,36 @@
+"""
+Environment Utils
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import random
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+
+from datetime import datetime
+
+
+def get_random_seed():
+ seed = (
+ os.getpid()
+ + int(datetime.now().strftime("%S%f"))
+ + int.from_bytes(os.urandom(2), "big")
+ )
+ return seed
+
+
+def set_seed(seed=None):
+ if seed is None:
+ seed = get_random_seed()
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+ cudnn.benchmark = False
+ cudnn.deterministic = True
+ os.environ["PYTHONHASHSEED"] = str(seed)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/events.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/events.py
new file mode 100644
index 0000000000000000000000000000000000000000..831638a2111f425113925cca5cd2d2bbb91c1c52
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/events.py
@@ -0,0 +1,593 @@
+"""
+Events Utils
+
+Modified from Detectron2
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import datetime
+import json
+import logging
+import os
+import time
+import torch
+import numpy as np
+
+from typing import List, Optional, Tuple
+from collections import defaultdict
+from contextlib import contextmanager
+
+__all__ = [
+ "get_event_storage",
+ "JSONWriter",
+ "TensorboardXWriter",
+ "CommonMetricPrinter",
+ "EventStorage",
+]
+
+_CURRENT_STORAGE_STACK = []
+
+
+def get_event_storage():
+ """
+ Returns:
+ The :class:`EventStorage` object that's currently being used.
+ Throws an error if no :class:`EventStorage` is currently enabled.
+ """
+ assert len(
+ _CURRENT_STORAGE_STACK
+ ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!"
+ return _CURRENT_STORAGE_STACK[-1]
+
+
+class EventWriter:
+ """
+ Base class for writers that obtain events from :class:`EventStorage` and process them.
+ """
+
+ def write(self):
+ raise NotImplementedError
+
+ def close(self):
+ pass
+
+
+class JSONWriter(EventWriter):
+ """
+ Write scalars to a json file.
+ It saves scalars as one json per line (instead of a big json) for easy parsing.
+ Examples parsing such a json file:
+ ::
+ $ cat metrics.json | jq -s '.[0:2]'
+ [
+ {
+ "data_time": 0.008433341979980469,
+ "iteration": 19,
+ "loss": 1.9228371381759644,
+ "loss_box_reg": 0.050025828182697296,
+ "loss_classifier": 0.5316952466964722,
+ "loss_mask": 0.7236229181289673,
+ "loss_rpn_box": 0.0856662318110466,
+ "loss_rpn_cls": 0.48198649287223816,
+ "lr": 0.007173333333333333,
+ "time": 0.25401854515075684
+ },
+ {
+ "data_time": 0.007216215133666992,
+ "iteration": 39,
+ "loss": 1.282649278640747,
+ "loss_box_reg": 0.06222952902317047,
+ "loss_classifier": 0.30682939291000366,
+ "loss_mask": 0.6970193982124329,
+ "loss_rpn_box": 0.038663312792778015,
+ "loss_rpn_cls": 0.1471673548221588,
+ "lr": 0.007706666666666667,
+ "time": 0.2490077018737793
+ }
+ ]
+ $ cat metrics.json | jq '.loss_mask'
+ 0.7126231789588928
+ 0.689423680305481
+ 0.6776131987571716
+ ...
+ """
+
+ def __init__(self, json_file, window_size=20):
+ """
+ Args:
+ json_file (str): path to the json file. New data will be appended if the file exists.
+ window_size (int): the window size of median smoothing for the scalars whose
+ `smoothing_hint` are True.
+ """
+ self._file_handle = open(json_file, "a")
+ self._window_size = window_size
+ self._last_write = -1
+
+ def write(self):
+ storage = get_event_storage()
+ to_save = defaultdict(dict)
+
+ for k, (v, iter) in storage.latest_with_smoothing_hint(
+ self._window_size
+ ).items():
+ # keep scalars that have not been written
+ if iter <= self._last_write:
+ continue
+ to_save[iter][k] = v
+ if len(to_save):
+ all_iters = sorted(to_save.keys())
+ self._last_write = max(all_iters)
+
+ for itr, scalars_per_iter in to_save.items():
+ scalars_per_iter["iteration"] = itr
+ self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n")
+ self._file_handle.flush()
+ try:
+ os.fsync(self._file_handle.fileno())
+ except AttributeError:
+ pass
+
+ def close(self):
+ self._file_handle.close()
+
+
+class TensorboardXWriter(EventWriter):
+ """
+ Write all scalars to a tensorboard file.
+ """
+
+ def __init__(self, log_dir: str, window_size: int = 20, **kwargs):
+ """
+ Args:
+ log_dir (str): the directory to save the output events
+ window_size (int): the scalars will be median-smoothed by this window size
+ kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`
+ """
+ self._window_size = window_size
+ from torch.utils.tensorboard import SummaryWriter
+
+ self._writer = SummaryWriter(log_dir, **kwargs)
+ self._last_write = -1
+
+ def write(self):
+ storage = get_event_storage()
+ new_last_write = self._last_write
+ for k, (v, iter) in storage.latest_with_smoothing_hint(
+ self._window_size
+ ).items():
+ if iter > self._last_write:
+ self._writer.add_scalar(k, v, iter)
+ new_last_write = max(new_last_write, iter)
+ self._last_write = new_last_write
+
+ # storage.put_{image,histogram} is only meant to be used by
+ # tensorboard writer. So we access its internal fields directly from here.
+ if len(storage._vis_data) >= 1:
+ for img_name, img, step_num in storage._vis_data:
+ self._writer.add_image(img_name, img, step_num)
+ # Storage stores all image data and rely on this writer to clear them.
+ # As a result it assumes only one writer will use its image data.
+ # An alternative design is to let storage store limited recent
+ # data (e.g. only the most recent image) that all writers can access.
+ # In that case a writer may not see all image data if its period is long.
+ storage.clear_images()
+
+ if len(storage._histograms) >= 1:
+ for params in storage._histograms:
+ self._writer.add_histogram_raw(**params)
+ storage.clear_histograms()
+
+ def close(self):
+ if hasattr(self, "_writer"): # doesn't exist when the code fails at import
+ self._writer.close()
+
+
+class CommonMetricPrinter(EventWriter):
+ """
+ Print **common** metrics to the terminal, including
+ iteration time, ETA, memory, all losses, and the learning rate.
+ It also applies smoothing using a window of 20 elements.
+ It's meant to print common metrics in common ways.
+ To print something in more customized ways, please implement a similar printer by yourself.
+ """
+
+ def __init__(self, max_iter: Optional[int] = None, window_size: int = 20):
+ """
+ Args:
+ max_iter: the maximum number of iterations to train.
+ Used to compute ETA. If not given, ETA will not be printed.
+ window_size (int): the losses will be median-smoothed by this window size
+ """
+ self.logger = logging.getLogger(__name__)
+ self._max_iter = max_iter
+ self._window_size = window_size
+ self._last_write = (
+ None # (step, time) of last call to write(). Used to compute ETA
+ )
+
+ def _get_eta(self, storage) -> Optional[str]:
+ if self._max_iter is None:
+ return ""
+ iteration = storage.iter
+ try:
+ eta_seconds = storage.history("time").median(1000) * (
+ self._max_iter - iteration - 1
+ )
+ storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False)
+ return str(datetime.timedelta(seconds=int(eta_seconds)))
+ except KeyError:
+ # estimate eta on our own - more noisy
+ eta_string = None
+ if self._last_write is not None:
+ estimate_iter_time = (time.perf_counter() - self._last_write[1]) / (
+ iteration - self._last_write[0]
+ )
+ eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1)
+ eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+ self._last_write = (iteration, time.perf_counter())
+ return eta_string
+
+ def write(self):
+ storage = get_event_storage()
+ iteration = storage.iter
+ if iteration == self._max_iter:
+ # This hook only reports training progress (loss, ETA, etc) but not other data,
+ # therefore do not write anything after training succeeds, even if this method
+ # is called.
+ return
+
+ try:
+ data_time = storage.history("data_time").avg(20)
+ except KeyError:
+ # they may not exist in the first few iterations (due to warmup)
+ # or when SimpleTrainer is not used
+ data_time = None
+ try:
+ iter_time = storage.history("time").global_avg()
+ except KeyError:
+ iter_time = None
+ try:
+ lr = "{:.5g}".format(storage.history("lr").latest())
+ except KeyError:
+ lr = "N/A"
+
+ eta_string = self._get_eta(storage)
+
+ if torch.cuda.is_available():
+ max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
+ else:
+ max_mem_mb = None
+
+ # NOTE: max_mem is parsed by grep in "dev/parse_results.sh"
+ self.logger.info(
+ " {eta}iter: {iter} {losses} {time}{data_time}lr: {lr} {memory}".format(
+ eta=f"eta: {eta_string} " if eta_string else "",
+ iter=iteration,
+ losses=" ".join(
+ [
+ "{}: {:.4g}".format(k, v.median(self._window_size))
+ for k, v in storage.histories().items()
+ if "loss" in k
+ ]
+ ),
+ time=(
+ "time: {:.4f} ".format(iter_time) if iter_time is not None else ""
+ ),
+ data_time=(
+ "data_time: {:.4f} ".format(data_time)
+ if data_time is not None
+ else ""
+ ),
+ lr=lr,
+ memory=(
+ "max_mem: {:.0f}M".format(max_mem_mb)
+ if max_mem_mb is not None
+ else ""
+ ),
+ )
+ )
+
+
+class EventStorage:
+ """
+ The user-facing class that provides metric storage functionalities.
+ In the future we may add support for storing / logging other types of data if needed.
+ """
+
+ def __init__(self, start_iter=0):
+ """
+ Args:
+ start_iter (int): the iteration number to start with
+ """
+ self._history = defaultdict(AverageMeter)
+ self._smoothing_hints = {}
+ self._latest_scalars = {}
+ self._iter = start_iter
+ self._current_prefix = ""
+ self._vis_data = []
+ self._histograms = []
+
+ # def put_image(self, img_name, img_tensor):
+ # """
+ # Add an `img_tensor` associated with `img_name`, to be shown on
+ # tensorboard.
+ # Args:
+ # img_name (str): The name of the image to put into tensorboard.
+ # img_tensor (torch.Tensor or numpy.array): An `uint8` or `float`
+ # Tensor of shape `[channel, height, width]` where `channel` is
+ # 3. The image format should be RGB. The elements in img_tensor
+ # can either have values in [0, 1] (float32) or [0, 255] (uint8).
+ # The `img_tensor` will be visualized in tensorboard.
+ # """
+ # self._vis_data.append((img_name, img_tensor, self._iter))
+
+ def put_scalar(self, name, value, n=1, smoothing_hint=False):
+ """
+ Add a scalar `value` to the `HistoryBuffer` associated with `name`.
+ Args:
+ smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be
+ smoothed when logged. The hint will be accessible through
+ :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint
+ and apply custom smoothing rule.
+ It defaults to True because most scalars we save need to be smoothed to
+ provide any useful signal.
+ """
+ name = self._current_prefix + name
+ history = self._history[name]
+ history.update(value, n)
+ self._latest_scalars[name] = (value, self._iter)
+
+ existing_hint = self._smoothing_hints.get(name)
+ if existing_hint is not None:
+ assert (
+ existing_hint == smoothing_hint
+ ), "Scalar {} was put with a different smoothing_hint!".format(name)
+ else:
+ self._smoothing_hints[name] = smoothing_hint
+
+ # def put_scalars(self, *, smoothing_hint=True, **kwargs):
+ # """
+ # Put multiple scalars from keyword arguments.
+ # Examples:
+ # storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True)
+ # """
+ # for k, v in kwargs.items():
+ # self.put_scalar(k, v, smoothing_hint=smoothing_hint)
+ #
+ # def put_histogram(self, hist_name, hist_tensor, bins=1000):
+ # """
+ # Create a histogram from a tensor.
+ # Args:
+ # hist_name (str): The name of the histogram to put into tensorboard.
+ # hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted
+ # into a histogram.
+ # bins (int): Number of histogram bins.
+ # """
+ # ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item()
+ #
+ # # Create a histogram with PyTorch
+ # hist_counts = torch.histc(hist_tensor, bins=bins)
+ # hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32)
+ #
+ # # Parameter for the add_histogram_raw function of SummaryWriter
+ # hist_params = dict(
+ # tag=hist_name,
+ # min=ht_min,
+ # max=ht_max,
+ # num=len(hist_tensor),
+ # sum=float(hist_tensor.sum()),
+ # sum_squares=float(torch.sum(hist_tensor**2)),
+ # bucket_limits=hist_edges[1:].tolist(),
+ # bucket_counts=hist_counts.tolist(),
+ # global_step=self._iter,
+ # )
+ # self._histograms.append(hist_params)
+
+ def history(self, name):
+ """
+ Returns:
+ AverageMeter: the history for name
+ """
+ ret = self._history.get(name, None)
+ if ret is None:
+ raise KeyError("No history metric available for {}!".format(name))
+ return ret
+
+ def histories(self):
+ """
+ Returns:
+ dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars
+ """
+ return self._history
+
+ def latest(self):
+ """
+ Returns:
+ dict[str -> (float, int)]: mapping from the name of each scalar to the most
+ recent value and the iteration number its added.
+ """
+ return self._latest_scalars
+
+ def latest_with_smoothing_hint(self, window_size=20):
+ """
+ Similar to :meth:`latest`, but the returned values
+ are either the un-smoothed original latest value,
+ or a median of the given window_size,
+ depend on whether the smoothing_hint is True.
+ This provides a default behavior that other writers can use.
+ """
+ result = {}
+ for k, (v, itr) in self._latest_scalars.items():
+ result[k] = (
+ self._history[k].median(window_size) if self._smoothing_hints[k] else v,
+ itr,
+ )
+ return result
+
+ def smoothing_hints(self):
+ """
+ Returns:
+ dict[name -> bool]: the user-provided hint on whether the scalar
+ is noisy and needs smoothing.
+ """
+ return self._smoothing_hints
+
+ def step(self):
+ """
+ User should either: (1) Call this function to increment storage.iter when needed. Or
+ (2) Set `storage.iter` to the correct iteration number before each iteration.
+ The storage will then be able to associate the new data with an iteration number.
+ """
+ self._iter += 1
+
+ @property
+ def iter(self):
+ """
+ Returns:
+ int: The current iteration number. When used together with a trainer,
+ this is ensured to be the same as trainer.iter.
+ """
+ return self._iter
+
+ @iter.setter
+ def iter(self, val):
+ self._iter = int(val)
+
+ @property
+ def iteration(self):
+ # for backward compatibility
+ return self._iter
+
+ def __enter__(self):
+ _CURRENT_STORAGE_STACK.append(self)
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ assert _CURRENT_STORAGE_STACK[-1] == self
+ _CURRENT_STORAGE_STACK.pop()
+
+ @contextmanager
+ def name_scope(self, name):
+ """
+ Yields:
+ A context within which all the events added to this storage
+ will be prefixed by the name scope.
+ """
+ old_prefix = self._current_prefix
+ self._current_prefix = name.rstrip("/") + "/"
+ yield
+ self._current_prefix = old_prefix
+
+ def clear_images(self):
+ """
+ Delete all the stored images for visualization. This should be called
+ after images are written to tensorboard.
+ """
+ self._vis_data = []
+
+ def clear_histograms(self):
+ """
+ Delete all the stored histograms for visualization.
+ This should be called after histograms are written to tensorboard.
+ """
+ self._histograms = []
+
+ def reset_history(self, name):
+ ret = self._history.get(name, None)
+ if ret is None:
+ raise KeyError("No history metric available for {}!".format(name))
+ ret.reset()
+
+ def reset_histories(self):
+ for name in self._history.keys():
+ self._history[name].reset()
+
+
+class AverageMeter:
+ """Computes and stores the average and current value"""
+
+ def __init__(self):
+ self.val = 0
+ self.avg = 0
+ self.total = 0
+ self.count = 0
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.total = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ self.val = val
+ self.total += val * n
+ self.count += n
+ self.avg = self.total / self.count
+
+
+class HistoryBuffer:
+ """
+ Track a series of scalar values and provide access to smoothed values over a
+ window or the global average of the series.
+ """
+
+ def __init__(self, max_length: int = 1000000) -> None:
+ """
+ Args:
+ max_length: maximal number of values that can be stored in the
+ buffer. When the capacity of the buffer is exhausted, old
+ values will be removed.
+ """
+ self._max_length: int = max_length
+ self._data: List[Tuple[float, float]] = [] # (value, iteration) pairs
+ self._count: int = 0
+ self._global_avg: float = 0
+
+ def update(self, value: float, iteration: Optional[float] = None) -> None:
+ """
+ Add a new scalar value produced at certain iteration. If the length
+ of the buffer exceeds self._max_length, the oldest element will be
+ removed from the buffer.
+ """
+ if iteration is None:
+ iteration = self._count
+ if len(self._data) == self._max_length:
+ self._data.pop(0)
+ self._data.append((value, iteration))
+
+ self._count += 1
+ self._global_avg += (value - self._global_avg) / self._count
+
+ def latest(self) -> float:
+ """
+ Return the latest scalar value added to the buffer.
+ """
+ return self._data[-1][0]
+
+ def median(self, window_size: int) -> float:
+ """
+ Return the median of the latest `window_size` values in the buffer.
+ """
+ return np.median([x[0] for x in self._data[-window_size:]])
+
+ def avg(self, window_size: int) -> float:
+ """
+ Return the mean of the latest `window_size` values in the buffer.
+ """
+ return np.mean([x[0] for x in self._data[-window_size:]])
+
+ def global_avg(self) -> float:
+ """
+ Return the mean of all the elements in the buffer. Note that this
+ includes those getting removed due to limited buffer storage.
+ """
+ return self._global_avg
+
+ def values(self) -> List[Tuple[float, float]]:
+ """
+ Returns:
+ list[(number, iteration)]: content of the current buffer.
+ """
+ return self._data
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/logger.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddaf2c5a765c9f1325737c3cbc73e1169f13cdd4
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/logger.py
@@ -0,0 +1,172 @@
+"""
+Logger Utils
+
+Modified from mmcv
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import logging
+import torch
+import torch.distributed as dist
+
+from termcolor import colored
+
+logger_initialized = {}
+root_status = 0
+
+
+class _ColorfulFormatter(logging.Formatter):
+ def __init__(self, *args, **kwargs):
+ self._root_name = kwargs.pop("root_name") + "."
+ super(_ColorfulFormatter, self).__init__(*args, **kwargs)
+
+ def formatMessage(self, record):
+ log = super(_ColorfulFormatter, self).formatMessage(record)
+ if record.levelno == logging.WARNING:
+ prefix = colored("WARNING", "red", attrs=["blink"])
+ elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
+ prefix = colored("ERROR", "red", attrs=["blink", "underline"])
+ else:
+ return log
+ return prefix + " " + log
+
+
+def get_logger(name, log_file=None, log_level=logging.INFO, file_mode="a", color=False):
+ """Initialize and get a logger by name.
+
+ If the logger has not been initialized, this method will initialize the
+ logger by adding one or two handlers, otherwise the initialized logger will
+ be directly returned. During initialization, a StreamHandler will always be
+ added. If `log_file` is specified and the process rank is 0, a FileHandler
+ will also be added.
+
+ Args:
+ name (str): Logger name.
+ log_file (str | None): The log filename. If specified, a FileHandler
+ will be added to the logger.
+ log_level (int): The logger level. Note that only the process of
+ rank 0 is affected, and other processes will set the level to
+ "Error" thus be silent most of the time.
+ file_mode (str): The file mode used in opening log file.
+ Defaults to 'a'.
+ color (bool): Colorful log output. Defaults to True
+
+ Returns:
+ logging.Logger: The expected logger.
+ """
+ logger = logging.getLogger(name)
+
+ if name in logger_initialized:
+ return logger
+ # handle hierarchical names
+ # e.g., logger "a" is initialized, then logger "a.b" will skip the
+ # initialization since it is a child of "a".
+ for logger_name in logger_initialized:
+ if name.startswith(logger_name):
+ return logger
+
+ logger.propagate = False
+
+ stream_handler = logging.StreamHandler()
+ handlers = [stream_handler]
+
+ if dist.is_available() and dist.is_initialized():
+ rank = dist.get_rank()
+ else:
+ rank = 0
+
+ # only rank 0 will add a FileHandler
+ if rank == 0 and log_file is not None:
+ # Here, the default behaviour of the official logger is 'a'. Thus, we
+ # provide an interface to change the file mode to the default
+ # behaviour.
+ file_handler = logging.FileHandler(log_file, file_mode)
+ handlers.append(file_handler)
+
+ plain_formatter = logging.Formatter(
+ "[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s"
+ )
+ if color:
+ formatter = _ColorfulFormatter(
+ colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s",
+ datefmt="%m/%d %H:%M:%S",
+ root_name=name,
+ )
+ else:
+ formatter = plain_formatter
+ for handler in handlers:
+ handler.setFormatter(formatter)
+ handler.setLevel(log_level)
+ logger.addHandler(handler)
+
+ if rank == 0:
+ logger.setLevel(log_level)
+ else:
+ logger.setLevel(logging.ERROR)
+
+ logger_initialized[name] = True
+
+ return logger
+
+
+def print_log(msg, logger=None, level=logging.INFO):
+ """Print a log message.
+
+ Args:
+ msg (str): The message to be logged.
+ logger (logging.Logger | str | None): The logger to be used.
+ Some special loggers are:
+ - "silent": no message will be printed.
+ - other str: the logger obtained with `get_root_logger(logger)`.
+ - None: The `print()` method will be used to print log messages.
+ level (int): Logging level. Only available when `logger` is a Logger
+ object or "root".
+ """
+ if logger is None:
+ print(msg)
+ elif isinstance(logger, logging.Logger):
+ logger.log(level, msg)
+ elif logger == "silent":
+ pass
+ elif isinstance(logger, str):
+ _logger = get_logger(logger)
+ _logger.log(level, msg)
+ else:
+ raise TypeError(
+ "logger should be either a logging.Logger object, str, "
+ f'"silent" or None, but got {type(logger)}'
+ )
+
+
+def get_root_logger(log_file=None, log_level=logging.INFO, file_mode="a"):
+ """Get the root logger.
+
+ The logger will be initialized if it has not been initialized. By default a
+ StreamHandler will be added. If `log_file` is specified, a FileHandler will
+ also be added. The name of the root logger is the top-level package name.
+
+ Args:
+ log_file (str | None): The log filename. If specified, a FileHandler
+ will be added to the root logger.
+ log_level (int): The root logger level. Note that only the process of
+ rank 0 is affected, while other processes will set the level to
+ "Error" and be silent most of the time.
+ file_mode (str): File Mode of logger. (w or a)
+
+ Returns:
+ logging.Logger: The root logger.
+ """
+ logger = get_logger(
+ name="pointcept", log_file=log_file, log_level=log_level, file_mode=file_mode
+ )
+ return logger
+
+
+def _log_api_usage(identifier: str):
+ """
+ Internal function used to log the usage of different detectron2 components
+ inside facebook's infra.
+ """
+ torch._C._log_api_usage_once("pointcept." + identifier)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/misc.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..3177bae3882ccad347002165d2b34d5dc2540359
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/misc.py
@@ -0,0 +1,164 @@
+"""
+Misc
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import warnings
+from collections import abc
+import numpy as np
+import torch
+from importlib import import_module
+
+
+class AverageMeter(object):
+ """Computes and stores the average and current value"""
+
+ def __init__(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ self.val = val
+ self.sum += val * n
+ self.count += n
+ self.avg = self.sum / self.count
+
+
+def intersection_and_union(output, target, K, ignore_index=-1):
+ # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.
+ assert output.ndim in [1, 2, 3]
+ assert output.shape == target.shape
+ output = output.reshape(output.size).copy()
+ target = target.reshape(target.size)
+ output[np.where(target == ignore_index)[0]] = ignore_index
+ intersection = output[np.where(output == target)[0]]
+ area_intersection, _ = np.histogram(intersection, bins=np.arange(K + 1))
+ area_output, _ = np.histogram(output, bins=np.arange(K + 1))
+ area_target, _ = np.histogram(target, bins=np.arange(K + 1))
+ area_union = area_output + area_target - area_intersection
+ return area_intersection, area_union, area_target
+
+
+def intersection_and_union_gpu(output, target, k, ignore_index=-1):
+ # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.
+ assert output.dim() in [1, 2, 3]
+ assert output.shape == target.shape
+ output = output.view(-1)
+ target = target.view(-1)
+ output[target == ignore_index] = ignore_index
+ intersection = output[output == target]
+ area_intersection = torch.histc(intersection, bins=k, min=0, max=k - 1)
+ area_output = torch.histc(output, bins=k, min=0, max=k - 1)
+ area_target = torch.histc(target, bins=k, min=0, max=k - 1)
+ area_union = area_output + area_target - area_intersection
+ return area_intersection, area_union, area_target
+
+
+def make_dirs(dir_name):
+ if not os.path.exists(dir_name):
+ os.makedirs(dir_name, exist_ok=True)
+
+
+def find_free_port():
+ import socket
+
+ sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ # Binding to port 0 will cause the OS to find an available port for us
+ sock.bind(("", 0))
+ port = sock.getsockname()[1]
+ sock.close()
+ # NOTE: there is still a chance the port could be taken by other processes.
+ return port
+
+
+def is_seq_of(seq, expected_type, seq_type=None):
+ """Check whether it is a sequence of some type.
+
+ Args:
+ seq (Sequence): The sequence to be checked.
+ expected_type (type): Expected type of sequence items.
+ seq_type (type, optional): Expected sequence type.
+
+ Returns:
+ bool: Whether the sequence is valid.
+ """
+ if seq_type is None:
+ exp_seq_type = abc.Sequence
+ else:
+ assert isinstance(seq_type, type)
+ exp_seq_type = seq_type
+ if not isinstance(seq, exp_seq_type):
+ return False
+ for item in seq:
+ if not isinstance(item, expected_type):
+ return False
+ return True
+
+
+def is_str(x):
+ """Whether the input is an string instance.
+
+ Note: This method is deprecated since python 2 is no longer supported.
+ """
+ return isinstance(x, str)
+
+
+def import_modules_from_strings(imports, allow_failed_imports=False):
+ """Import modules from the given list of strings.
+
+ Args:
+ imports (list | str | None): The given module names to be imported.
+ allow_failed_imports (bool): If True, the failed imports will return
+ None. Otherwise, an ImportError is raise. Default: False.
+
+ Returns:
+ list[module] | module | None: The imported modules.
+
+ Examples:
+ >>> osp, sys = import_modules_from_strings(
+ ... ['os.path', 'sys'])
+ >>> import os.path as osp_
+ >>> import sys as sys_
+ >>> assert osp == osp_
+ >>> assert sys == sys_
+ """
+ if not imports:
+ return
+ single_import = False
+ if isinstance(imports, str):
+ single_import = True
+ imports = [imports]
+ if not isinstance(imports, list):
+ raise TypeError(f"custom_imports must be a list but got type {type(imports)}")
+ imported = []
+ for imp in imports:
+ if not isinstance(imp, str):
+ raise TypeError(f"{imp} is of type {type(imp)} and cannot be imported.")
+ try:
+ imported_tmp = import_module(imp)
+ except ImportError:
+ if allow_failed_imports:
+ warnings.warn(f"{imp} failed to import and is ignored.", UserWarning)
+ imported_tmp = None
+ else:
+ raise ImportError
+ imported.append(imported_tmp)
+ if single_import:
+ imported = imported[0]
+ return imported
+
+
+class DummyClass:
+ def __init__(self):
+ pass
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/optimizer.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..355ec8916ad041ca02b404029983b0f59933fb8c
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/optimizer.py
@@ -0,0 +1,55 @@
+"""
+Optimizer
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.registry import Registry
+
+OPTIMIZERS = Registry("optimizers")
+
+
+OPTIMIZERS.register_module(module=torch.optim.SGD, name="SGD")
+OPTIMIZERS.register_module(module=torch.optim.Adam, name="Adam")
+OPTIMIZERS.register_module(module=torch.optim.AdamW, name="AdamW")
+
+
+def build_optimizer(cfg, model, param_dicts=None):
+ if param_dicts is None:
+ cfg.params = model.parameters()
+ else:
+ cfg.params = [dict(names=[], params=[], lr=cfg.lr)]
+ for i in range(len(param_dicts)):
+ param_group = dict(names=[], params=[])
+ if "lr" in param_dicts[i].keys():
+ param_group["lr"] = param_dicts[i].lr
+ if "momentum" in param_dicts[i].keys():
+ param_group["momentum"] = param_dicts[i].momentum
+ if "weight_decay" in param_dicts[i].keys():
+ param_group["weight_decay"] = param_dicts[i].weight_decay
+ cfg.params.append(param_group)
+
+ for n, p in model.named_parameters():
+ flag = False
+ for i in range(len(param_dicts)):
+ if param_dicts[i].keyword in n:
+ cfg.params[i + 1]["names"].append(n)
+ cfg.params[i + 1]["params"].append(p)
+ flag = True
+ break
+ if not flag:
+ cfg.params[0]["names"].append(n)
+ cfg.params[0]["params"].append(p)
+
+ logger = get_root_logger()
+ for i in range(len(cfg.params)):
+ param_names = cfg.params[i].pop("names")
+ message = ""
+ for key in cfg.params[i].keys():
+ if key != "params":
+ message += f" {key}: {cfg.params[i][key]};"
+ logger.info(f"Params Group {i+1} -{message} Params: {param_names}.")
+ return OPTIMIZERS.build(cfg=cfg)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/path.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/path.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce98fa5fd0dfbf6e1d61e833ecc35fea4ab2782b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/path.py
@@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+from pathlib import Path
+
+from .misc import is_str
+
+
+def is_filepath(x):
+ return is_str(x) or isinstance(x, Path)
+
+
+def fopen(filepath, *args, **kwargs):
+ if is_str(filepath):
+ return open(filepath, *args, **kwargs)
+ elif isinstance(filepath, Path):
+ return filepath.open(*args, **kwargs)
+ raise ValueError("`filepath` should be a string or a Path")
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+ if not osp.isfile(filename):
+ raise FileNotFoundError(msg_tmpl.format(filename))
+
+
+def mkdir_or_exist(dir_name, mode=0o777):
+ if dir_name == "":
+ return
+ dir_name = osp.expanduser(dir_name)
+ os.makedirs(dir_name, mode=mode, exist_ok=True)
+
+
+def symlink(src, dst, overwrite=True, **kwargs):
+ if os.path.lexists(dst) and overwrite:
+ os.remove(dst)
+ os.symlink(src, dst, **kwargs)
+
+
+def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True):
+ """Scan a directory to find the interested files.
+
+ Args:
+ dir_path (str | obj:`Path`): Path of the directory.
+ suffix (str | tuple(str), optional): File suffix that we are
+ interested in. Default: None.
+ recursive (bool, optional): If set to True, recursively scan the
+ directory. Default: False.
+ case_sensitive (bool, optional) : If set to False, ignore the case of
+ suffix. Default: True.
+
+ Returns:
+ A generator for all the interested files with relative paths.
+ """
+ if isinstance(dir_path, (str, Path)):
+ dir_path = str(dir_path)
+ else:
+ raise TypeError('"dir_path" must be a string or Path object')
+
+ if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+ raise TypeError('"suffix" must be a string or tuple of strings')
+
+ if suffix is not None and not case_sensitive:
+ suffix = (
+ suffix.lower()
+ if isinstance(suffix, str)
+ else tuple(item.lower() for item in suffix)
+ )
+
+ root = dir_path
+
+ def _scandir(dir_path, suffix, recursive, case_sensitive):
+ for entry in os.scandir(dir_path):
+ if not entry.name.startswith(".") and entry.is_file():
+ rel_path = osp.relpath(entry.path, root)
+ _rel_path = rel_path if case_sensitive else rel_path.lower()
+ if suffix is None or _rel_path.endswith(suffix):
+ yield rel_path
+ elif recursive and os.path.isdir(entry.path):
+ # scan recursively if entry.path is a directory
+ yield from _scandir(entry.path, suffix, recursive, case_sensitive)
+
+ return _scandir(dir_path, suffix, recursive, case_sensitive)
+
+
+def find_vcs_root(path, markers=(".git",)):
+ """Finds the root directory (including itself) of specified markers.
+
+ Args:
+ path (str): Path of directory or file.
+ markers (list[str], optional): List of file or directory names.
+
+ Returns:
+ The directory contained one of the markers or None if not found.
+ """
+ if osp.isfile(path):
+ path = osp.dirname(path)
+
+ prev, cur = None, osp.abspath(osp.expanduser(path))
+ while cur != prev:
+ if any(osp.exists(osp.join(cur, marker)) for marker in markers):
+ return cur
+ prev, cur = cur, osp.split(cur)[0]
+ return None
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/registry.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ac308a87d38ff61da14d6b4d5c73b4c68c15a58
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/registry.py
@@ -0,0 +1,316 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import inspect
+import warnings
+from functools import partial
+
+from .misc import is_seq_of
+
+
+def build_from_cfg(cfg, registry, default_args=None):
+ """Build a module from configs dict.
+
+ Args:
+ cfg (dict): Config dict. It should at least contain the key "type".
+ registry (:obj:`Registry`): The registry to search the type from.
+ default_args (dict, optional): Default initialization arguments.
+
+ Returns:
+ object: The constructed object.
+ """
+ if not isinstance(cfg, dict):
+ raise TypeError(f"cfg must be a dict, but got {type(cfg)}")
+ if "type" not in cfg:
+ if default_args is None or "type" not in default_args:
+ raise KeyError(
+ '`cfg` or `default_args` must contain the key "type", '
+ f"but got {cfg}\n{default_args}"
+ )
+ if not isinstance(registry, Registry):
+ raise TypeError(
+ "registry must be an mmcv.Registry object, " f"but got {type(registry)}"
+ )
+ if not (isinstance(default_args, dict) or default_args is None):
+ raise TypeError(
+ "default_args must be a dict or None, " f"but got {type(default_args)}"
+ )
+
+ args = cfg.copy()
+
+ if default_args is not None:
+ for name, value in default_args.items():
+ args.setdefault(name, value)
+
+ obj_type = args.pop("type")
+ if isinstance(obj_type, str):
+ obj_cls = registry.get(obj_type)
+ if obj_cls is None:
+ raise KeyError(f"{obj_type} is not in the {registry.name} registry")
+ elif inspect.isclass(obj_type):
+ obj_cls = obj_type
+ else:
+ raise TypeError(f"type must be a str or valid type, but got {type(obj_type)}")
+ try:
+ return obj_cls(**args)
+ except Exception as e:
+ # Normal TypeError does not print class name.
+ raise type(e)(f"{obj_cls.__name__}: {e}")
+
+
+class Registry:
+ """A registry to map strings to classes.
+
+ Registered object could be built from registry.
+ Example:
+ >>> MODELS = Registry('models')
+ >>> @MODELS.register_module()
+ >>> class ResNet:
+ >>> pass
+ >>> resnet = MODELS.build(dict(type='ResNet'))
+
+ Please refer to
+ https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
+ advanced usage.
+
+ Args:
+ name (str): Registry name.
+ build_func(func, optional): Build function to construct instance from
+ Registry, func:`build_from_cfg` is used if neither ``parent`` or
+ ``build_func`` is specified. If ``parent`` is specified and
+ ``build_func`` is not given, ``build_func`` will be inherited
+ from ``parent``. Default: None.
+ parent (Registry, optional): Parent registry. The class registered in
+ children registry could be built from parent. Default: None.
+ scope (str, optional): The scope of registry. It is the key to search
+ for children registry. If not specified, scope will be the name of
+ the package where class is defined, e.g. mmdet, mmcls, mmseg.
+ Default: None.
+ """
+
+ def __init__(self, name, build_func=None, parent=None, scope=None):
+ self._name = name
+ self._module_dict = dict()
+ self._children = dict()
+ self._scope = self.infer_scope() if scope is None else scope
+
+ # self.build_func will be set with the following priority:
+ # 1. build_func
+ # 2. parent.build_func
+ # 3. build_from_cfg
+ if build_func is None:
+ if parent is not None:
+ self.build_func = parent.build_func
+ else:
+ self.build_func = build_from_cfg
+ else:
+ self.build_func = build_func
+ if parent is not None:
+ assert isinstance(parent, Registry)
+ parent._add_children(self)
+ self.parent = parent
+ else:
+ self.parent = None
+
+ def __len__(self):
+ return len(self._module_dict)
+
+ def __contains__(self, key):
+ return self.get(key) is not None
+
+ def __repr__(self):
+ format_str = (
+ self.__class__.__name__ + f"(name={self._name}, "
+ f"items={self._module_dict})"
+ )
+ return format_str
+
+ @staticmethod
+ def infer_scope():
+ """Infer the scope of registry.
+
+ The name of the package where registry is defined will be returned.
+
+ Example:
+ # in mmdet/models/backbone/resnet.py
+ >>> MODELS = Registry('models')
+ >>> @MODELS.register_module()
+ >>> class ResNet:
+ >>> pass
+ The scope of ``ResNet`` will be ``mmdet``.
+
+
+ Returns:
+ scope (str): The inferred scope name.
+ """
+ # inspect.stack() trace where this function is called, the index-2
+ # indicates the frame where `infer_scope()` is called
+ filename = inspect.getmodule(inspect.stack()[2][0]).__name__
+ split_filename = filename.split(".")
+ return split_filename[0]
+
+ @staticmethod
+ def split_scope_key(key):
+ """Split scope and key.
+
+ The first scope will be split from key.
+
+ Examples:
+ >>> Registry.split_scope_key('mmdet.ResNet')
+ 'mmdet', 'ResNet'
+ >>> Registry.split_scope_key('ResNet')
+ None, 'ResNet'
+
+ Return:
+ scope (str, None): The first scope.
+ key (str): The remaining key.
+ """
+ split_index = key.find(".")
+ if split_index != -1:
+ return key[:split_index], key[split_index + 1 :]
+ else:
+ return None, key
+
+ @property
+ def name(self):
+ return self._name
+
+ @property
+ def scope(self):
+ return self._scope
+
+ @property
+ def module_dict(self):
+ return self._module_dict
+
+ @property
+ def children(self):
+ return self._children
+
+ def get(self, key):
+ """Get the registry record.
+
+ Args:
+ key (str): The class name in string format.
+
+ Returns:
+ class: The corresponding class.
+ """
+ scope, real_key = self.split_scope_key(key)
+ if scope is None or scope == self._scope:
+ # get from self
+ if real_key in self._module_dict:
+ return self._module_dict[real_key]
+ else:
+ # get from self._children
+ if scope in self._children:
+ return self._children[scope].get(real_key)
+ else:
+ # goto root
+ parent = self.parent
+ while parent.parent is not None:
+ parent = parent.parent
+ return parent.get(key)
+
+ def build(self, *args, **kwargs):
+ return self.build_func(*args, **kwargs, registry=self)
+
+ def _add_children(self, registry):
+ """Add children for a registry.
+
+ The ``registry`` will be added as children based on its scope.
+ The parent registry could build objects from children registry.
+
+ Example:
+ >>> models = Registry('models')
+ >>> mmdet_models = Registry('models', parent=models)
+ >>> @mmdet_models.register_module()
+ >>> class ResNet:
+ >>> pass
+ >>> resnet = models.build(dict(type='mmdet.ResNet'))
+ """
+
+ assert isinstance(registry, Registry)
+ assert registry.scope is not None
+ assert (
+ registry.scope not in self.children
+ ), f"scope {registry.scope} exists in {self.name} registry"
+ self.children[registry.scope] = registry
+
+ def _register_module(self, module_class, module_name=None, force=False):
+ if not inspect.isclass(module_class):
+ raise TypeError("module must be a class, " f"but got {type(module_class)}")
+
+ if module_name is None:
+ module_name = module_class.__name__
+ if isinstance(module_name, str):
+ module_name = [module_name]
+ for name in module_name:
+ if not force and name in self._module_dict:
+ raise KeyError(f"{name} is already registered " f"in {self.name}")
+ self._module_dict[name] = module_class
+
+ def deprecated_register_module(self, cls=None, force=False):
+ warnings.warn(
+ "The old API of register_module(module, force=False) "
+ "is deprecated and will be removed, please use the new API "
+ "register_module(name=None, force=False, module=None) instead."
+ )
+ if cls is None:
+ return partial(self.deprecated_register_module, force=force)
+ self._register_module(cls, force=force)
+ return cls
+
+ def register_module(self, name=None, force=False, module=None):
+ """Register a module.
+
+ A record will be added to `self._module_dict`, whose key is the class
+ name or the specified name, and value is the class itself.
+ It can be used as a decorator or a normal function.
+
+ Example:
+ >>> backbones = Registry('backbone')
+ >>> @backbones.register_module()
+ >>> class ResNet:
+ >>> pass
+
+ >>> backbones = Registry('backbone')
+ >>> @backbones.register_module(name='mnet')
+ >>> class MobileNet:
+ >>> pass
+
+ >>> backbones = Registry('backbone')
+ >>> class ResNet:
+ >>> pass
+ >>> backbones.register_module(ResNet)
+
+ Args:
+ name (str | None): The module name to be registered. If not
+ specified, the class name will be used.
+ force (bool, optional): Whether to override an existing class with
+ the same name. Default: False.
+ module (type): Module class to be registered.
+ """
+ if not isinstance(force, bool):
+ raise TypeError(f"force must be a boolean, but got {type(force)}")
+ # NOTE: This is a walkaround to be compatible with the old api,
+ # while it may introduce unexpected bugs.
+ if isinstance(name, type):
+ return self.deprecated_register_module(name, force=force)
+
+ # raise the error ahead of time
+ if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
+ raise TypeError(
+ "name must be either of None, an instance of str or a sequence"
+ f" of str, but got {type(name)}"
+ )
+
+ # use it as a normal method: x.register_module(module=SomeClass)
+ if module is not None:
+ self._register_module(module_class=module, module_name=name, force=force)
+ return module
+
+ # use it as a decorator: @x.register_module()
+ def _register(cls):
+ self._register_module(module_class=cls, module_name=name, force=force)
+ return cls
+
+ return _register
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/scheduler.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e2e29fdde2e2668c023af36afdb89e73fb9ce53
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/scheduler.py
@@ -0,0 +1,147 @@
+"""
+Scheduler
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import torch.optim.lr_scheduler as lr_scheduler
+from .registry import Registry
+
+SCHEDULERS = Registry("schedulers")
+
+
+@SCHEDULERS.register_module()
+class MultiStepLR(lr_scheduler.MultiStepLR):
+ def __init__(
+ self,
+ optimizer,
+ milestones,
+ total_steps,
+ gamma=0.1,
+ last_epoch=-1,
+ verbose=False,
+ ):
+ super().__init__(
+ optimizer=optimizer,
+ milestones=[rate * total_steps for rate in milestones],
+ gamma=gamma,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class MultiStepWithWarmupLR(lr_scheduler.LambdaLR):
+ def __init__(
+ self,
+ optimizer,
+ milestones,
+ total_steps,
+ gamma=0.1,
+ warmup_rate=0.05,
+ warmup_scale=1e-6,
+ last_epoch=-1,
+ verbose=False,
+ ):
+ milestones = [rate * total_steps for rate in milestones]
+
+ def multi_step_with_warmup(s):
+ factor = 1.0
+ for i in range(len(milestones)):
+ if s < milestones[i]:
+ break
+ factor *= gamma
+
+ if s <= warmup_rate * total_steps:
+ warmup_coefficient = 1 - (1 - s / warmup_rate / total_steps) * (
+ 1 - warmup_scale
+ )
+ else:
+ warmup_coefficient = 1.0
+ return warmup_coefficient * factor
+
+ super().__init__(
+ optimizer=optimizer,
+ lr_lambda=multi_step_with_warmup,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class PolyLR(lr_scheduler.LambdaLR):
+ def __init__(self, optimizer, total_steps, power=0.9, last_epoch=-1, verbose=False):
+ super().__init__(
+ optimizer=optimizer,
+ lr_lambda=lambda s: (1 - s / (total_steps + 1)) ** power,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class ExpLR(lr_scheduler.LambdaLR):
+ def __init__(self, optimizer, total_steps, gamma=0.9, last_epoch=-1, verbose=False):
+ super().__init__(
+ optimizer=optimizer,
+ lr_lambda=lambda s: gamma ** (s / total_steps),
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class CosineAnnealingLR(lr_scheduler.CosineAnnealingLR):
+ def __init__(self, optimizer, total_steps, eta_min=0, last_epoch=-1, verbose=False):
+ super().__init__(
+ optimizer=optimizer,
+ T_max=total_steps,
+ eta_min=eta_min,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+@SCHEDULERS.register_module()
+class OneCycleLR(lr_scheduler.OneCycleLR):
+ r"""
+ torch.optim.lr_scheduler.OneCycleLR, Block total_steps
+ """
+
+ def __init__(
+ self,
+ optimizer,
+ max_lr,
+ total_steps=None,
+ pct_start=0.3,
+ anneal_strategy="cos",
+ cycle_momentum=True,
+ base_momentum=0.85,
+ max_momentum=0.95,
+ div_factor=25.0,
+ final_div_factor=1e4,
+ three_phase=False,
+ last_epoch=-1,
+ verbose=False,
+ ):
+ super().__init__(
+ optimizer=optimizer,
+ max_lr=max_lr,
+ total_steps=total_steps,
+ pct_start=pct_start,
+ anneal_strategy=anneal_strategy,
+ cycle_momentum=cycle_momentum,
+ base_momentum=base_momentum,
+ max_momentum=max_momentum,
+ div_factor=div_factor,
+ final_div_factor=final_div_factor,
+ three_phase=three_phase,
+ last_epoch=last_epoch,
+ verbose=verbose,
+ )
+
+
+def build_scheduler(cfg, optimizer):
+ cfg.optimizer = optimizer
+ return SCHEDULERS.build(cfg=cfg)
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/timer.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3de4a16e33c43fe61ea3088f82216fd62eb6e959
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/timer.py
@@ -0,0 +1,70 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# -*- coding: utf-8 -*-
+
+from time import perf_counter
+from typing import Optional
+
+
+class Timer:
+ """
+ A timer which computes the time elapsed since the start/reset of the timer.
+ """
+
+ def __init__(self) -> None:
+ self.reset()
+
+ def reset(self) -> None:
+ """
+ Reset the timer.
+ """
+ self._start = perf_counter()
+ self._paused: Optional[float] = None
+ self._total_paused = 0
+ self._count_start = 1
+
+ def pause(self) -> None:
+ """
+ Pause the timer.
+ """
+ if self._paused is not None:
+ raise ValueError("Trying to pause a Timer that is already paused!")
+ self._paused = perf_counter()
+
+ def is_paused(self) -> bool:
+ """
+ Returns:
+ bool: whether the timer is currently paused
+ """
+ return self._paused is not None
+
+ def resume(self) -> None:
+ """
+ Resume the timer.
+ """
+ if self._paused is None:
+ raise ValueError("Trying to resume a Timer that is not paused!")
+ # pyre-fixme[58]: `-` is not supported for operand types `float` and
+ # `Optional[float]`.
+ self._total_paused += perf_counter() - self._paused
+ self._paused = None
+ self._count_start += 1
+
+ def seconds(self) -> float:
+ """
+ Returns:
+ (float): the total number of seconds since the start/reset of the
+ timer, excluding the time when the timer is paused.
+ """
+ if self._paused is not None:
+ end_time: float = self._paused # type: ignore
+ else:
+ end_time = perf_counter()
+ return end_time - self._start - self._total_paused
+
+ def avg_seconds(self) -> float:
+ """
+ Returns:
+ (float): the average number of seconds between every start/reset and
+ pause.
+ """
+ return self.seconds() / self._count_start
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/visualization.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/visualization.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a010dd8289f60119d1bfbccdff65edb908e24f6
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/pointcept/utils/visualization.py
@@ -0,0 +1,89 @@
+"""
+Visualization Utils
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import open3d as o3d
+import numpy as np
+import torch
+
+
+def to_numpy(x):
+ if isinstance(x, torch.Tensor):
+ x = x.clone().detach().cpu().numpy()
+ assert isinstance(x, np.ndarray)
+ return x
+
+
+def save_point_cloud(coord, color=None, file_path="pc.ply", logger=None):
+ os.makedirs(os.path.dirname(file_path), exist_ok=True)
+ coord = to_numpy(coord)
+ if color is not None:
+ color = to_numpy(color)
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(coord)
+ pcd.colors = o3d.utility.Vector3dVector(
+ np.ones_like(coord) if color is None else color
+ )
+ o3d.io.write_point_cloud(file_path, pcd)
+ if logger is not None:
+ logger.info(f"Save Point Cloud to: {file_path}")
+
+
+def save_bounding_boxes(
+ bboxes_corners, color=(1.0, 0.0, 0.0), file_path="bbox.ply", logger=None
+):
+ bboxes_corners = to_numpy(bboxes_corners)
+ # point list
+ points = bboxes_corners.reshape(-1, 3)
+ # line list
+ box_lines = np.array(
+ [
+ [0, 1],
+ [1, 2],
+ [2, 3],
+ [3, 0],
+ [4, 5],
+ [5, 6],
+ [6, 7],
+ [7, 0],
+ [0, 4],
+ [1, 5],
+ [2, 6],
+ [3, 7],
+ ]
+ )
+ lines = []
+ for i, _ in enumerate(bboxes_corners):
+ lines.append(box_lines + i * 8)
+ lines = np.concatenate(lines)
+ # color list
+ color = np.array([color for _ in range(len(lines))])
+ # generate line set
+ line_set = o3d.geometry.LineSet()
+ line_set.points = o3d.utility.Vector3dVector(points)
+ line_set.lines = o3d.utility.Vector2iVector(lines)
+ line_set.colors = o3d.utility.Vector3dVector(color)
+ o3d.io.write_line_set(file_path, line_set)
+
+ if logger is not None:
+ logger.info(f"Save Boxes to: {file_path}")
+
+
+def save_lines(
+ points, lines, color=(1.0, 0.0, 0.0), file_path="lines.ply", logger=None
+):
+ points = to_numpy(points)
+ lines = to_numpy(lines)
+ colors = np.array([color for _ in range(len(lines))])
+ line_set = o3d.geometry.LineSet()
+ line_set.points = o3d.utility.Vector3dVector(points)
+ line_set.lines = o3d.utility.Vector2iVector(lines)
+ line_set.colors = o3d.utility.Vector3dVector(colors)
+ o3d.io.write_line_set(file_path, line_set)
+
+ if logger is not None:
+ logger.info(f"Save Lines to: {file_path}")
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/build_image.sh b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/build_image.sh
new file mode 100644
index 0000000000000000000000000000000000000000..31a6a7fc23e57b3b738450d5c42fed4cc45b9b65
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/build_image.sh
@@ -0,0 +1,83 @@
+TORCH_VERSION=2.0.1
+CUDA_VERSION=11.7
+CUDNN_VERSION=8
+
+ARGS=`getopt -o t:c: -l torch:,cuda:,cudnn: -n "$0" -- "$@"`
+[ $? != 0 ] && exit 1
+eval set -- "${ARGS}"
+while true ; do
+ case "$1" in
+ -t | --torch)
+ TORCH_VERSION=$2
+ shift 2
+ ;;
+ -c | --cuda)
+ CUDA_VERSION=$2
+ shift 2
+ ;;
+ --cudnn)
+ CUDNN_VERSION=$2
+ shift 2
+ ;;
+ --)
+ break
+ ;;
+ *)
+ echo "Invalid option: $1"
+ exit 1
+ ;;
+ esac
+done
+
+CUDA_VERSION_NO_DOT=`echo ${CUDA_VERSION} | tr -d "."`
+BASE_TORCH_TAG=${TORCH_VERSION}-cuda${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel
+IMG_TAG=pointcept/pointcept:pytorch${BASE_TORCH_TAG}
+
+echo "TORCH VERSION: ${TORCH_VERSION}"
+echo "CUDA VERSION: ${CUDA_VERSION}"
+echo "CUDNN VERSION: ${CUDNN_VERSION}"
+
+
+cat > ./Dockerfile <<- EOM
+FROM pytorch/pytorch:${BASE_TORCH_TAG}
+
+# Fix nvidia-key error issue (NO_PUBKEY A4B469963BF863CC)
+RUN rm /etc/apt/sources.list.d/*.list
+
+# Installing apt packages
+RUN export DEBIAN_FRONTEND=noninteractive \
+ && apt -y update --no-install-recommends \
+ && apt -y install --no-install-recommends \
+ git wget tmux vim zsh build-essential cmake ninja-build libopenblas-dev libsparsehash-dev \
+ && apt autoremove -y \
+ && apt clean -y \
+ && export DEBIAN_FRONTEND=dialog
+
+# Install Pointcept environment
+RUN conda install h5py pyyaml -c anaconda -y
+RUN conda install sharedarray tensorboard tensorboardx yapf addict einops scipy plyfile termcolor timm -c conda-forge -y
+RUN conda install pytorch-cluster pytorch-scatter pytorch-sparse -c pyg -y
+
+RUN pip install --upgrade pip
+RUN pip install torch-geometric
+RUN pip install spconv-cu${CUDA_VERSION_NO_DOT}
+RUN pip install open3d
+
+# Build MinkowskiEngine
+RUN git clone https://github.com/NVIDIA/MinkowskiEngine.git
+WORKDIR /workspace/MinkowskiEngine
+RUN TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0+PTX 8.0" python setup.py install --blas=openblas --force_cuda
+WORKDIR /workspace
+
+# Build pointops
+RUN git clone https://github.com/Pointcept/Pointcept.git
+RUN TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0+PTX 8.0" pip install Pointcept/libs/pointops -v
+
+# Build pointgroup_ops
+RUN TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0+PTX 8.0" pip install Pointcept/libs/pointgroup_ops -v
+
+# Build swin3d
+RUN TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX 8.0" pip install -U git+https://github.com/microsoft/Swin3D.git -v
+EOM
+
+docker build . -f ./Dockerfile -t $IMG_TAG
\ No newline at end of file
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/create_tars.sh b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/create_tars.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8bd990b2fc6d3448202a04db63c2adb707c2652b
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/create_tars.sh
@@ -0,0 +1,67 @@
+#!/bin/sh
+
+# Variables
+SOURCE_DIR=$1
+DEST_DIR=$2
+MAX_SIZE=$(awk "BEGIN {printf \"%d\", $3 * 1024 * 1024}") # Convert GB to KB as an integer
+
+# Get the base name of the source directory to use as TAR_NAME
+TAR_NAME=$(basename "$SOURCE_DIR")
+
+# Create destination directory if it doesn't exist
+mkdir -p "$DEST_DIR"
+
+# Function to create a new tar file
+create_tar() {
+ tar_number=$1
+ file_list=$2
+ tar_name=$(printf "%s/${TAR_NAME}_%0${width}d.tar.gz" "$DEST_DIR" "$tar_number")
+ tar -zcvf "$tar_name" -C "$SOURCE_DIR" -T "$file_list"
+}
+
+# Initialize
+tar_number=1
+current_size=0
+temp_dir=$(mktemp -d)
+file_list="$temp_dir/file_list_$tar_number"
+echo Start indexing "file_list_$tar_number"
+
+cd "$SOURCE_DIR" || exit 1
+
+# Iterate over all files in the source directory
+find . -type f | while IFS= read -r file; do
+ file_size=$(du -k "$file" | cut -f1)
+
+ if [ $(( current_size + file_size )) -gt $MAX_SIZE ]; then
+ tar_number=$((tar_number + 1))
+ file_list="$temp_dir/file_list_$tar_number"
+ echo Start indexing "file_list_$tar_number"
+ current_size=0
+ fi
+
+ echo "$file" >> "$file_list"
+ current_size=$((current_size + file_size))
+done
+
+# Determine the width for the tar file numbers
+total_files=$(find "$temp_dir" -name 'file_list_*' | wc -l)
+width=${#total_files}
+
+# Set PARALLEL_PROCESSES to the number of file lists if not provided
+PARALLEL_PROCESSES=${4:-$total_files}
+
+# Debug information
+echo "Total files: $total_files"
+echo "Width: $width"
+echo "Parallel processes: $PARALLEL_PROCESSES"
+
+# Run tar creation in parallel
+find "$temp_dir" -name 'file_list_*' | xargs -P "$PARALLEL_PROCESSES" -I {} sh -c '
+ file_list={}
+ tar_number=$(basename "$file_list" | cut -d_ -f3)
+ tar_name=$(printf "%s/'"$TAR_NAME"'_%0'"$width"'d.tar.gz" "'"$DEST_DIR"'" "$tar_number")
+ tar -zcvf "$tar_name" -C "'"$SOURCE_DIR"'" -T "$file_list"
+'
+
+# Clean up
+rm -rf "$temp_dir"
\ No newline at end of file
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/test.sh b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a104f98e67873c7741711b63da6cdbd8c88b73f4
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/test.sh
@@ -0,0 +1,74 @@
+#!/bin/sh
+
+cd $(dirname $(dirname "$0")) || exit
+PYTHON=python
+
+TEST_CODE=test.py
+
+DATASET=scannet
+CONFIG="None"
+EXP_NAME=debug
+WEIGHT=model_best
+GPU=None
+
+while getopts "p:d:c:n:w:g:" opt; do
+ case $opt in
+ p)
+ PYTHON=$OPTARG
+ ;;
+ d)
+ DATASET=$OPTARG
+ ;;
+ c)
+ CONFIG=$OPTARG
+ ;;
+ n)
+ EXP_NAME=$OPTARG
+ ;;
+ w)
+ WEIGHT=$OPTARG
+ ;;
+ g)
+ GPU=$OPTARG
+ ;;
+ \?)
+ echo "Invalid option: -$OPTARG"
+ ;;
+ esac
+done
+
+if [ "${NUM_GPU}" = 'None' ]
+then
+ NUM_GPU=`$PYTHON -c 'import torch; print(torch.cuda.device_count())'`
+fi
+
+echo "Experiment name: $EXP_NAME"
+echo "Python interpreter dir: $PYTHON"
+echo "Dataset: $DATASET"
+echo "GPU Num: $GPU"
+
+EXP_DIR=exp/${DATASET}/${EXP_NAME}
+MODEL_DIR=${EXP_DIR}/model
+CODE_DIR=${EXP_DIR}/code
+CONFIG_DIR=${EXP_DIR}/config.py
+
+if [ "${CONFIG}" = "None" ]
+then
+ CONFIG_DIR=${EXP_DIR}/config.py
+else
+ CONFIG_DIR=configs/${DATASET}/${CONFIG}.py
+fi
+
+echo "Loading config in:" $CONFIG_DIR
+#export PYTHONPATH=./$CODE_DIR
+export PYTHONPATH=./
+echo "Running code in: $CODE_DIR"
+
+
+echo " =========> RUN TASK <========="
+
+#$PYTHON -u "$CODE_DIR"/tools/$TEST_CODE \
+$PYTHON -u tools/$TEST_CODE \
+ --config-file "$CONFIG_DIR" \
+ --num-gpus "$GPU" \
+ --options save_path="$EXP_DIR" weight="${MODEL_DIR}"/"${WEIGHT}".pth
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/train.sh b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2910ba1e92423ce8decf40eeeb4d5115da60b8b9
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/scripts/train.sh
@@ -0,0 +1,92 @@
+#!/bin/sh
+
+cd $(dirname $(dirname "$0")) || exit
+ROOT_DIR=$(pwd)
+PYTHON=python
+
+TRAIN_CODE=train.py
+
+DATASET=scannet
+CONFIG="None"
+EXP_NAME=debug
+WEIGHT="None"
+RESUME=false
+GPU=None
+
+
+while getopts "p:d:c:n:w:g:r:" opt; do
+ case $opt in
+ p)
+ PYTHON=$OPTARG
+ ;;
+ d)
+ DATASET=$OPTARG
+ ;;
+ c)
+ CONFIG=$OPTARG
+ ;;
+ n)
+ EXP_NAME=$OPTARG
+ ;;
+ w)
+ WEIGHT=$OPTARG
+ ;;
+ r)
+ RESUME=$OPTARG
+ ;;
+ g)
+ GPU=$OPTARG
+ ;;
+ \?)
+ echo "Invalid option: -$OPTARG"
+ ;;
+ esac
+done
+
+if [ "${NUM_GPU}" = 'None' ]
+then
+ NUM_GPU=`$PYTHON -c 'import torch; print(torch.cuda.device_count())'`
+fi
+
+echo "Experiment name: $EXP_NAME"
+echo "Python interpreter dir: $PYTHON"
+echo "Dataset: $DATASET"
+echo "Config: $CONFIG"
+echo "GPU Num: $GPU"
+
+EXP_DIR=exp/${DATASET}/${EXP_NAME}
+MODEL_DIR=${EXP_DIR}/model
+CODE_DIR=${EXP_DIR}/code
+CONFIG_DIR=configs/${DATASET}/${CONFIG}.py
+
+
+echo " =========> CREATE EXP DIR <========="
+echo "Experiment dir: $ROOT_DIR/$EXP_DIR"
+if ${RESUME}
+then
+ CONFIG_DIR=${EXP_DIR}/config.py
+ WEIGHT=$MODEL_DIR/model_last.pth
+else
+ mkdir -p "$MODEL_DIR" "$CODE_DIR"
+ cp -r scripts tools pointcept "$CODE_DIR"
+fi
+
+echo "Loading config in:" $CONFIG_DIR
+export PYTHONPATH=./$CODE_DIR
+echo "Running code in: $CODE_DIR"
+
+
+echo " =========> RUN TASK <========="
+
+if [ "${WEIGHT}" = "None" ]
+then
+ $PYTHON "$CODE_DIR"/tools/$TRAIN_CODE \
+ --config-file "$CONFIG_DIR" \
+ --num-gpus "$GPU" \
+ --options save_path="$EXP_DIR"
+else
+ $PYTHON "$CODE_DIR"/tools/$TRAIN_CODE \
+ --config-file "$CONFIG_DIR" \
+ --num-gpus "$GPU" \
+ --options save_path="$EXP_DIR" resume="$RESUME" weight="$WEIGHT"
+fi
\ No newline at end of file
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/create_waymo_semseg_submission.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/create_waymo_semseg_submission.py
new file mode 100644
index 0000000000000000000000000000000000000000..ded9f68bde40015a1bc7d1b7197ae909ff5831fe
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/create_waymo_semseg_submission.py
@@ -0,0 +1,131 @@
+"""
+Script for Creating Waymo Semantic Segmentation Submission
+
+The Waymo dataset toolkit relies on an old version of Tensorflow
+which share a conflicting dependency with the Pointcept environment,
+therefore we detach the submission generation from the test process
+and the script require the following environment:
+
+```bash
+conda create -n waymo python=3.8 -y
+conda activate waymo
+pip3 install waymo-open-dataset-tf-2-11-0
+```
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import tqdm
+import argparse
+import numpy as np
+import zlib
+import waymo_open_dataset.dataset_pb2 as open_dataset
+from waymo_open_dataset.protos import segmentation_metrics_pb2
+from waymo_open_dataset.protos import segmentation_submission_pb2
+
+
+def compress_array(array: np.ndarray, is_int32: bool = False):
+ """Compress a numpy array to ZLIP compressed serialized MatrixFloat/Int32.
+
+ Args:
+ array: A numpy array.
+ is_int32: If true, use MatrixInt32, otherwise use MatrixFloat.
+
+ Returns:
+ The compressed bytes.
+ """
+ if is_int32:
+ m = open_dataset.MatrixInt32()
+ else:
+ m = open_dataset.MatrixFloat()
+ m.shape.dims.extend(list(array.shape))
+ m.data.extend(array.reshape([-1]).tolist())
+ return zlib.compress(m.SerializeToString())
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--record_path",
+ required=True,
+ help="Path to the prediction result folder of Waymo dataset",
+ )
+ parser.add_argument(
+ "--dataset_path",
+ required=True,
+ help="Path to the processed Waymo dataset",
+ )
+ parser.add_argument(
+ "--split",
+ required=True,
+ choices=["validation", "testing"],
+ help="Split of the prediction ([training, validation, testing]).",
+ )
+ args = parser.parse_args()
+ file_list = [file for file in os.listdir(args.record_path) if file.endswith(".npy")]
+ submission = segmentation_submission_pb2.SemanticSegmentationSubmission()
+ frames = segmentation_metrics_pb2.SegmentationFrameList()
+ bar = tqdm.tqdm(file_list)
+ for file in bar:
+ bar.set_postfix(file=file)
+ context_name, frame_timestamp_micros = file.strip("segment-*_pred.npy").split(
+ "_with_camera_labels_"
+ )
+ # Load prediction.
+ # In Pointcept waymo dataset, we minus 1 to label to ignore UNLABELLED class (0 -> -1)
+ pred = np.load(os.path.join(args.record_path, file)) + 1
+ masks = np.load(
+ os.path.join(
+ args.dataset_path,
+ args.split,
+ f"segment-{context_name}_with_camera_labels",
+ frame_timestamp_micros,
+ "mask.npy",
+ ),
+ allow_pickle=True,
+ )
+ offset = np.cumsum([mask.sum() for mask in masks.reshape(-1)])
+ pred = np.split(pred[: offset[-1]], offset[:-1])
+ pred_ri1 = pred[0]
+ pred_ri2 = pred[5]
+ mask_ri1 = np.expand_dims(masks[0, 0], -1)
+ mask_ri2 = np.expand_dims(masks[1, 0], -1)
+ range_dummy = np.zeros_like(mask_ri1, dtype=np.int32)
+ range_pred_ri1 = np.zeros_like(mask_ri1, dtype=np.int32)
+ range_pred_ri1[mask_ri1] = pred_ri1
+ range_pred_ri1 = np.concatenate([range_dummy, range_pred_ri1], axis=-1)
+ range_pred_ri2 = np.zeros_like(mask_ri2, dtype=np.int32)
+ range_pred_ri2[mask_ri2] = pred_ri2
+ range_pred_ri2 = np.concatenate([range_dummy, range_pred_ri2], axis=-1)
+
+ # generate frame submission
+ segmentation_label = open_dataset.Laser()
+ segmentation_label.name = open_dataset.LaserName.TOP
+ segmentation_label.ri_return1.segmentation_label_compressed = compress_array(
+ range_pred_ri1, is_int32=True
+ )
+ segmentation_label.ri_return2.segmentation_label_compressed = compress_array(
+ range_pred_ri2, is_int32=True
+ )
+ frame = segmentation_metrics_pb2.SegmentationFrame()
+ frame.segmentation_labels.append(segmentation_label)
+ frame.context_name = context_name
+ frame.frame_timestamp_micros = int(frame_timestamp_micros)
+ frames.frames.append(frame)
+ submission.account_name = "***"
+ submission.unique_method_name = "***"
+ submission.authors.append("***")
+ submission.affiliation = "***"
+ submission.method_link = "***"
+ submission.sensor_type = (
+ segmentation_submission_pb2.SemanticSegmentationSubmission.LIDAR_ALL
+ )
+ submission.number_past_frames_exclude_current = 0
+ submission.number_future_frames_exclude_current = 0
+ submission.inference_results.CopyFrom(frames)
+ output_filename = os.path.join(args.record_path, "submission.bin")
+ f = open(output_filename, "wb")
+ f.write(submission.SerializeToString())
+ f.close()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/test.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c66708d417082451f23cb635bf4dd1c59082f625
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/test.py
@@ -0,0 +1,38 @@
+"""
+Main Testing Script
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.engines.defaults import (
+ default_argument_parser,
+ default_config_parser,
+ default_setup,
+)
+from pointcept.engines.test import TESTERS
+from pointcept.engines.launch import launch
+
+
+def main_worker(cfg):
+ cfg = default_setup(cfg)
+ tester = TESTERS.build(dict(type=cfg.test.type, cfg=cfg))
+ tester.test()
+
+
+def main():
+ args = default_argument_parser().parse_args()
+ cfg = default_config_parser(args.config_file, args.options)
+
+ launch(
+ main_worker,
+ num_gpus_per_machine=args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ cfg=(cfg,),
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/test_s3dis_6fold.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/test_s3dis_6fold.py
new file mode 100644
index 0000000000000000000000000000000000000000..711ad42c956412cb9cb68adf596b679e25f48d19
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/test_s3dis_6fold.py
@@ -0,0 +1,102 @@
+"""
+Test script for S3DIS 6-fold cross validation
+
+Gathering Area_X.pth from result folder of experiment record of each area as follows:
+|- RECORDS_PATH
+ |- Area_1.pth
+ |- Area_2.pth
+ |- Area_3.pth
+ |- Area_4.pth
+ |- Area_5.pth
+ |- Area_6.pth
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import os
+
+import torch
+import numpy as np
+import glob
+from pointcept.utils.logger import get_root_logger
+
+CLASS_NAMES = [
+ "ceiling",
+ "floor",
+ "wall",
+ "beam",
+ "column",
+ "window",
+ "door",
+ "table",
+ "chair",
+ "sofa",
+ "bookcase",
+ "board",
+ "clutter",
+]
+
+
+def evaluation(intersection, union, target, logger=None):
+ iou_class = intersection / (union + 1e-10)
+ accuracy_class = intersection / (target + 1e-10)
+ mIoU = np.mean(iou_class)
+ mAcc = np.mean(accuracy_class)
+ allAcc = sum(intersection) / (sum(target) + 1e-10)
+
+ if logger is not None:
+ logger.info(
+ "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}".format(
+ mIoU, mAcc, allAcc
+ )
+ )
+ for i in range(len(CLASS_NAMES)):
+ logger.info(
+ "Class_{idx} - {name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format(
+ idx=i,
+ name=CLASS_NAMES[i],
+ iou=iou_class[i],
+ accuracy=accuracy_class[i],
+ )
+ )
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--record_root",
+ required=True,
+ help="Path to the S3DIS record of each split",
+ )
+ config = parser.parse_args()
+ logger = get_root_logger(
+ log_file=os.path.join(config.record_root, "6-fold.log"),
+ file_mode="w",
+ )
+
+ records = sorted(glob.glob(os.path.join(config.record_root, "Area_*.pth")))
+ assert len(records) == 6
+ intersection_ = np.zeros(len(CLASS_NAMES), dtype=int)
+ union_ = np.zeros(len(CLASS_NAMES), dtype=int)
+ target_ = np.zeros(len(CLASS_NAMES), dtype=int)
+
+ for record in records:
+ area = os.path.basename(record).split(".")[0]
+ info = torch.load(record)
+ logger.info(f"<<<<<<<<<<<<<<<<< Parsing {area} <<<<<<<<<<<<<<<<<")
+ intersection = info["intersection"]
+ union = info["union"]
+ target = info["target"]
+ evaluation(intersection, union, target, logger=logger)
+ intersection_ += intersection
+ union_ += union
+ target_ += target
+
+ logger.info(f"<<<<<<<<<<<<<<<<< Parsing 6-fold <<<<<<<<<<<<<<<<<")
+ evaluation(intersection_, union_, target_, logger=logger)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/train.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3ed749c4d0bae2c3ad26487d92c46c5695341a2
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/code/tools/train.py
@@ -0,0 +1,38 @@
+"""
+Main Training Script
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.engines.defaults import (
+ default_argument_parser,
+ default_config_parser,
+ default_setup,
+)
+from pointcept.engines.train import TRAINERS
+from pointcept.engines.launch import launch
+
+
+def main_worker(cfg):
+ cfg = default_setup(cfg)
+ trainer = TRAINERS.build(dict(type=cfg.train.type, cfg=cfg))
+ trainer.train()
+
+
+def main():
+ args = default_argument_parser().parse_args()
+ cfg = default_config_parser(args.config_file, args.options)
+
+ launch(
+ main_worker,
+ num_gpus_per_machine=args.num_gpus,
+ num_machines=args.num_machines,
+ machine_rank=args.machine_rank,
+ dist_url=args.dist_url,
+ cfg=(cfg,),
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/config.py b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..78c0f5c3e70cce56082d67276eb391e2e25f0739
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/config.py
@@ -0,0 +1,613 @@
+weight = 'exp/scannet/semseg-pt-v3m1-1-ppt-extreme-alc-20240823-massive-no-val/model/model_last.pth'
+resume = False
+evaluate = True
+test_only = False
+seed = 39084076
+save_path = 'exp/scannet/semseg-pt-v3m1-1-ppt-extreme-alc-20240823-massive-no-val'
+num_worker = 32
+batch_size = 16
+batch_size_val = None
+batch_size_test = None
+epoch = 800
+eval_epoch = 100
+sync_bn = False
+enable_amp = True
+empty_cache = False
+empty_cache_per_epoch = False
+find_unused_parameters = True
+mix_prob = 0.8
+param_dicts = [dict(keyword='block', lr=0.0005)]
+hooks = [
+ dict(type='CheckpointLoader'),
+ dict(type='IterationTimer', warmup_iter=2),
+ dict(type='InformationWriter'),
+ dict(type='SemSegEvaluator'),
+ dict(type='CheckpointSaver', save_freq=None),
+ dict(type='PreciseEvaluator', test_last=False)
+]
+train = dict(type='MultiDatasetTrainer')
+test = dict(type='SemSegTester', verbose=True)
+CLASS_LABELS_200 = (
+ 'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet', 'shelf',
+ 'desk', 'office chair', 'bed', 'pillow', 'sink', 'picture', 'window',
+ 'toilet', 'bookshelf', 'monitor', 'curtain', 'book', 'armchair',
+ 'coffee table', 'box', 'refrigerator', 'lamp', 'kitchen cabinet', 'towel',
+ 'clothes', 'tv', 'nightstand', 'counter', 'dresser', 'stool', 'cushion',
+ 'plant', 'ceiling', 'bathtub', 'end table', 'dining table', 'keyboard',
+ 'bag', 'backpack', 'toilet paper', 'printer', 'tv stand', 'whiteboard',
+ 'blanket', 'shower curtain', 'trash can', 'closet', 'stairs', 'microwave',
+ 'stove', 'shoe', 'computer tower', 'bottle', 'bin', 'ottoman', 'bench',
+ 'board', 'washing machine', 'mirror', 'copier', 'basket', 'sofa chair',
+ 'file cabinet', 'fan', 'laptop', 'shower', 'paper', 'person',
+ 'paper towel dispenser', 'oven', 'blinds', 'rack', 'plate', 'blackboard',
+ 'piano', 'suitcase', 'rail', 'radiator', 'recycling bin', 'container',
+ 'wardrobe', 'soap dispenser', 'telephone', 'bucket', 'clock', 'stand',
+ 'light', 'laundry basket', 'pipe', 'clothes dryer', 'guitar',
+ 'toilet paper holder', 'seat', 'speaker', 'column', 'bicycle', 'ladder',
+ 'bathroom stall', 'shower wall', 'cup', 'jacket', 'storage bin',
+ 'coffee maker', 'dishwasher', 'paper towel roll', 'machine', 'mat',
+ 'windowsill', 'bar', 'toaster', 'bulletin board', 'ironing board',
+ 'fireplace', 'soap dish', 'kitchen counter', 'doorframe',
+ 'toilet paper dispenser', 'mini fridge', 'fire extinguisher', 'ball',
+ 'hat', 'shower curtain rod', 'water cooler', 'paper cutter', 'tray',
+ 'shower door', 'pillar', 'ledge', 'toaster oven', 'mouse',
+ 'toilet seat cover dispenser', 'furniture', 'cart', 'storage container',
+ 'scale', 'tissue box', 'light switch', 'crate', 'power outlet',
+ 'decoration', 'sign', 'projector', 'closet door', 'vacuum cleaner',
+ 'candle', 'plunger', 'stuffed animal', 'headphones', 'dish rack', 'broom',
+ 'guitar case', 'range hood', 'dustpan', 'hair dryer', 'water bottle',
+ 'handicap bar', 'purse', 'vent', 'shower floor', 'water pitcher',
+ 'mailbox', 'bowl', 'paper bag', 'alarm clock', 'music stand',
+ 'projector screen', 'divider', 'laundry detergent', 'bathroom counter',
+ 'object', 'bathroom vanity', 'closet wall', 'laundry hamper',
+ 'bathroom stall door', 'ceiling light', 'trash bin', 'dumbbell',
+ 'stair rail', 'tube', 'bathroom cabinet', 'cd case', 'closet rod',
+ 'coffee kettle', 'structure', 'shower head', 'keyboard piano',
+ 'case of water bottles', 'coat rack', 'storage organizer', 'folded chair',
+ 'fire alarm', 'power strip', 'calendar', 'poster', 'potted plant',
+ 'luggage', 'mattress')
+model = dict(
+ type='PPT-v1m2',
+ backbone=dict(
+ type='PT-v3m1',
+ in_channels=6,
+ order=('z', 'z-trans', 'hilbert', 'hilbert-trans'),
+ stride=(2, 2, 2, 2),
+ enc_depths=(3, 3, 3, 6, 3),
+ enc_channels=(48, 96, 192, 384, 512),
+ enc_num_head=(3, 6, 12, 24, 32),
+ enc_patch_size=(1024, 1024, 1024, 1024, 1024),
+ dec_depths=(3, 3, 3, 3),
+ dec_channels=(64, 96, 192, 384),
+ dec_num_head=(4, 6, 12, 24),
+ dec_patch_size=(1024, 1024, 1024, 1024),
+ mlp_ratio=4,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ drop_path=0.3,
+ shuffle_orders=True,
+ pre_norm=True,
+ enable_rpe=False,
+ enable_flash=True,
+ upcast_attention=False,
+ upcast_softmax=False,
+ cls_mode=False,
+ pdnorm_bn=True,
+ pdnorm_ln=True,
+ pdnorm_decouple=True,
+ pdnorm_adaptive=False,
+ pdnorm_affine=True,
+ pdnorm_conditions=('ScanNet', 'ScanNet200', 'ScanNet++',
+ 'Structured3D', 'ALC')),
+ criteria=[
+ dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1),
+ dict(
+ type='LovaszLoss',
+ mode='multiclass',
+ loss_weight=1.0,
+ ignore_index=-1)
+ ],
+ backbone_out_channels=64,
+ context_channels=256,
+ conditions=('ScanNet', 'ScanNet200', 'ScanNet++', 'Structured3D', 'ALC'),
+ num_classes=(20, 200, 100, 25, 185))
+optimizer = dict(type='AdamW', lr=0.005, weight_decay=0.05)
+scheduler = dict(
+ type='OneCycleLR',
+ max_lr=[0.005, 0.0005],
+ pct_start=0.05,
+ anneal_strategy='cos',
+ div_factor=10.0,
+ final_div_factor=1000.0)
+data = dict(
+ num_classes=200,
+ ignore_index=-1,
+ names=(
+ 'wall', 'chair', 'floor', 'table', 'door', 'couch', 'cabinet', 'shelf',
+ 'desk', 'office chair', 'bed', 'pillow', 'sink', 'picture', 'window',
+ 'toilet', 'bookshelf', 'monitor', 'curtain', 'book', 'armchair',
+ 'coffee table', 'box', 'refrigerator', 'lamp', 'kitchen cabinet',
+ 'towel', 'clothes', 'tv', 'nightstand', 'counter', 'dresser', 'stool',
+ 'cushion', 'plant', 'ceiling', 'bathtub', 'end table', 'dining table',
+ 'keyboard', 'bag', 'backpack', 'toilet paper', 'printer', 'tv stand',
+ 'whiteboard', 'blanket', 'shower curtain', 'trash can', 'closet',
+ 'stairs', 'microwave', 'stove', 'shoe', 'computer tower', 'bottle',
+ 'bin', 'ottoman', 'bench', 'board', 'washing machine', 'mirror',
+ 'copier', 'basket', 'sofa chair', 'file cabinet', 'fan', 'laptop',
+ 'shower', 'paper', 'person', 'paper towel dispenser', 'oven', 'blinds',
+ 'rack', 'plate', 'blackboard', 'piano', 'suitcase', 'rail', 'radiator',
+ 'recycling bin', 'container', 'wardrobe', 'soap dispenser',
+ 'telephone', 'bucket', 'clock', 'stand', 'light', 'laundry basket',
+ 'pipe', 'clothes dryer', 'guitar', 'toilet paper holder', 'seat',
+ 'speaker', 'column', 'bicycle', 'ladder', 'bathroom stall',
+ 'shower wall', 'cup', 'jacket', 'storage bin', 'coffee maker',
+ 'dishwasher', 'paper towel roll', 'machine', 'mat', 'windowsill',
+ 'bar', 'toaster', 'bulletin board', 'ironing board', 'fireplace',
+ 'soap dish', 'kitchen counter', 'doorframe', 'toilet paper dispenser',
+ 'mini fridge', 'fire extinguisher', 'ball', 'hat',
+ 'shower curtain rod', 'water cooler', 'paper cutter', 'tray',
+ 'shower door', 'pillar', 'ledge', 'toaster oven', 'mouse',
+ 'toilet seat cover dispenser', 'furniture', 'cart',
+ 'storage container', 'scale', 'tissue box', 'light switch', 'crate',
+ 'power outlet', 'decoration', 'sign', 'projector', 'closet door',
+ 'vacuum cleaner', 'candle', 'plunger', 'stuffed animal', 'headphones',
+ 'dish rack', 'broom', 'guitar case', 'range hood', 'dustpan',
+ 'hair dryer', 'water bottle', 'handicap bar', 'purse', 'vent',
+ 'shower floor', 'water pitcher', 'mailbox', 'bowl', 'paper bag',
+ 'alarm clock', 'music stand', 'projector screen', 'divider',
+ 'laundry detergent', 'bathroom counter', 'object', 'bathroom vanity',
+ 'closet wall', 'laundry hamper', 'bathroom stall door',
+ 'ceiling light', 'trash bin', 'dumbbell', 'stair rail', 'tube',
+ 'bathroom cabinet', 'cd case', 'closet rod', 'coffee kettle',
+ 'structure', 'shower head', 'keyboard piano', 'case of water bottles',
+ 'coat rack', 'storage organizer', 'folded chair', 'fire alarm',
+ 'power strip', 'calendar', 'poster', 'potted plant', 'luggage',
+ 'mattress'),
+ train=dict(
+ type='ConcatDataset',
+ datasets=[
+ dict(
+ type='ScanNetDataset',
+ split='train',
+ data_root='data/scannet',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='RandomDropout',
+ dropout_ratio=0.2,
+ dropout_application_ratio=0.2),
+ dict(
+ type='RandomRotate',
+ angle=[-1, 1],
+ axis='z',
+ center=[0, 0, 0],
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='x',
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='y',
+ p=0.5),
+ dict(type='RandomScale', scale=[0.9, 1.1]),
+ dict(type='RandomFlip', p=0.5),
+ dict(type='RandomJitter', sigma=0.005, clip=0.02),
+ dict(
+ type='ElasticDistortion',
+ distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
+ dict(
+ type='ChromaticAutoContrast', p=0.2,
+ blend_factor=None),
+ dict(type='ChromaticTranslation', p=0.95, ratio=0.05),
+ dict(type='ChromaticJitter', p=0.95, std=0.05),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True),
+ dict(type='SphereCrop', point_max=102400, mode='random'),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(type='ShufflePoint'),
+ dict(type='Add', keys_dict=dict(condition='ScanNet')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False,
+ loop=1),
+ dict(
+ type='ScanNetPPDataset',
+ split=[
+ 'train_grid1mm_chunk6x6_stride3x3',
+ 'val_grid1mm_chunk6x6_stride3x3'
+ ],
+ data_root='data/scannetpp',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='RandomDropout',
+ dropout_ratio=0.2,
+ dropout_application_ratio=0.2),
+ dict(
+ type='RandomRotate',
+ angle=[-1, 1],
+ axis='z',
+ center=[0, 0, 0],
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='x',
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='y',
+ p=0.5),
+ dict(type='RandomScale', scale=[0.9, 1.1]),
+ dict(type='RandomFlip', p=0.5),
+ dict(type='RandomJitter', sigma=0.005, clip=0.02),
+ dict(
+ type='ElasticDistortion',
+ distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
+ dict(
+ type='ChromaticAutoContrast', p=0.2,
+ blend_factor=None),
+ dict(type='ChromaticTranslation', p=0.95, ratio=0.05),
+ dict(type='ChromaticJitter', p=0.95, std=0.05),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True),
+ dict(type='SphereCrop', point_max=204800, mode='random'),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(type='Add', keys_dict=dict(condition='ScanNet++')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False,
+ loop=1),
+ dict(
+ type='Structured3DDataset',
+ split=['train', 'test'],
+ data_root='data/structured3d',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='RandomDropout',
+ dropout_ratio=0.2,
+ dropout_application_ratio=0.2),
+ dict(
+ type='RandomRotate',
+ angle=[-1, 1],
+ axis='z',
+ center=[0, 0, 0],
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='x',
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='y',
+ p=0.5),
+ dict(type='RandomScale', scale=[0.9, 1.1]),
+ dict(type='RandomFlip', p=0.5),
+ dict(type='RandomJitter', sigma=0.005, clip=0.02),
+ dict(
+ type='ElasticDistortion',
+ distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
+ dict(
+ type='ChromaticAutoContrast', p=0.2,
+ blend_factor=None),
+ dict(type='ChromaticTranslation', p=0.95, ratio=0.05),
+ dict(type='ChromaticJitter', p=0.95, std=0.05),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True),
+ dict(type='SphereCrop', sample_rate=0.8, mode='random'),
+ dict(type='SphereCrop', point_max=102400, mode='random'),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(type='Add', keys_dict=dict(condition='Structured3D')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False,
+ loop=2),
+ dict(
+ type='ScanNet200Dataset',
+ split='train',
+ data_root='data/scannet',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='RandomDropout',
+ dropout_ratio=0.2,
+ dropout_application_ratio=0.2),
+ dict(
+ type='RandomRotate',
+ angle=[-1, 1],
+ axis='z',
+ center=[0, 0, 0],
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='x',
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='y',
+ p=0.5),
+ dict(type='RandomScale', scale=[0.9, 1.1]),
+ dict(type='RandomFlip', p=0.5),
+ dict(type='RandomJitter', sigma=0.005, clip=0.02),
+ dict(
+ type='ElasticDistortion',
+ distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
+ dict(
+ type='ChromaticAutoContrast', p=0.2,
+ blend_factor=None),
+ dict(type='ChromaticTranslation', p=0.95, ratio=0.05),
+ dict(type='ChromaticJitter', p=0.95, std=0.05),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True),
+ dict(type='SphereCrop', point_max=102400, mode='random'),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(type='ShufflePoint'),
+ dict(type='Add', keys_dict=dict(condition='ScanNet200')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False,
+ loop=1),
+ dict(
+ type='ARKitScenesLabelMakerConsensusDataset',
+ split=['train', 'val'],
+ data_root='data/alc',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='RandomDropout',
+ dropout_ratio=0.2,
+ dropout_application_ratio=0.2),
+ dict(
+ type='RandomRotate',
+ angle=[-1, 1],
+ axis='z',
+ center=[0, 0, 0],
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='x',
+ p=0.5),
+ dict(
+ type='RandomRotate',
+ angle=[-0.015625, 0.015625],
+ axis='y',
+ p=0.5),
+ dict(type='RandomScale', scale=[0.9, 1.1]),
+ dict(type='RandomFlip', p=0.5),
+ dict(type='RandomJitter', sigma=0.005, clip=0.02),
+ dict(
+ type='ElasticDistortion',
+ distortion_params=[[0.2, 0.4], [0.8, 1.6]]),
+ dict(
+ type='ChromaticAutoContrast', p=0.2,
+ blend_factor=None),
+ dict(type='ChromaticTranslation', p=0.95, ratio=0.05),
+ dict(type='ChromaticJitter', p=0.95, std=0.05),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True),
+ dict(type='SphereCrop', point_max=102400, mode='random'),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(type='Add', keys_dict=dict(condition='ALC')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False,
+ loop=2)
+ ],
+ loop=8),
+ val=dict(
+ type='ScanNetDataset',
+ split='val',
+ data_root='data/scannet',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='train',
+ return_grid_coord=True),
+ dict(type='CenterShift', apply_z=False),
+ dict(type='NormalizeColor'),
+ dict(type='ToTensor'),
+ dict(type='Add', keys_dict=dict(condition='ScanNet')),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'segment', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ test_mode=False),
+ test=dict(
+ type='ScanNet200Dataset',
+ split='val',
+ data_root='data/scannet',
+ transform=[
+ dict(type='CenterShift', apply_z=True),
+ dict(type='NormalizeColor')
+ ],
+ test_mode=True,
+ test_cfg=dict(
+ voxelize=dict(
+ type='GridSample',
+ grid_size=0.02,
+ hash_type='fnv',
+ mode='test',
+ keys=('coord', 'color', 'normal'),
+ return_grid_coord=True),
+ crop=None,
+ post_transform=[
+ dict(type='CenterShift', apply_z=False),
+ dict(type='Add', keys_dict=dict(condition='ScanNet200')),
+ dict(type='ToTensor'),
+ dict(
+ type='Collect',
+ keys=('coord', 'grid_coord', 'index', 'condition'),
+ feat_keys=('color', 'normal'))
+ ],
+ aug_transform=[[{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [0],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [0.5],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [1],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [1.5],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [0],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [0.95, 0.95]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [0.5],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [0.95, 0.95]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [1],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [0.95, 0.95]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [1.5],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [0.95, 0.95]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [0],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [1.05, 1.05]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [0.5],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [1.05, 1.05]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [1],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [1.05, 1.05]
+ }],
+ [{
+ 'type': 'RandomRotateTargetAngle',
+ 'angle': [1.5],
+ 'axis': 'z',
+ 'center': [0, 0, 0],
+ 'p': 1
+ }, {
+ 'type': 'RandomScale',
+ 'scale': [1.05, 1.05]
+ }], [{
+ 'type': 'RandomFlip',
+ 'p': 1
+ }]])))
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/events.out.tfevents.1728045179.eu-g4-014 b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/events.out.tfevents.1728045179.eu-g4-014
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diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/events.out.tfevents.1728212217.eu-g4-025 b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/events.out.tfevents.1728212217.eu-g4-025
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diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/events.out.tfevents.1728248293.eu-g4-024 b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/events.out.tfevents.1728248293.eu-g4-024
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+[2024-10-13 15:29:04,932 INFO test.py line 41 25394] => Loading config ...
+[2024-10-13 15:29:04,932 INFO test.py line 48 25394] => Building model ...
+[2024-10-13 15:29:05,525 INFO test.py line 61 25394] Num params: 97544258
+[2024-10-13 15:29:05,836 INFO test.py line 68 25394] Loading weight at: exp/scannet/semseg-pt-v3m1-1-ppt-extreme-alc-20240823-massive-no-val/model/model_last.pth
+[2024-10-13 15:29:16,210 INFO test.py line 80 25394] => Loaded weight 'exp/scannet/semseg-pt-v3m1-1-ppt-extreme-alc-20240823-massive-no-val/model/model_last.pth' (epoch 100)
+[2024-10-13 15:29:16,217 INFO test.py line 53 25394] => Building test dataset & dataloader ...
+[2024-10-13 15:29:16,222 INFO scannet.py line 65 25394] Totally 312 x 1 samples in val set.
+[2024-10-13 15:29:16,222 INFO test.py line 116 25394] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
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+[2024-10-13 15:29:29,488 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 102/112
+[2024-10-13 15:29:29,561 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 103/112
+[2024-10-13 15:29:29,629 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 104/112
+[2024-10-13 15:29:29,697 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 105/112
+[2024-10-13 15:29:29,765 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 106/112
+[2024-10-13 15:29:29,833 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 107/112
+[2024-10-13 15:29:29,901 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 108/112
+[2024-10-13 15:29:29,969 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 109/112
+[2024-10-13 15:29:30,038 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 110/112
+[2024-10-13 15:29:30,105 INFO test.py line 186 25394] Test: 1/312-scene0461_00, Batch: 111/112
+[2024-10-13 15:29:30,191 INFO test.py line 272 25394] Test: scene0461_00 [1/312]-59134 Batch 11.701 (11.701) Accuracy 0.9392 (0.0286) mIoU 0.7408 (0.0259)
+[2024-10-13 15:29:30,333 INFO test.py line 186 25394] Test: 2/312-scene0655_00, Batch: 0/135
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+[2024-10-13 15:29:47,004 INFO test.py line 186 25394] Test: 2/312-scene0655_00, Batch: 133/135
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+[2024-10-13 15:29:47,304 INFO test.py line 272 25394] Test: scene0655_00 [2/312]-139138 Batch 17.113 (14.407) Accuracy 0.7620 (0.0384) mIoU 0.4946 (0.0311)
+[2024-10-13 15:29:47,442 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 0/134
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+[2024-10-13 15:30:02,610 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 126/134
+[2024-10-13 15:30:02,730 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 127/134
+[2024-10-13 15:30:02,850 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 128/134
+[2024-10-13 15:30:02,970 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 129/134
+[2024-10-13 15:30:03,090 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 130/134
+[2024-10-13 15:30:03,210 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 131/134
+[2024-10-13 15:30:03,330 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 132/134
+[2024-10-13 15:30:03,450 INFO test.py line 186 25394] Test: 3/312-scene0300_00, Batch: 133/134
+[2024-10-13 15:30:03,634 INFO test.py line 272 25394] Test: scene0300_00 [3/312]-140216 Batch 16.329 (15.048) Accuracy 0.8101 (0.0587) mIoU 0.5709 (0.0520)
+[2024-10-13 15:30:03,797 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 0/138
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+[2024-10-13 15:30:21,598 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 130/138
+[2024-10-13 15:30:21,735 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 131/138
+[2024-10-13 15:30:21,872 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 132/138
+[2024-10-13 15:30:22,010 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 133/138
+[2024-10-13 15:30:22,147 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 134/138
+[2024-10-13 15:30:22,285 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 135/138
+[2024-10-13 15:30:22,422 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 136/138
+[2024-10-13 15:30:22,560 INFO test.py line 186 25394] Test: 4/312-scene0699_00, Batch: 137/138
+[2024-10-13 15:30:22,756 INFO test.py line 272 25394] Test: scene0699_00 [4/312]-153041 Batch 19.122 (16.066) Accuracy 0.7688 (0.1053) mIoU 0.4014 (0.0911)
+[2024-10-13 15:30:22,852 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 0/116
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+[2024-10-13 15:30:30,213 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 88/116
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+[2024-10-13 15:30:30,386 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 90/116
+[2024-10-13 15:30:30,473 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 91/116
+[2024-10-13 15:30:30,560 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 92/116
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+[2024-10-13 15:30:30,733 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 94/116
+[2024-10-13 15:30:30,820 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 95/116
+[2024-10-13 15:30:30,908 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 96/116
+[2024-10-13 15:30:31,001 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 97/116
+[2024-10-13 15:30:31,087 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 98/116
+[2024-10-13 15:30:31,174 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 99/116
+[2024-10-13 15:30:31,261 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 100/116
+[2024-10-13 15:30:31,347 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 101/116
+[2024-10-13 15:30:31,434 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 102/116
+[2024-10-13 15:30:31,521 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 103/116
+[2024-10-13 15:30:31,607 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 104/116
+[2024-10-13 15:30:31,694 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 105/116
+[2024-10-13 15:30:31,781 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 106/116
+[2024-10-13 15:30:31,867 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 107/116
+[2024-10-13 15:30:31,951 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 108/116
+[2024-10-13 15:30:32,034 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 109/116
+[2024-10-13 15:30:32,118 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 110/116
+[2024-10-13 15:30:32,202 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 111/116
+[2024-10-13 15:30:32,285 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 112/116
+[2024-10-13 15:30:32,369 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 113/116
+[2024-10-13 15:30:32,452 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 114/116
+[2024-10-13 15:30:32,536 INFO test.py line 186 25394] Test: 5/312-scene0063_00, Batch: 115/116
+[2024-10-13 15:30:32,638 INFO test.py line 272 25394] Test: scene0063_00 [5/312]-76966 Batch 9.881 (14.829) Accuracy 0.9410 (0.1239) mIoU 0.5118 (0.1085)
+[2024-10-13 15:30:32,715 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 0/108
+[2024-10-13 15:30:32,782 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 1/108
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+[2024-10-13 15:30:33,184 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 7/108
+[2024-10-13 15:30:33,251 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 8/108
+[2024-10-13 15:30:33,317 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 9/108
+[2024-10-13 15:30:33,384 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 10/108
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+[2024-10-13 15:30:33,517 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 12/108
+[2024-10-13 15:30:33,583 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 13/108
+[2024-10-13 15:30:33,650 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 14/108
+[2024-10-13 15:30:33,717 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 15/108
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+[2024-10-13 15:30:34,051 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 20/108
+[2024-10-13 15:30:34,117 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 21/108
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+[2024-10-13 15:30:34,518 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 27/108
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+[2024-10-13 15:30:34,785 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 31/108
+[2024-10-13 15:30:34,849 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 32/108
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+[2024-10-13 15:30:38,754 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 90/108
+[2024-10-13 15:30:38,823 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 91/108
+[2024-10-13 15:30:38,892 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 92/108
+[2024-10-13 15:30:38,961 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 93/108
+[2024-10-13 15:30:39,030 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 94/108
+[2024-10-13 15:30:39,099 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 95/108
+[2024-10-13 15:30:39,168 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 96/108
+[2024-10-13 15:30:39,237 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 97/108
+[2024-10-13 15:30:39,306 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 98/108
+[2024-10-13 15:30:39,375 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 99/108
+[2024-10-13 15:30:39,442 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 100/108
+[2024-10-13 15:30:39,509 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 101/108
+[2024-10-13 15:30:39,575 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 102/108
+[2024-10-13 15:30:39,642 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 103/108
+[2024-10-13 15:30:39,708 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 104/108
+[2024-10-13 15:30:39,775 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 105/108
+[2024-10-13 15:30:39,841 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 106/108
+[2024-10-13 15:30:39,908 INFO test.py line 186 25394] Test: 6/312-scene0574_00, Batch: 107/108
+[2024-10-13 15:30:39,987 INFO test.py line 272 25394] Test: scene0574_00 [6/312]-55521 Batch 7.349 (13.583) Accuracy 0.8276 (0.1484) mIoU 0.4094 (0.1285)
+[2024-10-13 15:30:40,093 INFO test.py line 186 25394] Test: 7/312-scene0382_01, Batch: 0/121
+[2024-10-13 15:30:40,187 INFO test.py line 186 25394] Test: 7/312-scene0382_01, Batch: 1/121
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+[2024-10-13 15:30:43,409 INFO test.py line 186 25394] Test: 7/312-scene0382_01, Batch: 35/121
+[2024-10-13 15:30:43,499 INFO test.py line 186 25394] Test: 7/312-scene0382_01, Batch: 36/121
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+[2024-10-13 15:30:43,766 INFO test.py line 186 25394] Test: 7/312-scene0382_01, Batch: 39/121
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+[2024-10-13 15:30:51,084 INFO test.py line 186 25394] Test: 7/312-scene0382_01, Batch: 117/121
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+[2024-10-13 15:30:51,494 INFO test.py line 272 25394] Test: scene0382_01 [7/312]-96933 Batch 11.507 (13.286) Accuracy 0.8685 (0.1667) mIoU 0.4938 (0.1421)
+[2024-10-13 15:30:51,621 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 0/142
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+[2024-10-13 15:31:04,520 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 116/142
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+[2024-10-13 15:31:05,346 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 123/142
+[2024-10-13 15:31:05,464 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 124/142
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+[2024-10-13 15:31:05,699 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 126/142
+[2024-10-13 15:31:05,817 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 127/142
+[2024-10-13 15:31:05,935 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 128/142
+[2024-10-13 15:31:06,054 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 129/142
+[2024-10-13 15:31:06,172 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 130/142
+[2024-10-13 15:31:06,290 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 131/142
+[2024-10-13 15:31:06,400 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 132/142
+[2024-10-13 15:31:06,511 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 133/142
+[2024-10-13 15:31:06,622 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 134/142
+[2024-10-13 15:31:06,732 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 135/142
+[2024-10-13 15:31:06,843 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 136/142
+[2024-10-13 15:31:06,954 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 137/142
+[2024-10-13 15:31:07,065 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 138/142
+[2024-10-13 15:31:07,176 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 139/142
+[2024-10-13 15:31:07,287 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 140/142
+[2024-10-13 15:31:07,398 INFO test.py line 186 25394] Test: 8/312-scene0578_02, Batch: 141/142
+[2024-10-13 15:31:07,565 INFO test.py line 272 25394] Test: scene0578_02 [8/312]-128553 Batch 16.071 (13.634) Accuracy 0.9444 (0.1659) mIoU 0.6349 (0.1438)
+[2024-10-13 15:31:07,660 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 0/141
+[2024-10-13 15:31:07,742 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 1/141
+[2024-10-13 15:31:07,825 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 2/141
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+[2024-10-13 15:31:08,237 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 7/141
+[2024-10-13 15:31:08,320 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 8/141
+[2024-10-13 15:31:08,402 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 9/141
+[2024-10-13 15:31:08,484 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 10/141
+[2024-10-13 15:31:08,567 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 11/141
+[2024-10-13 15:31:08,649 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 12/141
+[2024-10-13 15:31:08,732 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 13/141
+[2024-10-13 15:31:08,814 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 14/141
+[2024-10-13 15:31:08,897 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 15/141
+[2024-10-13 15:31:08,979 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 16/141
+[2024-10-13 15:31:09,061 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 17/141
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+[2024-10-13 15:31:09,226 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 19/141
+[2024-10-13 15:31:09,308 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 20/141
+[2024-10-13 15:31:09,390 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 21/141
+[2024-10-13 15:31:09,473 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 22/141
+[2024-10-13 15:31:09,555 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 23/141
+[2024-10-13 15:31:09,638 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 24/141
+[2024-10-13 15:31:09,720 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 25/141
+[2024-10-13 15:31:09,803 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 26/141
+[2024-10-13 15:31:09,885 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 27/141
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+[2024-10-13 15:31:10,342 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 32/141
+[2024-10-13 15:31:10,426 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 33/141
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+[2024-10-13 15:31:15,340 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 96/141
+[2024-10-13 15:31:15,425 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 97/141
+[2024-10-13 15:31:15,511 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 98/141
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+[2024-10-13 15:31:15,681 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 100/141
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+[2024-10-13 15:31:15,937 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 103/141
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+[2024-10-13 15:31:16,449 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 109/141
+[2024-10-13 15:31:16,535 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 110/141
+[2024-10-13 15:31:16,620 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 111/141
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+[2024-10-13 15:31:16,876 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 114/141
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+[2024-10-13 15:31:17,558 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 122/141
+[2024-10-13 15:31:17,643 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 123/141
+[2024-10-13 15:31:17,728 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 124/141
+[2024-10-13 15:31:17,813 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 125/141
+[2024-10-13 15:31:17,898 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 126/141
+[2024-10-13 15:31:17,984 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 127/141
+[2024-10-13 15:31:18,069 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 128/141
+[2024-10-13 15:31:18,154 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 129/141
+[2024-10-13 15:31:18,239 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 130/141
+[2024-10-13 15:31:18,324 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 131/141
+[2024-10-13 15:31:18,407 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 132/141
+[2024-10-13 15:31:18,489 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 133/141
+[2024-10-13 15:31:18,571 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 134/141
+[2024-10-13 15:31:18,654 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 135/141
+[2024-10-13 15:31:18,736 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 136/141
+[2024-10-13 15:31:18,818 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 137/141
+[2024-10-13 15:31:18,901 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 138/141
+[2024-10-13 15:31:18,983 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 139/141
+[2024-10-13 15:31:19,066 INFO test.py line 186 25394] Test: 9/312-scene0278_00, Batch: 140/141
+[2024-10-13 15:31:19,173 INFO test.py line 272 25394] Test: scene0278_00 [9/312]-77020 Batch 11.607 (13.409) Accuracy 0.8844 (0.1877) mIoU 0.4492 (0.1629)
+[2024-10-13 15:31:19,304 INFO test.py line 186 25394] Test: 10/312-scene0430_01, Batch: 0/125
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+[2024-10-13 15:31:33,721 INFO test.py line 272 25394] Test: scene0430_01 [10/312]-126166 Batch 14.547 (13.523) Accuracy 0.8646 (0.1874) mIoU 0.5168 (0.1629)
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+[2024-10-13 15:31:46,877 INFO test.py line 186 25394] Test: 11/312-scene0660_00, Batch: 128/135
+[2024-10-13 15:31:46,979 INFO test.py line 186 25394] Test: 11/312-scene0660_00, Batch: 129/135
+[2024-10-13 15:31:47,080 INFO test.py line 186 25394] Test: 11/312-scene0660_00, Batch: 130/135
+[2024-10-13 15:31:47,182 INFO test.py line 186 25394] Test: 11/312-scene0660_00, Batch: 131/135
+[2024-10-13 15:31:47,283 INFO test.py line 186 25394] Test: 11/312-scene0660_00, Batch: 132/135
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+[2024-10-13 15:31:47,631 INFO test.py line 272 25394] Test: scene0660_00 [11/312]-110960 Batch 13.910 (13.558) Accuracy 0.9957 (0.1876) mIoU 0.9887 (0.1634)
+[2024-10-13 15:31:47,728 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 0/140
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+[2024-10-13 15:31:58,938 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 131/140
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+[2024-10-13 15:31:59,110 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 133/140
+[2024-10-13 15:31:59,196 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 134/140
+[2024-10-13 15:31:59,282 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 135/140
+[2024-10-13 15:31:59,367 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 136/140
+[2024-10-13 15:31:59,453 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 137/140
+[2024-10-13 15:31:59,539 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 138/140
+[2024-10-13 15:31:59,625 INFO test.py line 186 25394] Test: 12/312-scene0277_02, Batch: 139/140
+[2024-10-13 15:31:59,735 INFO test.py line 272 25394] Test: scene0277_02 [12/312]-82251 Batch 12.104 (13.437) Accuracy 0.8927 (0.1929) mIoU 0.6585 (0.1707)
+[2024-10-13 15:31:59,811 INFO test.py line 186 25394] Test: 13/312-scene0081_00, Batch: 0/117
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+[2024-10-13 15:32:07,091 INFO test.py line 186 25394] Test: 13/312-scene0081_00, Batch: 110/117
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+[2024-10-13 15:32:07,290 INFO test.py line 186 25394] Test: 13/312-scene0081_00, Batch: 113/117
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+[2024-10-13 15:32:07,489 INFO test.py line 186 25394] Test: 13/312-scene0081_00, Batch: 116/117
+[2024-10-13 15:32:07,558 INFO test.py line 272 25394] Test: scene0081_00 [13/312]-51381 Batch 7.823 (13.005) Accuracy 0.9758 (0.2026) mIoU 0.6601 (0.1784)
+[2024-10-13 15:32:07,734 INFO test.py line 186 25394] Test: 14/312-scene0187_00, Batch: 0/148
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+[2024-10-13 15:32:30,524 INFO test.py line 272 25394] Test: scene0187_00 [14/312]-173752 Batch 22.966 (13.717) Accuracy 0.9005 (0.2066) mIoU 0.4304 (0.1800)
+[2024-10-13 15:32:30,900 INFO test.py line 186 25394] Test: 15/312-scene0500_00, Batch: 0/138
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+[2024-10-13 15:33:12,085 INFO test.py line 186 25394] Test: 15/312-scene0500_00, Batch: 132/138
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+[2024-10-13 15:33:12,711 INFO test.py line 186 25394] Test: 15/312-scene0500_00, Batch: 134/138
+[2024-10-13 15:33:13,025 INFO test.py line 186 25394] Test: 15/312-scene0500_00, Batch: 135/138
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+[2024-10-13 15:33:14,143 INFO test.py line 272 25394] Test: scene0500_00 [15/312]-396942 Batch 43.618 (15.710) Accuracy 0.9361 (0.2127) mIoU 0.3718 (0.1864)
+[2024-10-13 15:33:14,341 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 0/139
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+[2024-10-13 15:33:31,863 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 106/139
+[2024-10-13 15:33:32,040 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 107/139
+[2024-10-13 15:33:32,217 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 108/139
+[2024-10-13 15:33:32,393 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 109/139
+[2024-10-13 15:33:32,570 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 110/139
+[2024-10-13 15:33:32,747 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 111/139
+[2024-10-13 15:33:32,924 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 112/139
+[2024-10-13 15:33:33,101 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 113/139
+[2024-10-13 15:33:33,278 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 114/139
+[2024-10-13 15:33:33,455 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 115/139
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+[2024-10-13 15:33:33,809 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 117/139
+[2024-10-13 15:33:33,985 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 118/139
+[2024-10-13 15:33:34,162 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 119/139
+[2024-10-13 15:33:34,338 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 120/139
+[2024-10-13 15:33:34,515 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 121/139
+[2024-10-13 15:33:34,691 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 122/139
+[2024-10-13 15:33:34,868 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 123/139
+[2024-10-13 15:33:35,044 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 124/139
+[2024-10-13 15:33:35,221 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 125/139
+[2024-10-13 15:33:35,398 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 126/139
+[2024-10-13 15:33:35,574 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 127/139
+[2024-10-13 15:33:35,742 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 128/139
+[2024-10-13 15:33:35,908 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 129/139
+[2024-10-13 15:33:36,075 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 130/139
+[2024-10-13 15:33:36,241 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 131/139
+[2024-10-13 15:33:36,408 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 132/139
+[2024-10-13 15:33:36,575 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 133/139
+[2024-10-13 15:33:36,741 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 134/139
+[2024-10-13 15:33:36,908 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 135/139
+[2024-10-13 15:33:37,075 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 136/139
+[2024-10-13 15:33:37,242 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 137/139
+[2024-10-13 15:33:37,408 INFO test.py line 186 25394] Test: 16/312-scene0378_01, Batch: 138/139
+[2024-10-13 15:33:37,657 INFO test.py line 272 25394] Test: scene0378_01 [16/312]-196608 Batch 23.513 (16.198) Accuracy 0.8995 (0.2400) mIoU 0.4715 (0.2057)
+[2024-10-13 15:33:37,882 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 0/138
+[2024-10-13 15:33:38,072 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 1/138
+[2024-10-13 15:33:38,261 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 2/138
+[2024-10-13 15:33:38,451 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 3/138
+[2024-10-13 15:33:38,641 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 4/138
+[2024-10-13 15:33:38,830 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 5/138
+[2024-10-13 15:33:39,020 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 6/138
+[2024-10-13 15:33:39,210 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 7/138
+[2024-10-13 15:33:39,399 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 8/138
+[2024-10-13 15:33:39,589 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 9/138
+[2024-10-13 15:33:39,780 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 10/138
+[2024-10-13 15:33:39,970 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 11/138
+[2024-10-13 15:33:40,160 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 12/138
+[2024-10-13 15:33:40,349 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 13/138
+[2024-10-13 15:33:40,539 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 14/138
+[2024-10-13 15:33:40,729 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 15/138
+[2024-10-13 15:33:40,919 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 16/138
+[2024-10-13 15:33:41,108 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 17/138
+[2024-10-13 15:33:41,297 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 18/138
+[2024-10-13 15:33:41,488 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 19/138
+[2024-10-13 15:33:41,678 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 20/138
+[2024-10-13 15:33:41,868 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 21/138
+[2024-10-13 15:33:42,083 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 22/138
+[2024-10-13 15:33:42,272 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 23/138
+[2024-10-13 15:33:42,463 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 24/138
+[2024-10-13 15:33:42,652 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 25/138
+[2024-10-13 15:33:42,841 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 26/138
+[2024-10-13 15:33:43,031 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 27/138
+[2024-10-13 15:33:43,220 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 28/138
+[2024-10-13 15:33:43,409 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 29/138
+[2024-10-13 15:33:43,598 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 30/138
+[2024-10-13 15:33:43,787 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 31/138
+[2024-10-13 15:33:43,975 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 32/138
+[2024-10-13 15:33:44,164 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 33/138
+[2024-10-13 15:33:44,353 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 34/138
+[2024-10-13 15:33:44,541 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 35/138
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+[2024-10-13 15:33:45,108 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 38/138
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+[2024-10-13 15:33:45,474 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 40/138
+[2024-10-13 15:33:45,652 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 41/138
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+[2024-10-13 15:33:46,183 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 44/138
+[2024-10-13 15:33:46,360 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 45/138
+[2024-10-13 15:33:46,538 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 46/138
+[2024-10-13 15:33:46,715 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 47/138
+[2024-10-13 15:33:46,892 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 48/138
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+[2024-10-13 15:33:48,841 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 59/138
+[2024-10-13 15:33:49,018 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 60/138
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+[2024-10-13 15:33:53,091 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 83/138
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+[2024-10-13 15:33:53,698 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 86/138
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+[2024-10-13 15:33:54,102 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 88/138
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+[2024-10-13 15:33:54,709 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 91/138
+[2024-10-13 15:33:54,911 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 92/138
+[2024-10-13 15:33:55,113 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 93/138
+[2024-10-13 15:33:55,316 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 94/138
+[2024-10-13 15:33:55,518 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 95/138
+[2024-10-13 15:33:55,720 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 96/138
+[2024-10-13 15:33:55,922 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 97/138
+[2024-10-13 15:33:56,124 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 98/138
+[2024-10-13 15:33:56,326 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 99/138
+[2024-10-13 15:33:56,528 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 100/138
+[2024-10-13 15:33:56,730 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 101/138
+[2024-10-13 15:33:56,932 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 102/138
+[2024-10-13 15:33:57,134 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 103/138
+[2024-10-13 15:33:57,336 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 104/138
+[2024-10-13 15:33:57,538 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 105/138
+[2024-10-13 15:33:57,740 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 106/138
+[2024-10-13 15:33:57,942 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 107/138
+[2024-10-13 15:33:58,145 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 108/138
+[2024-10-13 15:33:58,347 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 109/138
+[2024-10-13 15:33:58,549 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 110/138
+[2024-10-13 15:33:58,752 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 111/138
+[2024-10-13 15:33:58,954 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 112/138
+[2024-10-13 15:33:59,156 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 113/138
+[2024-10-13 15:33:59,358 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 114/138
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+[2024-10-13 15:33:59,763 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 116/138
+[2024-10-13 15:33:59,965 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 117/138
+[2024-10-13 15:34:00,167 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 118/138
+[2024-10-13 15:34:00,368 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 119/138
+[2024-10-13 15:34:00,570 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 120/138
+[2024-10-13 15:34:00,773 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 121/138
+[2024-10-13 15:34:00,974 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 122/138
+[2024-10-13 15:34:01,176 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 123/138
+[2024-10-13 15:34:01,378 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 124/138
+[2024-10-13 15:34:01,580 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 125/138
+[2024-10-13 15:34:01,782 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 126/138
+[2024-10-13 15:34:01,984 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 127/138
+[2024-10-13 15:34:02,174 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 128/138
+[2024-10-13 15:34:02,363 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 129/138
+[2024-10-13 15:34:02,552 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 130/138
+[2024-10-13 15:34:02,741 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 131/138
+[2024-10-13 15:34:02,930 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 132/138
+[2024-10-13 15:34:03,119 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 133/138
+[2024-10-13 15:34:03,309 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 134/138
+[2024-10-13 15:34:03,498 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 135/138
+[2024-10-13 15:34:03,688 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 136/138
+[2024-10-13 15:34:03,877 INFO test.py line 186 25394] Test: 17/312-scene0568_00, Batch: 137/138
+[2024-10-13 15:34:04,172 INFO test.py line 272 25394] Test: scene0568_00 [17/312]-232453 Batch 26.515 (16.805) Accuracy 0.7704 (0.2475) mIoU 0.3444 (0.2066)
+[2024-10-13 15:34:04,355 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 0/126
+[2024-10-13 15:34:04,512 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 1/126
+[2024-10-13 15:34:04,667 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 2/126
+[2024-10-13 15:34:04,822 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 3/126
+[2024-10-13 15:34:04,977 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 4/126
+[2024-10-13 15:34:05,131 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 5/126
+[2024-10-13 15:34:05,286 INFO test.py line 186 25394] Test: 18/312-scene0704_00, Batch: 6/126
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+[2024-10-13 15:34:23,886 INFO test.py line 272 25394] Test: scene0704_00 [18/312]-183029 Batch 19.714 (16.966) Accuracy 0.8807 (0.2553) mIoU 0.3844 (0.2135)
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+[2024-10-13 15:34:42,729 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 116/125
+[2024-10-13 15:34:42,891 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 117/125
+[2024-10-13 15:34:43,052 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 118/125
+[2024-10-13 15:34:43,214 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 119/125
+[2024-10-13 15:34:43,375 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 120/125
+[2024-10-13 15:34:43,536 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 121/125
+[2024-10-13 15:34:43,698 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 122/125
+[2024-10-13 15:34:43,859 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 123/125
+[2024-10-13 15:34:44,020 INFO test.py line 186 25394] Test: 19/312-scene0663_00, Batch: 124/125
+[2024-10-13 15:34:44,258 INFO test.py line 272 25394] Test: scene0663_00 [19/312]-184428 Batch 20.372 (17.145) Accuracy 0.8735 (0.2729) mIoU 0.4039 (0.2261)
+[2024-10-13 15:34:44,342 INFO test.py line 186 25394] Test: 20/312-scene0574_02, Batch: 0/108
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+[2024-10-13 15:34:51,891 INFO test.py line 186 25394] Test: 20/312-scene0574_02, Batch: 104/108
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+[2024-10-13 15:34:52,107 INFO test.py line 186 25394] Test: 20/312-scene0574_02, Batch: 107/108
+[2024-10-13 15:34:52,192 INFO test.py line 272 25394] Test: scene0574_02 [20/312]-62475 Batch 7.933 (16.685) Accuracy 0.8866 (0.2737) mIoU 0.5230 (0.2272)
+[2024-10-13 15:34:52,351 INFO test.py line 186 25394] Test: 21/312-scene0081_02, Batch: 0/135
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+[2024-10-13 15:35:10,803 INFO test.py line 272 25394] Test: scene0081_02 [21/312]-153929 Batch 18.611 (16.777) Accuracy 0.9920 (0.2739) mIoU 0.9816 (0.2275)
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+[2024-10-13 15:35:24,793 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 112/121
+[2024-10-13 15:35:24,916 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 113/121
+[2024-10-13 15:35:25,039 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 114/121
+[2024-10-13 15:35:25,163 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 115/121
+[2024-10-13 15:35:25,286 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 116/121
+[2024-10-13 15:35:25,409 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 117/121
+[2024-10-13 15:35:25,532 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 118/121
+[2024-10-13 15:35:25,655 INFO test.py line 186 25394] Test: 22/312-scene0426_01, Batch: 119/121
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+[2024-10-13 15:35:25,968 INFO test.py line 272 25394] Test: scene0426_01 [22/312]-137586 Batch 15.164 (16.703) Accuracy 0.8523 (0.2775) mIoU 0.3482 (0.2303)
+[2024-10-13 15:35:26,326 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 0/157
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+[2024-10-13 15:36:08,882 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 144/157
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+[2024-10-13 15:36:09,479 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 146/157
+[2024-10-13 15:36:09,777 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 147/157
+[2024-10-13 15:36:10,076 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 148/157
+[2024-10-13 15:36:10,374 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 149/157
+[2024-10-13 15:36:10,673 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 150/157
+[2024-10-13 15:36:10,971 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 151/157
+[2024-10-13 15:36:11,269 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 152/157
+[2024-10-13 15:36:11,567 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 153/157
+[2024-10-13 15:36:11,867 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 154/157
+[2024-10-13 15:36:12,165 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 155/157
+[2024-10-13 15:36:12,464 INFO test.py line 186 25394] Test: 23/312-scene0678_01, Batch: 156/157
+[2024-10-13 15:36:12,930 INFO test.py line 272 25394] Test: scene0678_01 [23/312]-377683 Batch 46.962 (18.019) Accuracy 0.8414 (0.2972) mIoU 0.2534 (0.2490)
+[2024-10-13 15:36:13,136 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 0/130
+[2024-10-13 15:36:13,309 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 1/130
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+[2024-10-13 15:36:14,694 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 9/130
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+[2024-10-13 15:36:15,563 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 14/130
+[2024-10-13 15:36:15,736 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 15/130
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+[2024-10-13 15:36:16,775 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 21/130
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+[2024-10-13 15:36:33,089 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 116/130
+[2024-10-13 15:36:33,273 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 117/130
+[2024-10-13 15:36:33,456 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 118/130
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+[2024-10-13 15:36:33,812 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 120/130
+[2024-10-13 15:36:33,985 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 121/130
+[2024-10-13 15:36:34,159 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 122/130
+[2024-10-13 15:36:34,331 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 123/130
+[2024-10-13 15:36:34,505 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 124/130
+[2024-10-13 15:36:34,678 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 125/130
+[2024-10-13 15:36:34,851 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 126/130
+[2024-10-13 15:36:35,024 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 127/130
+[2024-10-13 15:36:35,197 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 128/130
+[2024-10-13 15:36:35,370 INFO test.py line 186 25394] Test: 24/312-scene0095_01, Batch: 129/130
+[2024-10-13 15:36:35,646 INFO test.py line 272 25394] Test: scene0095_01 [24/312]-216254 Batch 22.715 (18.215) Accuracy 0.9479 (0.2967) mIoU 0.5040 (0.2455)
+[2024-10-13 15:36:35,758 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 0/112
+[2024-10-13 15:36:35,859 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 1/112
+[2024-10-13 15:36:35,960 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 2/112
+[2024-10-13 15:36:36,061 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 3/112
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+[2024-10-13 15:36:36,257 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 5/112
+[2024-10-13 15:36:36,356 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 6/112
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+[2024-10-13 15:36:36,551 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 8/112
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+[2024-10-13 15:36:46,432 INFO test.py line 186 25394] Test: 25/312-scene0633_00, Batch: 108/112
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+[2024-10-13 15:36:46,856 INFO test.py line 272 25394] Test: scene0633_00 [25/312]-101007 Batch 11.210 (17.934) Accuracy 0.7853 (0.3079) mIoU 0.3420 (0.2565)
+[2024-10-13 15:36:46,954 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 0/117
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+[2024-10-13 15:36:56,106 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 107/117
+[2024-10-13 15:36:56,191 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 108/117
+[2024-10-13 15:36:56,276 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 109/117
+[2024-10-13 15:36:56,361 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 110/117
+[2024-10-13 15:36:56,445 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 111/117
+[2024-10-13 15:36:56,530 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 112/117
+[2024-10-13 15:36:56,615 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 113/117
+[2024-10-13 15:36:56,700 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 114/117
+[2024-10-13 15:36:56,785 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 115/117
+[2024-10-13 15:36:56,870 INFO test.py line 186 25394] Test: 26/312-scene0686_02, Batch: 116/117
+[2024-10-13 15:36:56,993 INFO test.py line 272 25394] Test: scene0686_02 [26/312]-84543 Batch 10.137 (17.634) Accuracy 0.8799 (0.3360) mIoU 0.6823 (0.2799)
+[2024-10-13 15:36:57,088 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 0/142
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+[2024-10-13 15:36:58,366 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 15/142
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+[2024-10-13 15:36:58,864 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 21/142
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+[2024-10-13 15:37:07,757 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 130/142
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+[2024-10-13 15:37:07,926 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 132/142
+[2024-10-13 15:37:08,008 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 133/142
+[2024-10-13 15:37:08,091 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 134/142
+[2024-10-13 15:37:08,174 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 135/142
+[2024-10-13 15:37:08,256 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 136/142
+[2024-10-13 15:37:08,339 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 137/142
+[2024-10-13 15:37:08,422 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 138/142
+[2024-10-13 15:37:08,504 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 139/142
+[2024-10-13 15:37:08,587 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 140/142
+[2024-10-13 15:37:08,670 INFO test.py line 186 25394] Test: 27/312-scene0607_01, Batch: 141/142
+[2024-10-13 15:37:08,779 INFO test.py line 272 25394] Test: scene0607_01 [27/312]-78387 Batch 11.786 (17.418) Accuracy 0.9364 (0.3446) mIoU 0.7125 (0.2851)
+[2024-10-13 15:37:08,894 INFO test.py line 186 25394] Test: 28/312-scene0256_00, Batch: 0/121
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+[2024-10-13 15:37:21,136 INFO test.py line 272 25394] Test: scene0256_00 [28/312]-108517 Batch 12.357 (17.237) Accuracy 0.9232 (0.3421) mIoU 0.2936 (0.2817)
+[2024-10-13 15:37:21,356 INFO test.py line 186 25394] Test: 29/312-scene0064_00, Batch: 0/134
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+[2024-10-13 15:37:45,908 INFO test.py line 272 25394] Test: scene0064_00 [29/312]-230672 Batch 24.772 (17.497) Accuracy 0.8288 (0.3423) mIoU 0.4583 (0.2833)
+[2024-10-13 15:37:46,085 INFO test.py line 186 25394] Test: 30/312-scene0351_00, Batch: 0/121
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+[2024-10-13 15:38:03,798 INFO test.py line 186 25394] Test: 30/312-scene0351_00, Batch: 118/121
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+[2024-10-13 15:38:04,314 INFO test.py line 272 25394] Test: scene0351_00 [30/312]-172134 Batch 18.406 (17.527) Accuracy 0.9322 (0.3451) mIoU 0.5391 (0.2865)
+[2024-10-13 15:38:04,410 INFO test.py line 186 25394] Test: 31/312-scene0609_00, Batch: 0/122
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+[2024-10-13 15:38:14,544 INFO test.py line 272 25394] Test: scene0609_00 [31/312]-78836 Batch 10.229 (17.292) Accuracy 0.8191 (0.3451) mIoU 0.5686 (0.2860)
+[2024-10-13 15:38:14,926 INFO test.py line 186 25394] Test: 32/312-scene0307_02, Batch: 0/148
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+[2024-10-13 15:39:01,849 INFO test.py line 272 25394] Test: scene0307_02 [32/312]-408633 Batch 47.305 (18.230) Accuracy 0.8448 (0.3462) mIoU 0.2122 (0.2859)
+[2024-10-13 15:39:01,954 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 0/113
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+[2024-10-13 15:39:11,556 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 103/113
+[2024-10-13 15:39:11,648 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 104/113
+[2024-10-13 15:39:11,739 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 105/113
+[2024-10-13 15:39:11,831 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 106/113
+[2024-10-13 15:39:11,922 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 107/113
+[2024-10-13 15:39:12,014 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 108/113
+[2024-10-13 15:39:12,105 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 109/113
+[2024-10-13 15:39:12,197 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 110/113
+[2024-10-13 15:39:12,289 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 111/113
+[2024-10-13 15:39:12,380 INFO test.py line 186 25394] Test: 33/312-scene0559_00, Batch: 112/113
+[2024-10-13 15:39:12,503 INFO test.py line 272 25394] Test: scene0559_00 [33/312]-89796 Batch 10.653 (18.000) Accuracy 0.9865 (0.3535) mIoU 0.9691 (0.2933)
+[2024-10-13 15:39:12,719 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 0/126
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+[2024-10-13 15:39:14,368 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 9/126
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+[2024-10-13 15:39:32,752 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 110/126
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+[2024-10-13 15:39:33,902 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 116/126
+[2024-10-13 15:39:34,085 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 117/126
+[2024-10-13 15:39:34,267 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 118/126
+[2024-10-13 15:39:34,450 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 119/126
+[2024-10-13 15:39:34,632 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 120/126
+[2024-10-13 15:39:34,815 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 121/126
+[2024-10-13 15:39:34,997 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 122/126
+[2024-10-13 15:39:35,180 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 123/126
+[2024-10-13 15:39:35,362 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 124/126
+[2024-10-13 15:39:35,545 INFO test.py line 186 25394] Test: 34/312-scene0334_01, Batch: 125/126
+[2024-10-13 15:39:35,837 INFO test.py line 272 25394] Test: scene0334_01 [34/312]-226580 Batch 23.334 (18.157) Accuracy 0.8452 (0.3520) mIoU 0.3263 (0.2920)
+[2024-10-13 15:39:35,945 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 0/139
+[2024-10-13 15:39:36,040 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 1/139
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+[2024-10-13 15:39:36,712 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 8/139
+[2024-10-13 15:39:36,807 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 9/139
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+[2024-10-13 15:39:36,996 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 11/139
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+[2024-10-13 15:39:37,185 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 13/139
+[2024-10-13 15:39:37,280 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 14/139
+[2024-10-13 15:39:37,375 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 15/139
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+[2024-10-13 15:39:37,564 INFO test.py line 186 25394] Test: 35/312-scene0084_02, Batch: 17/139
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+[2024-10-13 15:39:49,358 INFO test.py line 272 25394] Test: scene0084_02 [35/312]-98346 Batch 13.520 (18.025) Accuracy 0.9208 (0.3668) mIoU 0.4258 (0.3041)
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+[2024-10-13 15:40:26,886 INFO test.py line 186 25394] Test: 36/312-scene0231_02, Batch: 135/139
+[2024-10-13 15:40:27,161 INFO test.py line 186 25394] Test: 36/312-scene0231_02, Batch: 136/139
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+[2024-10-13 15:40:28,156 INFO test.py line 272 25394] Test: scene0231_02 [36/312]-353013 Batch 38.798 (18.602) Accuracy 0.8072 (0.3742) mIoU 0.3307 (0.3044)
+[2024-10-13 15:40:28,394 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 0/143
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+[2024-10-13 15:40:54,758 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 133/143
+[2024-10-13 15:40:54,958 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 134/143
+[2024-10-13 15:40:55,158 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 135/143
+[2024-10-13 15:40:55,358 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 136/143
+[2024-10-13 15:40:55,558 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 137/143
+[2024-10-13 15:40:55,757 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 138/143
+[2024-10-13 15:40:55,957 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 139/143
+[2024-10-13 15:40:56,157 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 140/143
+[2024-10-13 15:40:56,357 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 141/143
+[2024-10-13 15:40:56,556 INFO test.py line 186 25394] Test: 37/312-scene0629_01, Batch: 142/143
+[2024-10-13 15:40:56,862 INFO test.py line 272 25394] Test: scene0629_01 [37/312]-244962 Batch 28.706 (18.875) Accuracy 0.7418 (0.3757) mIoU 0.3651 (0.3037)
+[2024-10-13 15:40:57,090 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 0/125
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+[2024-10-13 15:41:19,797 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 118/125
+[2024-10-13 15:41:19,989 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 119/125
+[2024-10-13 15:41:20,181 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 120/125
+[2024-10-13 15:41:20,372 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 121/125
+[2024-10-13 15:41:20,564 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 122/125
+[2024-10-13 15:41:20,756 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 123/125
+[2024-10-13 15:41:20,947 INFO test.py line 186 25394] Test: 38/312-scene0608_02, Batch: 124/125
+[2024-10-13 15:41:21,239 INFO test.py line 272 25394] Test: scene0608_02 [38/312]-235030 Batch 24.377 (19.019) Accuracy 0.7308 (0.3735) mIoU 0.3369 (0.3041)
+[2024-10-13 15:41:21,341 INFO test.py line 186 25394] Test: 39/312-scene0651_00, Batch: 0/143
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+[2024-10-13 15:41:34,240 INFO test.py line 272 25394] Test: scene0651_00 [39/312]-88450 Batch 13.000 (18.865) Accuracy 0.9669 (0.3831) mIoU 0.7596 (0.3136)
+[2024-10-13 15:41:34,402 INFO test.py line 186 25394] Test: 40/312-scene0149_00, Batch: 0/138
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+[2024-10-13 15:41:52,699 INFO test.py line 186 25394] Test: 40/312-scene0149_00, Batch: 134/138
+[2024-10-13 15:41:52,835 INFO test.py line 186 25394] Test: 40/312-scene0149_00, Batch: 135/138
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+[2024-10-13 15:41:53,107 INFO test.py line 186 25394] Test: 40/312-scene0149_00, Batch: 137/138
+[2024-10-13 15:41:53,319 INFO test.py line 272 25394] Test: scene0149_00 [40/312]-162396 Batch 19.079 (18.870) Accuracy 0.9437 (0.3897) mIoU 0.5550 (0.3186)
+[2024-10-13 15:41:53,534 INFO test.py line 186 25394] Test: 41/312-scene0015_00, Batch: 0/139
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+[2024-10-13 15:42:18,870 INFO test.py line 272 25394] Test: scene0015_00 [41/312]-217086 Batch 25.551 (19.033) Accuracy 0.9199 (0.3899) mIoU 0.5274 (0.3178)
+[2024-10-13 15:42:19,122 INFO test.py line 186 25394] Test: 42/312-scene0670_01, Batch: 0/144
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+[2024-10-13 15:42:49,893 INFO test.py line 186 25394] Test: 42/312-scene0670_01, Batch: 143/144
+[2024-10-13 15:42:50,223 INFO test.py line 272 25394] Test: scene0670_01 [42/312]-256524 Batch 31.353 (19.327) Accuracy 0.7921 (0.3870) mIoU 0.3590 (0.3139)
+[2024-10-13 15:42:50,374 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 0/143
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+[2024-10-13 15:43:07,405 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 134/143
+[2024-10-13 15:43:07,533 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 135/143
+[2024-10-13 15:43:07,660 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 136/143
+[2024-10-13 15:43:07,788 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 137/143
+[2024-10-13 15:43:07,916 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 138/143
+[2024-10-13 15:43:08,043 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 139/143
+[2024-10-13 15:43:08,171 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 140/143
+[2024-10-13 15:43:08,298 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 141/143
+[2024-10-13 15:43:08,426 INFO test.py line 186 25394] Test: 43/312-scene0088_01, Batch: 142/143
+[2024-10-13 15:43:08,611 INFO test.py line 272 25394] Test: scene0088_01 [43/312]-145650 Batch 18.387 (19.305) Accuracy 0.8048 (0.3870) mIoU 0.6062 (0.3136)
+[2024-10-13 15:43:08,925 INFO test.py line 186 25394] Test: 44/312-scene0678_02, Batch: 0/160
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+[2024-10-13 15:43:51,036 INFO test.py line 272 25394] Test: scene0678_02 [44/312]-328973 Batch 42.425 (19.830) Accuracy 0.8960 (0.3829) mIoU 0.2569 (0.3108)
+[2024-10-13 15:43:51,215 INFO test.py line 186 25394] Test: 45/312-scene0025_00, Batch: 0/143
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+[2024-10-13 15:44:12,249 INFO test.py line 186 25394] Test: 45/312-scene0025_00, Batch: 139/143
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+[2024-10-13 15:44:12,929 INFO test.py line 272 25394] Test: scene0025_00 [45/312]-173392 Batch 21.893 (19.876) Accuracy 0.6909 (0.3833) mIoU 0.2694 (0.3089)
+[2024-10-13 15:44:13,165 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 0/113
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+[2024-10-13 15:44:34,396 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 107/113
+[2024-10-13 15:44:34,594 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 108/113
+[2024-10-13 15:44:34,792 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 109/113
+[2024-10-13 15:44:34,990 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 110/113
+[2024-10-13 15:44:35,188 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 111/113
+[2024-10-13 15:44:35,386 INFO test.py line 186 25394] Test: 46/312-scene0203_02, Batch: 112/113
+[2024-10-13 15:44:35,694 INFO test.py line 272 25394] Test: scene0203_02 [46/312]-238392 Batch 22.764 (19.939) Accuracy 0.6573 (0.3858) mIoU 0.3803 (0.3103)
+[2024-10-13 15:44:35,803 INFO test.py line 186 25394] Test: 47/312-scene0633_01, Batch: 0/134
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+[2024-10-13 15:44:48,395 INFO test.py line 186 25394] Test: 47/312-scene0633_01, Batch: 130/134
+[2024-10-13 15:44:48,491 INFO test.py line 186 25394] Test: 47/312-scene0633_01, Batch: 131/134
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+[2024-10-13 15:44:48,810 INFO test.py line 272 25394] Test: scene0633_01 [47/312]-99171 Batch 13.115 (19.794) Accuracy 0.7948 (0.3862) mIoU 0.3017 (0.3096)
+[2024-10-13 15:44:48,988 INFO test.py line 186 25394] Test: 48/312-scene0088_02, Batch: 0/135
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+[2024-10-13 15:45:08,296 INFO test.py line 186 25394] Test: 48/312-scene0088_02, Batch: 130/135
+[2024-10-13 15:45:08,445 INFO test.py line 186 25394] Test: 48/312-scene0088_02, Batch: 131/135
+[2024-10-13 15:45:08,595 INFO test.py line 186 25394] Test: 48/312-scene0088_02, Batch: 132/135
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+[2024-10-13 15:45:08,895 INFO test.py line 186 25394] Test: 48/312-scene0088_02, Batch: 134/135
+[2024-10-13 15:45:09,116 INFO test.py line 272 25394] Test: scene0088_02 [48/312]-170685 Batch 20.306 (19.804) Accuracy 0.8199 (0.3852) mIoU 0.4136 (0.3100)
+[2024-10-13 15:45:09,362 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 0/159
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+[2024-10-13 15:45:40,229 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 149/159
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+[2024-10-13 15:45:40,855 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 152/159
+[2024-10-13 15:45:41,063 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 153/159
+[2024-10-13 15:45:41,272 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 154/159
+[2024-10-13 15:45:41,481 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 155/159
+[2024-10-13 15:45:41,690 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 156/159
+[2024-10-13 15:45:41,899 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 157/159
+[2024-10-13 15:45:42,108 INFO test.py line 186 25394] Test: 49/312-scene0474_02, Batch: 158/159
+[2024-10-13 15:45:42,431 INFO test.py line 272 25394] Test: scene0474_02 [49/312]-252355 Batch 33.315 (20.080) Accuracy 0.8665 (0.3892) mIoU 0.3738 (0.3121)
+[2024-10-13 15:45:42,645 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 0/139
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+[2024-10-13 15:46:00,312 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 99/139
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+[2024-10-13 15:46:00,702 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 101/139
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+[2024-10-13 15:46:04,796 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 122/139
+[2024-10-13 15:46:04,991 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 123/139
+[2024-10-13 15:46:05,186 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 124/139
+[2024-10-13 15:46:05,381 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 125/139
+[2024-10-13 15:46:05,576 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 126/139
+[2024-10-13 15:46:05,770 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 127/139
+[2024-10-13 15:46:05,952 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 128/139
+[2024-10-13 15:46:06,134 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 129/139
+[2024-10-13 15:46:06,317 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 130/139
+[2024-10-13 15:46:06,498 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 131/139
+[2024-10-13 15:46:06,680 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 132/139
+[2024-10-13 15:46:06,862 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 133/139
+[2024-10-13 15:46:07,044 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 134/139
+[2024-10-13 15:46:07,226 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 135/139
+[2024-10-13 15:46:07,408 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 136/139
+[2024-10-13 15:46:07,590 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 137/139
+[2024-10-13 15:46:07,773 INFO test.py line 186 25394] Test: 50/312-scene0474_01, Batch: 138/139
+[2024-10-13 15:46:08,047 INFO test.py line 272 25394] Test: scene0474_01 [50/312]-213949 Batch 25.616 (20.191) Accuracy 0.8042 (0.3885) mIoU 0.3646 (0.3108)
+[2024-10-13 15:46:08,189 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 0/130
+[2024-10-13 15:46:08,315 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 1/130
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+[2024-10-13 15:46:20,891 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 103/130
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+[2024-10-13 15:46:22,598 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 116/130
+[2024-10-13 15:46:22,729 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 117/130
+[2024-10-13 15:46:22,860 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 118/130
+[2024-10-13 15:46:22,991 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 119/130
+[2024-10-13 15:46:23,116 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 120/130
+[2024-10-13 15:46:23,241 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 121/130
+[2024-10-13 15:46:23,366 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 122/130
+[2024-10-13 15:46:23,491 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 123/130
+[2024-10-13 15:46:23,616 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 124/130
+[2024-10-13 15:46:23,741 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 125/130
+[2024-10-13 15:46:23,866 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 126/130
+[2024-10-13 15:46:23,991 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 127/130
+[2024-10-13 15:46:24,117 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 128/130
+[2024-10-13 15:46:24,241 INFO test.py line 186 25394] Test: 51/312-scene0019_00, Batch: 129/130
+[2024-10-13 15:46:24,421 INFO test.py line 272 25394] Test: scene0019_00 [51/312]-136769 Batch 16.374 (20.116) Accuracy 0.7642 (0.3883) mIoU 0.3853 (0.3086)
+[2024-10-13 15:46:24,520 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 0/139
+[2024-10-13 15:46:24,608 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 1/139
+[2024-10-13 15:46:24,696 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 2/139
+[2024-10-13 15:46:24,789 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 3/139
+[2024-10-13 15:46:24,879 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 4/139
+[2024-10-13 15:46:24,971 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 5/139
+[2024-10-13 15:46:25,062 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 6/139
+[2024-10-13 15:46:25,149 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 7/139
+[2024-10-13 15:46:25,237 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 8/139
+[2024-10-13 15:46:25,325 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 9/139
+[2024-10-13 15:46:25,413 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 10/139
+[2024-10-13 15:46:25,500 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 11/139
+[2024-10-13 15:46:25,588 INFO test.py line 186 25394] Test: 52/312-scene0356_02, Batch: 12/139
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+[2024-10-13 15:46:36,763 INFO test.py line 272 25394] Test: scene0356_02 [52/312]-83613 Batch 12.342 (19.967) Accuracy 0.8380 (0.3915) mIoU 0.5374 (0.3113)
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+[2024-10-13 15:46:53,450 INFO test.py line 272 25394] Test: scene0217_00 [53/312]-126385 Batch 16.686 (19.905) Accuracy 0.9356 (0.3930) mIoU 0.7457 (0.3130)
+[2024-10-13 15:46:53,562 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 0/127
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+[2024-10-13 15:47:04,498 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 112/127
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+[2024-10-13 15:47:04,807 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 115/127
+[2024-10-13 15:47:04,904 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 116/127
+[2024-10-13 15:47:05,002 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 117/127
+[2024-10-13 15:47:05,099 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 118/127
+[2024-10-13 15:47:05,196 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 119/127
+[2024-10-13 15:47:05,294 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 120/127
+[2024-10-13 15:47:05,391 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 121/127
+[2024-10-13 15:47:05,488 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 122/127
+[2024-10-13 15:47:05,585 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 123/127
+[2024-10-13 15:47:05,683 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 124/127
+[2024-10-13 15:47:05,780 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 125/127
+[2024-10-13 15:47:05,878 INFO test.py line 186 25394] Test: 54/312-scene0652_00, Batch: 126/127
+[2024-10-13 15:47:06,008 INFO test.py line 272 25394] Test: scene0652_00 [54/312]-100286 Batch 12.558 (19.769) Accuracy 0.8416 (0.3921) mIoU 0.3811 (0.3115)
+[2024-10-13 15:47:06,134 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 0/142
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+[2024-10-13 15:47:19,804 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 123/142
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+[2024-10-13 15:47:20,279 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 127/142
+[2024-10-13 15:47:20,397 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 128/142
+[2024-10-13 15:47:20,516 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 129/142
+[2024-10-13 15:47:20,635 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 130/142
+[2024-10-13 15:47:20,753 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 131/142
+[2024-10-13 15:47:20,865 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 132/142
+[2024-10-13 15:47:20,977 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 133/142
+[2024-10-13 15:47:21,089 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 134/142
+[2024-10-13 15:47:21,200 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 135/142
+[2024-10-13 15:47:21,312 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 136/142
+[2024-10-13 15:47:21,423 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 137/142
+[2024-10-13 15:47:21,535 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 138/142
+[2024-10-13 15:47:21,647 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 139/142
+[2024-10-13 15:47:21,759 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 140/142
+[2024-10-13 15:47:21,870 INFO test.py line 186 25394] Test: 55/312-scene0599_02, Batch: 141/142
+[2024-10-13 15:47:22,027 INFO test.py line 272 25394] Test: scene0599_02 [55/312]-122711 Batch 16.019 (19.700) Accuracy 0.9562 (0.3923) mIoU 0.8845 (0.3120)
+[2024-10-13 15:47:22,121 INFO test.py line 186 25394] Test: 56/312-scene0701_01, Batch: 0/139
+[2024-10-13 15:47:22,203 INFO test.py line 186 25394] Test: 56/312-scene0701_01, Batch: 1/139
+[2024-10-13 15:47:22,286 INFO test.py line 186 25394] Test: 56/312-scene0701_01, Batch: 2/139
+[2024-10-13 15:47:22,368 INFO test.py line 186 25394] Test: 56/312-scene0701_01, Batch: 3/139
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+[2024-10-13 15:47:33,551 INFO test.py line 272 25394] Test: scene0701_01 [56/312]-77206 Batch 11.524 (19.554) Accuracy 0.9785 (0.3925) mIoU 0.7786 (0.3122)
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+[2024-10-13 15:47:46,798 INFO test.py line 186 25394] Test: 57/312-scene0607_00, Batch: 130/134
+[2024-10-13 15:47:46,899 INFO test.py line 186 25394] Test: 57/312-scene0607_00, Batch: 131/134
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+[2024-10-13 15:47:47,244 INFO test.py line 272 25394] Test: scene0607_00 [57/312]-113156 Batch 13.693 (19.452) Accuracy 0.9260 (0.3924) mIoU 0.5629 (0.3122)
+[2024-10-13 15:47:47,409 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 0/148
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+[2024-10-13 15:48:03,722 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 118/148
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+[2024-10-13 15:48:05,048 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 127/148
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+[2024-10-13 15:48:05,491 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 130/148
+[2024-10-13 15:48:05,638 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 131/148
+[2024-10-13 15:48:05,786 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 132/148
+[2024-10-13 15:48:05,934 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 133/148
+[2024-10-13 15:48:06,081 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 134/148
+[2024-10-13 15:48:06,228 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 135/148
+[2024-10-13 15:48:06,368 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 136/148
+[2024-10-13 15:48:06,507 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 137/148
+[2024-10-13 15:48:06,646 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 138/148
+[2024-10-13 15:48:06,786 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 139/148
+[2024-10-13 15:48:06,926 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 140/148
+[2024-10-13 15:48:07,066 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 141/148
+[2024-10-13 15:48:07,205 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 142/148
+[2024-10-13 15:48:07,345 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 143/148
+[2024-10-13 15:48:07,484 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 144/148
+[2024-10-13 15:48:07,623 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 145/148
+[2024-10-13 15:48:07,763 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 146/148
+[2024-10-13 15:48:07,903 INFO test.py line 186 25394] Test: 58/312-scene0304_00, Batch: 147/148
+[2024-10-13 15:48:08,117 INFO test.py line 272 25394] Test: scene0304_00 [58/312]-164016 Batch 20.873 (19.476) Accuracy 0.5670 (0.3920) mIoU 0.2428 (0.3113)
+[2024-10-13 15:48:08,395 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 0/126
+[2024-10-13 15:48:08,632 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 1/126
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+[2024-10-13 15:48:09,799 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 6/126
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+[2024-10-13 15:48:10,265 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 8/126
+[2024-10-13 15:48:10,498 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 9/126
+[2024-10-13 15:48:10,732 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 10/126
+[2024-10-13 15:48:10,968 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 11/126
+[2024-10-13 15:48:11,202 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 12/126
+[2024-10-13 15:48:11,436 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 13/126
+[2024-10-13 15:48:11,669 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 14/126
+[2024-10-13 15:48:11,903 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 15/126
+[2024-10-13 15:48:12,137 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 16/126
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+[2024-10-13 15:48:12,842 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 19/126
+[2024-10-13 15:48:13,077 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 20/126
+[2024-10-13 15:48:13,310 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 21/126
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+[2024-10-13 15:48:30,090 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 95/126
+[2024-10-13 15:48:30,338 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 96/126
+[2024-10-13 15:48:30,585 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 97/126
+[2024-10-13 15:48:30,833 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 98/126
+[2024-10-13 15:48:31,080 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 99/126
+[2024-10-13 15:48:31,328 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 100/126
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+[2024-10-13 15:48:32,818 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 106/126
+[2024-10-13 15:48:33,066 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 107/126
+[2024-10-13 15:48:33,313 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 108/126
+[2024-10-13 15:48:33,560 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 109/126
+[2024-10-13 15:48:33,807 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 110/126
+[2024-10-13 15:48:34,053 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 111/126
+[2024-10-13 15:48:34,300 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 112/126
+[2024-10-13 15:48:34,548 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 113/126
+[2024-10-13 15:48:34,795 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 114/126
+[2024-10-13 15:48:35,043 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 115/126
+[2024-10-13 15:48:35,276 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 116/126
+[2024-10-13 15:48:35,509 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 117/126
+[2024-10-13 15:48:35,742 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 118/126
+[2024-10-13 15:48:35,975 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 119/126
+[2024-10-13 15:48:36,208 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 120/126
+[2024-10-13 15:48:36,441 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 121/126
+[2024-10-13 15:48:36,675 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 122/126
+[2024-10-13 15:48:36,908 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 123/126
+[2024-10-13 15:48:37,141 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 124/126
+[2024-10-13 15:48:37,375 INFO test.py line 186 25394] Test: 59/312-scene0696_01, Batch: 125/126
+[2024-10-13 15:48:37,737 INFO test.py line 272 25394] Test: scene0696_01 [59/312]-288139 Batch 29.620 (19.648) Accuracy 0.7858 (0.3908) mIoU 0.2768 (0.3102)
+[2024-10-13 15:48:37,873 INFO test.py line 186 25394] Test: 60/312-scene0377_01, Batch: 0/122
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+[2024-10-13 15:48:52,508 INFO test.py line 272 25394] Test: scene0377_01 [60/312]-131297 Batch 14.771 (19.567) Accuracy 0.8541 (0.3943) mIoU 0.2668 (0.3110)
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+[2024-10-13 15:49:13,897 INFO test.py line 186 25394] Test: 61/312-scene0353_01, Batch: 118/125
+[2024-10-13 15:49:14,076 INFO test.py line 186 25394] Test: 61/312-scene0353_01, Batch: 119/125
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+[2024-10-13 15:49:14,435 INFO test.py line 186 25394] Test: 61/312-scene0353_01, Batch: 121/125
+[2024-10-13 15:49:14,614 INFO test.py line 186 25394] Test: 61/312-scene0353_01, Batch: 122/125
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+[2024-10-13 15:49:15,238 INFO test.py line 272 25394] Test: scene0353_01 [61/312]-203737 Batch 22.730 (19.619) Accuracy 0.6892 (0.3987) mIoU 0.3216 (0.3134)
+[2024-10-13 15:49:15,532 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 0/151
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+[2024-10-13 15:49:51,649 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 144/151
+[2024-10-13 15:49:51,896 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 145/151
+[2024-10-13 15:49:52,143 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 146/151
+[2024-10-13 15:49:52,391 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 147/151
+[2024-10-13 15:49:52,638 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 148/151
+[2024-10-13 15:49:52,886 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 149/151
+[2024-10-13 15:49:53,133 INFO test.py line 186 25394] Test: 62/312-scene0696_02, Batch: 150/151
+[2024-10-13 15:49:53,519 INFO test.py line 272 25394] Test: scene0696_02 [62/312]-305916 Batch 38.280 (19.920) Accuracy 0.7799 (0.3948) mIoU 0.2600 (0.3070)
+[2024-10-13 15:49:53,733 INFO test.py line 186 25394] Test: 63/312-scene0685_02, Batch: 0/152
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+[2024-10-13 15:50:19,879 INFO test.py line 186 25394] Test: 63/312-scene0685_02, Batch: 146/152
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+[2024-10-13 15:50:20,237 INFO test.py line 186 25394] Test: 63/312-scene0685_02, Batch: 148/152
+[2024-10-13 15:50:20,416 INFO test.py line 186 25394] Test: 63/312-scene0685_02, Batch: 149/152
+[2024-10-13 15:50:20,595 INFO test.py line 186 25394] Test: 63/312-scene0685_02, Batch: 150/152
+[2024-10-13 15:50:20,774 INFO test.py line 186 25394] Test: 63/312-scene0685_02, Batch: 151/152
+[2024-10-13 15:50:21,051 INFO test.py line 272 25394] Test: scene0685_02 [63/312]-222253 Batch 27.532 (20.040) Accuracy 0.9420 (0.3945) mIoU 0.3272 (0.3070)
+[2024-10-13 15:50:21,395 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 0/167
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+[2024-10-13 15:50:35,879 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 51/167
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+[2024-10-13 15:50:39,096 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 63/167
+[2024-10-13 15:50:39,364 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 64/167
+[2024-10-13 15:50:39,633 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 65/167
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+[2024-10-13 15:50:40,169 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 67/167
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+[2024-10-13 15:51:06,164 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 157/167
+[2024-10-13 15:51:06,451 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 158/167
+[2024-10-13 15:51:06,737 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 159/167
+[2024-10-13 15:51:07,023 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 160/167
+[2024-10-13 15:51:07,309 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 161/167
+[2024-10-13 15:51:07,596 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 162/167
+[2024-10-13 15:51:07,882 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 163/167
+[2024-10-13 15:51:08,169 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 164/167
+[2024-10-13 15:51:08,455 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 165/167
+[2024-10-13 15:51:08,742 INFO test.py line 186 25394] Test: 64/312-scene0678_00, Batch: 166/167
+[2024-10-13 15:51:09,202 INFO test.py line 272 25394] Test: scene0678_00 [64/312]-364811 Batch 48.150 (20.480) Accuracy 0.8477 (0.3936) mIoU 0.1999 (0.3057)
+[2024-10-13 15:51:09,276 INFO test.py line 186 25394] Test: 65/312-scene0671_01, Batch: 0/121
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+[2024-10-13 15:51:17,064 INFO test.py line 272 25394] Test: scene0671_01 [65/312]-51187 Batch 7.862 (20.286) Accuracy 0.8902 (0.3930) mIoU 0.3275 (0.3046)
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+[2024-10-13 15:51:34,743 INFO test.py line 186 25394] Test: 66/312-scene0704_01, Batch: 131/138
+[2024-10-13 15:51:34,877 INFO test.py line 186 25394] Test: 66/312-scene0704_01, Batch: 132/138
+[2024-10-13 15:51:35,011 INFO test.py line 186 25394] Test: 66/312-scene0704_01, Batch: 133/138
+[2024-10-13 15:51:35,145 INFO test.py line 186 25394] Test: 66/312-scene0704_01, Batch: 134/138
+[2024-10-13 15:51:35,278 INFO test.py line 186 25394] Test: 66/312-scene0704_01, Batch: 135/138
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+[2024-10-13 15:51:35,746 INFO test.py line 272 25394] Test: scene0704_01 [66/312]-149982 Batch 18.681 (20.261) Accuracy 0.8717 (0.3942) mIoU 0.3171 (0.3048)
+[2024-10-13 15:51:36,007 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 0/139
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+[2024-10-13 15:51:59,687 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 110/139
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+[2024-10-13 15:52:02,717 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 123/139
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+[2024-10-13 15:52:03,183 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 125/139
+[2024-10-13 15:52:03,416 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 126/139
+[2024-10-13 15:52:03,649 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 127/139
+[2024-10-13 15:52:03,867 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 128/139
+[2024-10-13 15:52:04,086 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 129/139
+[2024-10-13 15:52:04,305 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 130/139
+[2024-10-13 15:52:04,524 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 131/139
+[2024-10-13 15:52:04,743 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 132/139
+[2024-10-13 15:52:04,962 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 133/139
+[2024-10-13 15:52:05,181 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 134/139
+[2024-10-13 15:52:05,399 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 135/139
+[2024-10-13 15:52:05,618 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 136/139
+[2024-10-13 15:52:05,837 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 137/139
+[2024-10-13 15:52:06,056 INFO test.py line 186 25394] Test: 67/312-scene0435_01, Batch: 138/139
+[2024-10-13 15:52:06,406 INFO test.py line 272 25394] Test: scene0435_01 [67/312]-273280 Batch 30.660 (20.416) Accuracy 0.9192 (0.4050) mIoU 0.5609 (0.3171)
+[2024-10-13 15:52:06,602 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 0/130
+[2024-10-13 15:52:06,766 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 1/130
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+[2024-10-13 15:52:08,907 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 14/130
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+[2024-10-13 15:52:17,575 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 68/130
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+[2024-10-13 15:52:26,555 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 121/130
+[2024-10-13 15:52:26,720 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 122/130
+[2024-10-13 15:52:26,885 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 123/130
+[2024-10-13 15:52:27,050 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 124/130
+[2024-10-13 15:52:27,214 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 125/130
+[2024-10-13 15:52:27,379 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 126/130
+[2024-10-13 15:52:27,544 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 127/130
+[2024-10-13 15:52:27,709 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 128/130
+[2024-10-13 15:52:27,874 INFO test.py line 186 25394] Test: 68/312-scene0575_01, Batch: 129/130
+[2024-10-13 15:52:28,134 INFO test.py line 272 25394] Test: scene0575_01 [68/312]-201935 Batch 21.727 (20.436) Accuracy 0.9511 (0.4054) mIoU 0.7041 (0.3178)
+[2024-10-13 15:52:28,329 INFO test.py line 186 25394] Test: 69/312-scene0695_03, Batch: 0/129
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+[2024-10-13 15:52:29,810 INFO test.py line 186 25394] Test: 69/312-scene0695_03, Batch: 9/129
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+[2024-10-13 15:52:30,468 INFO test.py line 186 25394] Test: 69/312-scene0695_03, Batch: 13/129
+[2024-10-13 15:52:30,632 INFO test.py line 186 25394] Test: 69/312-scene0695_03, Batch: 14/129
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+[2024-10-13 15:52:49,512 INFO test.py line 272 25394] Test: scene0695_03 [69/312]-205565 Batch 21.378 (20.449) Accuracy 0.8460 (0.4058) mIoU 0.2986 (0.3178)
+[2024-10-13 15:52:49,741 INFO test.py line 186 25394] Test: 70/312-scene0011_00, Batch: 0/159
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+[2024-10-13 15:53:20,246 INFO test.py line 272 25394] Test: scene0011_00 [70/312]-237360 Batch 30.734 (20.596) Accuracy 0.9130 (0.4059) mIoU 0.6105 (0.3179)
+[2024-10-13 15:53:20,484 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 0/148
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+[2024-10-13 15:53:48,094 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 139/148
+[2024-10-13 15:53:48,293 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 140/148
+[2024-10-13 15:53:48,493 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 141/148
+[2024-10-13 15:53:48,692 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 142/148
+[2024-10-13 15:53:48,892 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 143/148
+[2024-10-13 15:53:49,091 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 144/148
+[2024-10-13 15:53:49,291 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 145/148
+[2024-10-13 15:53:49,491 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 146/148
+[2024-10-13 15:53:49,690 INFO test.py line 186 25394] Test: 71/312-scene0568_01, Batch: 147/148
+[2024-10-13 15:53:49,991 INFO test.py line 272 25394] Test: scene0568_01 [71/312]-239388 Batch 29.745 (20.725) Accuracy 0.8769 (0.4072) mIoU 0.3651 (0.3188)
+[2024-10-13 15:53:50,185 INFO test.py line 186 25394] Test: 72/312-scene0050_02, Batch: 0/125
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+[2024-10-13 15:54:09,650 INFO test.py line 186 25394] Test: 72/312-scene0050_02, Batch: 120/125
+[2024-10-13 15:54:09,813 INFO test.py line 186 25394] Test: 72/312-scene0050_02, Batch: 121/125
+[2024-10-13 15:54:09,976 INFO test.py line 186 25394] Test: 72/312-scene0050_02, Batch: 122/125
+[2024-10-13 15:54:10,138 INFO test.py line 186 25394] Test: 72/312-scene0050_02, Batch: 123/125
+[2024-10-13 15:54:10,301 INFO test.py line 186 25394] Test: 72/312-scene0050_02, Batch: 124/125
+[2024-10-13 15:54:10,552 INFO test.py line 272 25394] Test: scene0050_02 [72/312]-196815 Batch 20.560 (20.723) Accuracy 0.8040 (0.4123) mIoU 0.3628 (0.3238)
+[2024-10-13 15:54:10,640 INFO test.py line 186 25394] Test: 73/312-scene0553_02, Batch: 0/138
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+[2024-10-13 15:54:21,377 INFO test.py line 272 25394] Test: scene0553_02 [73/312]-67632 Batch 10.825 (20.587) Accuracy 0.9232 (0.4171) mIoU 0.4223 (0.3281)
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+[2024-10-13 15:54:46,951 INFO test.py line 272 25394] Test: scene0046_02 [74/312]-199774 Batch 25.574 (20.655) Accuracy 0.8166 (0.4203) mIoU 0.4008 (0.3231)
+[2024-10-13 15:54:47,072 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 0/143
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+[2024-10-13 15:55:01,510 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 136/143
+[2024-10-13 15:55:01,617 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 137/143
+[2024-10-13 15:55:01,723 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 138/143
+[2024-10-13 15:55:01,830 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 139/143
+[2024-10-13 15:55:01,936 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 140/143
+[2024-10-13 15:55:02,043 INFO test.py line 186 25394] Test: 75/312-scene0701_02, Batch: 141/143
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+[2024-10-13 15:55:02,302 INFO test.py line 272 25394] Test: scene0701_02 [75/312]-110945 Batch 15.351 (20.584) Accuracy 0.8882 (0.4204) mIoU 0.5166 (0.3233)
+[2024-10-13 15:55:02,448 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 0/134
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+[2024-10-13 15:55:18,451 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 126/134
+[2024-10-13 15:55:18,577 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 127/134
+[2024-10-13 15:55:18,705 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 128/134
+[2024-10-13 15:55:18,832 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 129/134
+[2024-10-13 15:55:18,959 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 130/134
+[2024-10-13 15:55:19,086 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 131/134
+[2024-10-13 15:55:19,213 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 132/134
+[2024-10-13 15:55:19,341 INFO test.py line 186 25394] Test: 76/312-scene0697_00, Batch: 133/134
+[2024-10-13 15:55:19,520 INFO test.py line 272 25394] Test: scene0697_00 [76/312]-139300 Batch 17.217 (20.540) Accuracy 0.7804 (0.4222) mIoU 0.3768 (0.3250)
+[2024-10-13 15:55:19,623 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 0/143
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+[2024-10-13 15:55:31,089 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 128/143
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+[2024-10-13 15:55:31,277 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 130/143
+[2024-10-13 15:55:31,370 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 131/143
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+[2024-10-13 15:55:31,639 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 134/143
+[2024-10-13 15:55:31,729 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 135/143
+[2024-10-13 15:55:31,818 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 136/143
+[2024-10-13 15:55:31,908 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 137/143
+[2024-10-13 15:55:31,998 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 138/143
+[2024-10-13 15:55:32,087 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 139/143
+[2024-10-13 15:55:32,177 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 140/143
+[2024-10-13 15:55:32,266 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 141/143
+[2024-10-13 15:55:32,355 INFO test.py line 186 25394] Test: 77/312-scene0686_00, Batch: 142/143
+[2024-10-13 15:55:32,475 INFO test.py line 272 25394] Test: scene0686_00 [77/312]-89776 Batch 12.954 (20.441) Accuracy 0.9378 (0.4216) mIoU 0.7410 (0.3264)
+[2024-10-13 15:55:32,547 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 0/116
+[2024-10-13 15:55:32,610 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 1/116
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+[2024-10-13 15:55:33,050 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 8/116
+[2024-10-13 15:55:33,112 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 9/116
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+[2024-10-13 15:55:33,301 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 12/116
+[2024-10-13 15:55:33,406 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 13/116
+[2024-10-13 15:55:33,473 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 14/116
+[2024-10-13 15:55:33,537 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 15/116
+[2024-10-13 15:55:33,601 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 16/116
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+[2024-10-13 15:55:39,674 INFO test.py line 186 25394] Test: 78/312-scene0100_02, Batch: 112/116
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+[2024-10-13 15:55:39,936 INFO test.py line 272 25394] Test: scene0100_02 [78/312]-50328 Batch 7.461 (20.275) Accuracy 0.7047 (0.4248) mIoU 0.3642 (0.3304)
+[2024-10-13 15:55:40,034 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 0/139
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+[2024-10-13 15:55:50,538 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 123/139
+[2024-10-13 15:55:50,627 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 124/139
+[2024-10-13 15:55:50,716 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 125/139
+[2024-10-13 15:55:50,805 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 126/139
+[2024-10-13 15:55:50,894 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 127/139
+[2024-10-13 15:55:50,980 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 128/139
+[2024-10-13 15:55:51,069 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 129/139
+[2024-10-13 15:55:51,155 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 130/139
+[2024-10-13 15:55:51,241 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 131/139
+[2024-10-13 15:55:51,327 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 132/139
+[2024-10-13 15:55:51,413 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 133/139
+[2024-10-13 15:55:51,499 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 134/139
+[2024-10-13 15:55:51,585 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 135/139
+[2024-10-13 15:55:51,671 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 136/139
+[2024-10-13 15:55:51,757 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 137/139
+[2024-10-13 15:55:51,842 INFO test.py line 186 25394] Test: 79/312-scene0559_01, Batch: 138/139
+[2024-10-13 15:55:51,960 INFO test.py line 272 25394] Test: scene0559_01 [79/312]-81654 Batch 12.024 (20.170) Accuracy 0.9502 (0.4262) mIoU 0.6599 (0.3317)
+[2024-10-13 15:55:52,027 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 0/109
+[2024-10-13 15:55:52,087 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 1/109
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+[2024-10-13 15:55:52,498 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 8/109
+[2024-10-13 15:55:52,557 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 9/109
+[2024-10-13 15:55:52,616 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 10/109
+[2024-10-13 15:55:52,675 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 11/109
+[2024-10-13 15:55:52,734 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 12/109
+[2024-10-13 15:55:52,793 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 13/109
+[2024-10-13 15:55:52,852 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 14/109
+[2024-10-13 15:55:52,911 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 15/109
+[2024-10-13 15:55:52,970 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 16/109
+[2024-10-13 15:55:53,029 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 17/109
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+[2024-10-13 15:55:57,562 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 94/109
+[2024-10-13 15:55:57,622 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 95/109
+[2024-10-13 15:55:57,683 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 96/109
+[2024-10-13 15:55:57,744 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 97/109
+[2024-10-13 15:55:57,805 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 98/109
+[2024-10-13 15:55:57,866 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 99/109
+[2024-10-13 15:55:57,925 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 100/109
+[2024-10-13 15:55:57,984 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 101/109
+[2024-10-13 15:55:58,042 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 102/109
+[2024-10-13 15:55:58,101 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 103/109
+[2024-10-13 15:55:58,160 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 104/109
+[2024-10-13 15:55:58,218 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 105/109
+[2024-10-13 15:55:58,277 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 106/109
+[2024-10-13 15:55:58,336 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 107/109
+[2024-10-13 15:55:58,394 INFO test.py line 186 25394] Test: 80/312-scene0406_02, Batch: 108/109
+[2024-10-13 15:55:58,459 INFO test.py line 272 25394] Test: scene0406_02 [80/312]-41752 Batch 6.499 (19.999) Accuracy 0.9432 (0.4264) mIoU 0.6154 (0.3332)
+[2024-10-13 15:55:58,640 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 0/117
+[2024-10-13 15:55:58,792 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 1/117
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+[2024-10-13 15:56:00,017 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 9/117
+[2024-10-13 15:56:00,202 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 10/117
+[2024-10-13 15:56:00,360 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 11/117
+[2024-10-13 15:56:00,518 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 12/117
+[2024-10-13 15:56:00,675 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 13/117
+[2024-10-13 15:56:00,833 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 14/117
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+[2024-10-13 15:56:15,928 INFO test.py line 186 25394] Test: 81/312-scene0412_00, Batch: 113/117
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+[2024-10-13 15:56:16,613 INFO test.py line 272 25394] Test: scene0412_00 [81/312]-175557 Batch 18.154 (19.977) Accuracy 0.8455 (0.4363) mIoU 0.3395 (0.3428)
+[2024-10-13 15:56:16,930 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 0/139
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+[2024-10-13 15:56:51,292 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 130/139
+[2024-10-13 15:56:51,558 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 131/139
+[2024-10-13 15:56:51,824 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 132/139
+[2024-10-13 15:56:52,089 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 133/139
+[2024-10-13 15:56:52,353 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 134/139
+[2024-10-13 15:56:52,619 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 135/139
+[2024-10-13 15:56:52,884 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 136/139
+[2024-10-13 15:56:53,148 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 137/139
+[2024-10-13 15:56:53,414 INFO test.py line 186 25394] Test: 82/312-scene0249_00, Batch: 138/139
+[2024-10-13 15:56:53,818 INFO test.py line 272 25394] Test: scene0249_00 [82/312]-331031 Batch 37.204 (20.187) Accuracy 0.9642 (0.4380) mIoU 0.5299 (0.3438)
+[2024-10-13 15:56:53,955 INFO test.py line 186 25394] Test: 83/312-scene0357_01, Batch: 0/135
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+[2024-10-13 15:57:10,233 INFO test.py line 272 25394] Test: scene0357_01 [83/312]-133055 Batch 16.414 (20.141) Accuracy 0.8146 (0.4447) mIoU 0.3754 (0.3477)
+[2024-10-13 15:57:10,365 INFO test.py line 186 25394] Test: 84/312-scene0144_00, Batch: 0/117
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+[2024-10-13 15:57:23,950 INFO test.py line 272 25394] Test: scene0144_00 [84/312]-126879 Batch 13.717 (20.065) Accuracy 0.8310 (0.4455) mIoU 0.3802 (0.3475)
+[2024-10-13 15:57:24,050 INFO test.py line 186 25394] Test: 85/312-scene0441_00, Batch: 0/138
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+[2024-10-13 15:57:36,239 INFO test.py line 272 25394] Test: scene0441_00 [85/312]-86191 Batch 12.288 (19.973) Accuracy 0.8792 (0.4467) mIoU 0.4684 (0.3480)
+[2024-10-13 15:57:36,359 INFO test.py line 186 25394] Test: 86/312-scene0084_01, Batch: 0/157
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+[2024-10-13 15:57:52,568 INFO test.py line 186 25394] Test: 86/312-scene0084_01, Batch: 153/157
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+[2024-10-13 15:57:53,029 INFO test.py line 272 25394] Test: scene0084_01 [86/312]-111833 Batch 16.790 (19.936) Accuracy 0.7199 (0.4429) mIoU 0.3380 (0.3449)
+[2024-10-13 15:57:53,225 INFO test.py line 186 25394] Test: 87/312-scene0064_01, Batch: 0/121
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+[2024-10-13 15:58:12,394 INFO test.py line 186 25394] Test: 87/312-scene0064_01, Batch: 116/121
+[2024-10-13 15:58:12,560 INFO test.py line 186 25394] Test: 87/312-scene0064_01, Batch: 117/121
+[2024-10-13 15:58:12,725 INFO test.py line 186 25394] Test: 87/312-scene0064_01, Batch: 118/121
+[2024-10-13 15:58:12,890 INFO test.py line 186 25394] Test: 87/312-scene0064_01, Batch: 119/121
+[2024-10-13 15:58:13,056 INFO test.py line 186 25394] Test: 87/312-scene0064_01, Batch: 120/121
+[2024-10-13 15:58:13,306 INFO test.py line 272 25394] Test: scene0064_01 [87/312]-195252 Batch 20.277 (19.940) Accuracy 0.8439 (0.4433) mIoU 0.3326 (0.3457)
+[2024-10-13 15:58:13,511 INFO test.py line 186 25394] Test: 88/312-scene0050_00, Batch: 0/129
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+[2024-10-13 15:58:35,702 INFO test.py line 272 25394] Test: scene0050_00 [88/312]-211406 Batch 22.396 (19.968) Accuracy 0.7441 (0.4399) mIoU 0.3425 (0.3435)
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+[2024-10-13 15:59:04,908 INFO test.py line 272 25394] Test: scene0618_00 [89/312]-232740 Batch 29.206 (20.072) Accuracy 0.8732 (0.4395) mIoU 0.2960 (0.3428)
+[2024-10-13 15:59:05,050 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 0/117
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+[2024-10-13 15:59:16,378 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 92/117
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+[2024-10-13 15:59:17,035 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 97/117
+[2024-10-13 15:59:17,166 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 98/117
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+[2024-10-13 15:59:17,692 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 102/117
+[2024-10-13 15:59:17,824 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 103/117
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+[2024-10-13 15:59:18,087 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 105/117
+[2024-10-13 15:59:18,221 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 106/117
+[2024-10-13 15:59:18,353 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 107/117
+[2024-10-13 15:59:18,477 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 108/117
+[2024-10-13 15:59:18,602 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 109/117
+[2024-10-13 15:59:18,727 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 110/117
+[2024-10-13 15:59:18,851 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 111/117
+[2024-10-13 15:59:18,977 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 112/117
+[2024-10-13 15:59:19,102 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 113/117
+[2024-10-13 15:59:19,227 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 114/117
+[2024-10-13 15:59:19,351 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 115/117
+[2024-10-13 15:59:19,476 INFO test.py line 186 25394] Test: 90/312-scene0412_01, Batch: 116/117
+[2024-10-13 15:59:19,651 INFO test.py line 272 25394] Test: scene0412_01 [90/312]-133337 Batch 14.742 (20.013) Accuracy 0.8808 (0.4395) mIoU 0.3352 (0.3426)
+[2024-10-13 15:59:19,989 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 0/118
+[2024-10-13 15:59:20,271 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 1/118
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+[2024-10-13 15:59:22,250 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 8/118
+[2024-10-13 15:59:22,532 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 9/118
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+[2024-10-13 15:59:23,097 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 11/118
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+[2024-10-13 15:59:23,661 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 13/118
+[2024-10-13 15:59:23,943 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 14/118
+[2024-10-13 15:59:24,225 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 15/118
+[2024-10-13 15:59:24,508 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 16/118
+[2024-10-13 15:59:24,790 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 17/118
+[2024-10-13 15:59:25,075 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 18/118
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+[2024-10-13 15:59:25,639 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 20/118
+[2024-10-13 15:59:25,920 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 21/118
+[2024-10-13 15:59:26,201 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 22/118
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+[2024-10-13 15:59:27,045 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 25/118
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+[2024-10-13 15:59:28,731 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 31/118
+[2024-10-13 15:59:29,013 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 32/118
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+[2024-10-13 15:59:45,205 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 91/118
+[2024-10-13 15:59:45,506 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 92/118
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+[2024-10-13 15:59:50,021 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 107/118
+[2024-10-13 15:59:50,302 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 108/118
+[2024-10-13 15:59:50,583 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 109/118
+[2024-10-13 15:59:50,864 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 110/118
+[2024-10-13 15:59:51,145 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 111/118
+[2024-10-13 15:59:51,427 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 112/118
+[2024-10-13 15:59:51,708 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 113/118
+[2024-10-13 15:59:51,989 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 114/118
+[2024-10-13 15:59:52,271 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 115/118
+[2024-10-13 15:59:52,552 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 116/118
+[2024-10-13 15:59:52,833 INFO test.py line 186 25394] Test: 91/312-scene0329_01, Batch: 117/118
+[2024-10-13 15:59:53,293 INFO test.py line 272 25394] Test: scene0329_01 [91/312]-368473 Batch 33.643 (20.162) Accuracy 0.9094 (0.4401) mIoU 0.4059 (0.3412)
+[2024-10-13 15:59:53,465 INFO test.py line 186 25394] Test: 92/312-scene0700_00, Batch: 0/139
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+[2024-10-13 15:59:57,972 INFO test.py line 186 25394] Test: 92/312-scene0700_00, Batch: 31/139
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+[2024-10-13 16:00:13,710 INFO test.py line 272 25394] Test: scene0700_00 [92/312]-167755 Batch 20.417 (20.165) Accuracy 0.8380 (0.4374) mIoU 0.3684 (0.3401)
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+[2024-10-13 16:00:23,004 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 116/136
+[2024-10-13 16:00:23,087 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 117/136
+[2024-10-13 16:00:23,170 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 118/136
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+[2024-10-13 16:00:23,501 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 122/136
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+[2024-10-13 16:00:23,820 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 126/136
+[2024-10-13 16:00:23,899 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 127/136
+[2024-10-13 16:00:23,978 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 128/136
+[2024-10-13 16:00:24,056 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 129/136
+[2024-10-13 16:00:24,135 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 130/136
+[2024-10-13 16:00:24,214 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 131/136
+[2024-10-13 16:00:24,293 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 132/136
+[2024-10-13 16:00:24,372 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 133/136
+[2024-10-13 16:00:24,450 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 134/136
+[2024-10-13 16:00:24,529 INFO test.py line 186 25394] Test: 93/312-scene0139_00, Batch: 135/136
+[2024-10-13 16:00:24,631 INFO test.py line 272 25394] Test: scene0139_00 [93/312]-72007 Batch 10.920 (20.066) Accuracy 0.8753 (0.4361) mIoU 0.5803 (0.3394)
+[2024-10-13 16:00:24,801 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 0/155
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+[2024-10-13 16:00:42,357 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 124/155
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+[2024-10-13 16:00:44,331 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 137/155
+[2024-10-13 16:00:44,482 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 138/155
+[2024-10-13 16:00:44,634 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 139/155
+[2024-10-13 16:00:44,786 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 140/155
+[2024-10-13 16:00:44,937 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 141/155
+[2024-10-13 16:00:45,089 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 142/155
+[2024-10-13 16:00:45,241 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 143/155
+[2024-10-13 16:00:45,385 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 144/155
+[2024-10-13 16:00:45,529 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 145/155
+[2024-10-13 16:00:45,673 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 146/155
+[2024-10-13 16:00:45,817 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 147/155
+[2024-10-13 16:00:45,961 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 148/155
+[2024-10-13 16:00:46,105 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 149/155
+[2024-10-13 16:00:46,249 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 150/155
+[2024-10-13 16:00:46,392 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 151/155
+[2024-10-13 16:00:46,536 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 152/155
+[2024-10-13 16:00:46,681 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 153/155
+[2024-10-13 16:00:46,825 INFO test.py line 186 25394] Test: 94/312-scene0697_03, Batch: 154/155
+[2024-10-13 16:00:47,038 INFO test.py line 272 25394] Test: scene0697_03 [94/312]-165912 Batch 22.408 (20.091) Accuracy 0.7739 (0.4367) mIoU 0.4509 (0.3403)
+[2024-10-13 16:00:47,234 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 0/129
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+[2024-10-13 16:01:06,644 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 120/129
+[2024-10-13 16:01:06,807 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 121/129
+[2024-10-13 16:01:06,970 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 122/129
+[2024-10-13 16:01:07,133 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 123/129
+[2024-10-13 16:01:07,295 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 124/129
+[2024-10-13 16:01:07,459 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 125/129
+[2024-10-13 16:01:07,622 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 126/129
+[2024-10-13 16:01:07,785 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 127/129
+[2024-10-13 16:01:07,948 INFO test.py line 186 25394] Test: 95/312-scene0050_01, Batch: 128/129
+[2024-10-13 16:01:08,202 INFO test.py line 272 25394] Test: scene0050_01 [95/312]-199034 Batch 21.163 (20.102) Accuracy 0.7943 (0.4359) mIoU 0.3906 (0.3400)
+[2024-10-13 16:01:08,322 INFO test.py line 186 25394] Test: 96/312-scene0377_00, Batch: 0/125
+[2024-10-13 16:01:08,427 INFO test.py line 186 25394] Test: 96/312-scene0377_00, Batch: 1/125
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+[2024-10-13 16:01:09,301 INFO test.py line 186 25394] Test: 96/312-scene0377_00, Batch: 9/125
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+[2024-10-13 16:01:09,512 INFO test.py line 186 25394] Test: 96/312-scene0377_00, Batch: 11/125
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+[2024-10-13 16:01:21,404 INFO test.py line 272 25394] Test: scene0377_00 [96/312]-111811 Batch 13.202 (20.030) Accuracy 0.8251 (0.4354) mIoU 0.2321 (0.3399)
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+[2024-10-13 16:01:48,753 INFO test.py line 186 25394] Test: 97/312-scene0665_00, Batch: 132/138
+[2024-10-13 16:01:48,958 INFO test.py line 186 25394] Test: 97/312-scene0665_00, Batch: 133/138
+[2024-10-13 16:01:49,164 INFO test.py line 186 25394] Test: 97/312-scene0665_00, Batch: 134/138
+[2024-10-13 16:01:49,369 INFO test.py line 186 25394] Test: 97/312-scene0665_00, Batch: 135/138
+[2024-10-13 16:01:49,575 INFO test.py line 186 25394] Test: 97/312-scene0665_00, Batch: 136/138
+[2024-10-13 16:01:49,781 INFO test.py line 186 25394] Test: 97/312-scene0665_00, Batch: 137/138
+[2024-10-13 16:01:50,099 INFO test.py line 272 25394] Test: scene0665_00 [97/312]-252726 Batch 28.695 (20.119) Accuracy 0.9267 (0.4354) mIoU 0.4913 (0.3400)
+[2024-10-13 16:01:50,165 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 0/136
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+[2024-10-13 16:01:57,164 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 122/136
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+[2024-10-13 16:01:57,449 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 127/136
+[2024-10-13 16:01:57,506 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 128/136
+[2024-10-13 16:01:57,563 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 129/136
+[2024-10-13 16:01:57,619 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 130/136
+[2024-10-13 16:01:57,679 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 131/136
+[2024-10-13 16:01:57,735 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 132/136
+[2024-10-13 16:01:57,792 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 133/136
+[2024-10-13 16:01:57,849 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 134/136
+[2024-10-13 16:01:57,905 INFO test.py line 186 25394] Test: 98/312-scene0684_01, Batch: 135/136
+[2024-10-13 16:01:57,966 INFO test.py line 272 25394] Test: scene0684_01 [98/312]-39474 Batch 7.867 (19.994) Accuracy 0.8593 (0.4359) mIoU 0.5731 (0.3404)
+[2024-10-13 16:01:58,057 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 0/137
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+[2024-10-13 16:02:08,270 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 127/137
+[2024-10-13 16:02:08,350 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 128/137
+[2024-10-13 16:02:08,430 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 129/137
+[2024-10-13 16:02:08,510 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 130/137
+[2024-10-13 16:02:08,590 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 131/137
+[2024-10-13 16:02:08,670 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 132/137
+[2024-10-13 16:02:08,750 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 133/137
+[2024-10-13 16:02:08,831 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 134/137
+[2024-10-13 16:02:08,911 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 135/137
+[2024-10-13 16:02:08,991 INFO test.py line 186 25394] Test: 99/312-scene0427_00, Batch: 136/137
+[2024-10-13 16:02:09,090 INFO test.py line 272 25394] Test: scene0427_00 [99/312]-74074 Batch 11.123 (19.905) Accuracy 0.9746 (0.4360) mIoU 0.8360 (0.3404)
+[2024-10-13 16:02:09,318 INFO test.py line 186 25394] Test: 100/312-scene0169_00, Batch: 0/139
+[2024-10-13 16:02:09,511 INFO test.py line 186 25394] Test: 100/312-scene0169_00, Batch: 1/139
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+[2024-10-13 16:02:10,858 INFO test.py line 186 25394] Test: 100/312-scene0169_00, Batch: 8/139
+[2024-10-13 16:02:11,050 INFO test.py line 186 25394] Test: 100/312-scene0169_00, Batch: 9/139
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+[2024-10-13 16:02:12,012 INFO test.py line 186 25394] Test: 100/312-scene0169_00, Batch: 14/139
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+[2024-10-13 16:02:36,117 INFO test.py line 272 25394] Test: scene0169_00 [100/312]-239354 Batch 27.027 (19.976) Accuracy 0.9338 (0.4361) mIoU 0.8178 (0.3408)
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+[2024-10-13 16:02:47,035 INFO test.py line 186 25394] Test: 101/312-scene0342_00, Batch: 116/122
+[2024-10-13 16:02:47,129 INFO test.py line 186 25394] Test: 101/312-scene0342_00, Batch: 117/122
+[2024-10-13 16:02:47,222 INFO test.py line 186 25394] Test: 101/312-scene0342_00, Batch: 118/122
+[2024-10-13 16:02:47,315 INFO test.py line 186 25394] Test: 101/312-scene0342_00, Batch: 119/122
+[2024-10-13 16:02:47,408 INFO test.py line 186 25394] Test: 101/312-scene0342_00, Batch: 120/122
+[2024-10-13 16:02:47,502 INFO test.py line 186 25394] Test: 101/312-scene0342_00, Batch: 121/122
+[2024-10-13 16:02:47,619 INFO test.py line 272 25394] Test: scene0342_00 [101/312]-89958 Batch 11.502 (19.892) Accuracy 0.9915 (0.4362) mIoU 0.7333 (0.3409)
+[2024-10-13 16:02:47,824 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 0/142
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+[2024-10-13 16:03:11,089 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 135/142
+[2024-10-13 16:03:11,261 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 136/142
+[2024-10-13 16:03:11,433 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 137/142
+[2024-10-13 16:03:11,605 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 138/142
+[2024-10-13 16:03:11,777 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 139/142
+[2024-10-13 16:03:11,950 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 140/142
+[2024-10-13 16:03:12,122 INFO test.py line 186 25394] Test: 102/312-scene0095_00, Batch: 141/142
+[2024-10-13 16:03:12,394 INFO test.py line 272 25394] Test: scene0095_00 [102/312]-214895 Batch 24.774 (19.940) Accuracy 0.8990 (0.4404) mIoU 0.5264 (0.3412)
+[2024-10-13 16:03:12,539 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 0/144
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+[2024-10-13 16:03:29,867 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 135/144
+[2024-10-13 16:03:29,995 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 136/144
+[2024-10-13 16:03:30,123 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 137/144
+[2024-10-13 16:03:30,251 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 138/144
+[2024-10-13 16:03:30,379 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 139/144
+[2024-10-13 16:03:30,507 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 140/144
+[2024-10-13 16:03:30,635 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 141/144
+[2024-10-13 16:03:30,763 INFO test.py line 186 25394] Test: 103/312-scene0131_02, Batch: 142/144
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+[2024-10-13 16:03:31,066 INFO test.py line 272 25394] Test: scene0131_02 [103/312]-140599 Batch 18.672 (19.928) Accuracy 0.8477 (0.4413) mIoU 0.3929 (0.3410)
+[2024-10-13 16:03:31,132 INFO test.py line 186 25394] Test: 104/312-scene0684_00, Batch: 0/113
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+[2024-10-13 16:03:37,646 INFO test.py line 272 25394] Test: scene0684_00 [104/312]-41364 Batch 6.580 (19.799) Accuracy 0.8619 (0.4406) mIoU 0.4448 (0.3405)
+[2024-10-13 16:03:37,814 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 0/138
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+[2024-10-13 16:03:55,027 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 123/138
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+[2024-10-13 16:03:55,762 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 128/138
+[2024-10-13 16:03:55,903 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 129/138
+[2024-10-13 16:03:56,044 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 130/138
+[2024-10-13 16:03:56,185 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 131/138
+[2024-10-13 16:03:56,326 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 132/138
+[2024-10-13 16:03:56,466 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 133/138
+[2024-10-13 16:03:56,607 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 134/138
+[2024-10-13 16:03:56,749 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 135/138
+[2024-10-13 16:03:56,889 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 136/138
+[2024-10-13 16:03:57,030 INFO test.py line 186 25394] Test: 105/312-scene0462_00, Batch: 137/138
+[2024-10-13 16:03:57,232 INFO test.py line 272 25394] Test: scene0462_00 [105/312]-156714 Batch 19.586 (19.797) Accuracy 0.7847 (0.4419) mIoU 0.4142 (0.3414)
+[2024-10-13 16:03:57,509 INFO test.py line 186 25394] Test: 106/312-scene0030_01, Batch: 0/143
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+[2024-10-13 16:04:29,499 INFO test.py line 186 25394] Test: 106/312-scene0030_01, Batch: 139/143
+[2024-10-13 16:04:29,730 INFO test.py line 186 25394] Test: 106/312-scene0030_01, Batch: 140/143
+[2024-10-13 16:04:29,961 INFO test.py line 186 25394] Test: 106/312-scene0030_01, Batch: 141/143
+[2024-10-13 16:04:30,192 INFO test.py line 186 25394] Test: 106/312-scene0030_01, Batch: 142/143
+[2024-10-13 16:04:30,558 INFO test.py line 272 25394] Test: scene0030_01 [106/312]-284801 Batch 33.326 (19.925) Accuracy 0.9125 (0.4450) mIoU 0.5733 (0.3443)
+[2024-10-13 16:04:30,744 INFO test.py line 186 25394] Test: 107/312-scene0081_01, Batch: 0/130
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+[2024-10-13 16:04:50,539 INFO test.py line 186 25394] Test: 107/312-scene0081_01, Batch: 126/130
+[2024-10-13 16:04:50,696 INFO test.py line 186 25394] Test: 107/312-scene0081_01, Batch: 127/130
+[2024-10-13 16:04:50,854 INFO test.py line 186 25394] Test: 107/312-scene0081_01, Batch: 128/130
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+[2024-10-13 16:04:51,242 INFO test.py line 272 25394] Test: scene0081_01 [107/312]-179955 Batch 20.684 (19.932) Accuracy 0.8669 (0.4438) mIoU 0.2131 (0.3439)
+[2024-10-13 16:04:51,376 INFO test.py line 186 25394] Test: 108/312-scene0086_00, Batch: 0/131
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+[2024-10-13 16:05:05,993 INFO test.py line 186 25394] Test: 108/312-scene0086_00, Batch: 125/131
+[2024-10-13 16:05:06,110 INFO test.py line 186 25394] Test: 108/312-scene0086_00, Batch: 126/131
+[2024-10-13 16:05:06,228 INFO test.py line 186 25394] Test: 108/312-scene0086_00, Batch: 127/131
+[2024-10-13 16:05:06,345 INFO test.py line 186 25394] Test: 108/312-scene0086_00, Batch: 128/131
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+[2024-10-13 16:05:06,740 INFO test.py line 272 25394] Test: scene0086_00 [108/312]-124572 Batch 15.497 (19.891) Accuracy 0.7503 (0.4396) mIoU 0.3512 (0.3401)
+[2024-10-13 16:05:06,817 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 0/144
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+[2024-10-13 16:05:15,982 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 137/144
+[2024-10-13 16:05:16,049 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 138/144
+[2024-10-13 16:05:16,116 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 139/144
+[2024-10-13 16:05:16,183 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 140/144
+[2024-10-13 16:05:16,250 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 141/144
+[2024-10-13 16:05:16,317 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 142/144
+[2024-10-13 16:05:16,384 INFO test.py line 186 25394] Test: 109/312-scene0702_00, Batch: 143/144
+[2024-10-13 16:05:16,459 INFO test.py line 272 25394] Test: scene0702_00 [109/312]-54769 Batch 9.719 (19.798) Accuracy 0.9098 (0.4425) mIoU 0.7354 (0.3424)
+[2024-10-13 16:05:16,591 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 0/117
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+[2024-10-13 16:05:29,364 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 109/117
+[2024-10-13 16:05:29,480 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 110/117
+[2024-10-13 16:05:29,597 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 111/117
+[2024-10-13 16:05:29,714 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 112/117
+[2024-10-13 16:05:29,831 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 113/117
+[2024-10-13 16:05:29,948 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 114/117
+[2024-10-13 16:05:30,065 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 115/117
+[2024-10-13 16:05:30,182 INFO test.py line 186 25394] Test: 110/312-scene0552_00, Batch: 116/117
+[2024-10-13 16:05:30,356 INFO test.py line 272 25394] Test: scene0552_00 [110/312]-123103 Batch 13.897 (19.744) Accuracy 0.9523 (0.4473) mIoU 0.6822 (0.3471)
+[2024-10-13 16:05:30,424 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 0/116
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+[2024-10-13 16:05:36,760 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 109/116
+[2024-10-13 16:05:36,818 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 110/116
+[2024-10-13 16:05:36,876 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 111/116
+[2024-10-13 16:05:36,934 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 112/116
+[2024-10-13 16:05:36,992 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 113/116
+[2024-10-13 16:05:37,051 INFO test.py line 186 25394] Test: 111/312-scene0423_01, Batch: 114/116
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+[2024-10-13 16:05:37,168 INFO test.py line 272 25394] Test: scene0423_01 [111/312]-41518 Batch 6.811 (19.627) Accuracy 0.9994 (0.4477) mIoU 0.9943 (0.3475)
+[2024-10-13 16:05:37,303 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 0/142
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+[2024-10-13 16:05:52,298 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 127/142
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+[2024-10-13 16:05:52,670 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 130/142
+[2024-10-13 16:05:52,795 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 131/142
+[2024-10-13 16:05:52,914 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 132/142
+[2024-10-13 16:05:53,032 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 133/142
+[2024-10-13 16:05:53,151 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 134/142
+[2024-10-13 16:05:53,269 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 135/142
+[2024-10-13 16:05:53,388 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 136/142
+[2024-10-13 16:05:53,506 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 137/142
+[2024-10-13 16:05:53,625 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 138/142
+[2024-10-13 16:05:53,744 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 139/142
+[2024-10-13 16:05:53,863 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 140/142
+[2024-10-13 16:05:53,982 INFO test.py line 186 25394] Test: 112/312-scene0086_02, Batch: 141/142
+[2024-10-13 16:05:54,141 INFO test.py line 272 25394] Test: scene0086_02 [112/312]-124091 Batch 16.973 (19.604) Accuracy 0.7467 (0.4459) mIoU 0.4212 (0.3460)
+[2024-10-13 16:05:54,481 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 0/135
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+[2024-10-13 16:06:27,527 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 117/135
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+[2024-10-13 16:06:28,134 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 119/135
+[2024-10-13 16:06:28,437 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 120/135
+[2024-10-13 16:06:28,741 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 121/135
+[2024-10-13 16:06:29,044 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 122/135
+[2024-10-13 16:06:29,347 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 123/135
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+[2024-10-13 16:06:29,917 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 125/135
+[2024-10-13 16:06:30,202 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 126/135
+[2024-10-13 16:06:30,487 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 127/135
+[2024-10-13 16:06:30,771 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 128/135
+[2024-10-13 16:06:31,056 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 129/135
+[2024-10-13 16:06:31,341 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 130/135
+[2024-10-13 16:06:31,626 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 131/135
+[2024-10-13 16:06:31,911 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 132/135
+[2024-10-13 16:06:32,195 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 133/135
+[2024-10-13 16:06:32,480 INFO test.py line 186 25394] Test: 113/312-scene0329_02, Batch: 134/135
+[2024-10-13 16:06:32,936 INFO test.py line 272 25394] Test: scene0329_02 [113/312]-358910 Batch 38.794 (19.774) Accuracy 0.8680 (0.4449) mIoU 0.3670 (0.3437)
+[2024-10-13 16:06:33,208 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 0/143
+[2024-10-13 16:06:33,434 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 1/143
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+[2024-10-13 16:06:35,020 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 8/143
+[2024-10-13 16:06:35,246 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 9/143
+[2024-10-13 16:06:35,473 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 10/143
+[2024-10-13 16:06:35,699 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 11/143
+[2024-10-13 16:06:35,926 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 12/143
+[2024-10-13 16:06:36,152 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 13/143
+[2024-10-13 16:06:36,379 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 14/143
+[2024-10-13 16:06:36,605 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 15/143
+[2024-10-13 16:06:36,834 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 16/143
+[2024-10-13 16:06:37,060 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 17/143
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+[2024-10-13 16:06:37,866 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 20/143
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+[2024-10-13 16:06:39,906 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 29/143
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+[2024-10-13 16:06:40,360 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 31/143
+[2024-10-13 16:06:40,587 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 32/143
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+[2024-10-13 16:06:43,923 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 47/143
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+[2024-10-13 16:07:01,841 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 127/143
+[2024-10-13 16:07:02,082 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 128/143
+[2024-10-13 16:07:02,323 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 129/143
+[2024-10-13 16:07:02,565 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 130/143
+[2024-10-13 16:07:02,807 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 131/143
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+[2024-10-13 16:07:03,264 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 133/143
+[2024-10-13 16:07:03,491 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 134/143
+[2024-10-13 16:07:03,718 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 135/143
+[2024-10-13 16:07:03,945 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 136/143
+[2024-10-13 16:07:04,171 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 137/143
+[2024-10-13 16:07:04,399 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 138/143
+[2024-10-13 16:07:04,626 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 139/143
+[2024-10-13 16:07:04,853 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 140/143
+[2024-10-13 16:07:05,080 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 141/143
+[2024-10-13 16:07:05,308 INFO test.py line 186 25394] Test: 114/312-scene0616_01, Batch: 142/143
+[2024-10-13 16:07:05,687 INFO test.py line 272 25394] Test: scene0616_01 [114/312]-297626 Batch 32.751 (19.887) Accuracy 0.8455 (0.4488) mIoU 0.5069 (0.3472)
+[2024-10-13 16:07:05,884 INFO test.py line 186 25394] Test: 115/312-scene0608_01, Batch: 0/126
+[2024-10-13 16:07:06,051 INFO test.py line 186 25394] Test: 115/312-scene0608_01, Batch: 1/126
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+[2024-10-13 16:07:06,887 INFO test.py line 186 25394] Test: 115/312-scene0608_01, Batch: 6/126
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+[2024-10-13 16:07:07,221 INFO test.py line 186 25394] Test: 115/312-scene0608_01, Batch: 8/126
+[2024-10-13 16:07:07,387 INFO test.py line 186 25394] Test: 115/312-scene0608_01, Batch: 9/126
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+[2024-10-13 16:07:07,719 INFO test.py line 186 25394] Test: 115/312-scene0608_01, Batch: 11/126
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+[2024-10-13 16:07:26,950 INFO test.py line 272 25394] Test: scene0608_01 [115/312]-191107 Batch 21.263 (19.899) Accuracy 0.8184 (0.4476) mIoU 0.4317 (0.3468)
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+[2024-10-13 16:07:48,122 INFO test.py line 186 25394] Test: 116/312-scene0580_01, Batch: 147/151
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+[2024-10-13 16:07:48,776 INFO test.py line 272 25394] Test: scene0580_01 [116/312]-165009 Batch 21.826 (19.916) Accuracy 0.8334 (0.4474) mIoU 0.3843 (0.3463)
+[2024-10-13 16:07:48,921 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 0/130
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+[2024-10-13 16:08:03,628 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 116/130
+[2024-10-13 16:08:03,762 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 117/130
+[2024-10-13 16:08:03,897 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 118/130
+[2024-10-13 16:08:04,032 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 119/130
+[2024-10-13 16:08:04,160 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 120/130
+[2024-10-13 16:08:04,287 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 121/130
+[2024-10-13 16:08:04,415 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 122/130
+[2024-10-13 16:08:04,543 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 123/130
+[2024-10-13 16:08:04,670 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 124/130
+[2024-10-13 16:08:04,798 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 125/130
+[2024-10-13 16:08:04,925 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 126/130
+[2024-10-13 16:08:05,052 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 127/130
+[2024-10-13 16:08:05,180 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 128/130
+[2024-10-13 16:08:05,307 INFO test.py line 186 25394] Test: 117/312-scene0088_03, Batch: 129/130
+[2024-10-13 16:08:05,484 INFO test.py line 272 25394] Test: scene0088_03 [117/312]-136812 Batch 16.708 (19.889) Accuracy 0.7286 (0.4473) mIoU 0.5473 (0.3461)
+[2024-10-13 16:08:05,660 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 0/125
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+[2024-10-13 16:08:22,846 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 115/125
+[2024-10-13 16:08:22,996 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 116/125
+[2024-10-13 16:08:23,145 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 117/125
+[2024-10-13 16:08:23,294 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 118/125
+[2024-10-13 16:08:23,444 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 119/125
+[2024-10-13 16:08:23,593 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 120/125
+[2024-10-13 16:08:23,742 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 121/125
+[2024-10-13 16:08:23,892 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 122/125
+[2024-10-13 16:08:24,041 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 123/125
+[2024-10-13 16:08:24,190 INFO test.py line 186 25394] Test: 118/312-scene0558_02, Batch: 124/125
+[2024-10-13 16:08:24,410 INFO test.py line 272 25394] Test: scene0558_02 [118/312]-173904 Batch 18.926 (19.880) Accuracy 0.9168 (0.4478) mIoU 0.8870 (0.3466)
+[2024-10-13 16:08:24,534 INFO test.py line 186 25394] Test: 119/312-scene0084_00, Batch: 0/159
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+[2024-10-13 16:08:41,702 INFO test.py line 272 25394] Test: scene0084_00 [119/312]-116845 Batch 17.291 (19.859) Accuracy 0.7284 (0.4474) mIoU 0.3869 (0.3465)
+[2024-10-13 16:08:41,842 INFO test.py line 186 25394] Test: 120/312-scene0658_00, Batch: 0/126
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+[2024-10-13 16:08:56,925 INFO test.py line 272 25394] Test: scene0658_00 [120/312]-129951 Batch 15.223 (19.820) Accuracy 0.8143 (0.4469) mIoU 0.4549 (0.3461)
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+[2024-10-13 16:09:06,340 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 108/134
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+[2024-10-13 16:09:06,969 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 115/134
+[2024-10-13 16:09:07,059 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 116/134
+[2024-10-13 16:09:07,149 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 117/134
+[2024-10-13 16:09:07,239 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 118/134
+[2024-10-13 16:09:07,329 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 119/134
+[2024-10-13 16:09:07,419 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 120/134
+[2024-10-13 16:09:07,509 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 121/134
+[2024-10-13 16:09:07,598 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 122/134
+[2024-10-13 16:09:07,688 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 123/134
+[2024-10-13 16:09:07,775 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 124/134
+[2024-10-13 16:09:07,862 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 125/134
+[2024-10-13 16:09:07,948 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 126/134
+[2024-10-13 16:09:08,035 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 127/134
+[2024-10-13 16:09:08,122 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 128/134
+[2024-10-13 16:09:08,209 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 129/134
+[2024-10-13 16:09:08,295 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 130/134
+[2024-10-13 16:09:08,382 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 131/134
+[2024-10-13 16:09:08,469 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 132/134
+[2024-10-13 16:09:08,556 INFO test.py line 186 25394] Test: 121/312-scene0277_01, Batch: 133/134
+[2024-10-13 16:09:08,673 INFO test.py line 272 25394] Test: scene0277_01 [121/312]-82628 Batch 11.748 (19.753) Accuracy 0.8633 (0.4469) mIoU 0.5694 (0.3461)
+[2024-10-13 16:09:08,773 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 0/138
+[2024-10-13 16:09:08,860 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 1/138
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+[2024-10-13 16:09:09,033 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 3/138
+[2024-10-13 16:09:09,120 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 4/138
+[2024-10-13 16:09:09,207 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 5/138
+[2024-10-13 16:09:09,293 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 6/138
+[2024-10-13 16:09:09,380 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 7/138
+[2024-10-13 16:09:09,466 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 8/138
+[2024-10-13 16:09:09,553 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 9/138
+[2024-10-13 16:09:09,639 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 10/138
+[2024-10-13 16:09:09,726 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 11/138
+[2024-10-13 16:09:09,812 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 12/138
+[2024-10-13 16:09:09,899 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 13/138
+[2024-10-13 16:09:09,985 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 14/138
+[2024-10-13 16:09:10,071 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 15/138
+[2024-10-13 16:09:10,158 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 16/138
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+[2024-10-13 16:09:10,504 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 20/138
+[2024-10-13 16:09:10,591 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 21/138
+[2024-10-13 16:09:10,678 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 22/138
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+[2024-10-13 16:09:10,851 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 24/138
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+[2024-10-13 16:09:11,025 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 26/138
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+[2024-10-13 16:09:11,545 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 32/138
+[2024-10-13 16:09:11,632 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 33/138
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+[2024-10-13 16:09:12,736 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 46/138
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+[2024-10-13 16:09:19,041 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 119/138
+[2024-10-13 16:09:19,130 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 120/138
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+[2024-10-13 16:09:19,309 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 122/138
+[2024-10-13 16:09:19,398 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 123/138
+[2024-10-13 16:09:19,487 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 124/138
+[2024-10-13 16:09:19,576 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 125/138
+[2024-10-13 16:09:19,665 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 126/138
+[2024-10-13 16:09:19,754 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 127/138
+[2024-10-13 16:09:19,841 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 128/138
+[2024-10-13 16:09:19,928 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 129/138
+[2024-10-13 16:09:20,015 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 130/138
+[2024-10-13 16:09:20,101 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 131/138
+[2024-10-13 16:09:20,188 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 132/138
+[2024-10-13 16:09:20,275 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 133/138
+[2024-10-13 16:09:20,362 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 134/138
+[2024-10-13 16:09:20,448 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 135/138
+[2024-10-13 16:09:20,535 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 136/138
+[2024-10-13 16:09:20,622 INFO test.py line 186 25394] Test: 122/312-scene0278_01, Batch: 137/138
+[2024-10-13 16:09:20,734 INFO test.py line 272 25394] Test: scene0278_01 [122/312]-83269 Batch 12.061 (19.690) Accuracy 0.8880 (0.4485) mIoU 0.4606 (0.3474)
+[2024-10-13 16:09:20,817 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 0/104
+[2024-10-13 16:09:20,890 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 1/104
+[2024-10-13 16:09:20,962 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 2/104
+[2024-10-13 16:09:21,035 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 3/104
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+[2024-10-13 16:09:21,180 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 5/104
+[2024-10-13 16:09:21,253 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 6/104
+[2024-10-13 16:09:21,325 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 7/104
+[2024-10-13 16:09:21,396 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 8/104
+[2024-10-13 16:09:21,468 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 9/104
+[2024-10-13 16:09:21,539 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 10/104
+[2024-10-13 16:09:21,611 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 11/104
+[2024-10-13 16:09:21,682 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 12/104
+[2024-10-13 16:09:21,754 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 13/104
+[2024-10-13 16:09:21,870 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 14/104
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+[2024-10-13 16:09:28,031 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 100/104
+[2024-10-13 16:09:28,102 INFO test.py line 186 25394] Test: 123/312-scene0458_00, Batch: 101/104
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+[2024-10-13 16:09:28,338 INFO test.py line 272 25394] Test: scene0458_00 [123/312]-65264 Batch 7.604 (19.592) Accuracy 0.7776 (0.4476) mIoU 0.3738 (0.3480)
+[2024-10-13 16:09:28,584 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 0/135
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+[2024-10-13 16:09:46,767 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 91/135
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+[2024-10-13 16:09:53,103 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 120/135
+[2024-10-13 16:09:53,321 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 121/135
+[2024-10-13 16:09:53,540 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 122/135
+[2024-10-13 16:09:53,758 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 123/135
+[2024-10-13 16:09:53,964 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 124/135
+[2024-10-13 16:09:54,171 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 125/135
+[2024-10-13 16:09:54,377 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 126/135
+[2024-10-13 16:09:54,583 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 127/135
+[2024-10-13 16:09:54,789 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 128/135
+[2024-10-13 16:09:54,996 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 129/135
+[2024-10-13 16:09:55,202 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 130/135
+[2024-10-13 16:09:55,407 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 131/135
+[2024-10-13 16:09:55,614 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 132/135
+[2024-10-13 16:09:55,820 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 133/135
+[2024-10-13 16:09:56,025 INFO test.py line 186 25394] Test: 124/312-scene0435_00, Batch: 134/135
+[2024-10-13 16:09:56,349 INFO test.py line 272 25394] Test: scene0435_00 [124/312]-254998 Batch 28.011 (19.660) Accuracy 0.8967 (0.4481) mIoU 0.5076 (0.3486)
+[2024-10-13 16:09:56,424 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 0/116
+[2024-10-13 16:09:56,488 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 1/116
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+[2024-10-13 16:09:57,379 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 15/116
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+[2024-10-13 16:10:03,300 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 108/116
+[2024-10-13 16:10:03,363 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 109/116
+[2024-10-13 16:10:03,426 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 110/116
+[2024-10-13 16:10:03,489 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 111/116
+[2024-10-13 16:10:03,552 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 112/116
+[2024-10-13 16:10:03,615 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 113/116
+[2024-10-13 16:10:03,678 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 114/116
+[2024-10-13 16:10:03,741 INFO test.py line 186 25394] Test: 125/312-scene0574_01, Batch: 115/116
+[2024-10-13 16:10:03,820 INFO test.py line 272 25394] Test: scene0574_01 [125/312]-53641 Batch 7.470 (19.562) Accuracy 0.8127 (0.4480) mIoU 0.4307 (0.3481)
+[2024-10-13 16:10:03,917 INFO test.py line 186 25394] Test: 126/312-scene0671_00, Batch: 0/120
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+[2024-10-13 16:10:14,147 INFO test.py line 272 25394] Test: scene0671_00 [126/312]-81469 Batch 10.327 (19.489) Accuracy 0.8111 (0.4474) mIoU 0.3522 (0.3472)
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+[2024-10-13 16:11:01,154 INFO test.py line 186 25394] Test: 127/312-scene0231_00, Batch: 144/148
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+[2024-10-13 16:11:02,663 INFO test.py line 272 25394] Test: scene0231_00 [127/312]-419537 Batch 48.515 (19.718) Accuracy 0.8167 (0.4451) mIoU 0.3972 (0.3449)
+[2024-10-13 16:11:02,845 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 0/144
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+[2024-10-13 16:11:20,479 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 116/144
+[2024-10-13 16:11:20,642 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 117/144
+[2024-10-13 16:11:20,806 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 118/144
+[2024-10-13 16:11:20,970 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 119/144
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+[2024-10-13 16:11:21,624 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 123/144
+[2024-10-13 16:11:21,788 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 124/144
+[2024-10-13 16:11:21,952 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 125/144
+[2024-10-13 16:11:22,115 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 126/144
+[2024-10-13 16:11:22,279 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 127/144
+[2024-10-13 16:11:22,443 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 128/144
+[2024-10-13 16:11:22,607 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 129/144
+[2024-10-13 16:11:22,771 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 130/144
+[2024-10-13 16:11:22,934 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 131/144
+[2024-10-13 16:11:23,089 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 132/144
+[2024-10-13 16:11:23,243 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 133/144
+[2024-10-13 16:11:23,398 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 134/144
+[2024-10-13 16:11:23,552 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 135/144
+[2024-10-13 16:11:23,706 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 136/144
+[2024-10-13 16:11:23,860 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 137/144
+[2024-10-13 16:11:24,015 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 138/144
+[2024-10-13 16:11:24,169 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 139/144
+[2024-10-13 16:11:24,323 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 140/144
+[2024-10-13 16:11:24,478 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 141/144
+[2024-10-13 16:11:24,632 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 142/144
+[2024-10-13 16:11:24,786 INFO test.py line 186 25394] Test: 128/312-scene0591_00, Batch: 143/144
+[2024-10-13 16:11:25,009 INFO test.py line 272 25394] Test: scene0591_00 [128/312]-175600 Batch 22.345 (19.738) Accuracy 0.7572 (0.4454) mIoU 0.4846 (0.3451)
+[2024-10-13 16:11:25,086 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 0/130
+[2024-10-13 16:11:25,153 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 1/130
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+[2024-10-13 16:11:25,621 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 8/130
+[2024-10-13 16:11:25,688 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 9/130
+[2024-10-13 16:11:25,754 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 10/130
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+[2024-10-13 16:11:25,888 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 12/130
+[2024-10-13 16:11:25,954 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 13/130
+[2024-10-13 16:11:26,021 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 14/130
+[2024-10-13 16:11:26,088 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 15/130
+[2024-10-13 16:11:26,154 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 16/130
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+[2024-10-13 16:11:26,421 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 20/130
+[2024-10-13 16:11:26,488 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 21/130
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+[2024-10-13 16:11:32,891 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 116/130
+[2024-10-13 16:11:32,961 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 117/130
+[2024-10-13 16:11:33,031 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 118/130
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+[2024-10-13 16:11:33,168 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 120/130
+[2024-10-13 16:11:33,235 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 121/130
+[2024-10-13 16:11:33,301 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 122/130
+[2024-10-13 16:11:33,368 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 123/130
+[2024-10-13 16:11:33,435 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 124/130
+[2024-10-13 16:11:33,502 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 125/130
+[2024-10-13 16:11:33,568 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 126/130
+[2024-10-13 16:11:33,635 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 127/130
+[2024-10-13 16:11:33,702 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 128/130
+[2024-10-13 16:11:33,768 INFO test.py line 186 25394] Test: 129/312-scene0702_01, Batch: 129/130
+[2024-10-13 16:11:33,845 INFO test.py line 272 25394] Test: scene0702_01 [129/312]-56603 Batch 8.836 (19.654) Accuracy 0.8427 (0.4463) mIoU 0.5789 (0.3461)
+[2024-10-13 16:11:33,949 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 0/130
+[2024-10-13 16:11:34,040 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 1/130
+[2024-10-13 16:11:34,132 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 2/130
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+[2024-10-13 16:11:34,406 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 5/130
+[2024-10-13 16:11:34,497 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 6/130
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+[2024-10-13 16:11:34,680 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 8/130
+[2024-10-13 16:11:34,771 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 9/130
+[2024-10-13 16:11:34,862 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 10/130
+[2024-10-13 16:11:34,953 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 11/130
+[2024-10-13 16:11:35,044 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 12/130
+[2024-10-13 16:11:35,134 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 13/130
+[2024-10-13 16:11:35,225 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 14/130
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+[2024-10-13 16:11:35,407 INFO test.py line 186 25394] Test: 130/312-scene0193_01, Batch: 16/130
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+[2024-10-13 16:11:45,908 INFO test.py line 272 25394] Test: scene0193_01 [130/312]-93668 Batch 12.063 (19.595) Accuracy 0.8756 (0.4462) mIoU 0.5520 (0.3460)
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+[2024-10-13 16:11:52,562 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 73/113
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+[2024-10-13 16:11:53,238 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 80/113
+[2024-10-13 16:11:53,335 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 81/113
+[2024-10-13 16:11:53,431 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 82/113
+[2024-10-13 16:11:53,528 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 83/113
+[2024-10-13 16:11:53,624 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 84/113
+[2024-10-13 16:11:53,720 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 85/113
+[2024-10-13 16:11:53,817 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 86/113
+[2024-10-13 16:11:53,913 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 87/113
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+[2024-10-13 16:11:54,298 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 91/113
+[2024-10-13 16:11:54,394 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 92/113
+[2024-10-13 16:11:54,490 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 93/113
+[2024-10-13 16:11:54,586 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 94/113
+[2024-10-13 16:11:54,682 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 95/113
+[2024-10-13 16:11:54,779 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 96/113
+[2024-10-13 16:11:54,876 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 97/113
+[2024-10-13 16:11:54,973 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 98/113
+[2024-10-13 16:11:55,070 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 99/113
+[2024-10-13 16:11:55,167 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 100/113
+[2024-10-13 16:11:55,264 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 101/113
+[2024-10-13 16:11:55,360 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 102/113
+[2024-10-13 16:11:55,457 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 103/113
+[2024-10-13 16:11:55,548 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 104/113
+[2024-10-13 16:11:55,638 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 105/113
+[2024-10-13 16:11:55,731 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 106/113
+[2024-10-13 16:11:55,821 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 107/113
+[2024-10-13 16:11:55,912 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 108/113
+[2024-10-13 16:11:56,002 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 109/113
+[2024-10-13 16:11:56,093 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 110/113
+[2024-10-13 16:11:56,184 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 111/113
+[2024-10-13 16:11:56,275 INFO test.py line 186 25394] Test: 131/312-scene0077_01, Batch: 112/113
+[2024-10-13 16:11:56,394 INFO test.py line 272 25394] Test: scene0077_01 [131/312]-90381 Batch 10.486 (19.526) Accuracy 0.9438 (0.4458) mIoU 0.5846 (0.3462)
+[2024-10-13 16:11:56,594 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 0/129
+[2024-10-13 16:11:56,762 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 1/129
+[2024-10-13 16:11:56,930 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 2/129
+[2024-10-13 16:11:57,098 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 3/129
+[2024-10-13 16:11:57,267 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 4/129
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+[2024-10-13 16:11:57,607 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 6/129
+[2024-10-13 16:11:57,775 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 7/129
+[2024-10-13 16:11:57,943 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 8/129
+[2024-10-13 16:11:58,113 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 9/129
+[2024-10-13 16:11:58,280 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 10/129
+[2024-10-13 16:11:58,448 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 11/129
+[2024-10-13 16:11:58,616 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 12/129
+[2024-10-13 16:11:58,784 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 13/129
+[2024-10-13 16:11:58,952 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 14/129
+[2024-10-13 16:11:59,120 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 15/129
+[2024-10-13 16:11:59,287 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 16/129
+[2024-10-13 16:11:59,455 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 17/129
+[2024-10-13 16:11:59,623 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 18/129
+[2024-10-13 16:11:59,791 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 19/129
+[2024-10-13 16:11:59,959 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 20/129
+[2024-10-13 16:12:00,127 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 21/129
+[2024-10-13 16:12:00,295 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 22/129
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+[2024-10-13 16:12:01,807 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 31/129
+[2024-10-13 16:12:01,975 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 32/129
+[2024-10-13 16:12:02,143 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 33/129
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+[2024-10-13 16:12:04,376 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 47/129
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+[2024-10-13 16:12:04,693 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 49/129
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+[2024-10-13 16:12:07,386 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 66/129
+[2024-10-13 16:12:07,545 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 67/129
+[2024-10-13 16:12:07,703 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 68/129
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+[2024-10-13 16:12:10,521 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 85/129
+[2024-10-13 16:12:10,701 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 86/129
+[2024-10-13 16:12:10,880 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 87/129
+[2024-10-13 16:12:11,060 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 88/129
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+[2024-10-13 16:12:11,418 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 90/129
+[2024-10-13 16:12:11,598 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 91/129
+[2024-10-13 16:12:11,777 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 92/129
+[2024-10-13 16:12:11,956 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 93/129
+[2024-10-13 16:12:12,136 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 94/129
+[2024-10-13 16:12:12,316 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 95/129
+[2024-10-13 16:12:12,495 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 96/129
+[2024-10-13 16:12:12,674 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 97/129
+[2024-10-13 16:12:12,854 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 98/129
+[2024-10-13 16:12:13,033 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 99/129
+[2024-10-13 16:12:13,211 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 100/129
+[2024-10-13 16:12:13,390 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 101/129
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+[2024-10-13 16:12:13,748 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 103/129
+[2024-10-13 16:12:13,927 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 104/129
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+[2024-10-13 16:12:14,463 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 107/129
+[2024-10-13 16:12:14,643 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 108/129
+[2024-10-13 16:12:14,821 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 109/129
+[2024-10-13 16:12:15,001 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 110/129
+[2024-10-13 16:12:15,180 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 111/129
+[2024-10-13 16:12:15,359 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 112/129
+[2024-10-13 16:12:15,538 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 113/129
+[2024-10-13 16:12:15,718 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 114/129
+[2024-10-13 16:12:15,897 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 115/129
+[2024-10-13 16:12:16,076 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 116/129
+[2024-10-13 16:12:16,256 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 117/129
+[2024-10-13 16:12:16,435 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 118/129
+[2024-10-13 16:12:16,614 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 119/129
+[2024-10-13 16:12:16,782 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 120/129
+[2024-10-13 16:12:16,950 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 121/129
+[2024-10-13 16:12:17,118 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 122/129
+[2024-10-13 16:12:17,285 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 123/129
+[2024-10-13 16:12:17,454 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 124/129
+[2024-10-13 16:12:17,621 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 125/129
+[2024-10-13 16:12:17,789 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 126/129
+[2024-10-13 16:12:17,958 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 127/129
+[2024-10-13 16:12:18,125 INFO test.py line 186 25394] Test: 132/312-scene0549_01, Batch: 128/129
+[2024-10-13 16:12:18,380 INFO test.py line 272 25394] Test: scene0549_01 [132/312]-200947 Batch 21.985 (19.544) Accuracy 0.8199 (0.4435) mIoU 0.3537 (0.3453)
+[2024-10-13 16:12:18,556 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 0/125
+[2024-10-13 16:12:18,705 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 1/125
+[2024-10-13 16:12:18,854 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 2/125
+[2024-10-13 16:12:19,005 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 3/125
+[2024-10-13 16:12:19,154 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 4/125
+[2024-10-13 16:12:19,303 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 5/125
+[2024-10-13 16:12:19,452 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 6/125
+[2024-10-13 16:12:19,601 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 7/125
+[2024-10-13 16:12:19,751 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 8/125
+[2024-10-13 16:12:19,899 INFO test.py line 186 25394] Test: 133/312-scene0565_00, Batch: 9/125
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+[2024-10-13 16:12:37,260 INFO test.py line 272 25394] Test: scene0565_00 [133/312]-167558 Batch 18.879 (19.539) Accuracy 0.8055 (0.4445) mIoU 0.4583 (0.3462)
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+[2024-10-13 16:12:43,520 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 98/108
+[2024-10-13 16:12:43,586 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 99/108
+[2024-10-13 16:12:43,649 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 100/108
+[2024-10-13 16:12:43,713 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 101/108
+[2024-10-13 16:12:43,826 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 102/108
+[2024-10-13 16:12:43,890 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 103/108
+[2024-10-13 16:12:43,957 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 104/108
+[2024-10-13 16:12:44,021 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 105/108
+[2024-10-13 16:12:44,086 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 106/108
+[2024-10-13 16:12:44,151 INFO test.py line 186 25394] Test: 134/312-scene0693_02, Batch: 107/108
+[2024-10-13 16:12:44,218 INFO test.py line 272 25394] Test: scene0693_02 [134/312]-47996 Batch 6.958 (19.445) Accuracy 0.8476 (0.4444) mIoU 0.4135 (0.3471)
+[2024-10-13 16:12:44,627 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 0/138
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+[2024-10-13 16:13:26,394 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 123/138
+[2024-10-13 16:13:26,762 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 124/138
+[2024-10-13 16:13:27,128 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 125/138
+[2024-10-13 16:13:27,494 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 126/138
+[2024-10-13 16:13:27,863 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 127/138
+[2024-10-13 16:13:28,204 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 128/138
+[2024-10-13 16:13:28,546 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 129/138
+[2024-10-13 16:13:28,889 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 130/138
+[2024-10-13 16:13:29,232 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 131/138
+[2024-10-13 16:13:29,574 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 132/138
+[2024-10-13 16:13:29,916 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 133/138
+[2024-10-13 16:13:30,258 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 134/138
+[2024-10-13 16:13:30,601 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 135/138
+[2024-10-13 16:13:30,943 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 136/138
+[2024-10-13 16:13:31,286 INFO test.py line 186 25394] Test: 135/312-scene0231_01, Batch: 137/138
+[2024-10-13 16:13:31,840 INFO test.py line 272 25394] Test: scene0231_01 [135/312]-438565 Batch 47.622 (19.654) Accuracy 0.8507 (0.4446) mIoU 0.4179 (0.3479)
+[2024-10-13 16:13:32,105 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 0/138
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+[2024-10-13 16:14:01,285 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 131/138
+[2024-10-13 16:14:01,507 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 132/138
+[2024-10-13 16:14:01,729 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 133/138
+[2024-10-13 16:14:01,951 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 134/138
+[2024-10-13 16:14:02,174 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 135/138
+[2024-10-13 16:14:02,396 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 136/138
+[2024-10-13 16:14:02,618 INFO test.py line 186 25394] Test: 136/312-scene0207_01, Batch: 137/138
+[2024-10-13 16:14:02,978 INFO test.py line 272 25394] Test: scene0207_01 [136/312]-269274 Batch 31.137 (19.739) Accuracy 0.8548 (0.4463) mIoU 0.4696 (0.3505)
+[2024-10-13 16:14:03,120 INFO test.py line 186 25394] Test: 137/312-scene0609_02, Batch: 0/125
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+[2024-10-13 16:14:18,758 INFO test.py line 272 25394] Test: scene0609_02 [137/312]-134987 Batch 15.780 (19.710) Accuracy 0.7525 (0.4463) mIoU 0.3464 (0.3502)
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+[2024-10-13 16:14:41,016 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 116/130
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+[2024-10-13 16:14:41,423 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 118/130
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+[2024-10-13 16:14:41,818 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 120/130
+[2024-10-13 16:14:42,009 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 121/130
+[2024-10-13 16:14:42,200 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 122/130
+[2024-10-13 16:14:42,391 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 123/130
+[2024-10-13 16:14:42,582 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 124/130
+[2024-10-13 16:14:42,773 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 125/130
+[2024-10-13 16:14:42,964 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 126/130
+[2024-10-13 16:14:43,155 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 127/130
+[2024-10-13 16:14:43,346 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 128/130
+[2024-10-13 16:14:43,538 INFO test.py line 186 25394] Test: 138/312-scene0696_00, Batch: 129/130
+[2024-10-13 16:14:43,833 INFO test.py line 272 25394] Test: scene0696_00 [138/312]-231067 Batch 25.074 (19.749) Accuracy 0.8536 (0.4464) mIoU 0.2839 (0.3501)
+[2024-10-13 16:14:44,026 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 0/130
+[2024-10-13 16:14:44,189 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 1/130
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+[2024-10-13 16:14:46,001 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 12/130
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+[2024-10-13 16:15:03,690 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 121/130
+[2024-10-13 16:15:03,852 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 122/130
+[2024-10-13 16:15:04,015 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 123/130
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+[2024-10-13 16:15:04,343 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 125/130
+[2024-10-13 16:15:04,505 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 126/130
+[2024-10-13 16:15:04,668 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 127/130
+[2024-10-13 16:15:04,831 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 128/130
+[2024-10-13 16:15:04,994 INFO test.py line 186 25394] Test: 139/312-scene0575_00, Batch: 129/130
+[2024-10-13 16:15:05,243 INFO test.py line 272 25394] Test: scene0575_00 [139/312]-194207 Batch 21.410 (19.761) Accuracy 0.8606 (0.4469) mIoU 0.6099 (0.3506)
+[2024-10-13 16:15:05,470 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 0/121
+[2024-10-13 16:15:05,658 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 1/121
+[2024-10-13 16:15:05,847 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 2/121
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+[2024-10-13 16:15:07,171 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 9/121
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+[2024-10-13 16:15:07,927 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 13/121
+[2024-10-13 16:15:08,116 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 14/121
+[2024-10-13 16:15:08,337 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 15/121
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+[2024-10-13 16:15:26,814 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 113/121
+[2024-10-13 16:15:27,003 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 114/121
+[2024-10-13 16:15:27,192 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 115/121
+[2024-10-13 16:15:27,381 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 116/121
+[2024-10-13 16:15:27,569 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 117/121
+[2024-10-13 16:15:27,759 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 118/121
+[2024-10-13 16:15:27,947 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 119/121
+[2024-10-13 16:15:28,137 INFO test.py line 186 25394] Test: 140/312-scene0222_00, Batch: 120/121
+[2024-10-13 16:15:28,443 INFO test.py line 272 25394] Test: scene0222_00 [140/312]-234831 Batch 23.200 (19.785) Accuracy 0.8454 (0.4488) mIoU 0.4041 (0.3505)
+[2024-10-13 16:15:28,523 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 0/116
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+[2024-10-13 16:15:33,871 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 78/116
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+[2024-10-13 16:15:34,017 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 80/116
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+[2024-10-13 16:15:34,451 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 86/116
+[2024-10-13 16:15:34,524 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 87/116
+[2024-10-13 16:15:34,596 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 88/116
+[2024-10-13 16:15:34,669 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 89/116
+[2024-10-13 16:15:34,741 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 90/116
+[2024-10-13 16:15:34,814 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 91/116
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+[2024-10-13 16:15:35,031 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 94/116
+[2024-10-13 16:15:35,103 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 95/116
+[2024-10-13 16:15:35,176 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 96/116
+[2024-10-13 16:15:35,249 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 97/116
+[2024-10-13 16:15:35,321 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 98/116
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+[2024-10-13 16:15:35,466 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 100/116
+[2024-10-13 16:15:35,538 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 101/116
+[2024-10-13 16:15:35,611 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 102/116
+[2024-10-13 16:15:35,683 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 103/116
+[2024-10-13 16:15:35,755 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 104/116
+[2024-10-13 16:15:35,827 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 105/116
+[2024-10-13 16:15:35,900 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 106/116
+[2024-10-13 16:15:35,972 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 107/116
+[2024-10-13 16:15:36,041 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 108/116
+[2024-10-13 16:15:36,110 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 109/116
+[2024-10-13 16:15:36,179 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 110/116
+[2024-10-13 16:15:36,248 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 111/116
+[2024-10-13 16:15:36,317 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 112/116
+[2024-10-13 16:15:36,386 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 113/116
+[2024-10-13 16:15:36,455 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 114/116
+[2024-10-13 16:15:36,524 INFO test.py line 186 25394] Test: 141/312-scene0423_02, Batch: 115/116
+[2024-10-13 16:15:36,607 INFO test.py line 272 25394] Test: scene0423_02 [141/312]-60071 Batch 8.164 (19.703) Accuracy 0.9977 (0.4490) mIoU 0.7412 (0.3508)
+[2024-10-13 16:15:36,742 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 0/125
+[2024-10-13 16:15:36,860 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 1/125
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+[2024-10-13 16:15:37,571 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 7/125
+[2024-10-13 16:15:37,689 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 8/125
+[2024-10-13 16:15:37,808 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 9/125
+[2024-10-13 16:15:37,927 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 10/125
+[2024-10-13 16:15:38,046 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 11/125
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+[2024-10-13 16:15:38,404 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 14/125
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+[2024-10-13 16:15:38,997 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 19/125
+[2024-10-13 16:15:39,114 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 20/125
+[2024-10-13 16:15:39,232 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 21/125
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+[2024-10-13 16:15:39,467 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 23/125
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+[2024-10-13 16:15:40,526 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 32/125
+[2024-10-13 16:15:40,671 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 33/125
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+[2024-10-13 16:15:49,574 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 110/125
+[2024-10-13 16:15:49,698 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 111/125
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+[2024-10-13 16:15:50,196 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 115/125
+[2024-10-13 16:15:50,314 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 116/125
+[2024-10-13 16:15:50,432 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 117/125
+[2024-10-13 16:15:50,549 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 118/125
+[2024-10-13 16:15:50,667 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 119/125
+[2024-10-13 16:15:50,785 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 120/125
+[2024-10-13 16:15:50,902 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 121/125
+[2024-10-13 16:15:51,020 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 122/125
+[2024-10-13 16:15:51,137 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 123/125
+[2024-10-13 16:15:51,255 INFO test.py line 186 25394] Test: 142/312-scene0518_00, Batch: 124/125
+[2024-10-13 16:15:51,423 INFO test.py line 272 25394] Test: scene0518_00 [142/312]-130180 Batch 14.816 (19.668) Accuracy 0.7556 (0.4466) mIoU 0.3331 (0.3491)
+[2024-10-13 16:15:51,573 INFO test.py line 186 25394] Test: 143/312-scene0599_00, Batch: 0/145
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+[2024-10-13 16:15:52,586 INFO test.py line 186 25394] Test: 143/312-scene0599_00, Batch: 8/145
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+[2024-10-13 16:15:52,839 INFO test.py line 186 25394] Test: 143/312-scene0599_00, Batch: 10/145
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+[2024-10-13 16:16:09,970 INFO test.py line 272 25394] Test: scene0599_00 [143/312]-141933 Batch 18.547 (19.660) Accuracy 0.9758 (0.4467) mIoU 0.8731 (0.3494)
+[2024-10-13 16:16:10,150 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 0/126
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+[2024-10-13 16:16:27,748 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 116/126
+[2024-10-13 16:16:27,899 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 117/126
+[2024-10-13 16:16:28,051 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 118/126
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+[2024-10-13 16:16:28,353 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 120/126
+[2024-10-13 16:16:28,505 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 121/126
+[2024-10-13 16:16:28,657 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 122/126
+[2024-10-13 16:16:28,808 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 123/126
+[2024-10-13 16:16:28,960 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 124/126
+[2024-10-13 16:16:29,111 INFO test.py line 186 25394] Test: 144/312-scene0025_02, Batch: 125/126
+[2024-10-13 16:16:29,339 INFO test.py line 272 25394] Test: scene0025_02 [144/312]-177900 Batch 19.369 (19.658) Accuracy 0.7991 (0.4470) mIoU 0.4149 (0.3479)
+[2024-10-13 16:16:29,617 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 0/138
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+[2024-10-13 16:16:58,768 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 126/138
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+[2024-10-13 16:16:59,483 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 129/138
+[2024-10-13 16:16:59,716 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 130/138
+[2024-10-13 16:16:59,949 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 131/138
+[2024-10-13 16:17:00,182 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 132/138
+[2024-10-13 16:17:00,415 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 133/138
+[2024-10-13 16:17:00,648 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 134/138
+[2024-10-13 16:17:00,881 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 135/138
+[2024-10-13 16:17:01,114 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 136/138
+[2024-10-13 16:17:01,348 INFO test.py line 186 25394] Test: 145/312-scene0030_02, Batch: 137/138
+[2024-10-13 16:17:01,718 INFO test.py line 272 25394] Test: scene0030_02 [145/312]-284243 Batch 32.379 (19.746) Accuracy 0.9075 (0.4492) mIoU 0.4767 (0.3499)
+[2024-10-13 16:17:01,823 INFO test.py line 186 25394] Test: 146/312-scene0535_00, Batch: 0/125
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+[2024-10-13 16:17:13,262 INFO test.py line 186 25394] Test: 146/312-scene0535_00, Batch: 122/125
+[2024-10-13 16:17:13,355 INFO test.py line 186 25394] Test: 146/312-scene0535_00, Batch: 123/125
+[2024-10-13 16:17:13,448 INFO test.py line 186 25394] Test: 146/312-scene0535_00, Batch: 124/125
+[2024-10-13 16:17:13,565 INFO test.py line 272 25394] Test: scene0535_00 [146/312]-90268 Batch 11.847 (19.692) Accuracy 0.6108 (0.4493) mIoU 0.1928 (0.3499)
+[2024-10-13 16:17:13,710 INFO test.py line 186 25394] Test: 147/312-scene0655_02, Batch: 0/126
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+[2024-10-13 16:17:29,366 INFO test.py line 272 25394] Test: scene0655_02 [147/312]-133857 Batch 15.800 (19.666) Accuracy 0.8999 (0.4494) mIoU 0.6637 (0.3499)
+[2024-10-13 16:17:29,558 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 0/139
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+[2024-10-13 16:17:50,913 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 130/139
+[2024-10-13 16:17:51,078 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 131/139
+[2024-10-13 16:17:51,243 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 132/139
+[2024-10-13 16:17:51,407 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 133/139
+[2024-10-13 16:17:51,571 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 134/139
+[2024-10-13 16:17:51,735 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 135/139
+[2024-10-13 16:17:51,900 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 136/139
+[2024-10-13 16:17:52,064 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 137/139
+[2024-10-13 16:17:52,229 INFO test.py line 186 25394] Test: 148/312-scene0221_00, Batch: 138/139
+[2024-10-13 16:17:52,462 INFO test.py line 272 25394] Test: scene0221_00 [148/312]-185828 Batch 23.096 (19.689) Accuracy 0.8453 (0.4499) mIoU 0.3697 (0.3504)
+[2024-10-13 16:17:52,631 INFO test.py line 186 25394] Test: 149/312-scene0019_01, Batch: 0/134
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+[2024-10-13 16:18:11,784 INFO test.py line 272 25394] Test: scene0019_01 [149/312]-165356 Batch 19.321 (19.686) Accuracy 0.7542 (0.4494) mIoU 0.2577 (0.3494)
+[2024-10-13 16:18:11,956 INFO test.py line 186 25394] Test: 150/312-scene0606_00, Batch: 0/130
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+[2024-10-13 16:18:31,036 INFO test.py line 272 25394] Test: scene0606_00 [150/312]-164401 Batch 19.252 (19.683) Accuracy 0.7101 (0.4473) mIoU 0.1885 (0.3472)
+[2024-10-13 16:18:31,101 INFO test.py line 186 25394] Test: 151/312-scene0432_01, Batch: 0/100
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+[2024-10-13 16:18:36,545 INFO test.py line 186 25394] Test: 151/312-scene0432_01, Batch: 96/100
+[2024-10-13 16:18:36,601 INFO test.py line 186 25394] Test: 151/312-scene0432_01, Batch: 97/100
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+[2024-10-13 16:18:36,765 INFO test.py line 272 25394] Test: scene0432_01 [151/312]-37547 Batch 5.729 (19.591) Accuracy 0.9024 (0.4473) mIoU 0.4435 (0.3472)
+[2024-10-13 16:18:36,921 INFO test.py line 186 25394] Test: 152/312-scene0599_01, Batch: 0/125
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+[2024-10-13 16:18:53,320 INFO test.py line 272 25394] Test: scene0599_01 [152/312]-149922 Batch 16.554 (19.571) Accuracy 0.9639 (0.4473) mIoU 0.8513 (0.3474)
+[2024-10-13 16:18:53,518 INFO test.py line 186 25394] Test: 153/312-scene0353_02, Batch: 0/130
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+[2024-10-13 16:19:15,714 INFO test.py line 272 25394] Test: scene0353_02 [153/312]-191953 Batch 22.394 (19.589) Accuracy 0.7093 (0.4464) mIoU 0.3097 (0.3465)
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+[2024-10-13 16:19:39,386 INFO test.py line 272 25394] Test: scene0474_03 [154/312]-195084 Batch 23.672 (19.616) Accuracy 0.8561 (0.4451) mIoU 0.3837 (0.3456)
+[2024-10-13 16:19:39,466 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 0/117
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+[2024-10-13 16:19:45,661 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 89/117
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+[2024-10-13 16:19:45,806 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 91/117
+[2024-10-13 16:19:45,879 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 92/117
+[2024-10-13 16:19:45,952 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 93/117
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+[2024-10-13 16:19:46,097 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 95/117
+[2024-10-13 16:19:46,170 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 96/117
+[2024-10-13 16:19:46,242 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 97/117
+[2024-10-13 16:19:46,315 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 98/117
+[2024-10-13 16:19:46,387 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 99/117
+[2024-10-13 16:19:46,459 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 100/117
+[2024-10-13 16:19:46,531 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 101/117
+[2024-10-13 16:19:46,603 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 102/117
+[2024-10-13 16:19:46,675 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 103/117
+[2024-10-13 16:19:46,747 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 104/117
+[2024-10-13 16:19:46,820 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 105/117
+[2024-10-13 16:19:46,892 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 106/117
+[2024-10-13 16:19:46,964 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 107/117
+[2024-10-13 16:19:47,034 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 108/117
+[2024-10-13 16:19:47,103 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 109/117
+[2024-10-13 16:19:47,173 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 110/117
+[2024-10-13 16:19:47,242 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 111/117
+[2024-10-13 16:19:47,312 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 112/117
+[2024-10-13 16:19:47,382 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 113/117
+[2024-10-13 16:19:47,451 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 114/117
+[2024-10-13 16:19:47,521 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 115/117
+[2024-10-13 16:19:47,591 INFO test.py line 186 25394] Test: 155/312-scene0553_01, Batch: 116/117
+[2024-10-13 16:19:47,675 INFO test.py line 272 25394] Test: scene0553_01 [155/312]-61388 Batch 8.289 (19.543) Accuracy 0.9253 (0.4455) mIoU 0.5832 (0.3463)
+[2024-10-13 16:19:47,882 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 0/130
+[2024-10-13 16:19:48,057 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 1/130
+[2024-10-13 16:19:48,233 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 2/130
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+[2024-10-13 16:19:48,933 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 6/130
+[2024-10-13 16:19:49,109 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 7/130
+[2024-10-13 16:19:49,283 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 8/130
+[2024-10-13 16:19:49,458 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 9/130
+[2024-10-13 16:19:49,635 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 10/130
+[2024-10-13 16:19:49,830 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 11/130
+[2024-10-13 16:19:50,006 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 12/130
+[2024-10-13 16:19:50,180 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 13/130
+[2024-10-13 16:19:50,355 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 14/130
+[2024-10-13 16:19:50,530 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 15/130
+[2024-10-13 16:19:50,705 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 16/130
+[2024-10-13 16:19:50,880 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 17/130
+[2024-10-13 16:19:51,054 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 18/130
+[2024-10-13 16:19:51,229 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 19/130
+[2024-10-13 16:19:51,404 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 20/130
+[2024-10-13 16:19:51,579 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 21/130
+[2024-10-13 16:19:51,753 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 22/130
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+[2024-10-13 16:19:52,801 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 28/130
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+[2024-10-13 16:19:53,326 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 31/130
+[2024-10-13 16:19:53,501 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 32/130
+[2024-10-13 16:19:53,676 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 33/130
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+[2024-10-13 16:19:59,161 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 66/130
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+[2024-10-13 16:20:03,460 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 91/130
+[2024-10-13 16:20:03,648 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 92/130
+[2024-10-13 16:20:03,836 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 93/130
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+[2024-10-13 16:20:04,213 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 95/130
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+[2024-10-13 16:20:04,590 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 97/130
+[2024-10-13 16:20:04,778 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 98/130
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+[2024-10-13 16:20:05,156 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 100/130
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+[2024-10-13 16:20:07,042 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 110/130
+[2024-10-13 16:20:07,230 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 111/130
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+[2024-10-13 16:20:07,796 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 114/130
+[2024-10-13 16:20:07,985 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 115/130
+[2024-10-13 16:20:08,173 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 116/130
+[2024-10-13 16:20:08,362 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 117/130
+[2024-10-13 16:20:08,551 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 118/130
+[2024-10-13 16:20:08,739 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 119/130
+[2024-10-13 16:20:08,914 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 120/130
+[2024-10-13 16:20:09,089 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 121/130
+[2024-10-13 16:20:09,263 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 122/130
+[2024-10-13 16:20:09,438 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 123/130
+[2024-10-13 16:20:09,613 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 124/130
+[2024-10-13 16:20:09,788 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 125/130
+[2024-10-13 16:20:09,963 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 126/130
+[2024-10-13 16:20:10,138 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 127/130
+[2024-10-13 16:20:10,313 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 128/130
+[2024-10-13 16:20:10,487 INFO test.py line 186 25394] Test: 156/312-scene0430_00, Batch: 129/130
+[2024-10-13 16:20:10,750 INFO test.py line 272 25394] Test: scene0430_00 [156/312]-210249 Batch 23.075 (19.566) Accuracy 0.9300 (0.4455) mIoU 0.4916 (0.3462)
+[2024-10-13 16:20:10,829 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 0/155
+[2024-10-13 16:20:10,898 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 1/155
+[2024-10-13 16:20:10,966 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 2/155
+[2024-10-13 16:20:11,034 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 3/155
+[2024-10-13 16:20:11,103 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 4/155
+[2024-10-13 16:20:11,171 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 5/155
+[2024-10-13 16:20:11,239 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 6/155
+[2024-10-13 16:20:11,308 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 7/155
+[2024-10-13 16:20:11,376 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 8/155
+[2024-10-13 16:20:11,444 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 9/155
+[2024-10-13 16:20:11,513 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 10/155
+[2024-10-13 16:20:11,581 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 11/155
+[2024-10-13 16:20:11,650 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 12/155
+[2024-10-13 16:20:11,718 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 13/155
+[2024-10-13 16:20:11,787 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 14/155
+[2024-10-13 16:20:11,856 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 15/155
+[2024-10-13 16:20:11,924 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 16/155
+[2024-10-13 16:20:11,993 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 17/155
+[2024-10-13 16:20:12,062 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 18/155
+[2024-10-13 16:20:12,130 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 19/155
+[2024-10-13 16:20:12,199 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 20/155
+[2024-10-13 16:20:12,267 INFO test.py line 186 25394] Test: 157/312-scene0146_01, Batch: 21/155
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+[2024-10-13 16:20:21,490 INFO test.py line 272 25394] Test: scene0146_01 [157/312]-59330 Batch 10.740 (19.509) Accuracy 0.8414 (0.4454) mIoU 0.2805 (0.3460)
+[2024-10-13 16:20:21,594 INFO test.py line 186 25394] Test: 158/312-scene0077_00, Batch: 0/121
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+[2024-10-13 16:20:32,697 INFO test.py line 272 25394] Test: scene0077_00 [158/312]-92807 Batch 11.207 (19.457) Accuracy 0.9042 (0.4453) mIoU 0.7022 (0.3460)
+[2024-10-13 16:20:32,854 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 0/121
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+[2024-10-13 16:20:46,947 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 108/121
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+[2024-10-13 16:20:47,223 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 110/121
+[2024-10-13 16:20:47,361 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 111/121
+[2024-10-13 16:20:47,492 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 112/121
+[2024-10-13 16:20:47,622 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 113/121
+[2024-10-13 16:20:47,752 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 114/121
+[2024-10-13 16:20:47,882 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 115/121
+[2024-10-13 16:20:48,013 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 116/121
+[2024-10-13 16:20:48,143 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 117/121
+[2024-10-13 16:20:48,274 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 118/121
+[2024-10-13 16:20:48,404 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 119/121
+[2024-10-13 16:20:48,534 INFO test.py line 186 25394] Test: 159/312-scene0700_02, Batch: 120/121
+[2024-10-13 16:20:48,734 INFO test.py line 272 25394] Test: scene0700_02 [159/312]-148642 Batch 16.036 (19.435) Accuracy 0.6953 (0.4461) mIoU 0.3825 (0.3467)
+[2024-10-13 16:20:48,876 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 0/131
+[2024-10-13 16:20:49,000 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 1/131
+[2024-10-13 16:20:49,124 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 2/131
+[2024-10-13 16:20:49,249 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 3/131
+[2024-10-13 16:20:49,374 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 4/131
+[2024-10-13 16:20:49,499 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 5/131
+[2024-10-13 16:20:49,624 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 6/131
+[2024-10-13 16:20:49,752 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 7/131
+[2024-10-13 16:20:49,877 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 8/131
+[2024-10-13 16:20:50,004 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 9/131
+[2024-10-13 16:20:50,128 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 10/131
+[2024-10-13 16:20:50,253 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 11/131
+[2024-10-13 16:20:50,377 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 12/131
+[2024-10-13 16:20:50,501 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 13/131
+[2024-10-13 16:20:50,626 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 14/131
+[2024-10-13 16:20:50,750 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 15/131
+[2024-10-13 16:20:50,875 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 16/131
+[2024-10-13 16:20:50,999 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 17/131
+[2024-10-13 16:20:51,124 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 18/131
+[2024-10-13 16:20:51,248 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 19/131
+[2024-10-13 16:20:51,373 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 20/131
+[2024-10-13 16:20:51,498 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 21/131
+[2024-10-13 16:20:51,622 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 22/131
+[2024-10-13 16:20:51,746 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 23/131
+[2024-10-13 16:20:51,870 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 24/131
+[2024-10-13 16:20:51,994 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 25/131
+[2024-10-13 16:20:52,119 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 26/131
+[2024-10-13 16:20:52,243 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 27/131
+[2024-10-13 16:20:52,367 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 28/131
+[2024-10-13 16:20:52,491 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 29/131
+[2024-10-13 16:20:52,615 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 30/131
+[2024-10-13 16:20:52,740 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 31/131
+[2024-10-13 16:20:52,864 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 32/131
+[2024-10-13 16:20:52,988 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 33/131
+[2024-10-13 16:20:53,113 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 34/131
+[2024-10-13 16:20:53,238 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 35/131
+[2024-10-13 16:20:53,362 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 36/131
+[2024-10-13 16:20:53,487 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 37/131
+[2024-10-13 16:20:53,611 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 38/131
+[2024-10-13 16:20:53,736 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 39/131
+[2024-10-13 16:20:53,860 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 40/131
+[2024-10-13 16:20:53,985 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 41/131
+[2024-10-13 16:20:54,110 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 42/131
+[2024-10-13 16:20:54,235 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 43/131
+[2024-10-13 16:20:54,352 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 44/131
+[2024-10-13 16:20:54,469 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 45/131
+[2024-10-13 16:20:54,586 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 46/131
+[2024-10-13 16:20:54,703 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 47/131
+[2024-10-13 16:20:54,820 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 48/131
+[2024-10-13 16:20:54,937 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 49/131
+[2024-10-13 16:20:55,055 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 50/131
+[2024-10-13 16:20:55,172 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 51/131
+[2024-10-13 16:20:55,290 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 52/131
+[2024-10-13 16:20:55,407 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 53/131
+[2024-10-13 16:20:55,524 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 54/131
+[2024-10-13 16:20:55,641 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 55/131
+[2024-10-13 16:20:55,757 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 56/131
+[2024-10-13 16:20:55,892 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 57/131
+[2024-10-13 16:20:56,009 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 58/131
+[2024-10-13 16:20:56,126 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 59/131
+[2024-10-13 16:20:56,244 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 60/131
+[2024-10-13 16:20:56,360 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 61/131
+[2024-10-13 16:20:56,475 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 62/131
+[2024-10-13 16:20:56,591 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 63/131
+[2024-10-13 16:20:56,707 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 64/131
+[2024-10-13 16:20:56,823 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 65/131
+[2024-10-13 16:20:56,940 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 66/131
+[2024-10-13 16:20:57,057 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 67/131
+[2024-10-13 16:20:57,174 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 68/131
+[2024-10-13 16:20:57,291 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 69/131
+[2024-10-13 16:20:57,408 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 70/131
+[2024-10-13 16:20:57,525 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 71/131
+[2024-10-13 16:20:57,643 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 72/131
+[2024-10-13 16:20:57,760 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 73/131
+[2024-10-13 16:20:57,877 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 74/131
+[2024-10-13 16:20:57,994 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 75/131
+[2024-10-13 16:20:58,111 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 76/131
+[2024-10-13 16:20:58,228 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 77/131
+[2024-10-13 16:20:58,345 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 78/131
+[2024-10-13 16:20:58,463 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 79/131
+[2024-10-13 16:20:58,580 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 80/131
+[2024-10-13 16:20:58,697 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 81/131
+[2024-10-13 16:20:58,814 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 82/131
+[2024-10-13 16:20:58,932 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 83/131
+[2024-10-13 16:20:59,049 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 84/131
+[2024-10-13 16:20:59,166 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 85/131
+[2024-10-13 16:20:59,283 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 86/131
+[2024-10-13 16:20:59,400 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 87/131
+[2024-10-13 16:20:59,530 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 88/131
+[2024-10-13 16:20:59,661 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 89/131
+[2024-10-13 16:20:59,791 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 90/131
+[2024-10-13 16:20:59,922 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 91/131
+[2024-10-13 16:21:00,052 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 92/131
+[2024-10-13 16:21:00,183 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 93/131
+[2024-10-13 16:21:00,313 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 94/131
+[2024-10-13 16:21:00,443 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 95/131
+[2024-10-13 16:21:00,574 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 96/131
+[2024-10-13 16:21:00,705 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 97/131
+[2024-10-13 16:21:00,836 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 98/131
+[2024-10-13 16:21:00,966 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 99/131
+[2024-10-13 16:21:01,097 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 100/131
+[2024-10-13 16:21:01,228 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 101/131
+[2024-10-13 16:21:01,358 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 102/131
+[2024-10-13 16:21:01,489 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 103/131
+[2024-10-13 16:21:01,620 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 104/131
+[2024-10-13 16:21:01,751 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 105/131
+[2024-10-13 16:21:01,882 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 106/131
+[2024-10-13 16:21:02,013 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 107/131
+[2024-10-13 16:21:02,144 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 108/131
+[2024-10-13 16:21:02,275 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 109/131
+[2024-10-13 16:21:02,406 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 110/131
+[2024-10-13 16:21:02,536 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 111/131
+[2024-10-13 16:21:02,668 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 112/131
+[2024-10-13 16:21:02,800 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 113/131
+[2024-10-13 16:21:02,931 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 114/131
+[2024-10-13 16:21:03,063 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 115/131
+[2024-10-13 16:21:03,194 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 116/131
+[2024-10-13 16:21:03,326 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 117/131
+[2024-10-13 16:21:03,457 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 118/131
+[2024-10-13 16:21:03,589 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 119/131
+[2024-10-13 16:21:03,713 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 120/131
+[2024-10-13 16:21:03,837 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 121/131
+[2024-10-13 16:21:03,961 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 122/131
+[2024-10-13 16:21:04,085 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 123/131
+[2024-10-13 16:21:04,209 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 124/131
+[2024-10-13 16:21:04,333 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 125/131
+[2024-10-13 16:21:04,458 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 126/131
+[2024-10-13 16:21:04,582 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 127/131
+[2024-10-13 16:21:04,706 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 128/131
+[2024-10-13 16:21:04,830 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 129/131
+[2024-10-13 16:21:04,955 INFO test.py line 186 25394] Test: 160/312-scene0426_00, Batch: 130/131
+[2024-10-13 16:21:05,129 INFO test.py line 272 25394] Test: scene0426_00 [160/312]-135867 Batch 16.395 (19.416) Accuracy 0.7582 (0.4455) mIoU 0.3379 (0.3460)
+[2024-10-13 16:21:05,321 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 0/148
+[2024-10-13 16:21:05,481 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 1/148
+[2024-10-13 16:21:05,641 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 2/148
+[2024-10-13 16:21:05,801 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 3/148
+[2024-10-13 16:21:05,961 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 4/148
+[2024-10-13 16:21:06,121 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 5/148
+[2024-10-13 16:21:06,281 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 6/148
+[2024-10-13 16:21:06,441 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 7/148
+[2024-10-13 16:21:06,601 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 8/148
+[2024-10-13 16:21:06,761 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 9/148
+[2024-10-13 16:21:06,921 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 10/148
+[2024-10-13 16:21:07,081 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 11/148
+[2024-10-13 16:21:07,241 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 12/148
+[2024-10-13 16:21:07,401 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 13/148
+[2024-10-13 16:21:07,562 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 14/148
+[2024-10-13 16:21:07,746 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 15/148
+[2024-10-13 16:21:07,908 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 16/148
+[2024-10-13 16:21:08,068 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 17/148
+[2024-10-13 16:21:08,228 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 18/148
+[2024-10-13 16:21:08,388 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 19/148
+[2024-10-13 16:21:08,548 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 20/148
+[2024-10-13 16:21:08,708 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 21/148
+[2024-10-13 16:21:08,868 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 22/148
+[2024-10-13 16:21:09,027 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 23/148
+[2024-10-13 16:21:09,188 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 24/148
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+[2024-10-13 16:21:09,508 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 26/148
+[2024-10-13 16:21:09,669 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 27/148
+[2024-10-13 16:21:09,828 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 28/148
+[2024-10-13 16:21:09,989 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 29/148
+[2024-10-13 16:21:10,149 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 30/148
+[2024-10-13 16:21:10,309 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 31/148
+[2024-10-13 16:21:10,469 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 32/148
+[2024-10-13 16:21:10,630 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 33/148
+[2024-10-13 16:21:10,790 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 34/148
+[2024-10-13 16:21:10,950 INFO test.py line 186 25394] Test: 161/312-scene0647_01, Batch: 35/148
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+[2024-10-13 16:21:29,173 INFO test.py line 272 25394] Test: scene0647_01 [161/312]-194360 Batch 24.044 (19.445) Accuracy 0.9143 (0.4452) mIoU 0.5308 (0.3459)
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+[2024-10-13 16:21:58,073 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 117/126
+[2024-10-13 16:21:58,317 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 118/126
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+[2024-10-13 16:21:58,803 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 120/126
+[2024-10-13 16:21:59,046 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 121/126
+[2024-10-13 16:21:59,289 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 122/126
+[2024-10-13 16:21:59,532 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 123/126
+[2024-10-13 16:21:59,776 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 124/126
+[2024-10-13 16:22:00,018 INFO test.py line 186 25394] Test: 162/312-scene0644_00, Batch: 125/126
+[2024-10-13 16:22:00,399 INFO test.py line 272 25394] Test: scene0644_00 [162/312]-299472 Batch 31.225 (19.518) Accuracy 0.8405 (0.4462) mIoU 0.4168 (0.3465)
+[2024-10-13 16:22:00,515 INFO test.py line 186 25394] Test: 163/312-scene0256_01, Batch: 0/125
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+[2024-10-13 16:22:12,864 INFO test.py line 186 25394] Test: 163/312-scene0256_01, Batch: 121/125
+[2024-10-13 16:22:12,965 INFO test.py line 186 25394] Test: 163/312-scene0256_01, Batch: 122/125
+[2024-10-13 16:22:13,066 INFO test.py line 186 25394] Test: 163/312-scene0256_01, Batch: 123/125
+[2024-10-13 16:22:13,168 INFO test.py line 186 25394] Test: 163/312-scene0256_01, Batch: 124/125
+[2024-10-13 16:22:13,312 INFO test.py line 272 25394] Test: scene0256_01 [163/312]-111617 Batch 12.913 (19.477) Accuracy 0.8327 (0.4460) mIoU 0.1779 (0.3460)
+[2024-10-13 16:22:13,516 INFO test.py line 186 25394] Test: 164/312-scene0334_02, Batch: 0/138
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+[2024-10-13 16:22:36,520 INFO test.py line 186 25394] Test: 164/312-scene0334_02, Batch: 134/138
+[2024-10-13 16:22:36,692 INFO test.py line 186 25394] Test: 164/312-scene0334_02, Batch: 135/138
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+[2024-10-13 16:22:37,038 INFO test.py line 186 25394] Test: 164/312-scene0334_02, Batch: 137/138
+[2024-10-13 16:22:37,299 INFO test.py line 272 25394] Test: scene0334_02 [164/312]-211497 Batch 23.986 (19.505) Accuracy 0.9636 (0.4467) mIoU 0.4247 (0.3466)
+[2024-10-13 16:22:37,365 INFO test.py line 186 25394] Test: 165/312-scene0432_00, Batch: 0/127
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+[2024-10-13 16:22:44,657 INFO test.py line 272 25394] Test: scene0432_00 [165/312]-39529 Batch 7.358 (19.431) Accuracy 0.8619 (0.4467) mIoU 0.5265 (0.3466)
+[2024-10-13 16:22:44,793 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 0/138
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+[2024-10-13 16:23:00,073 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 128/138
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+[2024-10-13 16:23:00,311 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 130/138
+[2024-10-13 16:23:00,431 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 131/138
+[2024-10-13 16:23:00,550 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 132/138
+[2024-10-13 16:23:00,669 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 133/138
+[2024-10-13 16:23:00,789 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 134/138
+[2024-10-13 16:23:00,908 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 135/138
+[2024-10-13 16:23:01,028 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 136/138
+[2024-10-13 16:23:01,147 INFO test.py line 186 25394] Test: 166/312-scene0695_00, Batch: 137/138
+[2024-10-13 16:23:01,320 INFO test.py line 272 25394] Test: scene0695_00 [166/312]-133418 Batch 16.662 (19.414) Accuracy 0.7596 (0.4466) mIoU 0.4100 (0.3465)
+[2024-10-13 16:23:01,406 INFO test.py line 186 25394] Test: 167/312-scene0355_01, Batch: 0/122
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+[2024-10-13 16:23:10,513 INFO test.py line 186 25394] Test: 167/312-scene0355_01, Batch: 121/122
+[2024-10-13 16:23:10,609 INFO test.py line 272 25394] Test: scene0355_01 [167/312]-67623 Batch 9.289 (19.354) Accuracy 0.9420 (0.4467) mIoU 0.5269 (0.3465)
+[2024-10-13 16:23:10,684 INFO test.py line 186 25394] Test: 168/312-scene0100_00, Batch: 0/142
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+[2024-10-13 16:23:20,122 INFO test.py line 272 25394] Test: scene0100_00 [168/312]-52304 Batch 9.513 (19.295) Accuracy 0.7344 (0.4468) mIoU 0.4250 (0.3462)
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+[2024-10-13 16:23:40,043 INFO test.py line 272 25394] Test: scene0378_00 [169/312]-156298 Batch 19.921 (19.299) Accuracy 0.8831 (0.4474) mIoU 0.5400 (0.3469)
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+[2024-10-13 16:23:57,477 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 110/125
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+[2024-10-13 16:23:57,812 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 112/125
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+[2024-10-13 16:23:58,473 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 116/125
+[2024-10-13 16:23:58,631 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 117/125
+[2024-10-13 16:23:58,790 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 118/125
+[2024-10-13 16:23:58,948 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 119/125
+[2024-10-13 16:23:59,106 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 120/125
+[2024-10-13 16:23:59,264 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 121/125
+[2024-10-13 16:23:59,423 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 122/125
+[2024-10-13 16:23:59,581 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 123/125
+[2024-10-13 16:23:59,739 INFO test.py line 186 25394] Test: 170/312-scene0474_00, Batch: 124/125
+[2024-10-13 16:23:59,971 INFO test.py line 272 25394] Test: scene0474_00 [170/312]-182281 Batch 19.928 (19.303) Accuracy 0.8687 (0.4526) mIoU 0.4407 (0.3518)
+[2024-10-13 16:24:00,164 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 0/143
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+[2024-10-13 16:24:21,327 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 130/143
+[2024-10-13 16:24:21,500 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 131/143
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+[2024-10-13 16:24:21,987 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 134/143
+[2024-10-13 16:24:22,150 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 135/143
+[2024-10-13 16:24:22,312 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 136/143
+[2024-10-13 16:24:22,474 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 137/143
+[2024-10-13 16:24:22,636 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 138/143
+[2024-10-13 16:24:22,799 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 139/143
+[2024-10-13 16:24:22,961 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 140/143
+[2024-10-13 16:24:23,124 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 141/143
+[2024-10-13 16:24:23,287 INFO test.py line 186 25394] Test: 171/312-scene0334_00, Batch: 142/143
+[2024-10-13 16:24:23,531 INFO test.py line 272 25394] Test: scene0334_00 [171/312]-197174 Batch 23.560 (19.327) Accuracy 0.8551 (0.4530) mIoU 0.4902 (0.3522)
+[2024-10-13 16:24:23,715 INFO test.py line 186 25394] Test: 172/312-scene0257_00, Batch: 0/130
+[2024-10-13 16:24:23,870 INFO test.py line 186 25394] Test: 172/312-scene0257_00, Batch: 1/130
+[2024-10-13 16:24:24,025 INFO test.py line 186 25394] Test: 172/312-scene0257_00, Batch: 2/130
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+[2024-10-13 16:24:25,110 INFO test.py line 186 25394] Test: 172/312-scene0257_00, Batch: 9/130
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+[2024-10-13 16:24:25,576 INFO test.py line 186 25394] Test: 172/312-scene0257_00, Batch: 12/130
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+[2024-10-13 16:24:43,876 INFO test.py line 272 25394] Test: scene0257_00 [172/312]-180928 Batch 20.344 (19.333) Accuracy 0.8627 (0.4537) mIoU 0.3532 (0.3525)
+[2024-10-13 16:24:44,052 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 0/134
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+[2024-10-13 16:25:03,016 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 127/134
+[2024-10-13 16:25:03,166 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 128/134
+[2024-10-13 16:25:03,316 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 129/134
+[2024-10-13 16:25:03,466 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 130/134
+[2024-10-13 16:25:03,616 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 131/134
+[2024-10-13 16:25:03,766 INFO test.py line 186 25394] Test: 173/312-scene0300_01, Batch: 132/134
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+[2024-10-13 16:25:04,135 INFO test.py line 272 25394] Test: scene0300_01 [173/312]-173223 Batch 20.259 (19.339) Accuracy 0.7176 (0.4536) mIoU 0.5063 (0.3522)
+[2024-10-13 16:25:04,472 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 0/125
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+[2024-10-13 16:25:36,387 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 114/125
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+[2024-10-13 16:25:36,969 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 116/125
+[2024-10-13 16:25:37,249 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 117/125
+[2024-10-13 16:25:37,529 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 118/125
+[2024-10-13 16:25:37,810 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 119/125
+[2024-10-13 16:25:38,091 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 120/125
+[2024-10-13 16:25:38,371 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 121/125
+[2024-10-13 16:25:38,651 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 122/125
+[2024-10-13 16:25:38,931 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 123/125
+[2024-10-13 16:25:39,212 INFO test.py line 186 25394] Test: 174/312-scene0329_00, Batch: 124/125
+[2024-10-13 16:25:39,660 INFO test.py line 272 25394] Test: scene0329_00 [174/312]-355413 Batch 35.524 (19.432) Accuracy 0.9096 (0.4534) mIoU 0.3327 (0.3516)
+[2024-10-13 16:25:39,999 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 0/156
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+[2024-10-13 16:26:22,093 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 148/156
+[2024-10-13 16:26:22,378 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 149/156
+[2024-10-13 16:26:22,662 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 150/156
+[2024-10-13 16:26:22,946 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 151/156
+[2024-10-13 16:26:23,231 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 152/156
+[2024-10-13 16:26:23,515 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 153/156
+[2024-10-13 16:26:23,801 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 154/156
+[2024-10-13 16:26:24,085 INFO test.py line 186 25394] Test: 175/312-scene0645_00, Batch: 155/156
+[2024-10-13 16:26:24,537 INFO test.py line 272 25394] Test: scene0645_00 [175/312]-352477 Batch 44.876 (19.577) Accuracy 0.9327 (0.4554) mIoU 0.5545 (0.3526)
+[2024-10-13 16:26:24,652 INFO test.py line 186 25394] Test: 176/312-scene0256_02, Batch: 0/108
+[2024-10-13 16:26:24,744 INFO test.py line 186 25394] Test: 176/312-scene0256_02, Batch: 1/108
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+[2024-10-13 16:26:34,677 INFO test.py line 272 25394] Test: scene0256_02 [176/312]-92069 Batch 10.140 (19.524) Accuracy 0.7824 (0.4541) mIoU 0.1905 (0.3512)
+[2024-10-13 16:26:34,885 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 0/139
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+[2024-10-13 16:26:50,080 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 95/139
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+[2024-10-13 16:26:54,572 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 121/139
+[2024-10-13 16:26:54,745 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 122/139
+[2024-10-13 16:26:54,920 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 123/139
+[2024-10-13 16:26:55,094 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 124/139
+[2024-10-13 16:26:55,268 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 125/139
+[2024-10-13 16:26:55,441 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 126/139
+[2024-10-13 16:26:55,613 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 127/139
+[2024-10-13 16:26:55,776 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 128/139
+[2024-10-13 16:26:55,939 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 129/139
+[2024-10-13 16:26:56,102 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 130/139
+[2024-10-13 16:26:56,264 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 131/139
+[2024-10-13 16:26:56,425 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 132/139
+[2024-10-13 16:26:56,588 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 133/139
+[2024-10-13 16:26:56,750 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 134/139
+[2024-10-13 16:26:56,913 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 135/139
+[2024-10-13 16:26:57,075 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 136/139
+[2024-10-13 16:26:57,237 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 137/139
+[2024-10-13 16:26:57,399 INFO test.py line 186 25394] Test: 177/312-scene0496_00, Batch: 138/139
+[2024-10-13 16:26:57,649 INFO test.py line 272 25394] Test: scene0496_00 [177/312]-192962 Batch 22.972 (19.543) Accuracy 0.8619 (0.4532) mIoU 0.4544 (0.3502)
+[2024-10-13 16:26:57,803 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 0/126
+[2024-10-13 16:26:57,932 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 1/126
+[2024-10-13 16:26:58,060 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 2/126
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+[2024-10-13 16:26:58,575 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 6/126
+[2024-10-13 16:26:58,705 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 7/126
+[2024-10-13 16:26:58,833 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 8/126
+[2024-10-13 16:26:58,961 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 9/126
+[2024-10-13 16:26:59,091 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 10/126
+[2024-10-13 16:26:59,220 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 11/126
+[2024-10-13 16:26:59,349 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 12/126
+[2024-10-13 16:26:59,477 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 13/126
+[2024-10-13 16:26:59,607 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 14/126
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+[2024-10-13 16:27:10,080 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 96/126
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+[2024-10-13 16:27:11,719 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 108/126
+[2024-10-13 16:27:11,856 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 109/126
+[2024-10-13 16:27:11,993 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 110/126
+[2024-10-13 16:27:12,131 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 111/126
+[2024-10-13 16:27:12,268 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 112/126
+[2024-10-13 16:27:12,404 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 113/126
+[2024-10-13 16:27:12,541 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 114/126
+[2024-10-13 16:27:12,678 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 115/126
+[2024-10-13 16:27:12,807 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 116/126
+[2024-10-13 16:27:12,937 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 117/126
+[2024-10-13 16:27:13,066 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 118/126
+[2024-10-13 16:27:13,194 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 119/126
+[2024-10-13 16:27:13,323 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 120/126
+[2024-10-13 16:27:13,453 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 121/126
+[2024-10-13 16:27:13,582 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 122/126
+[2024-10-13 16:27:13,712 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 123/126
+[2024-10-13 16:27:13,841 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 124/126
+[2024-10-13 16:27:13,971 INFO test.py line 186 25394] Test: 178/312-scene0598_00, Batch: 125/126
+[2024-10-13 16:27:14,154 INFO test.py line 272 25394] Test: scene0598_00 [178/312]-145562 Batch 16.505 (19.526) Accuracy 0.4414 (0.4518) mIoU 0.3920 (0.3499)
+[2024-10-13 16:27:14,295 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 0/135
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+[2024-10-13 16:27:29,674 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 126/135
+[2024-10-13 16:27:29,797 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 127/135
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+[2024-10-13 16:27:30,165 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 130/135
+[2024-10-13 16:27:30,288 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 131/135
+[2024-10-13 16:27:30,411 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 132/135
+[2024-10-13 16:27:30,533 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 133/135
+[2024-10-13 16:27:30,656 INFO test.py line 186 25394] Test: 179/312-scene0685_00, Batch: 134/135
+[2024-10-13 16:27:30,829 INFO test.py line 272 25394] Test: scene0685_00 [179/312]-132720 Batch 16.674 (19.510) Accuracy 0.8731 (0.4520) mIoU 0.3276 (0.3499)
+[2024-10-13 16:27:30,938 INFO test.py line 186 25394] Test: 180/312-scene0356_01, Batch: 0/143
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+[2024-10-13 16:27:44,645 INFO test.py line 272 25394] Test: scene0356_01 [180/312]-96610 Batch 13.817 (19.478) Accuracy 0.9395 (0.4525) mIoU 0.6338 (0.3503)
+[2024-10-13 16:27:44,817 INFO test.py line 186 25394] Test: 181/312-scene0088_00, Batch: 0/155
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+[2024-10-13 16:28:06,465 INFO test.py line 186 25394] Test: 181/312-scene0088_00, Batch: 149/155
+[2024-10-13 16:28:06,610 INFO test.py line 186 25394] Test: 181/312-scene0088_00, Batch: 150/155
+[2024-10-13 16:28:06,755 INFO test.py line 186 25394] Test: 181/312-scene0088_00, Batch: 151/155
+[2024-10-13 16:28:06,900 INFO test.py line 186 25394] Test: 181/312-scene0088_00, Batch: 152/155
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+[2024-10-13 16:28:07,401 INFO test.py line 272 25394] Test: scene0088_00 [181/312]-166872 Batch 22.755 (19.496) Accuracy 0.6786 (0.4524) mIoU 0.4401 (0.3500)
+[2024-10-13 16:28:07,683 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 0/130
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+[2024-10-13 16:28:34,490 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 115/130
+[2024-10-13 16:28:34,740 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 116/130
+[2024-10-13 16:28:34,990 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 117/130
+[2024-10-13 16:28:35,240 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 118/130
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+[2024-10-13 16:28:35,958 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 121/130
+[2024-10-13 16:28:36,193 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 122/130
+[2024-10-13 16:28:36,428 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 123/130
+[2024-10-13 16:28:36,662 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 124/130
+[2024-10-13 16:28:36,895 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 125/130
+[2024-10-13 16:28:37,130 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 126/130
+[2024-10-13 16:28:37,364 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 127/130
+[2024-10-13 16:28:37,597 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 128/130
+[2024-10-13 16:28:37,832 INFO test.py line 186 25394] Test: 182/312-scene0030_00, Batch: 129/130
+[2024-10-13 16:28:38,205 INFO test.py line 272 25394] Test: scene0030_00 [182/312]-293811 Batch 30.804 (19.559) Accuracy 0.8819 (0.4527) mIoU 0.3912 (0.3502)
+[2024-10-13 16:28:38,304 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 0/152
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+[2024-10-13 16:28:50,986 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 146/152
+[2024-10-13 16:28:51,073 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 147/152
+[2024-10-13 16:28:51,160 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 148/152
+[2024-10-13 16:28:51,247 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 149/152
+[2024-10-13 16:28:51,334 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 150/152
+[2024-10-13 16:28:51,421 INFO test.py line 186 25394] Test: 183/312-scene0356_00, Batch: 151/152
+[2024-10-13 16:28:51,527 INFO test.py line 272 25394] Test: scene0356_00 [183/312]-81172 Batch 13.322 (19.525) Accuracy 0.9260 (0.4531) mIoU 0.6832 (0.3507)
+[2024-10-13 16:28:51,593 INFO test.py line 186 25394] Test: 184/312-scene0693_00, Batch: 0/90
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+[2024-10-13 16:28:56,809 INFO test.py line 272 25394] Test: scene0693_00 [184/312]-41327 Batch 5.282 (19.447) Accuracy 0.5634 (0.4537) mIoU 0.3423 (0.3510)
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+[2024-10-13 16:29:12,764 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 128/140
+[2024-10-13 16:29:12,887 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 129/140
+[2024-10-13 16:29:13,011 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 130/140
+[2024-10-13 16:29:13,135 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 131/140
+[2024-10-13 16:29:13,259 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 132/140
+[2024-10-13 16:29:13,382 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 133/140
+[2024-10-13 16:29:13,505 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 134/140
+[2024-10-13 16:29:13,629 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 135/140
+[2024-10-13 16:29:13,752 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 136/140
+[2024-10-13 16:29:13,875 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 137/140
+[2024-10-13 16:29:13,998 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 138/140
+[2024-10-13 16:29:14,122 INFO test.py line 186 25394] Test: 185/312-scene0355_00, Batch: 139/140
+[2024-10-13 16:29:14,297 INFO test.py line 272 25394] Test: scene0355_00 [185/312]-136135 Batch 17.488 (19.437) Accuracy 0.9639 (0.4537) mIoU 0.6835 (0.3511)
+[2024-10-13 16:29:14,503 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 0/143
+[2024-10-13 16:29:14,673 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 1/143
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+[2024-10-13 16:29:16,040 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 9/143
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+[2024-10-13 16:29:16,895 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 14/143
+[2024-10-13 16:29:17,065 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 15/143
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+[2024-10-13 16:29:36,223 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 128/143
+[2024-10-13 16:29:36,403 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 129/143
+[2024-10-13 16:29:36,584 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 130/143
+[2024-10-13 16:29:36,764 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 131/143
+[2024-10-13 16:29:36,935 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 132/143
+[2024-10-13 16:29:37,107 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 133/143
+[2024-10-13 16:29:37,277 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 134/143
+[2024-10-13 16:29:37,447 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 135/143
+[2024-10-13 16:29:37,618 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 136/143
+[2024-10-13 16:29:37,788 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 137/143
+[2024-10-13 16:29:37,959 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 138/143
+[2024-10-13 16:29:38,130 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 139/143
+[2024-10-13 16:29:38,301 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 140/143
+[2024-10-13 16:29:38,472 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 141/143
+[2024-10-13 16:29:38,643 INFO test.py line 186 25394] Test: 186/312-scene0575_02, Batch: 142/143
+[2024-10-13 16:29:38,899 INFO test.py line 272 25394] Test: scene0575_02 [186/312]-202882 Batch 24.602 (19.464) Accuracy 0.7485 (0.4535) mIoU 0.5824 (0.3510)
+[2024-10-13 16:29:38,970 INFO test.py line 186 25394] Test: 187/312-scene0664_01, Batch: 0/104
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+[2024-10-13 16:29:44,918 INFO test.py line 186 25394] Test: 187/312-scene0664_01, Batch: 96/104
+[2024-10-13 16:29:44,979 INFO test.py line 186 25394] Test: 187/312-scene0664_01, Batch: 97/104
+[2024-10-13 16:29:45,040 INFO test.py line 186 25394] Test: 187/312-scene0664_01, Batch: 98/104
+[2024-10-13 16:29:45,101 INFO test.py line 186 25394] Test: 187/312-scene0664_01, Batch: 99/104
+[2024-10-13 16:29:45,161 INFO test.py line 186 25394] Test: 187/312-scene0664_01, Batch: 100/104
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+[2024-10-13 16:29:45,409 INFO test.py line 272 25394] Test: scene0664_01 [187/312]-45436 Batch 6.509 (19.395) Accuracy 0.6741 (0.4532) mIoU 0.5140 (0.3506)
+[2024-10-13 16:29:45,730 INFO test.py line 186 25394] Test: 188/312-scene0645_01, Batch: 0/152
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+[2024-10-13 16:30:26,313 INFO test.py line 272 25394] Test: scene0645_01 [188/312]-330321 Batch 40.904 (19.509) Accuracy 0.9312 (0.4551) mIoU 0.5862 (0.3526)
+[2024-10-13 16:30:26,470 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 0/144
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+[2024-10-13 16:30:41,354 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 114/144
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+[2024-10-13 16:30:41,632 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 116/144
+[2024-10-13 16:30:41,771 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 117/144
+[2024-10-13 16:30:41,910 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 118/144
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+[2024-10-13 16:30:42,883 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 125/144
+[2024-10-13 16:30:43,022 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 126/144
+[2024-10-13 16:30:43,161 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 127/144
+[2024-10-13 16:30:43,301 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 128/144
+[2024-10-13 16:30:43,440 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 129/144
+[2024-10-13 16:30:43,579 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 130/144
+[2024-10-13 16:30:43,718 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 131/144
+[2024-10-13 16:30:43,851 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 132/144
+[2024-10-13 16:30:43,983 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 133/144
+[2024-10-13 16:30:44,116 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 134/144
+[2024-10-13 16:30:44,248 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 135/144
+[2024-10-13 16:30:44,380 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 136/144
+[2024-10-13 16:30:44,512 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 137/144
+[2024-10-13 16:30:44,644 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 138/144
+[2024-10-13 16:30:44,776 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 139/144
+[2024-10-13 16:30:44,907 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 140/144
+[2024-10-13 16:30:45,039 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 141/144
+[2024-10-13 16:30:45,171 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 142/144
+[2024-10-13 16:30:45,303 INFO test.py line 186 25394] Test: 189/312-scene0583_00, Batch: 143/144
+[2024-10-13 16:30:45,503 INFO test.py line 272 25394] Test: scene0583_00 [189/312]-146782 Batch 19.190 (19.508) Accuracy 0.8083 (0.4541) mIoU 0.4703 (0.3519)
+[2024-10-13 16:30:45,611 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 0/126
+[2024-10-13 16:30:45,704 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 1/126
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+[2024-10-13 16:30:46,169 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 6/126
+[2024-10-13 16:30:46,262 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 7/126
+[2024-10-13 16:30:46,355 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 8/126
+[2024-10-13 16:30:46,448 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 9/126
+[2024-10-13 16:30:46,541 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 10/126
+[2024-10-13 16:30:46,635 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 11/126
+[2024-10-13 16:30:46,728 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 12/126
+[2024-10-13 16:30:46,822 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 13/126
+[2024-10-13 16:30:46,915 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 14/126
+[2024-10-13 16:30:47,008 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 15/126
+[2024-10-13 16:30:47,101 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 16/126
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+[2024-10-13 16:30:47,474 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 20/126
+[2024-10-13 16:30:47,567 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 21/126
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+[2024-10-13 16:30:55,595 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 107/126
+[2024-10-13 16:30:55,694 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 108/126
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+[2024-10-13 16:30:55,891 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 110/126
+[2024-10-13 16:30:55,989 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 111/126
+[2024-10-13 16:30:56,087 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 112/126
+[2024-10-13 16:30:56,186 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 113/126
+[2024-10-13 16:30:56,285 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 114/126
+[2024-10-13 16:30:56,384 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 115/126
+[2024-10-13 16:30:56,477 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 116/126
+[2024-10-13 16:30:56,571 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 117/126
+[2024-10-13 16:30:56,664 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 118/126
+[2024-10-13 16:30:56,757 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 119/126
+[2024-10-13 16:30:56,850 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 120/126
+[2024-10-13 16:30:56,943 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 121/126
+[2024-10-13 16:30:57,036 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 122/126
+[2024-10-13 16:30:57,130 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 123/126
+[2024-10-13 16:30:57,223 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 124/126
+[2024-10-13 16:30:57,316 INFO test.py line 186 25394] Test: 190/312-scene0552_01, Batch: 125/126
+[2024-10-13 16:30:57,443 INFO test.py line 272 25394] Test: scene0552_01 [190/312]-91978 Batch 11.939 (19.468) Accuracy 0.8659 (0.4535) mIoU 0.4815 (0.3517)
+[2024-10-13 16:30:57,503 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 0/125
+[2024-10-13 16:30:57,558 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 1/125
+[2024-10-13 16:30:57,611 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 2/125
+[2024-10-13 16:30:57,665 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 3/125
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+[2024-10-13 16:30:57,772 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 5/125
+[2024-10-13 16:30:57,825 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 6/125
+[2024-10-13 16:30:57,879 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 7/125
+[2024-10-13 16:30:57,932 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 8/125
+[2024-10-13 16:30:57,985 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 9/125
+[2024-10-13 16:30:58,038 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 10/125
+[2024-10-13 16:30:58,091 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 11/125
+[2024-10-13 16:30:58,144 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 12/125
+[2024-10-13 16:30:58,197 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 13/125
+[2024-10-13 16:30:58,250 INFO test.py line 186 25394] Test: 191/312-scene0702_02, Batch: 14/125
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+[2024-10-13 16:31:04,255 INFO test.py line 272 25394] Test: scene0702_02 [191/312]-32788 Batch 6.812 (19.402) Accuracy 0.8868 (0.4544) mIoU 0.7131 (0.3524)
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+[2024-10-13 16:31:32,540 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 127/155
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+[2024-10-13 16:31:34,204 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 134/155
+[2024-10-13 16:31:34,441 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 135/155
+[2024-10-13 16:31:34,679 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 136/155
+[2024-10-13 16:31:34,917 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 137/155
+[2024-10-13 16:31:35,154 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 138/155
+[2024-10-13 16:31:35,392 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 139/155
+[2024-10-13 16:31:35,629 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 140/155
+[2024-10-13 16:31:35,866 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 141/155
+[2024-10-13 16:31:36,103 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 142/155
+[2024-10-13 16:31:36,340 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 143/155
+[2024-10-13 16:31:36,563 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 144/155
+[2024-10-13 16:31:36,784 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 145/155
+[2024-10-13 16:31:37,006 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 146/155
+[2024-10-13 16:31:37,228 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 147/155
+[2024-10-13 16:31:37,450 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 148/155
+[2024-10-13 16:31:37,671 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 149/155
+[2024-10-13 16:31:37,895 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 150/155
+[2024-10-13 16:31:38,117 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 151/155
+[2024-10-13 16:31:38,338 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 152/155
+[2024-10-13 16:31:38,560 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 153/155
+[2024-10-13 16:31:38,781 INFO test.py line 186 25394] Test: 192/312-scene0645_02, Batch: 154/155
+[2024-10-13 16:31:39,128 INFO test.py line 272 25394] Test: scene0645_02 [192/312]-269529 Batch 34.873 (19.482) Accuracy 0.9110 (0.4546) mIoU 0.5557 (0.3528)
+[2024-10-13 16:31:39,326 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 0/138
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+[2024-10-13 16:31:54,920 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 97/138
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+[2024-10-13 16:31:55,268 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 99/138
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+[2024-10-13 16:31:56,490 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 106/138
+[2024-10-13 16:31:56,664 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 107/138
+[2024-10-13 16:31:56,838 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 108/138
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+[2024-10-13 16:31:57,186 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 110/138
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+[2024-10-13 16:31:58,406 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 117/138
+[2024-10-13 16:31:58,581 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 118/138
+[2024-10-13 16:31:58,755 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 119/138
+[2024-10-13 16:31:58,930 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 120/138
+[2024-10-13 16:31:59,105 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 121/138
+[2024-10-13 16:31:59,279 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 122/138
+[2024-10-13 16:31:59,453 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 123/138
+[2024-10-13 16:31:59,627 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 124/138
+[2024-10-13 16:31:59,802 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 125/138
+[2024-10-13 16:31:59,976 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 126/138
+[2024-10-13 16:32:00,150 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 127/138
+[2024-10-13 16:32:00,316 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 128/138
+[2024-10-13 16:32:00,481 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 129/138
+[2024-10-13 16:32:00,646 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 130/138
+[2024-10-13 16:32:00,811 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 131/138
+[2024-10-13 16:32:00,977 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 132/138
+[2024-10-13 16:32:01,142 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 133/138
+[2024-10-13 16:32:01,307 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 134/138
+[2024-10-13 16:32:01,472 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 135/138
+[2024-10-13 16:32:01,638 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 136/138
+[2024-10-13 16:32:01,804 INFO test.py line 186 25394] Test: 193/312-scene0414_00, Batch: 137/138
+[2024-10-13 16:32:02,051 INFO test.py line 272 25394] Test: scene0414_00 [193/312]-192685 Batch 22.922 (19.500) Accuracy 0.9167 (0.4546) mIoU 0.6556 (0.3528)
+[2024-10-13 16:32:02,134 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 0/130
+[2024-10-13 16:32:02,207 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 1/130
+[2024-10-13 16:32:02,279 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 2/130
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+[2024-10-13 16:32:02,714 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 8/130
+[2024-10-13 16:32:02,786 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 9/130
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+[2024-10-13 16:32:02,932 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 11/130
+[2024-10-13 16:32:03,005 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 12/130
+[2024-10-13 16:32:03,077 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 13/130
+[2024-10-13 16:32:03,150 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 14/130
+[2024-10-13 16:32:03,223 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 15/130
+[2024-10-13 16:32:03,295 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 16/130
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+[2024-10-13 16:32:03,512 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 19/130
+[2024-10-13 16:32:03,584 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 20/130
+[2024-10-13 16:32:03,657 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 21/130
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+[2024-10-13 16:32:10,091 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 110/130
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+[2024-10-13 16:32:10,242 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 112/130
+[2024-10-13 16:32:10,316 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 113/130
+[2024-10-13 16:32:10,391 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 114/130
+[2024-10-13 16:32:10,466 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 115/130
+[2024-10-13 16:32:10,542 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 116/130
+[2024-10-13 16:32:10,616 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 117/130
+[2024-10-13 16:32:10,693 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 118/130
+[2024-10-13 16:32:10,769 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 119/130
+[2024-10-13 16:32:10,841 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 120/130
+[2024-10-13 16:32:10,913 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 121/130
+[2024-10-13 16:32:10,985 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 122/130
+[2024-10-13 16:32:11,057 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 123/130
+[2024-10-13 16:32:11,129 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 124/130
+[2024-10-13 16:32:11,202 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 125/130
+[2024-10-13 16:32:11,273 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 126/130
+[2024-10-13 16:32:11,346 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 127/130
+[2024-10-13 16:32:11,418 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 128/130
+[2024-10-13 16:32:11,491 INFO test.py line 186 25394] Test: 194/312-scene0553_00, Batch: 129/130
+[2024-10-13 16:32:11,581 INFO test.py line 272 25394] Test: scene0553_00 [194/312]-62854 Batch 9.530 (19.449) Accuracy 0.8780 (0.4546) mIoU 0.3947 (0.3529)
+[2024-10-13 16:32:11,707 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 0/134
+[2024-10-13 16:32:11,817 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 1/134
+[2024-10-13 16:32:11,926 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 2/134
+[2024-10-13 16:32:12,035 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 3/134
+[2024-10-13 16:32:12,144 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 4/134
+[2024-10-13 16:32:12,253 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 5/134
+[2024-10-13 16:32:12,361 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 6/134
+[2024-10-13 16:32:12,470 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 7/134
+[2024-10-13 16:32:12,579 INFO test.py line 186 25394] Test: 195/312-scene0690_01, Batch: 8/134
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+[2024-10-13 16:32:26,329 INFO test.py line 272 25394] Test: scene0690_01 [195/312]-109935 Batch 14.747 (19.425) Accuracy 0.8422 (0.4551) mIoU 0.4068 (0.3532)
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+[2024-10-13 16:32:39,605 INFO test.py line 186 25394] Test: 196/312-scene0559_02, Batch: 117/121
+[2024-10-13 16:32:39,717 INFO test.py line 186 25394] Test: 196/312-scene0559_02, Batch: 118/121
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+[2024-10-13 16:32:40,095 INFO test.py line 272 25394] Test: scene0559_02 [196/312]-118005 Batch 13.766 (19.396) Accuracy 0.9868 (0.4555) mIoU 0.7774 (0.3536)
+[2024-10-13 16:32:40,277 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 0/130
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+[2024-10-13 16:32:58,158 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 118/130
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+[2024-10-13 16:32:58,774 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 122/130
+[2024-10-13 16:32:58,925 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 123/130
+[2024-10-13 16:32:59,077 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 124/130
+[2024-10-13 16:32:59,228 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 125/130
+[2024-10-13 16:32:59,380 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 126/130
+[2024-10-13 16:32:59,531 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 127/130
+[2024-10-13 16:32:59,682 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 128/130
+[2024-10-13 16:32:59,834 INFO test.py line 186 25394] Test: 197/312-scene0593_01, Batch: 129/130
+[2024-10-13 16:33:00,085 INFO test.py line 272 25394] Test: scene0593_01 [197/312]-179252 Batch 19.990 (19.399) Accuracy 0.8705 (0.4568) mIoU 0.4990 (0.3550)
+[2024-10-13 16:33:00,158 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 0/127
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+[2024-10-13 16:33:07,754 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 121/127
+[2024-10-13 16:33:07,815 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 122/127
+[2024-10-13 16:33:07,877 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 123/127
+[2024-10-13 16:33:07,939 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 124/127
+[2024-10-13 16:33:08,001 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 125/127
+[2024-10-13 16:33:08,063 INFO test.py line 186 25394] Test: 198/312-scene0664_02, Batch: 126/127
+[2024-10-13 16:33:08,131 INFO test.py line 272 25394] Test: scene0664_02 [198/312]-46804 Batch 8.046 (19.341) Accuracy 0.8587 (0.4580) mIoU 0.5648 (0.3552)
+[2024-10-13 16:33:08,476 INFO test.py line 186 25394] Test: 199/312-scene0500_01, Batch: 0/173
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+[2024-10-13 16:33:58,315 INFO test.py line 272 25394] Test: scene0500_01 [199/312]-352674 Batch 50.184 (19.496) Accuracy 0.9003 (0.4577) mIoU 0.4225 (0.3550)
+[2024-10-13 16:33:58,487 INFO test.py line 186 25394] Test: 200/312-scene0025_01, Batch: 0/139
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+[2024-10-13 16:34:18,576 INFO test.py line 272 25394] Test: scene0025_01 [200/312]-165720 Batch 20.261 (19.500) Accuracy 0.7444 (0.4578) mIoU 0.3209 (0.3544)
+[2024-10-13 16:34:18,736 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 0/139
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+[2024-10-13 16:34:35,100 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 123/139
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+[2024-10-13 16:34:35,664 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 127/139
+[2024-10-13 16:34:35,798 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 128/139
+[2024-10-13 16:34:35,933 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 129/139
+[2024-10-13 16:34:36,067 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 130/139
+[2024-10-13 16:34:36,201 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 131/139
+[2024-10-13 16:34:36,335 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 132/139
+[2024-10-13 16:34:36,469 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 133/139
+[2024-10-13 16:34:36,603 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 134/139
+[2024-10-13 16:34:36,737 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 135/139
+[2024-10-13 16:34:36,873 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 136/139
+[2024-10-13 16:34:37,006 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 137/139
+[2024-10-13 16:34:37,141 INFO test.py line 186 25394] Test: 201/312-scene0695_02, Batch: 138/139
+[2024-10-13 16:34:37,346 INFO test.py line 272 25394] Test: scene0695_02 [201/312]-157025 Batch 18.770 (19.497) Accuracy 0.7743 (0.4568) mIoU 0.2734 (0.3536)
+[2024-10-13 16:34:37,452 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 0/122
+[2024-10-13 16:34:37,543 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 1/122
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+[2024-10-13 16:34:37,996 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 6/122
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+[2024-10-13 16:34:38,177 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 8/122
+[2024-10-13 16:34:38,268 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 9/122
+[2024-10-13 16:34:38,397 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 10/122
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+[2024-10-13 16:34:38,580 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 12/122
+[2024-10-13 16:34:38,671 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 13/122
+[2024-10-13 16:34:38,761 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 14/122
+[2024-10-13 16:34:38,851 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 15/122
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+[2024-10-13 16:34:47,260 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 108/122
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+[2024-10-13 16:34:47,449 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 110/122
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+[2024-10-13 16:34:47,634 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 112/122
+[2024-10-13 16:34:47,725 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 113/122
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+[2024-10-13 16:34:47,906 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 115/122
+[2024-10-13 16:34:47,996 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 116/122
+[2024-10-13 16:34:48,086 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 117/122
+[2024-10-13 16:34:48,177 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 118/122
+[2024-10-13 16:34:48,268 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 119/122
+[2024-10-13 16:34:48,359 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 120/122
+[2024-10-13 16:34:48,449 INFO test.py line 186 25394] Test: 202/312-scene0686_01, Batch: 121/122
+[2024-10-13 16:34:48,571 INFO test.py line 272 25394] Test: scene0686_01 [202/312]-91616 Batch 11.225 (19.456) Accuracy 0.9091 (0.4577) mIoU 0.5990 (0.3544)
+[2024-10-13 16:34:48,721 INFO test.py line 186 25394] Test: 203/312-scene0474_05, Batch: 0/130
+[2024-10-13 16:34:48,845 INFO test.py line 186 25394] Test: 203/312-scene0474_05, Batch: 1/130
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+[2024-10-13 16:34:49,842 INFO test.py line 186 25394] Test: 203/312-scene0474_05, Batch: 9/130
+[2024-10-13 16:34:49,967 INFO test.py line 186 25394] Test: 203/312-scene0474_05, Batch: 10/130
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+[2024-10-13 16:35:04,937 INFO test.py line 272 25394] Test: scene0474_05 [203/312]-131255 Batch 16.365 (19.440) Accuracy 0.9447 (0.4577) mIoU 0.5564 (0.3546)
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+[2024-10-13 16:35:11,576 INFO test.py line 186 25394] Test: 204/312-scene0664_00, Batch: 107/113
+[2024-10-13 16:35:11,638 INFO test.py line 186 25394] Test: 204/312-scene0664_00, Batch: 108/113
+[2024-10-13 16:35:11,699 INFO test.py line 186 25394] Test: 204/312-scene0664_00, Batch: 109/113
+[2024-10-13 16:35:11,761 INFO test.py line 186 25394] Test: 204/312-scene0664_00, Batch: 110/113
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+[2024-10-13 16:35:11,884 INFO test.py line 186 25394] Test: 204/312-scene0664_00, Batch: 112/113
+[2024-10-13 16:35:11,949 INFO test.py line 272 25394] Test: scene0664_00 [204/312]-44585 Batch 7.012 (19.379) Accuracy 0.7157 (0.4585) mIoU 0.5280 (0.3549)
+[2024-10-13 16:35:12,099 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 0/147
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+[2024-10-13 16:35:28,814 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 132/147
+[2024-10-13 16:35:28,947 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 133/147
+[2024-10-13 16:35:29,080 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 134/147
+[2024-10-13 16:35:29,212 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 135/147
+[2024-10-13 16:35:29,338 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 136/147
+[2024-10-13 16:35:29,463 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 137/147
+[2024-10-13 16:35:29,588 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 138/147
+[2024-10-13 16:35:29,714 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 139/147
+[2024-10-13 16:35:29,839 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 140/147
+[2024-10-13 16:35:29,965 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 141/147
+[2024-10-13 16:35:30,090 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 142/147
+[2024-10-13 16:35:30,215 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 143/147
+[2024-10-13 16:35:30,340 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 144/147
+[2024-10-13 16:35:30,466 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 145/147
+[2024-10-13 16:35:30,591 INFO test.py line 186 25394] Test: 205/312-scene0187_01, Batch: 146/147
+[2024-10-13 16:35:30,772 INFO test.py line 272 25394] Test: scene0187_01 [205/312]-144163 Batch 18.823 (19.377) Accuracy 0.8884 (0.4585) mIoU 0.4547 (0.3545)
+[2024-10-13 16:35:31,107 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 0/148
+[2024-10-13 16:35:31,388 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 1/148
+[2024-10-13 16:35:31,670 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 2/148
+[2024-10-13 16:35:31,950 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 3/148
+[2024-10-13 16:35:32,232 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 4/148
+[2024-10-13 16:35:32,513 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 5/148
+[2024-10-13 16:35:32,794 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 6/148
+[2024-10-13 16:35:33,074 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 7/148
+[2024-10-13 16:35:33,352 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 8/148
+[2024-10-13 16:35:33,633 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 9/148
+[2024-10-13 16:35:33,913 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 10/148
+[2024-10-13 16:35:34,193 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 11/148
+[2024-10-13 16:35:34,473 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 12/148
+[2024-10-13 16:35:34,753 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 13/148
+[2024-10-13 16:35:35,033 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 14/148
+[2024-10-13 16:35:35,312 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 15/148
+[2024-10-13 16:35:35,593 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 16/148
+[2024-10-13 16:35:35,873 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 17/148
+[2024-10-13 16:35:36,153 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 18/148
+[2024-10-13 16:35:36,434 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 19/148
+[2024-10-13 16:35:36,713 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 20/148
+[2024-10-13 16:35:36,993 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 21/148
+[2024-10-13 16:35:37,274 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 22/148
+[2024-10-13 16:35:37,554 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 23/148
+[2024-10-13 16:35:37,833 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 24/148
+[2024-10-13 16:35:38,113 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 25/148
+[2024-10-13 16:35:38,393 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 26/148
+[2024-10-13 16:35:38,673 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 27/148
+[2024-10-13 16:35:38,953 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 28/148
+[2024-10-13 16:35:39,233 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 29/148
+[2024-10-13 16:35:39,513 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 30/148
+[2024-10-13 16:35:39,792 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 31/148
+[2024-10-13 16:35:40,072 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 32/148
+[2024-10-13 16:35:40,352 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 33/148
+[2024-10-13 16:35:40,632 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 34/148
+[2024-10-13 16:35:40,912 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 35/148
+[2024-10-13 16:35:41,192 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 36/148
+[2024-10-13 16:35:41,498 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 37/148
+[2024-10-13 16:35:41,780 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 38/148
+[2024-10-13 16:35:42,060 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 39/148
+[2024-10-13 16:35:42,341 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 40/148
+[2024-10-13 16:35:42,621 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 41/148
+[2024-10-13 16:35:42,900 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 42/148
+[2024-10-13 16:35:43,180 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 43/148
+[2024-10-13 16:35:43,461 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 44/148
+[2024-10-13 16:35:43,740 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 45/148
+[2024-10-13 16:35:44,020 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 46/148
+[2024-10-13 16:35:44,300 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 47/148
+[2024-10-13 16:35:44,560 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 48/148
+[2024-10-13 16:35:44,820 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 49/148
+[2024-10-13 16:35:45,081 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 50/148
+[2024-10-13 16:35:45,342 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 51/148
+[2024-10-13 16:35:45,602 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 52/148
+[2024-10-13 16:35:45,863 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 53/148
+[2024-10-13 16:35:46,124 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 54/148
+[2024-10-13 16:35:46,385 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 55/148
+[2024-10-13 16:35:46,646 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 56/148
+[2024-10-13 16:35:46,906 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 57/148
+[2024-10-13 16:35:47,167 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 58/148
+[2024-10-13 16:35:47,428 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 59/148
+[2024-10-13 16:35:47,689 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 60/148
+[2024-10-13 16:35:47,950 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 61/148
+[2024-10-13 16:35:48,211 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 62/148
+[2024-10-13 16:35:48,472 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 63/148
+[2024-10-13 16:35:48,733 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 64/148
+[2024-10-13 16:35:48,994 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 65/148
+[2024-10-13 16:35:49,255 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 66/148
+[2024-10-13 16:35:49,516 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 67/148
+[2024-10-13 16:35:49,777 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 68/148
+[2024-10-13 16:35:50,037 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 69/148
+[2024-10-13 16:35:50,298 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 70/148
+[2024-10-13 16:35:50,559 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 71/148
+[2024-10-13 16:35:50,820 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 72/148
+[2024-10-13 16:35:51,081 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 73/148
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+[2024-10-13 16:35:51,602 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 75/148
+[2024-10-13 16:35:51,864 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 76/148
+[2024-10-13 16:35:52,125 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 77/148
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+[2024-10-13 16:35:52,647 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 79/148
+[2024-10-13 16:35:52,909 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 80/148
+[2024-10-13 16:35:53,170 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 81/148
+[2024-10-13 16:35:53,431 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 82/148
+[2024-10-13 16:35:53,692 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 83/148
+[2024-10-13 16:35:53,952 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 84/148
+[2024-10-13 16:35:54,213 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 85/148
+[2024-10-13 16:35:54,473 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 86/148
+[2024-10-13 16:35:54,734 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 87/148
+[2024-10-13 16:35:54,994 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 88/148
+[2024-10-13 16:35:55,255 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 89/148
+[2024-10-13 16:35:55,515 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 90/148
+[2024-10-13 16:35:55,776 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 91/148
+[2024-10-13 16:35:56,036 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 92/148
+[2024-10-13 16:35:56,297 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 93/148
+[2024-10-13 16:35:56,558 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 94/148
+[2024-10-13 16:35:56,818 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 95/148
+[2024-10-13 16:35:57,115 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 96/148
+[2024-10-13 16:35:57,412 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 97/148
+[2024-10-13 16:35:57,710 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 98/148
+[2024-10-13 16:35:58,008 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 99/148
+[2024-10-13 16:35:58,306 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 100/148
+[2024-10-13 16:35:58,603 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 101/148
+[2024-10-13 16:35:58,900 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 102/148
+[2024-10-13 16:35:59,198 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 103/148
+[2024-10-13 16:35:59,495 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 104/148
+[2024-10-13 16:35:59,793 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 105/148
+[2024-10-13 16:36:00,091 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 106/148
+[2024-10-13 16:36:00,389 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 107/148
+[2024-10-13 16:36:00,687 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 108/148
+[2024-10-13 16:36:00,985 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 109/148
+[2024-10-13 16:36:01,283 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 110/148
+[2024-10-13 16:36:01,581 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 111/148
+[2024-10-13 16:36:01,879 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 112/148
+[2024-10-13 16:36:02,176 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 113/148
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+[2024-10-13 16:36:02,771 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 115/148
+[2024-10-13 16:36:03,069 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 116/148
+[2024-10-13 16:36:03,367 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 117/148
+[2024-10-13 16:36:03,665 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 118/148
+[2024-10-13 16:36:03,963 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 119/148
+[2024-10-13 16:36:04,261 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 120/148
+[2024-10-13 16:36:04,559 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 121/148
+[2024-10-13 16:36:04,856 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 122/148
+[2024-10-13 16:36:05,154 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 123/148
+[2024-10-13 16:36:05,451 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 124/148
+[2024-10-13 16:36:05,750 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 125/148
+[2024-10-13 16:36:06,047 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 126/148
+[2024-10-13 16:36:06,345 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 127/148
+[2024-10-13 16:36:06,642 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 128/148
+[2024-10-13 16:36:06,940 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 129/148
+[2024-10-13 16:36:07,238 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 130/148
+[2024-10-13 16:36:07,536 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 131/148
+[2024-10-13 16:36:07,833 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 132/148
+[2024-10-13 16:36:08,131 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 133/148
+[2024-10-13 16:36:08,428 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 134/148
+[2024-10-13 16:36:08,726 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 135/148
+[2024-10-13 16:36:09,006 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 136/148
+[2024-10-13 16:36:09,286 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 137/148
+[2024-10-13 16:36:09,567 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 138/148
+[2024-10-13 16:36:09,847 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 139/148
+[2024-10-13 16:36:10,127 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 140/148
+[2024-10-13 16:36:10,408 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 141/148
+[2024-10-13 16:36:10,688 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 142/148
+[2024-10-13 16:36:10,968 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 143/148
+[2024-10-13 16:36:11,248 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 144/148
+[2024-10-13 16:36:11,528 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 145/148
+[2024-10-13 16:36:11,808 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 146/148
+[2024-10-13 16:36:12,088 INFO test.py line 186 25394] Test: 206/312-scene0207_02, Batch: 147/148
+[2024-10-13 16:36:12,528 INFO test.py line 272 25394] Test: scene0207_02 [206/312]-347322 Batch 41.755 (19.485) Accuracy 0.8504 (0.4597) mIoU 0.5455 (0.3573)
+[2024-10-13 16:36:12,701 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 0/121
+[2024-10-13 16:36:12,848 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 1/121
+[2024-10-13 16:36:12,995 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 2/121
+[2024-10-13 16:36:13,143 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 3/121
+[2024-10-13 16:36:13,290 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 4/121
+[2024-10-13 16:36:13,438 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 5/121
+[2024-10-13 16:36:13,585 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 6/121
+[2024-10-13 16:36:13,733 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 7/121
+[2024-10-13 16:36:13,885 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 8/121
+[2024-10-13 16:36:14,054 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 9/121
+[2024-10-13 16:36:14,201 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 10/121
+[2024-10-13 16:36:14,347 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 11/121
+[2024-10-13 16:36:14,493 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 12/121
+[2024-10-13 16:36:14,639 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 13/121
+[2024-10-13 16:36:14,785 INFO test.py line 186 25394] Test: 207/312-scene0643_00, Batch: 14/121
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+[2024-10-13 16:36:30,477 INFO test.py line 272 25394] Test: scene0643_00 [207/312]-167360 Batch 17.949 (19.478) Accuracy 0.7663 (0.4599) mIoU 0.2726 (0.3568)
+[2024-10-13 16:36:30,856 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 0/144
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+[2024-10-13 16:37:11,833 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 130/144
+[2024-10-13 16:37:12,171 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 131/144
+[2024-10-13 16:37:12,487 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 132/144
+[2024-10-13 16:37:12,801 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 133/144
+[2024-10-13 16:37:13,116 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 134/144
+[2024-10-13 16:37:13,430 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 135/144
+[2024-10-13 16:37:13,744 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 136/144
+[2024-10-13 16:37:14,058 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 137/144
+[2024-10-13 16:37:14,373 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 138/144
+[2024-10-13 16:37:14,687 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 139/144
+[2024-10-13 16:37:15,002 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 140/144
+[2024-10-13 16:37:15,316 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 141/144
+[2024-10-13 16:37:15,631 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 142/144
+[2024-10-13 16:37:15,946 INFO test.py line 186 25394] Test: 208/312-scene0307_01, Batch: 143/144
+[2024-10-13 16:37:16,462 INFO test.py line 272 25394] Test: scene0307_01 [208/312]-412511 Batch 45.984 (19.605) Accuracy 0.8948 (0.4613) mIoU 0.2197 (0.3579)
+[2024-10-13 16:37:16,546 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 0/112
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+[2024-10-13 16:37:24,238 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 103/112
+[2024-10-13 16:37:24,313 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 104/112
+[2024-10-13 16:37:24,388 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 105/112
+[2024-10-13 16:37:24,463 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 106/112
+[2024-10-13 16:37:24,538 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 107/112
+[2024-10-13 16:37:24,613 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 108/112
+[2024-10-13 16:37:24,687 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 109/112
+[2024-10-13 16:37:24,762 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 110/112
+[2024-10-13 16:37:24,837 INFO test.py line 186 25394] Test: 209/312-scene0527_00, Batch: 111/112
+[2024-10-13 16:37:24,926 INFO test.py line 272 25394] Test: scene0527_00 [209/312]-65893 Batch 8.464 (19.552) Accuracy 0.7984 (0.4613) mIoU 0.3880 (0.3580)
+[2024-10-13 16:37:25,078 INFO test.py line 186 25394] Test: 210/312-scene0578_00, Batch: 0/134
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+[2024-10-13 16:37:42,139 INFO test.py line 272 25394] Test: scene0578_00 [210/312]-147607 Batch 17.213 (19.541) Accuracy 0.9514 (0.4613) mIoU 0.6976 (0.3581)
+[2024-10-13 16:37:42,211 INFO test.py line 186 25394] Test: 211/312-scene0406_01, Batch: 0/112
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+[2024-10-13 16:37:48,937 INFO test.py line 186 25394] Test: 211/312-scene0406_01, Batch: 108/112
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+[2024-10-13 16:37:49,188 INFO test.py line 272 25394] Test: scene0406_01 [211/312]-45557 Batch 7.049 (19.482) Accuracy 0.7028 (0.4613) mIoU 0.5211 (0.3579)
+[2024-10-13 16:37:49,311 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 0/125
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+[2024-10-13 16:38:01,992 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 117/125
+[2024-10-13 16:38:02,101 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 118/125
+[2024-10-13 16:38:02,210 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 119/125
+[2024-10-13 16:38:02,318 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 120/125
+[2024-10-13 16:38:02,427 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 121/125
+[2024-10-13 16:38:02,535 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 122/125
+[2024-10-13 16:38:02,644 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 123/125
+[2024-10-13 16:38:02,752 INFO test.py line 186 25394] Test: 212/312-scene0144_01, Batch: 124/125
+[2024-10-13 16:38:02,909 INFO test.py line 272 25394] Test: scene0144_01 [212/312]-116341 Batch 13.720 (19.455) Accuracy 0.8397 (0.4612) mIoU 0.3382 (0.3577)
+[2024-10-13 16:38:03,067 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 0/148
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+[2024-10-13 16:38:05,104 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 15/148
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+[2024-10-13 16:38:20,419 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 130/148
+[2024-10-13 16:38:20,561 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 131/148
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+[2024-10-13 16:38:20,844 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 133/148
+[2024-10-13 16:38:20,986 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 134/148
+[2024-10-13 16:38:21,127 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 135/148
+[2024-10-13 16:38:21,261 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 136/148
+[2024-10-13 16:38:21,394 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 137/148
+[2024-10-13 16:38:21,527 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 138/148
+[2024-10-13 16:38:21,660 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 139/148
+[2024-10-13 16:38:21,793 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 140/148
+[2024-10-13 16:38:21,926 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 141/148
+[2024-10-13 16:38:22,059 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 142/148
+[2024-10-13 16:38:22,192 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 143/148
+[2024-10-13 16:38:22,325 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 144/148
+[2024-10-13 16:38:22,458 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 145/148
+[2024-10-13 16:38:22,591 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 146/148
+[2024-10-13 16:38:22,725 INFO test.py line 186 25394] Test: 213/312-scene0697_01, Batch: 147/148
+[2024-10-13 16:38:22,915 INFO test.py line 272 25394] Test: scene0697_01 [213/312]-146820 Batch 20.006 (19.457) Accuracy 0.7911 (0.4612) mIoU 0.3912 (0.3579)
+[2024-10-13 16:38:23,017 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 0/122
+[2024-10-13 16:38:23,106 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 1/122
+[2024-10-13 16:38:23,195 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 2/122
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+[2024-10-13 16:38:23,727 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 8/122
+[2024-10-13 16:38:23,816 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 9/122
+[2024-10-13 16:38:23,905 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 10/122
+[2024-10-13 16:38:23,995 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 11/122
+[2024-10-13 16:38:24,084 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 12/122
+[2024-10-13 16:38:24,173 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 13/122
+[2024-10-13 16:38:24,262 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 14/122
+[2024-10-13 16:38:24,351 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 15/122
+[2024-10-13 16:38:24,440 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 16/122
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+[2024-10-13 16:38:24,885 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 21/122
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+[2024-10-13 16:38:33,452 INFO test.py line 186 25394] Test: 214/312-scene0314_00, Batch: 118/122
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+[2024-10-13 16:38:33,832 INFO test.py line 272 25394] Test: scene0314_00 [214/312]-87446 Batch 10.917 (19.417) Accuracy 0.4565 (0.4612) mIoU 0.2905 (0.3578)
+[2024-10-13 16:38:33,905 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 0/112
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+[2024-10-13 16:38:40,576 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 103/112
+[2024-10-13 16:38:40,640 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 104/112
+[2024-10-13 16:38:40,704 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 105/112
+[2024-10-13 16:38:40,767 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 106/112
+[2024-10-13 16:38:40,831 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 107/112
+[2024-10-13 16:38:40,895 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 108/112
+[2024-10-13 16:38:40,959 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 109/112
+[2024-10-13 16:38:41,023 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 110/112
+[2024-10-13 16:38:41,087 INFO test.py line 186 25394] Test: 215/312-scene0423_00, Batch: 111/112
+[2024-10-13 16:38:41,162 INFO test.py line 272 25394] Test: scene0423_00 [215/312]-53074 Batch 7.329 (19.361) Accuracy 0.9602 (0.4612) mIoU 0.4978 (0.3578)
+[2024-10-13 16:38:41,293 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 0/144
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+[2024-10-13 16:38:56,918 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 134/144
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+[2024-10-13 16:38:57,268 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 137/144
+[2024-10-13 16:38:57,385 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 138/144
+[2024-10-13 16:38:57,501 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 139/144
+[2024-10-13 16:38:57,618 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 140/144
+[2024-10-13 16:38:57,735 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 141/144
+[2024-10-13 16:38:57,852 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 142/144
+[2024-10-13 16:38:57,969 INFO test.py line 186 25394] Test: 216/312-scene0086_01, Batch: 143/144
+[2024-10-13 16:38:58,134 INFO test.py line 272 25394] Test: scene0086_01 [216/312]-124210 Batch 16.972 (19.350) Accuracy 0.8023 (0.4647) mIoU 0.4991 (0.3574)
+[2024-10-13 16:38:58,277 INFO test.py line 186 25394] Test: 217/312-scene0357_00, Batch: 0/126
+[2024-10-13 16:38:58,398 INFO test.py line 186 25394] Test: 217/312-scene0357_00, Batch: 1/126
+[2024-10-13 16:38:58,520 INFO test.py line 186 25394] Test: 217/312-scene0357_00, Batch: 2/126
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+[2024-10-13 16:39:13,677 INFO test.py line 272 25394] Test: scene0357_00 [217/312]-136945 Batch 15.543 (19.332) Accuracy 0.8008 (0.4645) mIoU 0.3267 (0.3575)
+[2024-10-13 16:39:13,887 INFO test.py line 186 25394] Test: 218/312-scene0203_00, Batch: 0/121
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+[2024-10-13 16:39:34,409 INFO test.py line 186 25394] Test: 218/312-scene0203_00, Batch: 117/121
+[2024-10-13 16:39:34,586 INFO test.py line 186 25394] Test: 218/312-scene0203_00, Batch: 118/121
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+[2024-10-13 16:39:35,200 INFO test.py line 272 25394] Test: scene0203_00 [218/312]-205756 Batch 21.522 (19.342) Accuracy 0.7762 (0.4640) mIoU 0.3260 (0.3574)
+[2024-10-13 16:39:35,413 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 0/143
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+[2024-10-13 16:39:57,541 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 123/143
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+[2024-10-13 16:39:57,930 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 125/143
+[2024-10-13 16:39:58,124 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 126/143
+[2024-10-13 16:39:58,318 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 127/143
+[2024-10-13 16:39:58,513 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 128/143
+[2024-10-13 16:39:58,707 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 129/143
+[2024-10-13 16:39:58,902 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 130/143
+[2024-10-13 16:39:59,096 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 131/143
+[2024-10-13 16:39:59,279 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 132/143
+[2024-10-13 16:39:59,461 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 133/143
+[2024-10-13 16:39:59,644 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 134/143
+[2024-10-13 16:39:59,827 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 135/143
+[2024-10-13 16:40:00,009 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 136/143
+[2024-10-13 16:40:00,191 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 137/143
+[2024-10-13 16:40:00,374 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 138/143
+[2024-10-13 16:40:00,556 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 139/143
+[2024-10-13 16:40:00,739 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 140/143
+[2024-10-13 16:40:00,922 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 141/143
+[2024-10-13 16:40:01,104 INFO test.py line 186 25394] Test: 219/312-scene0353_00, Batch: 142/143
+[2024-10-13 16:40:01,378 INFO test.py line 272 25394] Test: scene0353_00 [219/312]-208782 Batch 26.178 (19.374) Accuracy 0.8118 (0.4637) mIoU 0.3113 (0.3562)
+[2024-10-13 16:40:01,549 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 0/147
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+[2024-10-13 16:40:19,772 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 127/147
+[2024-10-13 16:40:19,926 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 128/147
+[2024-10-13 16:40:20,080 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 129/147
+[2024-10-13 16:40:20,234 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 130/147
+[2024-10-13 16:40:20,388 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 131/147
+[2024-10-13 16:40:20,542 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 132/147
+[2024-10-13 16:40:20,696 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 133/147
+[2024-10-13 16:40:20,850 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 134/147
+[2024-10-13 16:40:21,004 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 135/147
+[2024-10-13 16:40:21,149 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 136/147
+[2024-10-13 16:40:21,294 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 137/147
+[2024-10-13 16:40:21,440 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 138/147
+[2024-10-13 16:40:21,585 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 139/147
+[2024-10-13 16:40:21,730 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 140/147
+[2024-10-13 16:40:21,876 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 141/147
+[2024-10-13 16:40:22,021 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 142/147
+[2024-10-13 16:40:22,166 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 143/147
+[2024-10-13 16:40:22,311 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 144/147
+[2024-10-13 16:40:22,456 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 145/147
+[2024-10-13 16:40:22,602 INFO test.py line 186 25394] Test: 220/312-scene0697_02, Batch: 146/147
+[2024-10-13 16:40:22,818 INFO test.py line 272 25394] Test: scene0697_02 [220/312]-166855 Batch 21.440 (19.383) Accuracy 0.7806 (0.4636) mIoU 0.4090 (0.3562)
+[2024-10-13 16:40:22,968 INFO test.py line 186 25394] Test: 221/312-scene0595_00, Batch: 0/130
+[2024-10-13 16:40:23,094 INFO test.py line 186 25394] Test: 221/312-scene0595_00, Batch: 1/130
+[2024-10-13 16:40:23,220 INFO test.py line 186 25394] Test: 221/312-scene0595_00, Batch: 2/130
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+[2024-10-13 16:40:24,101 INFO test.py line 186 25394] Test: 221/312-scene0595_00, Batch: 9/130
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+[2024-10-13 16:40:39,336 INFO test.py line 272 25394] Test: scene0595_00 [221/312]-142330 Batch 16.518 (19.370) Accuracy 0.8323 (0.4634) mIoU 0.3133 (0.3559)
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+[2024-10-13 16:41:03,355 INFO test.py line 272 25394] Test: scene0046_01 [222/312]-198471 Batch 24.019 (19.391) Accuracy 0.8122 (0.4636) mIoU 0.3850 (0.3561)
+[2024-10-13 16:41:03,555 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 0/121
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+[2024-10-13 16:41:22,492 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 113/121
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+[2024-10-13 16:41:22,829 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 115/121
+[2024-10-13 16:41:22,997 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 116/121
+[2024-10-13 16:41:23,165 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 117/121
+[2024-10-13 16:41:23,333 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 118/121
+[2024-10-13 16:41:23,501 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 119/121
+[2024-10-13 16:41:23,669 INFO test.py line 186 25394] Test: 223/312-scene0246_00, Batch: 120/121
+[2024-10-13 16:41:23,927 INFO test.py line 272 25394] Test: scene0246_00 [223/312]-201062 Batch 20.572 (19.396) Accuracy 0.7931 (0.4638) mIoU 0.4521 (0.3559)
+[2024-10-13 16:41:24,044 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 0/151
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+[2024-10-13 16:41:38,430 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 142/151
+[2024-10-13 16:41:38,532 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 143/151
+[2024-10-13 16:41:38,634 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 144/151
+[2024-10-13 16:41:38,736 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 145/151
+[2024-10-13 16:41:38,838 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 146/151
+[2024-10-13 16:41:38,940 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 147/151
+[2024-10-13 16:41:39,041 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 148/151
+[2024-10-13 16:41:39,143 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 149/151
+[2024-10-13 16:41:39,245 INFO test.py line 186 25394] Test: 224/312-scene0354_00, Batch: 150/151
+[2024-10-13 16:41:39,388 INFO test.py line 272 25394] Test: scene0354_00 [224/312]-110569 Batch 15.461 (19.379) Accuracy 0.9676 (0.4638) mIoU 0.8332 (0.3559)
+[2024-10-13 16:41:39,536 INFO test.py line 186 25394] Test: 225/312-scene0695_01, Batch: 0/112
+[2024-10-13 16:41:39,662 INFO test.py line 186 25394] Test: 225/312-scene0695_01, Batch: 1/112
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+[2024-10-13 16:41:53,623 INFO test.py line 272 25394] Test: scene0695_01 [225/312]-143776 Batch 14.235 (19.356) Accuracy 0.8113 (0.4632) mIoU 0.3836 (0.3553)
+[2024-10-13 16:41:53,846 INFO test.py line 186 25394] Test: 226/312-scene0251_00, Batch: 0/198
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+[2024-10-13 16:42:30,707 INFO test.py line 272 25394] Test: scene0251_00 [226/312]-226765 Batch 37.084 (19.434) Accuracy 0.9086 (0.4632) mIoU 0.6002 (0.3552)
+[2024-10-13 16:42:30,810 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 0/121
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+[2024-10-13 16:42:41,087 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 114/121
+[2024-10-13 16:42:41,176 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 115/121
+[2024-10-13 16:42:41,266 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 116/121
+[2024-10-13 16:42:41,356 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 117/121
+[2024-10-13 16:42:41,445 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 118/121
+[2024-10-13 16:42:41,535 INFO test.py line 186 25394] Test: 227/312-scene0598_01, Batch: 119/121
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+[2024-10-13 16:42:41,756 INFO test.py line 272 25394] Test: scene0598_01 [227/312]-91924 Batch 11.049 (19.397) Accuracy 0.6100 (0.4627) mIoU 0.5503 (0.3552)
+[2024-10-13 16:42:41,825 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 0/134
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+[2024-10-13 16:42:49,244 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 121/134
+[2024-10-13 16:42:49,307 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 122/134
+[2024-10-13 16:42:49,370 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 123/134
+[2024-10-13 16:42:49,431 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 124/134
+[2024-10-13 16:42:49,491 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 125/134
+[2024-10-13 16:42:49,552 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 126/134
+[2024-10-13 16:42:49,612 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 127/134
+[2024-10-13 16:42:49,672 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 128/134
+[2024-10-13 16:42:49,733 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 129/134
+[2024-10-13 16:42:49,793 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 130/134
+[2024-10-13 16:42:49,853 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 131/134
+[2024-10-13 16:42:49,914 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 132/134
+[2024-10-13 16:42:49,974 INFO test.py line 186 25394] Test: 228/312-scene0153_00, Batch: 133/134
+[2024-10-13 16:42:50,041 INFO test.py line 272 25394] Test: scene0153_00 [228/312]-47709 Batch 8.285 (19.349) Accuracy 0.8219 (0.4727) mIoU 0.5303 (0.3601)
+[2024-10-13 16:42:50,219 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 0/125
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+[2024-10-13 16:42:55,034 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 32/125
+[2024-10-13 16:42:55,184 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 33/125
+[2024-10-13 16:42:55,334 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 34/125
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+[2024-10-13 16:43:06,646 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 110/125
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+[2024-10-13 16:43:07,446 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 115/125
+[2024-10-13 16:43:07,595 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 116/125
+[2024-10-13 16:43:07,745 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 117/125
+[2024-10-13 16:43:07,894 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 118/125
+[2024-10-13 16:43:08,044 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 119/125
+[2024-10-13 16:43:08,193 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 120/125
+[2024-10-13 16:43:08,342 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 121/125
+[2024-10-13 16:43:08,492 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 122/125
+[2024-10-13 16:43:08,642 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 123/125
+[2024-10-13 16:43:08,791 INFO test.py line 186 25394] Test: 229/312-scene0670_00, Batch: 124/125
+[2024-10-13 16:43:09,019 INFO test.py line 272 25394] Test: scene0670_00 [229/312]-176038 Batch 18.977 (19.347) Accuracy 0.7923 (0.4729) mIoU 0.3313 (0.3603)
+[2024-10-13 16:43:09,155 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 0/138
+[2024-10-13 16:43:09,274 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 1/138
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+[2024-10-13 16:43:10,112 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 8/138
+[2024-10-13 16:43:10,232 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 9/138
+[2024-10-13 16:43:10,351 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 10/138
+[2024-10-13 16:43:10,470 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 11/138
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+[2024-10-13 16:43:10,826 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 14/138
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+[2024-10-13 16:43:11,538 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 20/138
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+[2024-10-13 16:43:24,812 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 132/138
+[2024-10-13 16:43:24,931 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 133/138
+[2024-10-13 16:43:25,051 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 134/138
+[2024-10-13 16:43:25,170 INFO test.py line 186 25394] Test: 230/312-scene0426_03, Batch: 135/138
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+[2024-10-13 16:43:25,577 INFO test.py line 272 25394] Test: scene0426_03 [230/312]-126181 Batch 16.558 (19.335) Accuracy 0.8083 (0.4726) mIoU 0.3428 (0.3598)
+[2024-10-13 16:43:25,697 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 0/129
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+[2024-10-13 16:43:38,431 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 121/129
+[2024-10-13 16:43:38,537 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 122/129
+[2024-10-13 16:43:38,643 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 123/129
+[2024-10-13 16:43:38,749 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 124/129
+[2024-10-13 16:43:38,855 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 125/129
+[2024-10-13 16:43:38,961 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 126/129
+[2024-10-13 16:43:39,067 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 127/129
+[2024-10-13 16:43:39,173 INFO test.py line 186 25394] Test: 231/312-scene0377_02, Batch: 128/129
+[2024-10-13 16:43:39,323 INFO test.py line 272 25394] Test: scene0377_02 [231/312]-113557 Batch 13.746 (19.311) Accuracy 0.8110 (0.4721) mIoU 0.1875 (0.3595)
+[2024-10-13 16:43:39,501 INFO test.py line 186 25394] Test: 232/312-scene0606_02, Batch: 0/122
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+[2024-10-13 16:43:57,210 INFO test.py line 186 25394] Test: 232/312-scene0606_02, Batch: 118/122
+[2024-10-13 16:43:57,361 INFO test.py line 186 25394] Test: 232/312-scene0606_02, Batch: 119/122
+[2024-10-13 16:43:57,512 INFO test.py line 186 25394] Test: 232/312-scene0606_02, Batch: 120/122
+[2024-10-13 16:43:57,663 INFO test.py line 186 25394] Test: 232/312-scene0606_02, Batch: 121/122
+[2024-10-13 16:43:57,875 INFO test.py line 272 25394] Test: scene0606_02 [232/312]-167932 Batch 18.552 (19.307) Accuracy 0.7361 (0.4716) mIoU 0.1985 (0.3587)
+[2024-10-13 16:43:57,956 INFO test.py line 186 25394] Test: 233/312-scene0494_00, Batch: 0/131
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+[2024-10-13 16:44:07,221 INFO test.py line 272 25394] Test: scene0494_00 [233/312]-62048 Batch 9.345 (19.265) Accuracy 0.9768 (0.4716) mIoU 0.7930 (0.3587)
+[2024-10-13 16:44:07,454 INFO test.py line 186 25394] Test: 234/312-scene0011_01, Batch: 0/126
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+[2024-10-13 16:44:31,137 INFO test.py line 186 25394] Test: 234/312-scene0011_01, Batch: 121/126
+[2024-10-13 16:44:31,333 INFO test.py line 186 25394] Test: 234/312-scene0011_01, Batch: 122/126
+[2024-10-13 16:44:31,529 INFO test.py line 186 25394] Test: 234/312-scene0011_01, Batch: 123/126
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+[2024-10-13 16:44:32,225 INFO test.py line 272 25394] Test: scene0011_01 [234/312]-239261 Batch 25.004 (19.289) Accuracy 0.7956 (0.4707) mIoU 0.4114 (0.3577)
+[2024-10-13 16:44:32,351 INFO test.py line 186 25394] Test: 235/312-scene0689_00, Batch: 0/143
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+[2024-10-13 16:44:48,317 INFO test.py line 272 25394] Test: scene0689_00 [235/312]-122129 Batch 16.092 (19.276) Accuracy 0.9506 (0.4708) mIoU 0.6552 (0.3578)
+[2024-10-13 16:44:48,402 INFO test.py line 186 25394] Test: 236/312-scene0458_01, Batch: 0/125
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+[2024-10-13 16:44:57,691 INFO test.py line 272 25394] Test: scene0458_01 [236/312]-69167 Batch 9.373 (19.234) Accuracy 0.7434 (0.4710) mIoU 0.3196 (0.3579)
+[2024-10-13 16:44:57,820 INFO test.py line 186 25394] Test: 237/312-scene0550_00, Batch: 0/126
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+[2024-10-13 16:45:11,873 INFO test.py line 272 25394] Test: scene0550_00 [237/312]-127677 Batch 14.182 (19.212) Accuracy 0.4964 (0.4737) mIoU 0.2597 (0.3581)
+[2024-10-13 16:45:12,041 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 0/130
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+[2024-10-13 16:45:26,101 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 100/130
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+[2024-10-13 16:45:27,613 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 110/130
+[2024-10-13 16:45:27,764 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 111/130
+[2024-10-13 16:45:27,915 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 112/130
+[2024-10-13 16:45:28,066 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 113/130
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+[2024-10-13 16:45:28,368 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 115/130
+[2024-10-13 16:45:28,520 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 116/130
+[2024-10-13 16:45:28,671 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 117/130
+[2024-10-13 16:45:28,822 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 118/130
+[2024-10-13 16:45:28,973 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 119/130
+[2024-10-13 16:45:29,114 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 120/130
+[2024-10-13 16:45:29,256 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 121/130
+[2024-10-13 16:45:29,398 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 122/130
+[2024-10-13 16:45:29,540 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 123/130
+[2024-10-13 16:45:29,682 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 124/130
+[2024-10-13 16:45:29,823 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 125/130
+[2024-10-13 16:45:29,965 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 126/130
+[2024-10-13 16:45:30,107 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 127/130
+[2024-10-13 16:45:30,249 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 128/130
+[2024-10-13 16:45:30,390 INFO test.py line 186 25394] Test: 238/312-scene0549_00, Batch: 129/130
+[2024-10-13 16:45:30,599 INFO test.py line 272 25394] Test: scene0549_00 [238/312]-161973 Batch 18.726 (19.210) Accuracy 0.8513 (0.4734) mIoU 0.4865 (0.3580)
+[2024-10-13 16:45:30,773 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 0/112
+[2024-10-13 16:45:30,921 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 1/112
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+[2024-10-13 16:45:31,956 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 8/112
+[2024-10-13 16:45:32,104 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 9/112
+[2024-10-13 16:45:32,252 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 10/112
+[2024-10-13 16:45:32,400 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 11/112
+[2024-10-13 16:45:32,548 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 12/112
+[2024-10-13 16:45:32,696 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 13/112
+[2024-10-13 16:45:32,844 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 14/112
+[2024-10-13 16:45:32,992 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 15/112
+[2024-10-13 16:45:33,139 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 16/112
+[2024-10-13 16:45:33,287 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 17/112
+[2024-10-13 16:45:33,435 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 18/112
+[2024-10-13 16:45:33,584 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 19/112
+[2024-10-13 16:45:33,732 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 20/112
+[2024-10-13 16:45:33,880 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 21/112
+[2024-10-13 16:45:34,067 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 22/112
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+[2024-10-13 16:45:44,081 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 91/112
+[2024-10-13 16:45:44,239 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 92/112
+[2024-10-13 16:45:44,397 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 93/112
+[2024-10-13 16:45:44,554 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 94/112
+[2024-10-13 16:45:44,711 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 95/112
+[2024-10-13 16:45:44,868 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 96/112
+[2024-10-13 16:45:45,025 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 97/112
+[2024-10-13 16:45:45,183 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 98/112
+[2024-10-13 16:45:45,340 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 99/112
+[2024-10-13 16:45:45,497 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 100/112
+[2024-10-13 16:45:45,654 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 101/112
+[2024-10-13 16:45:45,811 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 102/112
+[2024-10-13 16:45:45,968 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 103/112
+[2024-10-13 16:45:46,116 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 104/112
+[2024-10-13 16:45:46,264 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 105/112
+[2024-10-13 16:45:46,411 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 106/112
+[2024-10-13 16:45:46,559 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 107/112
+[2024-10-13 16:45:46,707 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 108/112
+[2024-10-13 16:45:46,855 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 109/112
+[2024-10-13 16:45:47,003 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 110/112
+[2024-10-13 16:45:47,151 INFO test.py line 186 25394] Test: 239/312-scene0648_00, Batch: 111/112
+[2024-10-13 16:45:47,363 INFO test.py line 272 25394] Test: scene0648_00 [239/312]-167915 Batch 16.764 (19.200) Accuracy 0.7652 (0.4749) mIoU 0.2738 (0.3583)
+[2024-10-13 16:45:47,559 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 0/138
+[2024-10-13 16:45:47,725 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 1/138
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+[2024-10-13 16:45:48,887 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 8/138
+[2024-10-13 16:45:49,053 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 9/138
+[2024-10-13 16:45:49,219 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 10/138
+[2024-10-13 16:45:49,386 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 11/138
+[2024-10-13 16:45:49,578 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 12/138
+[2024-10-13 16:45:49,747 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 13/138
+[2024-10-13 16:45:49,913 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 14/138
+[2024-10-13 16:45:50,079 INFO test.py line 186 25394] Test: 240/312-scene0647_00, Batch: 15/138
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+[2024-10-13 16:46:10,570 INFO test.py line 272 25394] Test: scene0647_00 [240/312]-196185 Batch 23.206 (19.217) Accuracy 0.8653 (0.4746) mIoU 0.4511 (0.3581)
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+[2024-10-13 16:46:27,079 INFO test.py line 186 25394] Test: 241/312-scene0558_01, Batch: 130/134
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+[2024-10-13 16:46:27,640 INFO test.py line 272 25394] Test: scene0558_01 [241/312]-141342 Batch 17.071 (19.208) Accuracy 0.9099 (0.4748) mIoU 0.7143 (0.3583)
+[2024-10-13 16:46:27,751 INFO test.py line 186 25394] Test: 242/312-scene0193_00, Batch: 0/117
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+[2024-10-13 16:46:39,057 INFO test.py line 272 25394] Test: scene0193_00 [242/312]-101783 Batch 11.417 (19.176) Accuracy 0.8820 (0.4747) mIoU 0.5466 (0.3583)
+[2024-10-13 16:46:39,229 INFO test.py line 186 25394] Test: 243/312-scene0343_00, Batch: 0/117
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+[2024-10-13 16:46:56,429 INFO test.py line 272 25394] Test: scene0343_00 [243/312]-165618 Batch 17.372 (19.168) Accuracy 0.7993 (0.4744) mIoU 0.2817 (0.3580)
+[2024-10-13 16:46:56,640 INFO test.py line 186 25394] Test: 244/312-scene0203_01, Batch: 0/130
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+[2024-10-13 16:47:18,722 INFO test.py line 186 25394] Test: 244/312-scene0203_01, Batch: 124/130
+[2024-10-13 16:47:18,901 INFO test.py line 186 25394] Test: 244/312-scene0203_01, Batch: 125/130
+[2024-10-13 16:47:19,080 INFO test.py line 186 25394] Test: 244/312-scene0203_01, Batch: 126/130
+[2024-10-13 16:47:19,259 INFO test.py line 186 25394] Test: 244/312-scene0203_01, Batch: 127/130
+[2024-10-13 16:47:19,438 INFO test.py line 186 25394] Test: 244/312-scene0203_01, Batch: 128/130
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+[2024-10-13 16:47:19,877 INFO test.py line 272 25394] Test: scene0203_01 [244/312]-206210 Batch 23.447 (19.186) Accuracy 0.6094 (0.4736) mIoU 0.3742 (0.3573)
+[2024-10-13 16:47:20,077 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 0/143
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+[2024-10-13 16:47:38,879 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 114/143
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+[2024-10-13 16:47:41,188 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 127/143
+[2024-10-13 16:47:41,365 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 128/143
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+[2024-10-13 16:47:41,898 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 131/143
+[2024-10-13 16:47:42,066 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 132/143
+[2024-10-13 16:47:42,233 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 133/143
+[2024-10-13 16:47:42,400 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 134/143
+[2024-10-13 16:47:42,568 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 135/143
+[2024-10-13 16:47:42,735 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 136/143
+[2024-10-13 16:47:42,902 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 137/143
+[2024-10-13 16:47:43,070 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 138/143
+[2024-10-13 16:47:43,236 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 139/143
+[2024-10-13 16:47:43,404 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 140/143
+[2024-10-13 16:47:43,571 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 141/143
+[2024-10-13 16:47:43,738 INFO test.py line 186 25394] Test: 245/312-scene0389_00, Batch: 142/143
+[2024-10-13 16:47:43,993 INFO test.py line 272 25394] Test: scene0389_00 [245/312]-201003 Batch 24.116 (19.206) Accuracy 0.9219 (0.4735) mIoU 0.5739 (0.3572)
+[2024-10-13 16:47:44,195 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 0/130
+[2024-10-13 16:47:44,366 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 1/130
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+[2024-10-13 16:47:45,734 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 9/130
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+[2024-10-13 16:47:46,248 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 12/130
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+[2024-10-13 16:47:46,590 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 14/130
+[2024-10-13 16:47:46,760 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 15/130
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+[2024-10-13 16:48:03,990 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 116/130
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+[2024-10-13 16:48:04,353 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 118/130
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+[2024-10-13 16:48:04,706 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 120/130
+[2024-10-13 16:48:04,877 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 121/130
+[2024-10-13 16:48:05,048 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 122/130
+[2024-10-13 16:48:05,218 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 123/130
+[2024-10-13 16:48:05,389 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 124/130
+[2024-10-13 16:48:05,559 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 125/130
+[2024-10-13 16:48:05,730 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 126/130
+[2024-10-13 16:48:05,901 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 127/130
+[2024-10-13 16:48:06,072 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 128/130
+[2024-10-13 16:48:06,243 INFO test.py line 186 25394] Test: 246/312-scene0665_01, Batch: 129/130
+[2024-10-13 16:48:06,495 INFO test.py line 272 25394] Test: scene0665_01 [246/312]-200688 Batch 22.501 (19.219) Accuracy 0.9526 (0.4735) mIoU 0.6002 (0.3573)
+[2024-10-13 16:48:06,597 INFO test.py line 186 25394] Test: 247/312-scene0164_03, Batch: 0/129
+[2024-10-13 16:48:06,686 INFO test.py line 186 25394] Test: 247/312-scene0164_03, Batch: 1/129
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+[2024-10-13 16:48:07,403 INFO test.py line 186 25394] Test: 247/312-scene0164_03, Batch: 9/129
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+[2024-10-13 16:48:07,761 INFO test.py line 186 25394] Test: 247/312-scene0164_03, Batch: 13/129
+[2024-10-13 16:48:07,850 INFO test.py line 186 25394] Test: 247/312-scene0164_03, Batch: 14/129
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+[2024-10-13 16:48:18,224 INFO test.py line 272 25394] Test: scene0164_03 [247/312]-89190 Batch 11.729 (19.189) Accuracy 0.9019 (0.4871) mIoU 0.6045 (0.3648)
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+[2024-10-13 16:48:29,196 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 73/108
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+[2024-10-13 16:48:30,001 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 78/108
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+[2024-10-13 16:48:30,323 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 80/108
+[2024-10-13 16:48:30,484 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 81/108
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+[2024-10-13 16:48:30,967 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 84/108
+[2024-10-13 16:48:31,129 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 85/108
+[2024-10-13 16:48:31,290 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 86/108
+[2024-10-13 16:48:31,451 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 87/108
+[2024-10-13 16:48:31,612 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 88/108
+[2024-10-13 16:48:31,774 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 89/108
+[2024-10-13 16:48:31,935 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 90/108
+[2024-10-13 16:48:32,096 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 91/108
+[2024-10-13 16:48:32,257 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 92/108
+[2024-10-13 16:48:32,418 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 93/108
+[2024-10-13 16:48:32,579 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 94/108
+[2024-10-13 16:48:32,740 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 95/108
+[2024-10-13 16:48:32,901 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 96/108
+[2024-10-13 16:48:33,062 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 97/108
+[2024-10-13 16:48:33,223 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 98/108
+[2024-10-13 16:48:33,384 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 99/108
+[2024-10-13 16:48:33,535 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 100/108
+[2024-10-13 16:48:33,686 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 101/108
+[2024-10-13 16:48:33,838 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 102/108
+[2024-10-13 16:48:33,990 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 103/108
+[2024-10-13 16:48:34,142 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 104/108
+[2024-10-13 16:48:34,293 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 105/108
+[2024-10-13 16:48:34,445 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 106/108
+[2024-10-13 16:48:34,596 INFO test.py line 186 25394] Test: 248/312-scene0648_01, Batch: 107/108
+[2024-10-13 16:48:34,823 INFO test.py line 272 25394] Test: scene0648_01 [248/312]-172602 Batch 16.598 (19.179) Accuracy 0.7879 (0.4868) mIoU 0.2830 (0.3642)
+[2024-10-13 16:48:35,003 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 0/126
+[2024-10-13 16:48:35,155 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 1/126
+[2024-10-13 16:48:35,307 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 2/126
+[2024-10-13 16:48:35,459 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 3/126
+[2024-10-13 16:48:35,612 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 4/126
+[2024-10-13 16:48:35,763 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 5/126
+[2024-10-13 16:48:35,915 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 6/126
+[2024-10-13 16:48:36,067 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 7/126
+[2024-10-13 16:48:36,220 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 8/126
+[2024-10-13 16:48:36,372 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 9/126
+[2024-10-13 16:48:36,524 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 10/126
+[2024-10-13 16:48:36,676 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 11/126
+[2024-10-13 16:48:36,828 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 12/126
+[2024-10-13 16:48:36,981 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 13/126
+[2024-10-13 16:48:37,133 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 14/126
+[2024-10-13 16:48:37,285 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 15/126
+[2024-10-13 16:48:37,437 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 16/126
+[2024-10-13 16:48:37,590 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 17/126
+[2024-10-13 16:48:37,741 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 18/126
+[2024-10-13 16:48:37,930 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 19/126
+[2024-10-13 16:48:38,083 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 20/126
+[2024-10-13 16:48:38,235 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 21/126
+[2024-10-13 16:48:38,387 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 22/126
+[2024-10-13 16:48:38,538 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 23/126
+[2024-10-13 16:48:38,690 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 24/126
+[2024-10-13 16:48:38,842 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 25/126
+[2024-10-13 16:48:38,994 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 26/126
+[2024-10-13 16:48:39,146 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 27/126
+[2024-10-13 16:48:39,297 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 28/126
+[2024-10-13 16:48:39,449 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 29/126
+[2024-10-13 16:48:39,601 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 30/126
+[2024-10-13 16:48:39,753 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 31/126
+[2024-10-13 16:48:39,905 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 32/126
+[2024-10-13 16:48:40,057 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 33/126
+[2024-10-13 16:48:40,209 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 34/126
+[2024-10-13 16:48:40,361 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 35/126
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+[2024-10-13 16:48:40,665 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 37/126
+[2024-10-13 16:48:40,817 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 38/126
+[2024-10-13 16:48:40,969 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 39/126
+[2024-10-13 16:48:41,113 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 40/126
+[2024-10-13 16:48:41,257 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 41/126
+[2024-10-13 16:48:41,400 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 42/126
+[2024-10-13 16:48:41,544 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 43/126
+[2024-10-13 16:48:41,688 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 44/126
+[2024-10-13 16:48:41,831 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 45/126
+[2024-10-13 16:48:41,974 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 46/126
+[2024-10-13 16:48:42,118 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 47/126
+[2024-10-13 16:48:42,262 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 48/126
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+[2024-10-13 16:48:42,548 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 50/126
+[2024-10-13 16:48:42,692 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 51/126
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+[2024-10-13 16:48:43,552 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 57/126
+[2024-10-13 16:48:43,695 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 58/126
+[2024-10-13 16:48:43,839 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 59/126
+[2024-10-13 16:48:43,982 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 60/126
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+[2024-10-13 16:48:44,269 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 62/126
+[2024-10-13 16:48:44,413 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 63/126
+[2024-10-13 16:48:44,557 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 64/126
+[2024-10-13 16:48:44,701 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 65/126
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+[2024-10-13 16:48:44,989 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 67/126
+[2024-10-13 16:48:45,133 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 68/126
+[2024-10-13 16:48:45,278 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 69/126
+[2024-10-13 16:48:45,422 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 70/126
+[2024-10-13 16:48:45,566 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 71/126
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+[2024-10-13 16:48:46,141 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 75/126
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+[2024-10-13 16:48:46,858 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 80/126
+[2024-10-13 16:48:47,002 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 81/126
+[2024-10-13 16:48:47,146 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 82/126
+[2024-10-13 16:48:47,290 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 83/126
+[2024-10-13 16:48:47,451 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 84/126
+[2024-10-13 16:48:47,612 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 85/126
+[2024-10-13 16:48:47,773 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 86/126
+[2024-10-13 16:48:47,934 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 87/126
+[2024-10-13 16:48:48,096 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 88/126
+[2024-10-13 16:48:48,257 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 89/126
+[2024-10-13 16:48:48,418 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 90/126
+[2024-10-13 16:48:48,580 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 91/126
+[2024-10-13 16:48:48,741 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 92/126
+[2024-10-13 16:48:48,903 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 93/126
+[2024-10-13 16:48:49,064 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 94/126
+[2024-10-13 16:48:49,226 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 95/126
+[2024-10-13 16:48:49,388 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 96/126
+[2024-10-13 16:48:49,549 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 97/126
+[2024-10-13 16:48:49,711 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 98/126
+[2024-10-13 16:48:49,872 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 99/126
+[2024-10-13 16:48:50,033 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 100/126
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+[2024-10-13 16:48:50,355 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 102/126
+[2024-10-13 16:48:50,517 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 103/126
+[2024-10-13 16:48:50,678 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 104/126
+[2024-10-13 16:48:50,840 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 105/126
+[2024-10-13 16:48:51,001 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 106/126
+[2024-10-13 16:48:51,163 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 107/126
+[2024-10-13 16:48:51,325 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 108/126
+[2024-10-13 16:48:51,487 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 109/126
+[2024-10-13 16:48:51,649 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 110/126
+[2024-10-13 16:48:51,811 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 111/126
+[2024-10-13 16:48:51,972 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 112/126
+[2024-10-13 16:48:52,134 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 113/126
+[2024-10-13 16:48:52,295 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 114/126
+[2024-10-13 16:48:52,457 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 115/126
+[2024-10-13 16:48:52,609 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 116/126
+[2024-10-13 16:48:52,761 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 117/126
+[2024-10-13 16:48:52,912 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 118/126
+[2024-10-13 16:48:53,064 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 119/126
+[2024-10-13 16:48:53,216 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 120/126
+[2024-10-13 16:48:53,368 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 121/126
+[2024-10-13 16:48:53,520 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 122/126
+[2024-10-13 16:48:53,672 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 123/126
+[2024-10-13 16:48:53,824 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 124/126
+[2024-10-13 16:48:53,976 INFO test.py line 186 25394] Test: 249/312-scene0474_04, Batch: 125/126
+[2024-10-13 16:48:54,202 INFO test.py line 272 25394] Test: scene0474_04 [249/312]-178388 Batch 19.379 (19.179) Accuracy 0.8315 (0.4868) mIoU 0.4805 (0.3643)
+[2024-10-13 16:48:54,312 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 0/130
+[2024-10-13 16:48:54,408 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 1/130
+[2024-10-13 16:48:54,504 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 2/130
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+[2024-10-13 16:48:54,792 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 5/130
+[2024-10-13 16:48:54,888 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 6/130
+[2024-10-13 16:48:54,984 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 7/130
+[2024-10-13 16:48:55,081 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 8/130
+[2024-10-13 16:48:55,177 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 9/130
+[2024-10-13 16:48:55,273 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 10/130
+[2024-10-13 16:48:55,369 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 11/130
+[2024-10-13 16:48:55,465 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 12/130
+[2024-10-13 16:48:55,561 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 13/130
+[2024-10-13 16:48:55,657 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 14/130
+[2024-10-13 16:48:55,754 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 15/130
+[2024-10-13 16:48:55,850 INFO test.py line 186 25394] Test: 250/312-scene0583_01, Batch: 16/130
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+[2024-10-13 16:49:06,847 INFO test.py line 272 25394] Test: scene0583_01 [250/312]-98239 Batch 12.645 (19.153) Accuracy 0.7299 (0.4854) mIoU 0.4848 (0.3636)
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+[2024-10-13 16:49:20,602 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 107/117
+[2024-10-13 16:49:20,728 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 108/117
+[2024-10-13 16:49:20,854 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 109/117
+[2024-10-13 16:49:20,980 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 110/117
+[2024-10-13 16:49:21,108 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 111/117
+[2024-10-13 16:49:21,234 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 112/117
+[2024-10-13 16:49:21,360 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 113/117
+[2024-10-13 16:49:21,486 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 114/117
+[2024-10-13 16:49:21,612 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 115/117
+[2024-10-13 16:49:21,738 INFO test.py line 186 25394] Test: 251/312-scene0685_01, Batch: 116/117
+[2024-10-13 16:49:21,915 INFO test.py line 272 25394] Test: scene0685_01 [251/312]-138130 Batch 15.067 (19.137) Accuracy 0.9107 (0.4854) mIoU 0.3581 (0.3636)
+[2024-10-13 16:49:22,122 INFO test.py line 186 25394] Test: 252/312-scene0435_02, Batch: 0/139
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+[2024-10-13 16:49:46,498 INFO test.py line 272 25394] Test: scene0435_02 [252/312]-210308 Batch 24.583 (19.159) Accuracy 0.9046 (0.4844) mIoU 0.5573 (0.3636)
+[2024-10-13 16:49:46,605 INFO test.py line 186 25394] Test: 253/312-scene0558_00, Batch: 0/134
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+[2024-10-13 16:49:59,259 INFO test.py line 272 25394] Test: scene0558_00 [253/312]-92953 Batch 12.760 (19.133) Accuracy 0.9229 (0.4845) mIoU 0.7316 (0.3638)
+[2024-10-13 16:49:59,380 INFO test.py line 186 25394] Test: 254/312-scene0578_01, Batch: 0/125
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+[2024-10-13 16:50:12,774 INFO test.py line 272 25394] Test: scene0578_01 [254/312]-114840 Batch 13.515 (19.111) Accuracy 0.9490 (0.4848) mIoU 0.7443 (0.3639)
+[2024-10-13 16:50:12,895 INFO test.py line 186 25394] Test: 255/312-scene0328_00, Batch: 0/126
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+[2024-10-13 16:50:25,880 INFO test.py line 186 25394] Test: 255/312-scene0328_00, Batch: 122/126
+[2024-10-13 16:50:25,988 INFO test.py line 186 25394] Test: 255/312-scene0328_00, Batch: 123/126
+[2024-10-13 16:50:26,095 INFO test.py line 186 25394] Test: 255/312-scene0328_00, Batch: 124/126
+[2024-10-13 16:50:26,202 INFO test.py line 186 25394] Test: 255/312-scene0328_00, Batch: 125/126
+[2024-10-13 16:50:26,348 INFO test.py line 272 25394] Test: scene0328_00 [255/312]-111467 Batch 13.574 (19.089) Accuracy 0.8875 (0.4848) mIoU 0.4542 (0.3636)
+[2024-10-13 16:50:26,468 INFO test.py line 186 25394] Test: 256/312-scene0690_00, Batch: 0/158
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+[2024-10-13 16:50:42,744 INFO test.py line 186 25394] Test: 256/312-scene0690_00, Batch: 153/158
+[2024-10-13 16:50:42,850 INFO test.py line 186 25394] Test: 256/312-scene0690_00, Batch: 154/158
+[2024-10-13 16:50:42,956 INFO test.py line 186 25394] Test: 256/312-scene0690_00, Batch: 155/158
+[2024-10-13 16:50:43,062 INFO test.py line 186 25394] Test: 256/312-scene0690_00, Batch: 156/158
+[2024-10-13 16:50:43,168 INFO test.py line 186 25394] Test: 256/312-scene0690_00, Batch: 157/158
+[2024-10-13 16:50:43,309 INFO test.py line 272 25394] Test: scene0690_00 [256/312]-106050 Batch 16.961 (19.081) Accuracy 0.8149 (0.4849) mIoU 0.4601 (0.3637)
+[2024-10-13 16:50:43,404 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 0/130
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+[2024-10-13 16:50:53,495 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 122/130
+[2024-10-13 16:50:53,578 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 123/130
+[2024-10-13 16:50:53,661 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 124/130
+[2024-10-13 16:50:53,744 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 125/130
+[2024-10-13 16:50:53,827 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 126/130
+[2024-10-13 16:50:53,910 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 127/130
+[2024-10-13 16:50:53,994 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 128/130
+[2024-10-13 16:50:54,077 INFO test.py line 186 25394] Test: 257/312-scene0316_00, Batch: 129/130
+[2024-10-13 16:50:54,181 INFO test.py line 272 25394] Test: scene0316_00 [257/312]-78769 Batch 10.872 (19.049) Accuracy 0.9143 (0.4851) mIoU 0.7612 (0.3639)
+[2024-10-13 16:50:54,299 INFO test.py line 186 25394] Test: 258/312-scene0488_00, Batch: 0/159
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+[2024-10-13 16:51:10,690 INFO test.py line 272 25394] Test: scene0488_00 [258/312]-108319 Batch 16.508 (19.039) Accuracy 0.9592 (0.4853) mIoU 0.8139 (0.3644)
+[2024-10-13 16:51:10,929 INFO test.py line 186 25394] Test: 259/312-scene0621_00, Batch: 0/135
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+[2024-10-13 16:51:38,158 INFO test.py line 272 25394] Test: scene0621_00 [259/312]-250821 Batch 27.468 (19.072) Accuracy 0.9741 (0.4854) mIoU 0.8003 (0.3645)
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+[2024-10-13 16:51:55,104 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 113/134
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+[2024-10-13 16:51:55,577 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 116/134
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+[2024-10-13 16:51:55,891 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 118/134
+[2024-10-13 16:51:56,048 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 119/134
+[2024-10-13 16:51:56,206 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 120/134
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+[2024-10-13 16:51:56,520 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 122/134
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+[2024-10-13 16:51:56,827 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 124/134
+[2024-10-13 16:51:56,976 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 125/134
+[2024-10-13 16:51:57,126 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 126/134
+[2024-10-13 16:51:57,275 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 127/134
+[2024-10-13 16:51:57,425 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 128/134
+[2024-10-13 16:51:57,574 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 129/134
+[2024-10-13 16:51:57,724 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 130/134
+[2024-10-13 16:51:57,873 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 131/134
+[2024-10-13 16:51:58,023 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 132/134
+[2024-10-13 16:51:58,172 INFO test.py line 186 25394] Test: 260/312-scene0131_01, Batch: 133/134
+[2024-10-13 16:51:58,392 INFO test.py line 272 25394] Test: scene0131_01 [260/312]-172221 Batch 20.234 (19.076) Accuracy 0.8666 (0.4847) mIoU 0.2941 (0.3645)
+[2024-10-13 16:51:58,484 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 0/143
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+[2024-10-13 16:51:59,200 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 9/143
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+[2024-10-13 16:51:59,517 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 13/143
+[2024-10-13 16:51:59,596 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 14/143
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+[2024-10-13 16:52:08,615 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 127/143
+[2024-10-13 16:52:08,699 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 128/143
+[2024-10-13 16:52:08,783 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 129/143
+[2024-10-13 16:52:08,866 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 130/143
+[2024-10-13 16:52:08,950 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 131/143
+[2024-10-13 16:52:09,029 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 132/143
+[2024-10-13 16:52:09,108 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 133/143
+[2024-10-13 16:52:09,187 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 134/143
+[2024-10-13 16:52:09,265 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 135/143
+[2024-10-13 16:52:09,344 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 136/143
+[2024-10-13 16:52:09,423 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 137/143
+[2024-10-13 16:52:09,502 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 138/143
+[2024-10-13 16:52:09,581 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 139/143
+[2024-10-13 16:52:09,660 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 140/143
+[2024-10-13 16:52:09,738 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 141/143
+[2024-10-13 16:52:09,817 INFO test.py line 186 25394] Test: 261/312-scene0701_00, Batch: 142/143
+[2024-10-13 16:52:09,914 INFO test.py line 272 25394] Test: scene0701_00 [261/312]-73550 Batch 11.522 (19.047) Accuracy 0.9064 (0.4847) mIoU 0.4162 (0.3644)
+[2024-10-13 16:52:10,161 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 0/156
+[2024-10-13 16:52:10,370 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 1/156
+[2024-10-13 16:52:10,578 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 2/156
+[2024-10-13 16:52:10,787 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 3/156
+[2024-10-13 16:52:10,996 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 4/156
+[2024-10-13 16:52:11,205 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 5/156
+[2024-10-13 16:52:11,413 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 6/156
+[2024-10-13 16:52:11,622 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 7/156
+[2024-10-13 16:52:11,830 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 8/156
+[2024-10-13 16:52:12,038 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 9/156
+[2024-10-13 16:52:12,247 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 10/156
+[2024-10-13 16:52:12,460 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 11/156
+[2024-10-13 16:52:12,703 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 12/156
+[2024-10-13 16:52:12,912 INFO test.py line 186 25394] Test: 262/312-scene0629_00, Batch: 13/156
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+[2024-10-13 16:52:42,621 INFO test.py line 272 25394] Test: scene0629_00 [262/312]-255083 Batch 32.706 (19.099) Accuracy 0.8175 (0.4856) mIoU 0.3494 (0.3651)
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+[2024-10-13 16:53:05,995 INFO test.py line 272 25394] Test: scene0593_00 [263/312]-211902 Batch 23.373 (19.116) Accuracy 0.8441 (0.4866) mIoU 0.5257 (0.3661)
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+[2024-10-13 16:53:18,469 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 117/125
+[2024-10-13 16:53:18,575 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 118/125
+[2024-10-13 16:53:18,681 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 119/125
+[2024-10-13 16:53:18,786 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 120/125
+[2024-10-13 16:53:18,892 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 121/125
+[2024-10-13 16:53:18,997 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 122/125
+[2024-10-13 16:53:19,103 INFO test.py line 186 25394] Test: 264/312-scene0609_01, Batch: 123/125
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+[2024-10-13 16:53:19,368 INFO test.py line 272 25394] Test: scene0609_01 [264/312]-115550 Batch 13.372 (19.094) Accuracy 0.8392 (0.4866) mIoU 0.4840 (0.3661)
+[2024-10-13 16:53:19,558 INFO test.py line 186 25394] Test: 265/312-scene0608_00, Batch: 0/121
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+[2024-10-13 16:53:39,140 INFO test.py line 272 25394] Test: scene0608_00 [265/312]-186579 Batch 19.773 (19.097) Accuracy 0.7885 (0.4860) mIoU 0.3707 (0.3659)
+[2024-10-13 16:53:39,242 INFO test.py line 186 25394] Test: 266/312-scene0164_01, Batch: 0/130
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+[2024-10-13 16:53:50,915 INFO test.py line 272 25394] Test: scene0164_01 [266/312]-88073 Batch 11.774 (19.069) Accuracy 0.8941 (0.4861) mIoU 0.5871 (0.3673)
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+[2024-10-13 16:54:11,186 INFO test.py line 186 25394] Test: 267/312-scene0435_03, Batch: 118/125
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+[2024-10-13 16:54:11,691 INFO test.py line 186 25394] Test: 267/312-scene0435_03, Batch: 121/125
+[2024-10-13 16:54:11,859 INFO test.py line 186 25394] Test: 267/312-scene0435_03, Batch: 122/125
+[2024-10-13 16:54:12,027 INFO test.py line 186 25394] Test: 267/312-scene0435_03, Batch: 123/125
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+[2024-10-13 16:54:12,455 INFO test.py line 272 25394] Test: scene0435_03 [267/312]-203722 Batch 21.540 (19.078) Accuracy 0.9310 (0.4861) mIoU 0.5562 (0.3676)
+[2024-10-13 16:54:12,588 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 0/151
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+[2024-10-13 16:54:27,751 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 130/151
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+[2024-10-13 16:54:28,123 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 133/151
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+[2024-10-13 16:54:28,495 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 136/151
+[2024-10-13 16:54:28,619 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 137/151
+[2024-10-13 16:54:28,744 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 138/151
+[2024-10-13 16:54:28,868 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 139/151
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+[2024-10-13 16:54:29,102 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 141/151
+[2024-10-13 16:54:29,218 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 142/151
+[2024-10-13 16:54:29,335 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 143/151
+[2024-10-13 16:54:29,452 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 144/151
+[2024-10-13 16:54:29,568 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 145/151
+[2024-10-13 16:54:29,685 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 146/151
+[2024-10-13 16:54:29,802 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 147/151
+[2024-10-13 16:54:29,919 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 148/151
+[2024-10-13 16:54:30,036 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 149/151
+[2024-10-13 16:54:30,152 INFO test.py line 186 25394] Test: 268/312-scene0490_00, Batch: 150/151
+[2024-10-13 16:54:30,317 INFO test.py line 272 25394] Test: scene0490_00 [268/312]-131283 Batch 17.862 (19.074) Accuracy 0.8232 (0.4861) mIoU 0.5145 (0.3676)
+[2024-10-13 16:54:30,435 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 0/113
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+[2024-10-13 16:54:31,989 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 15/113
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+[2024-10-13 16:54:32,539 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 20/113
+[2024-10-13 16:54:32,643 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 21/113
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+[2024-10-13 16:54:40,532 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 98/113
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+[2024-10-13 16:54:41,186 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 104/113
+[2024-10-13 16:54:41,289 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 105/113
+[2024-10-13 16:54:41,393 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 106/113
+[2024-10-13 16:54:41,496 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 107/113
+[2024-10-13 16:54:41,600 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 108/113
+[2024-10-13 16:54:41,703 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 109/113
+[2024-10-13 16:54:41,807 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 110/113
+[2024-10-13 16:54:41,910 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 111/113
+[2024-10-13 16:54:42,014 INFO test.py line 186 25394] Test: 269/312-scene0338_00, Batch: 112/113
+[2024-10-13 16:54:42,153 INFO test.py line 272 25394] Test: scene0338_00 [269/312]-106339 Batch 11.836 (19.047) Accuracy 0.7710 (0.4860) mIoU 0.4783 (0.3675)
+[2024-10-13 16:54:42,224 INFO test.py line 186 25394] Test: 270/312-scene0153_01, Batch: 0/117
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+[2024-10-13 16:54:49,267 INFO test.py line 186 25394] Test: 270/312-scene0153_01, Batch: 113/117
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+[2024-10-13 16:54:49,518 INFO test.py line 272 25394] Test: scene0153_01 [270/312]-49553 Batch 7.365 (19.004) Accuracy 0.7663 (0.4862) mIoU 0.4891 (0.3693)
+[2024-10-13 16:54:49,623 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 0/125
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+[2024-10-13 16:55:00,655 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 118/125
+[2024-10-13 16:55:00,747 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 119/125
+[2024-10-13 16:55:00,840 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 120/125
+[2024-10-13 16:55:00,932 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 121/125
+[2024-10-13 16:55:01,024 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 122/125
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+[2024-10-13 16:55:01,209 INFO test.py line 186 25394] Test: 271/312-scene0583_02, Batch: 124/125
+[2024-10-13 16:55:01,329 INFO test.py line 272 25394] Test: scene0583_02 [271/312]-89810 Batch 11.811 (18.977) Accuracy 0.7991 (0.4859) mIoU 0.4374 (0.3690)
+[2024-10-13 16:55:01,427 INFO test.py line 186 25394] Test: 272/312-scene0651_01, Batch: 0/122
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+[2024-10-13 16:55:11,080 INFO test.py line 186 25394] Test: 272/312-scene0651_01, Batch: 116/122
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+[2024-10-13 16:55:11,248 INFO test.py line 186 25394] Test: 272/312-scene0651_01, Batch: 118/122
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+[2024-10-13 16:55:11,417 INFO test.py line 186 25394] Test: 272/312-scene0651_01, Batch: 120/122
+[2024-10-13 16:55:11,501 INFO test.py line 186 25394] Test: 272/312-scene0651_01, Batch: 121/122
+[2024-10-13 16:55:11,605 INFO test.py line 272 25394] Test: scene0651_01 [272/312]-79213 Batch 10.276 (18.945) Accuracy 0.9339 (0.4862) mIoU 0.7271 (0.3693)
+[2024-10-13 16:55:11,769 INFO test.py line 186 25394] Test: 273/312-scene0580_00, Batch: 0/126
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+[2024-10-13 16:55:28,563 INFO test.py line 186 25394] Test: 273/312-scene0580_00, Batch: 122/126
+[2024-10-13 16:55:28,700 INFO test.py line 186 25394] Test: 273/312-scene0580_00, Batch: 123/126
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+[2024-10-13 16:55:29,178 INFO test.py line 272 25394] Test: scene0580_00 [273/312]-158047 Batch 17.573 (18.940) Accuracy 0.8690 (0.4862) mIoU 0.3673 (0.3691)
+[2024-10-13 16:55:29,404 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 0/126
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+[2024-10-13 16:55:47,814 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 98/126
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+[2024-10-13 16:55:49,633 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 107/126
+[2024-10-13 16:55:49,836 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 108/126
+[2024-10-13 16:55:50,038 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 109/126
+[2024-10-13 16:55:50,240 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 110/126
+[2024-10-13 16:55:50,442 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 111/126
+[2024-10-13 16:55:50,644 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 112/126
+[2024-10-13 16:55:50,846 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 113/126
+[2024-10-13 16:55:51,049 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 114/126
+[2024-10-13 16:55:51,252 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 115/126
+[2024-10-13 16:55:51,443 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 116/126
+[2024-10-13 16:55:51,633 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 117/126
+[2024-10-13 16:55:51,823 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 118/126
+[2024-10-13 16:55:52,014 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 119/126
+[2024-10-13 16:55:52,205 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 120/126
+[2024-10-13 16:55:52,395 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 121/126
+[2024-10-13 16:55:52,586 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 122/126
+[2024-10-13 16:55:52,776 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 123/126
+[2024-10-13 16:55:52,967 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 124/126
+[2024-10-13 16:55:53,157 INFO test.py line 186 25394] Test: 274/312-scene0222_01, Batch: 125/126
+[2024-10-13 16:55:53,453 INFO test.py line 272 25394] Test: scene0222_01 [274/312]-235777 Batch 24.275 (18.959) Accuracy 0.8497 (0.4865) mIoU 0.3335 (0.3693)
+[2024-10-13 16:55:53,528 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 0/104
+[2024-10-13 16:55:53,593 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 1/104
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+[2024-10-13 16:55:53,854 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 5/104
+[2024-10-13 16:55:53,919 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 6/104
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+[2024-10-13 16:55:54,049 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 8/104
+[2024-10-13 16:55:54,115 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 9/104
+[2024-10-13 16:55:54,180 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 10/104
+[2024-10-13 16:55:54,245 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 11/104
+[2024-10-13 16:55:54,310 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 12/104
+[2024-10-13 16:55:54,375 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 13/104
+[2024-10-13 16:55:54,440 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 14/104
+[2024-10-13 16:55:54,506 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 15/104
+[2024-10-13 16:55:54,570 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 16/104
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+[2024-10-13 16:55:54,829 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 20/104
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+[2024-10-13 16:55:59,519 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 92/104
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+[2024-10-13 16:55:59,653 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 94/104
+[2024-10-13 16:55:59,720 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 95/104
+[2024-10-13 16:55:59,785 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 96/104
+[2024-10-13 16:55:59,850 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 97/104
+[2024-10-13 16:55:59,915 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 98/104
+[2024-10-13 16:55:59,979 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 99/104
+[2024-10-13 16:56:00,044 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 100/104
+[2024-10-13 16:56:00,109 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 101/104
+[2024-10-13 16:56:00,174 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 102/104
+[2024-10-13 16:56:00,238 INFO test.py line 186 25394] Test: 275/312-scene0100_01, Batch: 103/104
+[2024-10-13 16:56:00,309 INFO test.py line 272 25394] Test: scene0100_01 [275/312]-51211 Batch 6.855 (18.915) Accuracy 0.7892 (0.4876) mIoU 0.5433 (0.3706)
+[2024-10-13 16:56:00,484 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 0/118
+[2024-10-13 16:56:00,633 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 1/118
+[2024-10-13 16:56:00,782 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 2/118
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+[2024-10-13 16:56:05,137 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 31/118
+[2024-10-13 16:56:05,286 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 32/118
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+[2024-10-13 16:56:06,892 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 43/118
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+[2024-10-13 16:56:17,192 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 112/118
+[2024-10-13 16:56:17,343 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 113/118
+[2024-10-13 16:56:17,493 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 114/118
+[2024-10-13 16:56:17,644 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 115/118
+[2024-10-13 16:56:17,795 INFO test.py line 186 25394] Test: 276/312-scene0221_01, Batch: 116/118
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+[2024-10-13 16:56:18,171 INFO test.py line 272 25394] Test: scene0221_01 [276/312]-170654 Batch 17.862 (18.912) Accuracy 0.8485 (0.4881) mIoU 0.3458 (0.3710)
+[2024-10-13 16:56:18,359 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 0/151
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+[2024-10-13 16:56:35,882 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 113/151
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+[2024-10-13 16:56:36,386 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 116/151
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+[2024-10-13 16:56:36,889 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 119/151
+[2024-10-13 16:56:37,057 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 120/151
+[2024-10-13 16:56:37,225 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 121/151
+[2024-10-13 16:56:37,393 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 122/151
+[2024-10-13 16:56:37,561 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 123/151
+[2024-10-13 16:56:37,729 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 124/151
+[2024-10-13 16:56:37,898 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 125/151
+[2024-10-13 16:56:38,066 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 126/151
+[2024-10-13 16:56:38,234 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 127/151
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+[2024-10-13 16:56:38,738 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 130/151
+[2024-10-13 16:56:38,906 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 131/151
+[2024-10-13 16:56:39,074 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 132/151
+[2024-10-13 16:56:39,243 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 133/151
+[2024-10-13 16:56:39,411 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 134/151
+[2024-10-13 16:56:39,580 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 135/151
+[2024-10-13 16:56:39,748 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 136/151
+[2024-10-13 16:56:39,916 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 137/151
+[2024-10-13 16:56:40,084 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 138/151
+[2024-10-13 16:56:40,252 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 139/151
+[2024-10-13 16:56:40,411 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 140/151
+[2024-10-13 16:56:40,569 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 141/151
+[2024-10-13 16:56:40,728 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 142/151
+[2024-10-13 16:56:40,887 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 143/151
+[2024-10-13 16:56:41,045 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 144/151
+[2024-10-13 16:56:41,204 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 145/151
+[2024-10-13 16:56:41,363 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 146/151
+[2024-10-13 16:56:41,522 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 147/151
+[2024-10-13 16:56:41,681 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 148/151
+[2024-10-13 16:56:41,840 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 149/151
+[2024-10-13 16:56:41,999 INFO test.py line 186 25394] Test: 277/312-scene0591_02, Batch: 150/151
+[2024-10-13 16:56:42,237 INFO test.py line 272 25394] Test: scene0591_02 [277/312]-187656 Batch 24.065 (18.930) Accuracy 0.9436 (0.4882) mIoU 0.5365 (0.3712)
+[2024-10-13 16:56:42,513 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 0/138
+[2024-10-13 16:56:42,741 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 1/138
+[2024-10-13 16:56:42,970 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 2/138
+[2024-10-13 16:56:43,198 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 3/138
+[2024-10-13 16:56:43,427 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 4/138
+[2024-10-13 16:56:43,656 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 5/138
+[2024-10-13 16:56:43,884 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 6/138
+[2024-10-13 16:56:44,112 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 7/138
+[2024-10-13 16:56:44,340 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 8/138
+[2024-10-13 16:56:44,568 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 9/138
+[2024-10-13 16:56:44,797 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 10/138
+[2024-10-13 16:56:45,025 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 11/138
+[2024-10-13 16:56:45,253 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 12/138
+[2024-10-13 16:56:45,481 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 13/138
+[2024-10-13 16:56:45,710 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 14/138
+[2024-10-13 16:56:45,938 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 15/138
+[2024-10-13 16:56:46,166 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 16/138
+[2024-10-13 16:56:46,394 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 17/138
+[2024-10-13 16:56:46,622 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 18/138
+[2024-10-13 16:56:46,850 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 19/138
+[2024-10-13 16:56:47,078 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 20/138
+[2024-10-13 16:56:47,307 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 21/138
+[2024-10-13 16:56:47,535 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 22/138
+[2024-10-13 16:56:47,763 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 23/138
+[2024-10-13 16:56:47,991 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 24/138
+[2024-10-13 16:56:48,219 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 25/138
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+[2024-10-13 16:56:48,905 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 28/138
+[2024-10-13 16:56:49,144 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 29/138
+[2024-10-13 16:56:49,376 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 30/138
+[2024-10-13 16:56:49,605 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 31/138
+[2024-10-13 16:56:49,834 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 32/138
+[2024-10-13 16:56:50,062 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 33/138
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+[2024-10-13 16:56:53,132 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 47/138
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+[2024-10-13 16:56:55,257 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 57/138
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+[2024-10-13 16:57:00,773 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 83/138
+[2024-10-13 16:57:01,021 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 84/138
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+[2024-10-13 16:57:02,742 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 91/138
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+[2024-10-13 16:57:03,233 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 93/138
+[2024-10-13 16:57:03,479 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 94/138
+[2024-10-13 16:57:03,725 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 95/138
+[2024-10-13 16:57:03,970 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 96/138
+[2024-10-13 16:57:04,215 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 97/138
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+[2024-10-13 16:57:04,952 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 100/138
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+[2024-10-13 16:57:07,407 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 110/138
+[2024-10-13 16:57:07,653 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 111/138
+[2024-10-13 16:57:07,899 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 112/138
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+[2024-10-13 16:57:09,375 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 118/138
+[2024-10-13 16:57:09,621 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 119/138
+[2024-10-13 16:57:09,866 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 120/138
+[2024-10-13 16:57:10,112 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 121/138
+[2024-10-13 16:57:10,358 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 122/138
+[2024-10-13 16:57:10,604 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 123/138
+[2024-10-13 16:57:10,850 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 124/138
+[2024-10-13 16:57:11,096 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 125/138
+[2024-10-13 16:57:11,341 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 126/138
+[2024-10-13 16:57:11,588 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 127/138
+[2024-10-13 16:57:11,815 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 128/138
+[2024-10-13 16:57:12,043 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 129/138
+[2024-10-13 16:57:12,271 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 130/138
+[2024-10-13 16:57:12,499 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 131/138
+[2024-10-13 16:57:12,727 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 132/138
+[2024-10-13 16:57:12,955 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 133/138
+[2024-10-13 16:57:13,183 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 134/138
+[2024-10-13 16:57:13,410 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 135/138
+[2024-10-13 16:57:13,638 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 136/138
+[2024-10-13 16:57:13,866 INFO test.py line 186 25394] Test: 278/312-scene0616_00, Batch: 137/138
+[2024-10-13 16:57:14,255 INFO test.py line 272 25394] Test: scene0616_00 [278/312]-310248 Batch 32.018 (18.977) Accuracy 0.8449 (0.4883) mIoU 0.5394 (0.3712)
+[2024-10-13 16:57:14,386 INFO test.py line 186 25394] Test: 279/312-scene0651_02, Batch: 0/121
+[2024-10-13 16:57:14,501 INFO test.py line 186 25394] Test: 279/312-scene0651_02, Batch: 1/121
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+[2024-10-13 16:57:28,370 INFO test.py line 272 25394] Test: scene0651_02 [279/312]-124048 Batch 14.114 (18.960) Accuracy 0.9404 (0.4885) mIoU 0.6503 (0.3715)
+[2024-10-13 16:57:28,666 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 0/126
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+[2024-10-13 16:57:55,235 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 107/126
+[2024-10-13 16:57:55,503 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 108/126
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+[2024-10-13 16:57:56,036 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 110/126
+[2024-10-13 16:57:56,304 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 111/126
+[2024-10-13 16:57:56,571 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 112/126
+[2024-10-13 16:57:56,838 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 113/126
+[2024-10-13 16:57:57,105 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 114/126
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+[2024-10-13 16:57:57,622 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 116/126
+[2024-10-13 16:57:57,874 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 117/126
+[2024-10-13 16:57:58,125 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 118/126
+[2024-10-13 16:57:58,374 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 119/126
+[2024-10-13 16:57:58,625 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 120/126
+[2024-10-13 16:57:58,875 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 121/126
+[2024-10-13 16:57:59,126 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 122/126
+[2024-10-13 16:57:59,377 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 123/126
+[2024-10-13 16:57:59,627 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 124/126
+[2024-10-13 16:57:59,877 INFO test.py line 186 25394] Test: 280/312-scene0207_00, Batch: 125/126
+[2024-10-13 16:58:00,267 INFO test.py line 272 25394] Test: scene0207_00 [280/312]-304396 Batch 31.897 (19.006) Accuracy 0.8005 (0.4884) mIoU 0.4335 (0.3706)
+[2024-10-13 16:58:00,437 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 0/131
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+[2024-10-13 16:58:17,285 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 117/131
+[2024-10-13 16:58:17,438 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 118/131
+[2024-10-13 16:58:17,591 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 119/131
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+[2024-10-13 16:58:18,026 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 122/131
+[2024-10-13 16:58:18,171 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 123/131
+[2024-10-13 16:58:18,316 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 124/131
+[2024-10-13 16:58:18,461 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 125/131
+[2024-10-13 16:58:18,606 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 126/131
+[2024-10-13 16:58:18,751 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 127/131
+[2024-10-13 16:58:18,896 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 128/131
+[2024-10-13 16:58:19,041 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 129/131
+[2024-10-13 16:58:19,186 INFO test.py line 186 25394] Test: 281/312-scene0378_02, Batch: 130/131
+[2024-10-13 16:58:19,400 INFO test.py line 272 25394] Test: scene0378_02 [281/312]-161593 Batch 19.133 (19.007) Accuracy 0.8946 (0.4886) mIoU 0.4775 (0.3708)
+[2024-10-13 16:58:19,501 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 0/113
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+[2024-10-13 16:58:28,807 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 107/113
+[2024-10-13 16:58:28,894 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 108/113
+[2024-10-13 16:58:28,980 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 109/113
+[2024-10-13 16:58:29,066 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 110/113
+[2024-10-13 16:58:29,152 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 111/113
+[2024-10-13 16:58:29,239 INFO test.py line 186 25394] Test: 282/312-scene0277_00, Batch: 112/113
+[2024-10-13 16:58:29,345 INFO test.py line 272 25394] Test: scene0277_00 [282/312]-79184 Batch 9.945 (18.974) Accuracy 0.9040 (0.4886) mIoU 0.6596 (0.3709)
+[2024-10-13 16:58:29,502 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 0/113
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+[2024-10-13 16:58:43,582 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 107/113
+[2024-10-13 16:58:43,714 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 108/113
+[2024-10-13 16:58:43,847 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 109/113
+[2024-10-13 16:58:43,979 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 110/113
+[2024-10-13 16:58:44,111 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 111/113
+[2024-10-13 16:58:44,243 INFO test.py line 186 25394] Test: 283/312-scene0196_00, Batch: 112/113
+[2024-10-13 16:58:44,427 INFO test.py line 272 25394] Test: scene0196_00 [283/312]-146032 Batch 15.081 (18.961) Accuracy 0.9037 (0.4886) mIoU 0.5745 (0.3709)
+[2024-10-13 16:58:44,599 INFO test.py line 186 25394] Test: 284/312-scene0351_01, Batch: 0/121
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+[2024-10-13 16:59:02,350 INFO test.py line 272 25394] Test: scene0351_01 [284/312]-170840 Batch 17.923 (18.957) Accuracy 0.8950 (0.4886) mIoU 0.4536 (0.3708)
+[2024-10-13 16:59:02,456 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 0/151
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+[2024-10-13 16:59:15,649 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 142/151
+[2024-10-13 16:59:15,741 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 143/151
+[2024-10-13 16:59:15,833 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 144/151
+[2024-10-13 16:59:15,925 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 145/151
+[2024-10-13 16:59:16,018 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 146/151
+[2024-10-13 16:59:16,110 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 147/151
+[2024-10-13 16:59:16,202 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 148/151
+[2024-10-13 16:59:16,294 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 149/151
+[2024-10-13 16:59:16,386 INFO test.py line 186 25394] Test: 285/312-scene0164_02, Batch: 150/151
+[2024-10-13 16:59:16,531 INFO test.py line 272 25394] Test: scene0164_02 [285/312]-92825 Batch 14.180 (18.940) Accuracy 0.7886 (0.4872) mIoU 0.5289 (0.3707)
+[2024-10-13 16:59:16,748 INFO test.py line 186 25394] Test: 286/312-scene0169_01, Batch: 0/151
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+[2024-10-13 16:59:44,419 INFO test.py line 272 25394] Test: scene0169_01 [286/312]-228074 Batch 27.888 (18.972) Accuracy 0.8585 (0.4872) mIoU 0.6508 (0.3708)
+[2024-10-13 16:59:44,609 INFO test.py line 186 25394] Test: 287/312-scene0046_00, Batch: 0/138
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+[2024-10-13 17:00:06,375 INFO test.py line 186 25394] Test: 287/312-scene0046_00, Batch: 134/138
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+[2024-10-13 17:00:07,098 INFO test.py line 272 25394] Test: scene0046_00 [287/312]-186857 Batch 22.679 (18.984) Accuracy 0.8346 (0.4875) mIoU 0.4583 (0.3706)
+[2024-10-13 17:00:07,204 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 0/130
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+[2024-10-13 17:00:16,744 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 104/130
+[2024-10-13 17:00:16,842 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 105/130
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+[2024-10-13 17:00:17,082 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 107/130
+[2024-10-13 17:00:17,180 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 108/130
+[2024-10-13 17:00:17,279 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 109/130
+[2024-10-13 17:00:17,378 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 110/130
+[2024-10-13 17:00:17,477 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 111/130
+[2024-10-13 17:00:17,575 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 112/130
+[2024-10-13 17:00:17,674 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 113/130
+[2024-10-13 17:00:17,772 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 114/130
+[2024-10-13 17:00:17,869 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 115/130
+[2024-10-13 17:00:17,967 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 116/130
+[2024-10-13 17:00:18,065 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 117/130
+[2024-10-13 17:00:18,163 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 118/130
+[2024-10-13 17:00:18,261 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 119/130
+[2024-10-13 17:00:18,354 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 120/130
+[2024-10-13 17:00:18,446 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 121/130
+[2024-10-13 17:00:18,539 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 122/130
+[2024-10-13 17:00:18,632 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 123/130
+[2024-10-13 17:00:18,725 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 124/130
+[2024-10-13 17:00:18,817 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 125/130
+[2024-10-13 17:00:18,910 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 126/130
+[2024-10-13 17:00:19,003 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 127/130
+[2024-10-13 17:00:19,095 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 128/130
+[2024-10-13 17:00:19,188 INFO test.py line 186 25394] Test: 288/312-scene0164_00, Batch: 129/130
+[2024-10-13 17:00:19,312 INFO test.py line 272 25394] Test: scene0164_00 [288/312]-94315 Batch 12.214 (18.961) Accuracy 0.8757 (0.4883) mIoU 0.5869 (0.3718)
+[2024-10-13 17:00:19,532 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 0/143
+[2024-10-13 17:00:19,718 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 1/143
+[2024-10-13 17:00:19,903 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 2/143
+[2024-10-13 17:00:20,089 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 3/143
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+[2024-10-13 17:00:20,460 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 5/143
+[2024-10-13 17:00:20,646 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 6/143
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+[2024-10-13 17:00:21,017 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 8/143
+[2024-10-13 17:00:21,203 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 9/143
+[2024-10-13 17:00:21,389 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 10/143
+[2024-10-13 17:00:21,575 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 11/143
+[2024-10-13 17:00:21,761 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 12/143
+[2024-10-13 17:00:21,948 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 13/143
+[2024-10-13 17:00:22,134 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 14/143
+[2024-10-13 17:00:22,320 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 15/143
+[2024-10-13 17:00:22,506 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 16/143
+[2024-10-13 17:00:22,693 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 17/143
+[2024-10-13 17:00:22,878 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 18/143
+[2024-10-13 17:00:23,064 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 19/143
+[2024-10-13 17:00:23,250 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 20/143
+[2024-10-13 17:00:23,436 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 21/143
+[2024-10-13 17:00:23,622 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 22/143
+[2024-10-13 17:00:23,807 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 23/143
+[2024-10-13 17:00:23,992 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 24/143
+[2024-10-13 17:00:24,177 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 25/143
+[2024-10-13 17:00:24,363 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 26/143
+[2024-10-13 17:00:24,548 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 27/143
+[2024-10-13 17:00:24,734 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 28/143
+[2024-10-13 17:00:24,920 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 29/143
+[2024-10-13 17:00:25,105 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 30/143
+[2024-10-13 17:00:25,290 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 31/143
+[2024-10-13 17:00:25,480 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 32/143
+[2024-10-13 17:00:25,676 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 33/143
+[2024-10-13 17:00:25,863 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 34/143
+[2024-10-13 17:00:26,049 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 35/143
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+[2024-10-13 17:00:26,792 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 39/143
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+[2024-10-13 17:00:27,163 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 41/143
+[2024-10-13 17:00:27,348 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 42/143
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+[2024-10-13 17:00:27,879 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 45/143
+[2024-10-13 17:00:28,051 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 46/143
+[2024-10-13 17:00:28,224 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 47/143
+[2024-10-13 17:00:28,396 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 48/143
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+[2024-10-13 17:00:28,741 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 50/143
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+[2024-10-13 17:00:30,470 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 60/143
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+[2024-10-13 17:00:30,815 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 62/143
+[2024-10-13 17:00:30,988 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 63/143
+[2024-10-13 17:00:31,161 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 64/143
+[2024-10-13 17:00:31,334 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 65/143
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+[2024-10-13 17:00:31,680 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 67/143
+[2024-10-13 17:00:31,853 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 68/143
+[2024-10-13 17:00:32,027 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 69/143
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+[2024-10-13 17:00:34,103 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 81/143
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+[2024-10-13 17:00:34,969 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 86/143
+[2024-10-13 17:00:35,141 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 87/143
+[2024-10-13 17:00:35,338 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 88/143
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+[2024-10-13 17:00:36,126 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 92/143
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+[2024-10-13 17:00:36,521 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 94/143
+[2024-10-13 17:00:36,718 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 95/143
+[2024-10-13 17:00:36,915 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 96/143
+[2024-10-13 17:00:37,112 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 97/143
+[2024-10-13 17:00:37,310 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 98/143
+[2024-10-13 17:00:37,507 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 99/143
+[2024-10-13 17:00:37,705 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 100/143
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+[2024-10-13 17:00:38,100 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 102/143
+[2024-10-13 17:00:38,298 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 103/143
+[2024-10-13 17:00:38,496 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 104/143
+[2024-10-13 17:00:38,693 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 105/143
+[2024-10-13 17:00:38,891 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 106/143
+[2024-10-13 17:00:39,089 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 107/143
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+[2024-10-13 17:00:39,683 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 110/143
+[2024-10-13 17:00:39,880 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 111/143
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+[2024-10-13 17:00:40,871 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 116/143
+[2024-10-13 17:00:41,069 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 117/143
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+[2024-10-13 17:00:42,058 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 122/143
+[2024-10-13 17:00:42,256 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 123/143
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+[2024-10-13 17:00:42,651 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 125/143
+[2024-10-13 17:00:42,848 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 126/143
+[2024-10-13 17:00:43,046 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 127/143
+[2024-10-13 17:00:43,243 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 128/143
+[2024-10-13 17:00:43,442 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 129/143
+[2024-10-13 17:00:43,639 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 130/143
+[2024-10-13 17:00:43,837 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 131/143
+[2024-10-13 17:00:44,023 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 132/143
+[2024-10-13 17:00:44,208 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 133/143
+[2024-10-13 17:00:44,394 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 134/143
+[2024-10-13 17:00:44,579 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 135/143
+[2024-10-13 17:00:44,764 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 136/143
+[2024-10-13 17:00:44,950 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 137/143
+[2024-10-13 17:00:45,135 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 138/143
+[2024-10-13 17:00:45,321 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 139/143
+[2024-10-13 17:00:45,506 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 140/143
+[2024-10-13 17:00:45,691 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 141/143
+[2024-10-13 17:00:45,876 INFO test.py line 186 25394] Test: 289/312-scene0629_02, Batch: 142/143
+[2024-10-13 17:00:46,165 INFO test.py line 272 25394] Test: scene0629_02 [289/312]-225983 Batch 26.853 (18.988) Accuracy 0.8341 (0.4886) mIoU 0.3648 (0.3722)
+[2024-10-13 17:00:46,514 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 0/148
+[2024-10-13 17:00:46,804 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 1/148
+[2024-10-13 17:00:47,095 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 2/148
+[2024-10-13 17:00:47,386 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 3/148
+[2024-10-13 17:00:47,677 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 4/148
+[2024-10-13 17:00:47,969 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 5/148
+[2024-10-13 17:00:48,260 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 6/148
+[2024-10-13 17:00:48,551 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 7/148
+[2024-10-13 17:00:48,842 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 8/148
+[2024-10-13 17:00:49,134 INFO test.py line 186 25394] Test: 290/312-scene0307_00, Batch: 9/148
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+[2024-10-13 17:01:29,648 INFO test.py line 272 25394] Test: scene0307_00 [290/312]-371616 Batch 43.483 (19.073) Accuracy 0.8476 (0.4887) mIoU 0.2258 (0.3722)
+[2024-10-13 17:01:29,900 INFO test.py line 186 25394] Test: 291/312-scene0663_01, Batch: 0/130
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+[2024-10-13 17:01:57,454 INFO test.py line 272 25394] Test: scene0663_01 [291/312]-258402 Batch 27.806 (19.103) Accuracy 0.8963 (0.4889) mIoU 0.4543 (0.3722)
+[2024-10-13 17:01:57,587 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 0/130
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+[2024-10-13 17:02:08,763 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 97/130
+[2024-10-13 17:02:08,887 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 98/130
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+[2024-10-13 17:02:09,510 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 103/130
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+[2024-10-13 17:02:10,258 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 109/130
+[2024-10-13 17:02:10,383 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 110/130
+[2024-10-13 17:02:10,507 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 111/130
+[2024-10-13 17:02:10,631 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 112/130
+[2024-10-13 17:02:10,755 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 113/130
+[2024-10-13 17:02:10,879 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 114/130
+[2024-10-13 17:02:11,004 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 115/130
+[2024-10-13 17:02:11,127 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 116/130
+[2024-10-13 17:02:11,252 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 117/130
+[2024-10-13 17:02:11,376 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 118/130
+[2024-10-13 17:02:11,500 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 119/130
+[2024-10-13 17:02:11,616 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 120/130
+[2024-10-13 17:02:11,734 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 121/130
+[2024-10-13 17:02:11,851 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 122/130
+[2024-10-13 17:02:11,968 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 123/130
+[2024-10-13 17:02:12,085 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 124/130
+[2024-10-13 17:02:12,202 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 125/130
+[2024-10-13 17:02:12,319 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 126/130
+[2024-10-13 17:02:12,436 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 127/130
+[2024-10-13 17:02:12,553 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 128/130
+[2024-10-13 17:02:12,670 INFO test.py line 186 25394] Test: 292/312-scene0426_02, Batch: 129/130
+[2024-10-13 17:02:12,831 INFO test.py line 272 25394] Test: scene0426_02 [292/312]-125149 Batch 15.377 (19.090) Accuracy 0.8193 (0.4888) mIoU 0.4080 (0.3720)
+[2024-10-13 17:02:12,910 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 0/113
+[2024-10-13 17:02:12,978 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 1/113
+[2024-10-13 17:02:13,046 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 2/113
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+[2024-10-13 17:02:13,319 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 6/113
+[2024-10-13 17:02:13,387 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 7/113
+[2024-10-13 17:02:13,455 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 8/113
+[2024-10-13 17:02:13,523 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 9/113
+[2024-10-13 17:02:13,591 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 10/113
+[2024-10-13 17:02:13,660 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 11/113
+[2024-10-13 17:02:13,728 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 12/113
+[2024-10-13 17:02:13,796 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 13/113
+[2024-10-13 17:02:13,864 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 14/113
+[2024-10-13 17:02:13,933 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 15/113
+[2024-10-13 17:02:14,001 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 16/113
+[2024-10-13 17:02:14,069 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 17/113
+[2024-10-13 17:02:14,137 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 18/113
+[2024-10-13 17:02:14,205 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 19/113
+[2024-10-13 17:02:14,273 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 20/113
+[2024-10-13 17:02:14,341 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 21/113
+[2024-10-13 17:02:14,409 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 22/113
+[2024-10-13 17:02:14,477 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 23/113
+[2024-10-13 17:02:14,545 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 24/113
+[2024-10-13 17:02:14,613 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 25/113
+[2024-10-13 17:02:14,681 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 26/113
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+[2024-10-13 17:02:14,886 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 29/113
+[2024-10-13 17:02:14,954 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 30/113
+[2024-10-13 17:02:15,022 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 31/113
+[2024-10-13 17:02:15,090 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 32/113
+[2024-10-13 17:02:15,158 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 33/113
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+[2024-10-13 17:02:19,242 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 93/113
+[2024-10-13 17:02:19,314 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 94/113
+[2024-10-13 17:02:19,386 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 95/113
+[2024-10-13 17:02:19,457 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 96/113
+[2024-10-13 17:02:19,529 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 97/113
+[2024-10-13 17:02:19,600 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 98/113
+[2024-10-13 17:02:19,671 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 99/113
+[2024-10-13 17:02:19,742 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 100/113
+[2024-10-13 17:02:19,813 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 101/113
+[2024-10-13 17:02:19,885 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 102/113
+[2024-10-13 17:02:19,956 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 103/113
+[2024-10-13 17:02:20,024 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 104/113
+[2024-10-13 17:02:20,092 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 105/113
+[2024-10-13 17:02:20,160 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 106/113
+[2024-10-13 17:02:20,228 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 107/113
+[2024-10-13 17:02:20,297 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 108/113
+[2024-10-13 17:02:20,365 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 109/113
+[2024-10-13 17:02:20,433 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 110/113
+[2024-10-13 17:02:20,501 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 111/113
+[2024-10-13 17:02:20,568 INFO test.py line 186 25394] Test: 293/312-scene0146_00, Batch: 112/113
+[2024-10-13 17:02:20,662 INFO test.py line 272 25394] Test: scene0146_00 [293/312]-60438 Batch 7.830 (19.052) Accuracy 0.8483 (0.4889) mIoU 0.3608 (0.3718)
+[2024-10-13 17:02:20,844 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 0/127
+[2024-10-13 17:02:20,997 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 1/127
+[2024-10-13 17:02:21,152 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 2/127
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+[2024-10-13 17:02:21,768 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 6/127
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+[2024-10-13 17:02:22,078 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 8/127
+[2024-10-13 17:02:22,233 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 9/127
+[2024-10-13 17:02:22,387 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 10/127
+[2024-10-13 17:02:22,541 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 11/127
+[2024-10-13 17:02:22,696 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 12/127
+[2024-10-13 17:02:22,882 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 13/127
+[2024-10-13 17:02:23,037 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 14/127
+[2024-10-13 17:02:23,192 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 15/127
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+[2024-10-13 17:02:25,503 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 30/127
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+[2024-10-13 17:02:39,877 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 123/127
+[2024-10-13 17:02:40,031 INFO test.py line 186 25394] Test: 294/312-scene0598_02, Batch: 124/127
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+[2024-10-13 17:02:40,561 INFO test.py line 272 25394] Test: scene0598_02 [294/312]-176139 Batch 19.899 (19.054) Accuracy 0.7566 (0.4889) mIoU 0.6439 (0.3716)
+[2024-10-13 17:02:40,946 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 0/130
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+[2024-10-13 17:03:19,754 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 122/130
+[2024-10-13 17:03:20,074 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 123/130
+[2024-10-13 17:03:20,394 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 124/130
+[2024-10-13 17:03:20,713 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 125/130
+[2024-10-13 17:03:21,032 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 126/130
+[2024-10-13 17:03:21,352 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 127/130
+[2024-10-13 17:03:21,672 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 128/130
+[2024-10-13 17:03:21,992 INFO test.py line 186 25394] Test: 295/312-scene0208_00, Batch: 129/130
+[2024-10-13 17:03:22,503 INFO test.py line 272 25394] Test: scene0208_00 [295/312]-407386 Batch 41.941 (19.132) Accuracy 0.7614 (0.4884) mIoU 0.2967 (0.3712)
+[2024-10-13 17:03:22,688 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 0/144
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+[2024-10-13 17:03:43,615 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 135/144
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+[2024-10-13 17:03:43,925 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 137/144
+[2024-10-13 17:03:44,080 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 138/144
+[2024-10-13 17:03:44,236 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 139/144
+[2024-10-13 17:03:44,391 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 140/144
+[2024-10-13 17:03:44,546 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 141/144
+[2024-10-13 17:03:44,702 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 142/144
+[2024-10-13 17:03:44,858 INFO test.py line 186 25394] Test: 296/312-scene0131_00, Batch: 143/144
+[2024-10-13 17:03:45,085 INFO test.py line 272 25394] Test: scene0131_00 [296/312]-177091 Batch 22.582 (19.144) Accuracy 0.8190 (0.4885) mIoU 0.3527 (0.3712)
+[2024-10-13 17:03:45,316 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 0/149
+[2024-10-13 17:03:45,514 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 1/149
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+[2024-10-13 17:03:46,901 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 8/149
+[2024-10-13 17:03:47,101 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 9/149
+[2024-10-13 17:03:47,294 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 10/149
+[2024-10-13 17:03:47,488 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 11/149
+[2024-10-13 17:03:47,682 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 12/149
+[2024-10-13 17:03:47,875 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 13/149
+[2024-10-13 17:03:48,067 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 14/149
+[2024-10-13 17:03:48,260 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 15/149
+[2024-10-13 17:03:48,454 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 16/149
+[2024-10-13 17:03:48,647 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 17/149
+[2024-10-13 17:03:48,840 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 18/149
+[2024-10-13 17:03:49,033 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 19/149
+[2024-10-13 17:03:49,226 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 20/149
+[2024-10-13 17:03:49,419 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 21/149
+[2024-10-13 17:03:49,612 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 22/149
+[2024-10-13 17:03:49,805 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 23/149
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+[2024-10-13 17:03:50,576 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 27/149
+[2024-10-13 17:03:50,769 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 28/149
+[2024-10-13 17:03:50,962 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 29/149
+[2024-10-13 17:03:51,154 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 30/149
+[2024-10-13 17:03:51,347 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 31/149
+[2024-10-13 17:03:51,540 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 32/149
+[2024-10-13 17:03:51,733 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 33/149
+[2024-10-13 17:03:51,925 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 34/149
+[2024-10-13 17:03:52,118 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 35/149
+[2024-10-13 17:03:52,311 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 36/149
+[2024-10-13 17:03:52,504 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 37/149
+[2024-10-13 17:03:52,697 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 38/149
+[2024-10-13 17:03:52,889 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 39/149
+[2024-10-13 17:03:53,082 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 40/149
+[2024-10-13 17:03:53,275 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 41/149
+[2024-10-13 17:03:53,467 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 42/149
+[2024-10-13 17:03:53,660 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 43/149
+[2024-10-13 17:03:53,852 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 44/149
+[2024-10-13 17:03:54,045 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 45/149
+[2024-10-13 17:03:54,238 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 46/149
+[2024-10-13 17:03:54,430 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 47/149
+[2024-10-13 17:03:54,623 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 48/149
+[2024-10-13 17:03:54,816 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 49/149
+[2024-10-13 17:03:55,008 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 50/149
+[2024-10-13 17:03:55,201 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 51/149
+[2024-10-13 17:03:55,381 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 52/149
+[2024-10-13 17:03:55,562 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 53/149
+[2024-10-13 17:03:55,743 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 54/149
+[2024-10-13 17:03:55,924 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 55/149
+[2024-10-13 17:03:56,105 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 56/149
+[2024-10-13 17:03:56,286 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 57/149
+[2024-10-13 17:03:56,467 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 58/149
+[2024-10-13 17:03:56,647 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 59/149
+[2024-10-13 17:03:56,829 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 60/149
+[2024-10-13 17:03:57,010 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 61/149
+[2024-10-13 17:03:57,191 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 62/149
+[2024-10-13 17:03:57,372 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 63/149
+[2024-10-13 17:03:57,553 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 64/149
+[2024-10-13 17:03:57,734 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 65/149
+[2024-10-13 17:03:57,915 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 66/149
+[2024-10-13 17:03:58,096 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 67/149
+[2024-10-13 17:03:58,277 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 68/149
+[2024-10-13 17:03:58,458 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 69/149
+[2024-10-13 17:03:58,639 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 70/149
+[2024-10-13 17:03:58,820 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 71/149
+[2024-10-13 17:03:59,000 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 72/149
+[2024-10-13 17:03:59,181 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 73/149
+[2024-10-13 17:03:59,361 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 74/149
+[2024-10-13 17:03:59,541 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 75/149
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+[2024-10-13 17:03:59,901 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 77/149
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+[2024-10-13 17:04:00,803 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 82/149
+[2024-10-13 17:04:00,984 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 83/149
+[2024-10-13 17:04:01,164 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 84/149
+[2024-10-13 17:04:01,345 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 85/149
+[2024-10-13 17:04:01,525 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 86/149
+[2024-10-13 17:04:01,706 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 87/149
+[2024-10-13 17:04:01,887 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 88/149
+[2024-10-13 17:04:02,068 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 89/149
+[2024-10-13 17:04:02,249 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 90/149
+[2024-10-13 17:04:02,429 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 91/149
+[2024-10-13 17:04:02,610 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 92/149
+[2024-10-13 17:04:02,791 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 93/149
+[2024-10-13 17:04:02,972 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 94/149
+[2024-10-13 17:04:03,152 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 95/149
+[2024-10-13 17:04:03,359 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 96/149
+[2024-10-13 17:04:03,566 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 97/149
+[2024-10-13 17:04:03,773 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 98/149
+[2024-10-13 17:04:03,979 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 99/149
+[2024-10-13 17:04:04,186 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 100/149
+[2024-10-13 17:04:04,393 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 101/149
+[2024-10-13 17:04:04,600 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 102/149
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+[2024-10-13 17:04:05,633 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 107/149
+[2024-10-13 17:04:05,839 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 108/149
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+[2024-10-13 17:04:06,252 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 110/149
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+[2024-10-13 17:04:08,108 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 119/149
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+[2024-10-13 17:04:08,726 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 122/149
+[2024-10-13 17:04:08,932 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 123/149
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+[2024-10-13 17:04:09,757 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 127/149
+[2024-10-13 17:04:09,963 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 128/149
+[2024-10-13 17:04:10,169 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 129/149
+[2024-10-13 17:04:10,376 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 130/149
+[2024-10-13 17:04:10,582 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 131/149
+[2024-10-13 17:04:10,788 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 132/149
+[2024-10-13 17:04:10,994 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 133/149
+[2024-10-13 17:04:11,201 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 134/149
+[2024-10-13 17:04:11,408 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 135/149
+[2024-10-13 17:04:11,600 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 136/149
+[2024-10-13 17:04:11,793 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 137/149
+[2024-10-13 17:04:11,986 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 138/149
+[2024-10-13 17:04:12,179 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 139/149
+[2024-10-13 17:04:12,372 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 140/149
+[2024-10-13 17:04:12,565 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 141/149
+[2024-10-13 17:04:12,757 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 142/149
+[2024-10-13 17:04:12,950 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 143/149
+[2024-10-13 17:04:13,142 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 144/149
+[2024-10-13 17:04:13,335 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 145/149
+[2024-10-13 17:04:13,528 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 146/149
+[2024-10-13 17:04:13,721 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 147/149
+[2024-10-13 17:04:13,914 INFO test.py line 186 25394] Test: 297/312-scene0568_02, Batch: 148/149
+[2024-10-13 17:04:14,208 INFO test.py line 272 25394] Test: scene0568_02 [297/312]-232577 Batch 29.122 (19.177) Accuracy 0.8359 (0.4883) mIoU 0.3720 (0.3711)
+[2024-10-13 17:04:14,351 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 0/39
+[2024-10-13 17:04:14,487 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 1/39
+[2024-10-13 17:04:14,622 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 2/39
+[2024-10-13 17:04:14,758 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 3/39
+[2024-10-13 17:04:14,894 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 4/39
+[2024-10-13 17:04:15,030 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 5/39
+[2024-10-13 17:04:15,166 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 6/39
+[2024-10-13 17:04:15,301 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 7/39
+[2024-10-13 17:04:15,437 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 8/39
+[2024-10-13 17:04:15,572 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 9/39
+[2024-10-13 17:04:15,708 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 10/39
+[2024-10-13 17:04:15,843 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 11/39
+[2024-10-13 17:04:15,971 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 12/39
+[2024-10-13 17:04:16,099 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 13/39
+[2024-10-13 17:04:16,228 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 14/39
+[2024-10-13 17:04:16,356 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 15/39
+[2024-10-13 17:04:16,485 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 16/39
+[2024-10-13 17:04:16,614 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 17/39
+[2024-10-13 17:04:16,742 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 18/39
+[2024-10-13 17:04:16,871 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 19/39
+[2024-10-13 17:04:17,000 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 20/39
+[2024-10-13 17:04:17,129 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 21/39
+[2024-10-13 17:04:17,258 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 22/39
+[2024-10-13 17:04:17,387 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 23/39
+[2024-10-13 17:04:17,528 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 24/39
+[2024-10-13 17:04:17,669 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 25/39
+[2024-10-13 17:04:17,810 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 26/39
+[2024-10-13 17:04:17,950 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 27/39
+[2024-10-13 17:04:18,091 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 28/39
+[2024-10-13 17:04:18,233 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 29/39
+[2024-10-13 17:04:18,374 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 30/39
+[2024-10-13 17:04:18,516 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 31/39
+[2024-10-13 17:04:18,657 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 32/39
+[2024-10-13 17:04:18,798 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 33/39
+[2024-10-13 17:04:18,939 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 34/39
+[2024-10-13 17:04:19,080 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 35/39
+[2024-10-13 17:04:19,215 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 36/39
+[2024-10-13 17:04:19,351 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 37/39
+[2024-10-13 17:04:19,487 INFO test.py line 186 25394] Test: 298/312-scene0663_02, Batch: 38/39
+[2024-10-13 17:04:19,555 INFO test.py line 272 25394] Test: scene0663_02 [298/312]-48076 Batch 5.347 (19.131) Accuracy 0.8415 (0.4885) mIoU 0.4085 (0.3711)
+[2024-10-13 17:04:19,654 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 0/107
+[2024-10-13 17:04:19,741 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 1/107
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+[2024-10-13 17:04:20,003 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 4/107
+[2024-10-13 17:04:20,090 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 5/107
+[2024-10-13 17:04:20,177 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 6/107
+[2024-10-13 17:04:20,264 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 7/107
+[2024-10-13 17:04:20,352 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 8/107
+[2024-10-13 17:04:20,439 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 9/107
+[2024-10-13 17:04:20,527 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 10/107
+[2024-10-13 17:04:20,614 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 11/107
+[2024-10-13 17:04:20,702 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 12/107
+[2024-10-13 17:04:20,790 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 13/107
+[2024-10-13 17:04:20,877 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 14/107
+[2024-10-13 17:04:20,964 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 15/107
+[2024-10-13 17:04:21,052 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 16/107
+[2024-10-13 17:04:21,164 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 17/107
+[2024-10-13 17:04:21,252 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 18/107
+[2024-10-13 17:04:21,340 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 19/107
+[2024-10-13 17:04:21,428 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 20/107
+[2024-10-13 17:04:21,516 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 21/107
+[2024-10-13 17:04:21,604 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 22/107
+[2024-10-13 17:04:21,692 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 23/107
+[2024-10-13 17:04:21,779 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 24/107
+[2024-10-13 17:04:21,866 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 25/107
+[2024-10-13 17:04:21,953 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 26/107
+[2024-10-13 17:04:22,040 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 27/107
+[2024-10-13 17:04:22,123 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 28/107
+[2024-10-13 17:04:22,205 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 29/107
+[2024-10-13 17:04:22,288 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 30/107
+[2024-10-13 17:04:22,371 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 31/107
+[2024-10-13 17:04:22,454 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 32/107
+[2024-10-13 17:04:22,536 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 33/107
+[2024-10-13 17:04:22,619 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 34/107
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+[2024-10-13 17:04:23,033 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 39/107
+[2024-10-13 17:04:23,116 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 40/107
+[2024-10-13 17:04:23,199 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 41/107
+[2024-10-13 17:04:23,282 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 42/107
+[2024-10-13 17:04:23,364 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 43/107
+[2024-10-13 17:04:23,447 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 44/107
+[2024-10-13 17:04:23,530 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 45/107
+[2024-10-13 17:04:23,613 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 46/107
+[2024-10-13 17:04:23,696 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 47/107
+[2024-10-13 17:04:23,779 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 48/107
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+[2024-10-13 17:04:25,080 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 63/107
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+[2024-10-13 17:04:26,585 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 80/107
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+[2024-10-13 17:04:27,307 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 88/107
+[2024-10-13 17:04:27,397 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 89/107
+[2024-10-13 17:04:27,488 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 90/107
+[2024-10-13 17:04:27,578 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 91/107
+[2024-10-13 17:04:27,669 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 92/107
+[2024-10-13 17:04:27,760 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 93/107
+[2024-10-13 17:04:27,850 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 94/107
+[2024-10-13 17:04:27,940 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 95/107
+[2024-10-13 17:04:28,031 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 96/107
+[2024-10-13 17:04:28,121 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 97/107
+[2024-10-13 17:04:28,212 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 98/107
+[2024-10-13 17:04:28,302 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 99/107
+[2024-10-13 17:04:28,390 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 100/107
+[2024-10-13 17:04:28,477 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 101/107
+[2024-10-13 17:04:28,564 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 102/107
+[2024-10-13 17:04:28,651 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 103/107
+[2024-10-13 17:04:28,738 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 104/107
+[2024-10-13 17:04:28,825 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 105/107
+[2024-10-13 17:04:28,912 INFO test.py line 186 25394] Test: 299/312-scene0338_02, Batch: 106/107
+[2024-10-13 17:04:29,025 INFO test.py line 272 25394] Test: scene0338_02 [299/312]-84445 Batch 9.470 (19.099) Accuracy 0.7971 (0.4886) mIoU 0.5227 (0.3712)
+[2024-10-13 17:04:29,300 INFO test.py line 186 25394] Test: 300/312-scene0653_00, Batch: 0/134
+[2024-10-13 17:04:29,532 INFO test.py line 186 25394] Test: 300/312-scene0653_00, Batch: 1/134
+[2024-10-13 17:04:29,764 INFO test.py line 186 25394] Test: 300/312-scene0653_00, Batch: 2/134
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+[2024-10-13 17:04:31,155 INFO test.py line 186 25394] Test: 300/312-scene0653_00, Batch: 8/134
+[2024-10-13 17:04:31,387 INFO test.py line 186 25394] Test: 300/312-scene0653_00, Batch: 9/134
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+[2024-10-13 17:04:32,312 INFO test.py line 186 25394] Test: 300/312-scene0653_00, Batch: 13/134
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+[2024-10-13 17:05:00,371 INFO test.py line 272 25394] Test: scene0653_00 [300/312]-284951 Batch 31.346 (19.139) Accuracy 0.8854 (0.4913) mIoU 0.4985 (0.3738)
+[2024-10-13 17:05:00,536 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 0/152
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+[2024-10-13 17:05:14,866 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 105/152
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+[2024-10-13 17:05:19,597 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 137/152
+[2024-10-13 17:05:19,746 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 138/152
+[2024-10-13 17:05:19,894 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 139/152
+[2024-10-13 17:05:20,034 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 140/152
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+[2024-10-13 17:05:20,315 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 142/152
+[2024-10-13 17:05:20,456 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 143/152
+[2024-10-13 17:05:20,596 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 144/152
+[2024-10-13 17:05:20,737 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 145/152
+[2024-10-13 17:05:20,878 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 146/152
+[2024-10-13 17:05:21,018 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 147/152
+[2024-10-13 17:05:21,158 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 148/152
+[2024-10-13 17:05:21,299 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 149/152
+[2024-10-13 17:05:21,439 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 150/152
+[2024-10-13 17:05:21,580 INFO test.py line 186 25394] Test: 301/312-scene0655_01, Batch: 151/152
+[2024-10-13 17:05:21,783 INFO test.py line 272 25394] Test: scene0655_01 [301/312]-159475 Batch 21.411 (19.147) Accuracy 0.8398 (0.4913) mIoU 0.4993 (0.3737)
+[2024-10-13 17:05:21,975 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 0/133
+[2024-10-13 17:05:22,137 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 1/133
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+[2024-10-13 17:05:23,105 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 7/133
+[2024-10-13 17:05:23,266 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 8/133
+[2024-10-13 17:05:23,462 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 9/133
+[2024-10-13 17:05:23,624 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 10/133
+[2024-10-13 17:05:23,785 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 11/133
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+[2024-10-13 17:05:24,109 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 13/133
+[2024-10-13 17:05:24,270 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 14/133
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+[2024-10-13 17:05:37,350 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 97/133
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+[2024-10-13 17:05:40,783 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 117/133
+[2024-10-13 17:05:40,954 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 118/133
+[2024-10-13 17:05:41,126 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 119/133
+[2024-10-13 17:05:41,298 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 120/133
+[2024-10-13 17:05:41,470 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 121/133
+[2024-10-13 17:05:41,642 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 122/133
+[2024-10-13 17:05:41,814 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 123/133
+[2024-10-13 17:05:41,975 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 124/133
+[2024-10-13 17:05:42,136 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 125/133
+[2024-10-13 17:05:42,298 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 126/133
+[2024-10-13 17:05:42,459 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 127/133
+[2024-10-13 17:05:42,620 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 128/133
+[2024-10-13 17:05:42,782 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 129/133
+[2024-10-13 17:05:42,943 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 130/133
+[2024-10-13 17:05:43,105 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 131/133
+[2024-10-13 17:05:43,267 INFO test.py line 186 25394] Test: 302/312-scene0591_01, Batch: 132/133
+[2024-10-13 17:05:43,506 INFO test.py line 272 25394] Test: scene0591_01 [302/312]-190792 Batch 21.724 (19.155) Accuracy 0.8332 (0.4917) mIoU 0.4726 (0.3739)
+[2024-10-13 17:05:43,606 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 0/123
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+[2024-10-13 17:05:44,304 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 8/123
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+[2024-10-13 17:05:44,739 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 13/123
+[2024-10-13 17:05:44,826 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 14/123
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+[2024-10-13 17:05:45,346 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 20/123
+[2024-10-13 17:05:45,433 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 21/123
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+[2024-10-13 17:05:45,953 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 27/123
+[2024-10-13 17:05:46,039 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 28/123
+[2024-10-13 17:05:46,126 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 29/123
+[2024-10-13 17:05:46,212 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 30/123
+[2024-10-13 17:05:46,299 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 31/123
+[2024-10-13 17:05:46,385 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 32/123
+[2024-10-13 17:05:46,473 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 33/123
+[2024-10-13 17:05:46,560 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 34/123
+[2024-10-13 17:05:46,647 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 35/123
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+[2024-10-13 17:05:47,001 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 39/123
+[2024-10-13 17:05:47,114 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 40/123
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+[2024-10-13 17:05:47,291 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 42/123
+[2024-10-13 17:05:47,378 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 43/123
+[2024-10-13 17:05:47,460 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 44/123
+[2024-10-13 17:05:47,542 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 45/123
+[2024-10-13 17:05:47,624 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 46/123
+[2024-10-13 17:05:47,706 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 47/123
+[2024-10-13 17:05:47,788 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 48/123
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+[2024-10-13 17:05:48,035 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 51/123
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+[2024-10-13 17:05:48,449 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 56/123
+[2024-10-13 17:05:48,532 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 57/123
+[2024-10-13 17:05:48,615 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 58/123
+[2024-10-13 17:05:48,698 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 59/123
+[2024-10-13 17:05:48,781 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 60/123
+[2024-10-13 17:05:48,864 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 61/123
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+[2024-10-13 17:05:49,359 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 67/123
+[2024-10-13 17:05:49,442 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 68/123
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+[2024-10-13 17:05:51,880 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 96/123
+[2024-10-13 17:05:51,970 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 97/123
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+[2024-10-13 17:05:52,877 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 107/123
+[2024-10-13 17:05:52,967 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 108/123
+[2024-10-13 17:05:53,058 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 109/123
+[2024-10-13 17:05:53,149 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 110/123
+[2024-10-13 17:05:53,240 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 111/123
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+[2024-10-13 17:05:53,500 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 114/123
+[2024-10-13 17:05:53,586 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 115/123
+[2024-10-13 17:05:53,673 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 116/123
+[2024-10-13 17:05:53,759 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 117/123
+[2024-10-13 17:05:53,846 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 118/123
+[2024-10-13 17:05:53,932 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 119/123
+[2024-10-13 17:05:54,019 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 120/123
+[2024-10-13 17:05:54,105 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 121/123
+[2024-10-13 17:05:54,192 INFO test.py line 186 25394] Test: 303/312-scene0338_01, Batch: 122/123
+[2024-10-13 17:05:54,310 INFO test.py line 272 25394] Test: scene0338_01 [303/312]-85459 Batch 10.804 (19.128) Accuracy 0.8144 (0.4918) mIoU 0.4516 (0.3738)
+[2024-10-13 17:05:54,401 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 0/138
+[2024-10-13 17:05:54,480 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 1/138
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+[2024-10-13 17:05:55,038 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 8/138
+[2024-10-13 17:05:55,118 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 9/138
+[2024-10-13 17:05:55,197 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 10/138
+[2024-10-13 17:05:55,277 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 11/138
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+[2024-10-13 17:06:04,999 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 134/138
+[2024-10-13 17:06:05,078 INFO test.py line 186 25394] Test: 304/312-scene0609_03, Batch: 135/138
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+[2024-10-13 17:06:05,338 INFO test.py line 272 25394] Test: scene0609_03 [304/312]-69895 Batch 11.028 (19.101) Accuracy 0.5851 (0.4918) mIoU 0.3617 (0.3737)
+[2024-10-13 17:06:05,498 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 0/126
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+[2024-10-13 17:06:18,773 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 101/126
+[2024-10-13 17:06:18,914 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 102/126
+[2024-10-13 17:06:19,055 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 103/126
+[2024-10-13 17:06:19,196 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 104/126
+[2024-10-13 17:06:19,337 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 105/126
+[2024-10-13 17:06:19,478 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 106/126
+[2024-10-13 17:06:19,620 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 107/126
+[2024-10-13 17:06:19,761 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 108/126
+[2024-10-13 17:06:19,902 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 109/126
+[2024-10-13 17:06:20,043 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 110/126
+[2024-10-13 17:06:20,183 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 111/126
+[2024-10-13 17:06:20,324 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 112/126
+[2024-10-13 17:06:20,465 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 113/126
+[2024-10-13 17:06:20,606 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 114/126
+[2024-10-13 17:06:20,747 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 115/126
+[2024-10-13 17:06:20,881 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 116/126
+[2024-10-13 17:06:21,014 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 117/126
+[2024-10-13 17:06:21,147 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 118/126
+[2024-10-13 17:06:21,281 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 119/126
+[2024-10-13 17:06:21,414 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 120/126
+[2024-10-13 17:06:21,547 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 121/126
+[2024-10-13 17:06:21,680 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 122/126
+[2024-10-13 17:06:21,814 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 123/126
+[2024-10-13 17:06:21,947 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 124/126
+[2024-10-13 17:06:22,080 INFO test.py line 186 25394] Test: 305/312-scene0700_01, Batch: 125/126
+[2024-10-13 17:06:22,277 INFO test.py line 272 25394] Test: scene0700_01 [305/312]-153877 Batch 16.939 (19.094) Accuracy 0.6854 (0.4918) mIoU 0.4218 (0.3736)
+[2024-10-13 17:06:22,396 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 0/135
+[2024-10-13 17:06:22,501 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 1/135
+[2024-10-13 17:06:22,605 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 2/135
+[2024-10-13 17:06:22,709 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 3/135
+[2024-10-13 17:06:22,814 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 4/135
+[2024-10-13 17:06:22,918 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 5/135
+[2024-10-13 17:06:23,022 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 6/135
+[2024-10-13 17:06:23,127 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 7/135
+[2024-10-13 17:06:23,231 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 8/135
+[2024-10-13 17:06:23,335 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 9/135
+[2024-10-13 17:06:23,439 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 10/135
+[2024-10-13 17:06:23,543 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 11/135
+[2024-10-13 17:06:23,647 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 12/135
+[2024-10-13 17:06:23,751 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 13/135
+[2024-10-13 17:06:23,856 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 14/135
+[2024-10-13 17:06:23,960 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 15/135
+[2024-10-13 17:06:24,064 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 16/135
+[2024-10-13 17:06:24,168 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 17/135
+[2024-10-13 17:06:24,272 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 18/135
+[2024-10-13 17:06:24,377 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 19/135
+[2024-10-13 17:06:24,481 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 20/135
+[2024-10-13 17:06:24,584 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 21/135
+[2024-10-13 17:06:24,688 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 22/135
+[2024-10-13 17:06:24,792 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 23/135
+[2024-10-13 17:06:24,896 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 24/135
+[2024-10-13 17:06:25,000 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 25/135
+[2024-10-13 17:06:25,104 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 26/135
+[2024-10-13 17:06:25,208 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 27/135
+[2024-10-13 17:06:25,312 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 28/135
+[2024-10-13 17:06:25,417 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 29/135
+[2024-10-13 17:06:25,521 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 30/135
+[2024-10-13 17:06:25,625 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 31/135
+[2024-10-13 17:06:25,729 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 32/135
+[2024-10-13 17:06:25,833 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 33/135
+[2024-10-13 17:06:25,937 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 34/135
+[2024-10-13 17:06:26,041 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 35/135
+[2024-10-13 17:06:26,146 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 36/135
+[2024-10-13 17:06:26,250 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 37/135
+[2024-10-13 17:06:26,355 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 38/135
+[2024-10-13 17:06:26,459 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 39/135
+[2024-10-13 17:06:26,563 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 40/135
+[2024-10-13 17:06:26,668 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 41/135
+[2024-10-13 17:06:26,772 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 42/135
+[2024-10-13 17:06:26,877 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 43/135
+[2024-10-13 17:06:26,975 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 44/135
+[2024-10-13 17:06:27,072 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 45/135
+[2024-10-13 17:06:27,170 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 46/135
+[2024-10-13 17:06:27,267 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 47/135
+[2024-10-13 17:06:27,365 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 48/135
+[2024-10-13 17:06:27,463 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 49/135
+[2024-10-13 17:06:27,561 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 50/135
+[2024-10-13 17:06:27,658 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 51/135
+[2024-10-13 17:06:27,756 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 52/135
+[2024-10-13 17:06:27,854 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 53/135
+[2024-10-13 17:06:27,952 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 54/135
+[2024-10-13 17:06:28,049 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 55/135
+[2024-10-13 17:06:28,147 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 56/135
+[2024-10-13 17:06:28,245 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 57/135
+[2024-10-13 17:06:28,342 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 58/135
+[2024-10-13 17:06:28,440 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 59/135
+[2024-10-13 17:06:28,537 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 60/135
+[2024-10-13 17:06:28,635 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 61/135
+[2024-10-13 17:06:28,733 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 62/135
+[2024-10-13 17:06:28,830 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 63/135
+[2024-10-13 17:06:28,928 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 64/135
+[2024-10-13 17:06:29,025 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 65/135
+[2024-10-13 17:06:29,123 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 66/135
+[2024-10-13 17:06:29,221 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 67/135
+[2024-10-13 17:06:29,319 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 68/135
+[2024-10-13 17:06:29,416 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 69/135
+[2024-10-13 17:06:29,514 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 70/135
+[2024-10-13 17:06:29,612 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 71/135
+[2024-10-13 17:06:29,710 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 72/135
+[2024-10-13 17:06:29,808 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 73/135
+[2024-10-13 17:06:29,906 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 74/135
+[2024-10-13 17:06:30,004 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 75/135
+[2024-10-13 17:06:30,105 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 76/135
+[2024-10-13 17:06:30,227 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 77/135
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+[2024-10-13 17:06:30,424 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 79/135
+[2024-10-13 17:06:30,523 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 80/135
+[2024-10-13 17:06:30,621 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 81/135
+[2024-10-13 17:06:30,720 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 82/135
+[2024-10-13 17:06:30,817 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 83/135
+[2024-10-13 17:06:30,927 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 84/135
+[2024-10-13 17:06:31,037 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 85/135
+[2024-10-13 17:06:31,147 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 86/135
+[2024-10-13 17:06:31,257 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 87/135
+[2024-10-13 17:06:31,367 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 88/135
+[2024-10-13 17:06:31,477 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 89/135
+[2024-10-13 17:06:31,588 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 90/135
+[2024-10-13 17:06:31,697 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 91/135
+[2024-10-13 17:06:31,808 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 92/135
+[2024-10-13 17:06:31,918 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 93/135
+[2024-10-13 17:06:32,028 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 94/135
+[2024-10-13 17:06:32,138 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 95/135
+[2024-10-13 17:06:32,247 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 96/135
+[2024-10-13 17:06:32,357 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 97/135
+[2024-10-13 17:06:32,466 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 98/135
+[2024-10-13 17:06:32,576 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 99/135
+[2024-10-13 17:06:32,686 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 100/135
+[2024-10-13 17:06:32,795 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 101/135
+[2024-10-13 17:06:32,905 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 102/135
+[2024-10-13 17:06:33,015 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 103/135
+[2024-10-13 17:06:33,125 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 104/135
+[2024-10-13 17:06:33,235 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 105/135
+[2024-10-13 17:06:33,345 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 106/135
+[2024-10-13 17:06:33,455 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 107/135
+[2024-10-13 17:06:33,565 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 108/135
+[2024-10-13 17:06:33,675 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 109/135
+[2024-10-13 17:06:33,785 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 110/135
+[2024-10-13 17:06:33,895 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 111/135
+[2024-10-13 17:06:34,005 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 112/135
+[2024-10-13 17:06:34,115 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 113/135
+[2024-10-13 17:06:34,225 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 114/135
+[2024-10-13 17:06:34,335 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 115/135
+[2024-10-13 17:06:34,445 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 116/135
+[2024-10-13 17:06:34,555 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 117/135
+[2024-10-13 17:06:34,665 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 118/135
+[2024-10-13 17:06:34,775 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 119/135
+[2024-10-13 17:06:34,885 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 120/135
+[2024-10-13 17:06:34,995 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 121/135
+[2024-10-13 17:06:35,105 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 122/135
+[2024-10-13 17:06:35,215 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 123/135
+[2024-10-13 17:06:35,318 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 124/135
+[2024-10-13 17:06:35,422 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 125/135
+[2024-10-13 17:06:35,526 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 126/135
+[2024-10-13 17:06:35,630 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 127/135
+[2024-10-13 17:06:35,734 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 128/135
+[2024-10-13 17:06:35,838 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 129/135
+[2024-10-13 17:06:35,941 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 130/135
+[2024-10-13 17:06:36,045 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 131/135
+[2024-10-13 17:06:36,149 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 132/135
+[2024-10-13 17:06:36,253 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 133/135
+[2024-10-13 17:06:36,357 INFO test.py line 186 25394] Test: 306/312-scene0488_01, Batch: 134/135
+[2024-10-13 17:06:36,502 INFO test.py line 272 25394] Test: scene0488_01 [306/312]-109452 Batch 14.225 (19.078) Accuracy 0.9033 (0.4919) mIoU 0.7199 (0.3738)
+[2024-10-13 17:06:36,575 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 0/130
+[2024-10-13 17:06:36,638 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 1/130
+[2024-10-13 17:06:36,701 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 2/130
+[2024-10-13 17:06:36,765 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 3/130
+[2024-10-13 17:06:36,828 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 4/130
+[2024-10-13 17:06:36,891 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 5/130
+[2024-10-13 17:06:36,954 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 6/130
+[2024-10-13 17:06:37,018 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 7/130
+[2024-10-13 17:06:37,081 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 8/130
+[2024-10-13 17:06:37,144 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 9/130
+[2024-10-13 17:06:37,207 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 10/130
+[2024-10-13 17:06:37,271 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 11/130
+[2024-10-13 17:06:37,334 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 12/130
+[2024-10-13 17:06:37,397 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 13/130
+[2024-10-13 17:06:37,461 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 14/130
+[2024-10-13 17:06:37,524 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 15/130
+[2024-10-13 17:06:37,587 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 16/130
+[2024-10-13 17:06:37,651 INFO test.py line 186 25394] Test: 307/312-scene0406_00, Batch: 17/130
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+[2024-10-13 17:06:44,823 INFO test.py line 272 25394] Test: scene0406_00 [307/312]-48864 Batch 8.321 (19.043) Accuracy 0.8749 (0.4920) mIoU 0.5757 (0.3739)
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+[2024-10-13 17:07:18,024 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 125/134
+[2024-10-13 17:07:18,288 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 126/134
+[2024-10-13 17:07:18,553 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 127/134
+[2024-10-13 17:07:18,817 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 128/134
+[2024-10-13 17:07:19,083 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 129/134
+[2024-10-13 17:07:19,347 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 130/134
+[2024-10-13 17:07:19,612 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 131/134
+[2024-10-13 17:07:19,877 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 132/134
+[2024-10-13 17:07:20,142 INFO test.py line 186 25394] Test: 308/312-scene0653_01, Batch: 133/134
+[2024-10-13 17:07:20,562 INFO test.py line 272 25394] Test: scene0653_01 [308/312]-331565 Batch 35.739 (19.097) Accuracy 0.9089 (0.4937) mIoU 0.4211 (0.3751)
+[2024-10-13 17:07:20,645 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 0/131
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+[2024-10-13 17:07:28,976 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 116/131
+[2024-10-13 17:07:29,050 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 117/131
+[2024-10-13 17:07:29,125 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 118/131
+[2024-10-13 17:07:29,200 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 119/131
+[2024-10-13 17:07:29,272 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 120/131
+[2024-10-13 17:07:29,344 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 121/131
+[2024-10-13 17:07:29,416 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 122/131
+[2024-10-13 17:07:29,488 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 123/131
+[2024-10-13 17:07:29,559 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 124/131
+[2024-10-13 17:07:29,631 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 125/131
+[2024-10-13 17:07:29,703 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 126/131
+[2024-10-13 17:07:29,775 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 127/131
+[2024-10-13 17:07:29,847 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 128/131
+[2024-10-13 17:07:29,919 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 129/131
+[2024-10-13 17:07:29,990 INFO test.py line 186 25394] Test: 309/312-scene0146_02, Batch: 130/131
+[2024-10-13 17:07:30,082 INFO test.py line 272 25394] Test: scene0146_02 [309/312]-64383 Batch 9.520 (19.066) Accuracy 0.8668 (0.4936) mIoU 0.4054 (0.3751)
+[2024-10-13 17:07:30,244 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 0/123
+[2024-10-13 17:07:30,384 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 1/123
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+[2024-10-13 17:07:31,082 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 6/123
+[2024-10-13 17:07:31,221 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 7/123
+[2024-10-13 17:07:31,361 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 8/123
+[2024-10-13 17:07:31,501 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 9/123
+[2024-10-13 17:07:31,640 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 10/123
+[2024-10-13 17:07:31,779 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 11/123
+[2024-10-13 17:07:31,919 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 12/123
+[2024-10-13 17:07:32,058 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 13/123
+[2024-10-13 17:07:32,198 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 14/123
+[2024-10-13 17:07:32,337 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 15/123
+[2024-10-13 17:07:32,479 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 16/123
+[2024-10-13 17:07:32,648 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 17/123
+[2024-10-13 17:07:32,789 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 18/123
+[2024-10-13 17:07:32,929 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 19/123
+[2024-10-13 17:07:33,068 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 20/123
+[2024-10-13 17:07:33,208 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 21/123
+[2024-10-13 17:07:33,348 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 22/123
+[2024-10-13 17:07:33,488 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 23/123
+[2024-10-13 17:07:33,628 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 24/123
+[2024-10-13 17:07:33,768 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 25/123
+[2024-10-13 17:07:33,908 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 26/123
+[2024-10-13 17:07:34,048 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 27/123
+[2024-10-13 17:07:34,188 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 28/123
+[2024-10-13 17:07:34,328 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 29/123
+[2024-10-13 17:07:34,468 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 30/123
+[2024-10-13 17:07:34,608 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 31/123
+[2024-10-13 17:07:34,748 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 32/123
+[2024-10-13 17:07:34,888 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 33/123
+[2024-10-13 17:07:35,028 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 34/123
+[2024-10-13 17:07:35,168 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 35/123
+[2024-10-13 17:07:35,308 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 36/123
+[2024-10-13 17:07:35,448 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 37/123
+[2024-10-13 17:07:35,588 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 38/123
+[2024-10-13 17:07:35,728 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 39/123
+[2024-10-13 17:07:35,868 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 40/123
+[2024-10-13 17:07:36,008 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 41/123
+[2024-10-13 17:07:36,149 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 42/123
+[2024-10-13 17:07:36,288 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 43/123
+[2024-10-13 17:07:36,420 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 44/123
+[2024-10-13 17:07:36,552 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 45/123
+[2024-10-13 17:07:36,683 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 46/123
+[2024-10-13 17:07:36,815 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 47/123
+[2024-10-13 17:07:36,946 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 48/123
+[2024-10-13 17:07:37,078 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 49/123
+[2024-10-13 17:07:37,209 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 50/123
+[2024-10-13 17:07:37,341 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 51/123
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+[2024-10-13 17:07:37,866 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 55/123
+[2024-10-13 17:07:37,997 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 56/123
+[2024-10-13 17:07:38,128 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 57/123
+[2024-10-13 17:07:38,260 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 58/123
+[2024-10-13 17:07:38,391 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 59/123
+[2024-10-13 17:07:38,522 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 60/123
+[2024-10-13 17:07:38,653 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 61/123
+[2024-10-13 17:07:38,785 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 62/123
+[2024-10-13 17:07:38,916 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 63/123
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+[2024-10-13 17:07:39,178 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 65/123
+[2024-10-13 17:07:39,310 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 66/123
+[2024-10-13 17:07:39,442 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 67/123
+[2024-10-13 17:07:39,573 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 68/123
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+[2024-10-13 17:07:45,171 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 107/123
+[2024-10-13 17:07:45,319 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 108/123
+[2024-10-13 17:07:45,467 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 109/123
+[2024-10-13 17:07:45,614 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 110/123
+[2024-10-13 17:07:45,762 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 111/123
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+[2024-10-13 17:07:46,042 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 113/123
+[2024-10-13 17:07:46,182 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 114/123
+[2024-10-13 17:07:46,322 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 115/123
+[2024-10-13 17:07:46,462 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 116/123
+[2024-10-13 17:07:46,601 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 117/123
+[2024-10-13 17:07:46,741 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 118/123
+[2024-10-13 17:07:46,881 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 119/123
+[2024-10-13 17:07:47,021 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 120/123
+[2024-10-13 17:07:47,162 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 121/123
+[2024-10-13 17:07:47,302 INFO test.py line 186 25394] Test: 310/312-scene0606_01, Batch: 122/123
+[2024-10-13 17:07:47,496 INFO test.py line 272 25394] Test: scene0606_01 [310/312]-152050 Batch 17.414 (19.061) Accuracy 0.7800 (0.4928) mIoU 0.1772 (0.3741)
+[2024-10-13 17:07:47,568 INFO test.py line 186 25394] Test: 311/312-scene0693_01, Batch: 0/95
+[2024-10-13 17:07:47,632 INFO test.py line 186 25394] Test: 311/312-scene0693_01, Batch: 1/95
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+[2024-10-13 17:07:53,182 INFO test.py line 186 25394] Test: 311/312-scene0693_01, Batch: 88/95
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+[2024-10-13 17:07:53,310 INFO test.py line 186 25394] Test: 311/312-scene0693_01, Batch: 90/95
+[2024-10-13 17:07:53,374 INFO test.py line 186 25394] Test: 311/312-scene0693_01, Batch: 91/95
+[2024-10-13 17:07:53,439 INFO test.py line 186 25394] Test: 311/312-scene0693_01, Batch: 92/95
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+[2024-10-13 17:07:53,637 INFO test.py line 272 25394] Test: scene0693_01 [311/312]-47986 Batch 6.140 (19.020) Accuracy 0.8448 (0.4936) mIoU 0.5038 (0.3747)
+[2024-10-13 17:07:53,744 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 0/145
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+[2024-10-13 17:08:05,841 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 128/145
+[2024-10-13 17:08:05,941 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 129/145
+[2024-10-13 17:08:06,040 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 130/145
+[2024-10-13 17:08:06,140 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 131/145
+[2024-10-13 17:08:06,234 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 132/145
+[2024-10-13 17:08:06,328 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 133/145
+[2024-10-13 17:08:06,423 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 134/145
+[2024-10-13 17:08:06,517 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 135/145
+[2024-10-13 17:08:06,612 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 136/145
+[2024-10-13 17:08:06,706 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 137/145
+[2024-10-13 17:08:06,800 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 138/145
+[2024-10-13 17:08:06,895 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 139/145
+[2024-10-13 17:08:06,989 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 140/145
+[2024-10-13 17:08:07,084 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 141/145
+[2024-10-13 17:08:07,178 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 142/145
+[2024-10-13 17:08:07,272 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 143/145
+[2024-10-13 17:08:07,367 INFO test.py line 186 25394] Test: 312/312-scene0382_00, Batch: 144/145
+[2024-10-13 17:08:07,497 INFO test.py line 272 25394] Test: scene0382_00 [312/312]-100285 Batch 13.860 (19.003) Accuracy 0.9090 (0.4936) mIoU 0.6091 (0.3747)
+[2024-10-13 17:08:07,544 INFO test.py line 289 25394] Syncing ...
+[2024-10-13 17:08:07,546 INFO test.py line 315 25394] Val result: mIoU/mAcc/allAcc 0.3747/0.4936/0.8477
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_0 - wall Result: iou/accuracy 0.8254/0.9327
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_1 - chair Result: iou/accuracy 0.7752/0.8820
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_2 - floor Result: iou/accuracy 0.9541/0.9794
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_3 - table Result: iou/accuracy 0.7058/0.8170
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_4 - door Result: iou/accuracy 0.7277/0.8318
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_5 - couch Result: iou/accuracy 0.8191/0.9179
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_6 - cabinet Result: iou/accuracy 0.4419/0.5549
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_7 - shelf Result: iou/accuracy 0.4875/0.7202
+[2024-10-13 17:08:07,546 INFO test.py line 317 25394] Class_8 - desk Result: iou/accuracy 0.6896/0.8458
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_9 - office chair Result: iou/accuracy 0.1949/0.2742
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_10 - bed Result: iou/accuracy 0.8324/0.9049
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_11 - pillow Result: iou/accuracy 0.6645/0.8413
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_12 - sink Result: iou/accuracy 0.7160/0.8400
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_13 - picture Result: iou/accuracy 0.3990/0.5091
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_14 - window Result: iou/accuracy 0.7182/0.8670
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_15 - toilet Result: iou/accuracy 0.9500/0.9731
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_16 - bookshelf Result: iou/accuracy 0.5410/0.6506
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_17 - monitor Result: iou/accuracy 0.9095/0.9615
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_18 - curtain Result: iou/accuracy 0.7881/0.9006
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_19 - book Result: iou/accuracy 0.5099/0.7970
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_20 - armchair Result: iou/accuracy 0.5591/0.7733
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_21 - coffee table Result: iou/accuracy 0.6972/0.8712
+[2024-10-13 17:08:07,547 INFO test.py line 317 25394] Class_22 - box Result: iou/accuracy 0.4522/0.6614
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_23 - refrigerator Result: iou/accuracy 0.8094/0.8745
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_24 - lamp Result: iou/accuracy 0.7785/0.8792
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_25 - kitchen cabinet Result: iou/accuracy 0.7628/0.8893
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_26 - towel Result: iou/accuracy 0.5526/0.6862
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_27 - clothes Result: iou/accuracy 0.2041/0.5158
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_28 - tv Result: iou/accuracy 0.9096/0.9632
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_29 - nightstand Result: iou/accuracy 0.7897/0.9000
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_30 - counter Result: iou/accuracy 0.4168/0.5889
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_31 - dresser Result: iou/accuracy 0.6016/0.7739
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_32 - stool Result: iou/accuracy 0.5949/0.7826
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_33 - cushion Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_34 - plant Result: iou/accuracy 0.7964/0.9488
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_35 - ceiling Result: iou/accuracy 0.9170/0.9562
+[2024-10-13 17:08:07,548 INFO test.py line 317 25394] Class_36 - bathtub Result: iou/accuracy 0.8284/0.9078
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_37 - end table Result: iou/accuracy 0.1441/0.1899
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_38 - dining table Result: iou/accuracy 0.0839/0.1000
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_39 - keyboard Result: iou/accuracy 0.4726/0.6218
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_40 - bag Result: iou/accuracy 0.3399/0.5008
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_41 - backpack Result: iou/accuracy 0.7082/0.7966
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_42 - toilet paper Result: iou/accuracy 0.4905/0.6251
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_43 - printer Result: iou/accuracy 0.6593/0.9030
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_44 - tv stand Result: iou/accuracy 0.4796/0.6940
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_45 - whiteboard Result: iou/accuracy 0.5940/0.8376
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_46 - blanket Result: iou/accuracy 0.0682/0.0823
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_47 - shower curtain Result: iou/accuracy 0.6684/0.7409
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_48 - trash can Result: iou/accuracy 0.6887/0.8205
+[2024-10-13 17:08:07,549 INFO test.py line 317 25394] Class_49 - closet Result: iou/accuracy 0.1062/0.1323
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_50 - stairs Result: iou/accuracy 0.5730/0.8140
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_51 - microwave Result: iou/accuracy 0.6842/0.9095
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_52 - stove Result: iou/accuracy 0.6781/0.9088
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_53 - shoe Result: iou/accuracy 0.5636/0.7351
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_54 - computer tower Result: iou/accuracy 0.6829/0.9247
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_55 - bottle Result: iou/accuracy 0.1428/0.4923
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_56 - bin Result: iou/accuracy 0.0004/0.0011
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_57 - ottoman Result: iou/accuracy 0.5272/0.6254
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_58 - bench Result: iou/accuracy 0.4267/0.4696
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_59 - board Result: iou/accuracy 0.2049/0.4391
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_60 - washing machine Result: iou/accuracy 0.8754/0.9650
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_61 - mirror Result: iou/accuracy 0.5259/0.6560
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_62 - copier Result: iou/accuracy 0.7128/0.9666
+[2024-10-13 17:08:07,550 INFO test.py line 317 25394] Class_63 - basket Result: iou/accuracy 0.0652/0.1003
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_64 - sofa chair Result: iou/accuracy 0.2422/0.2912
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_65 - file cabinet Result: iou/accuracy 0.6724/0.7679
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_66 - fan Result: iou/accuracy 0.4991/0.5912
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_67 - laptop Result: iou/accuracy 0.6650/0.9039
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_68 - shower Result: iou/accuracy 0.4175/0.4880
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_69 - paper Result: iou/accuracy 0.1882/0.2998
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_70 - person Result: iou/accuracy 0.4871/0.8074
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_71 - paper towel dispenser Result: iou/accuracy 0.7317/0.9021
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_72 - oven Result: iou/accuracy 0.1041/0.1075
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_73 - blinds Result: iou/accuracy 0.0033/0.0033
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_74 - rack Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_75 - plate Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_76 - blackboard Result: iou/accuracy 0.6407/0.6803
+[2024-10-13 17:08:07,551 INFO test.py line 317 25394] Class_77 - piano Result: iou/accuracy 0.9248/0.9801
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_78 - suitcase Result: iou/accuracy 0.6721/0.8726
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_79 - rail Result: iou/accuracy 0.0548/0.0566
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_80 - radiator Result: iou/accuracy 0.7437/0.8773
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_81 - recycling bin Result: iou/accuracy 0.5412/0.6992
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_82 - container Result: iou/accuracy 0.0819/0.1432
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_83 - wardrobe Result: iou/accuracy 0.1893/0.2356
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_84 - soap dispenser Result: iou/accuracy 0.7955/0.8896
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_85 - telephone Result: iou/accuracy 0.5354/0.7352
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_86 - bucket Result: iou/accuracy 0.2710/0.6164
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_87 - clock Result: iou/accuracy 0.3346/0.3901
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_88 - stand Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_89 - light Result: iou/accuracy 0.0298/0.0328
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_90 - laundry basket Result: iou/accuracy 0.3394/0.3588
+[2024-10-13 17:08:07,552 INFO test.py line 317 25394] Class_91 - pipe Result: iou/accuracy 0.1183/0.1517
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_92 - clothes dryer Result: iou/accuracy 0.7221/0.7293
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_93 - guitar Result: iou/accuracy 0.7009/0.8240
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_94 - toilet paper holder Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_95 - seat Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_96 - speaker Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_97 - column Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_98 - bicycle Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_99 - ladder Result: iou/accuracy 0.7990/0.8506
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_100 - bathroom stall Result: iou/accuracy 0.5051/0.5694
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_101 - shower wall Result: iou/accuracy 0.3986/0.5178
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_102 - cup Result: iou/accuracy 0.3021/0.7179
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_103 - jacket Result: iou/accuracy 0.2205/0.2967
+[2024-10-13 17:08:07,553 INFO test.py line 317 25394] Class_104 - storage bin Result: iou/accuracy 0.0418/0.0536
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_105 - coffee maker Result: iou/accuracy 0.8268/0.9921
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_106 - dishwasher Result: iou/accuracy 0.6335/0.7615
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_107 - paper towel roll Result: iou/accuracy 0.5697/0.6605
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_108 - machine Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_109 - mat Result: iou/accuracy 0.2743/0.3192
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_110 - windowsill Result: iou/accuracy 0.0908/0.1009
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_111 - bar Result: iou/accuracy 0.1134/0.2020
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_112 - toaster Result: iou/accuracy 0.4452/0.5581
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_113 - bulletin board Result: iou/accuracy 0.5121/0.7038
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_114 - ironing board Result: iou/accuracy 0.2393/0.2462
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_115 - fireplace Result: iou/accuracy 0.5735/0.6081
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_116 - soap dish Result: iou/accuracy 0.3712/0.5663
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_117 - kitchen counter Result: iou/accuracy 0.6526/0.7905
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_118 - doorframe Result: iou/accuracy 0.5438/0.7456
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_119 - toilet paper dispenser Result: iou/accuracy 0.4059/0.4878
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_120 - mini fridge Result: iou/accuracy 0.6758/0.7003
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_121 - fire extinguisher Result: iou/accuracy 0.4970/0.6281
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_122 - ball Result: iou/accuracy 0.2443/0.9535
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_123 - hat Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_124 - shower curtain rod Result: iou/accuracy 0.4933/0.9028
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_125 - water cooler Result: iou/accuracy 0.9155/0.9545
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_126 - paper cutter Result: iou/accuracy 0.3266/0.3881
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_127 - tray Result: iou/accuracy 0.0785/0.0983
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_128 - shower door Result: iou/accuracy 0.4796/0.5998
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_129 - pillar Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_130 - ledge Result: iou/accuracy 0.0673/0.0955
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_131 - toaster oven Result: iou/accuracy 0.0566/0.0566
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_132 - mouse Result: iou/accuracy 0.1318/0.6714
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_133 - toilet seat cover dispenser Result: iou/accuracy 0.7399/0.8027
+[2024-10-13 17:08:07,554 INFO test.py line 317 25394] Class_134 - furniture Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_135 - cart Result: iou/accuracy 0.6143/0.6587
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_136 - storage container Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_137 - scale Result: iou/accuracy 0.3412/0.9383
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_138 - tissue box Result: iou/accuracy 0.3666/0.5337
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_139 - light switch Result: iou/accuracy 0.0920/0.5208
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_140 - crate Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_141 - power outlet Result: iou/accuracy 0.2851/0.4880
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_142 - decoration Result: iou/accuracy 0.5001/0.6851
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_143 - sign Result: iou/accuracy 0.2511/0.2836
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_144 - projector Result: iou/accuracy 0.1472/1.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_145 - closet door Result: iou/accuracy 0.1828/0.1989
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_146 - vacuum cleaner Result: iou/accuracy 0.9463/0.9734
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_147 - candle Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_148 - plunger Result: iou/accuracy 0.5930/0.9625
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_149 - stuffed animal Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_150 - headphones Result: iou/accuracy 0.2356/0.3792
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_151 - dish rack Result: iou/accuracy 0.6662/0.9818
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_152 - broom Result: iou/accuracy 0.0945/0.1551
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_153 - guitar case Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_154 - range hood Result: iou/accuracy 0.5888/0.6907
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_155 - dustpan Result: iou/accuracy 0.1061/0.1260
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_156 - hair dryer Result: iou/accuracy 0.2415/0.3363
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_157 - water bottle Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_158 - handicap bar Result: iou/accuracy 0.0600/0.8982
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_159 - purse Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_160 - vent Result: iou/accuracy 0.0801/0.3974
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_161 - shower floor Result: iou/accuracy 0.5231/0.9835
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_162 - water pitcher Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_163 - mailbox Result: iou/accuracy 0.4367/0.4378
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_164 - bowl Result: iou/accuracy 0.5051/0.8113
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_165 - paper bag Result: iou/accuracy 0.0871/0.1141
+[2024-10-13 17:08:07,555 INFO test.py line 317 25394] Class_166 - alarm clock Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_167 - music stand Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_168 - projector screen Result: iou/accuracy 0.0057/0.0057
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_169 - divider Result: iou/accuracy 0.5312/0.6208
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_170 - laundry detergent Result: iou/accuracy 0.0744/0.0753
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_171 - bathroom counter Result: iou/accuracy 0.3101/0.3320
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_172 - object Result: iou/accuracy 0.0650/0.1345
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_173 - bathroom vanity Result: iou/accuracy 0.3760/0.5894
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_174 - closet wall Result: iou/accuracy 0.0424/0.0570
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_175 - laundry hamper Result: iou/accuracy 0.1795/0.2120
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_176 - bathroom stall door Result: iou/accuracy 0.5066/0.5240
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_177 - ceiling light Result: iou/accuracy 0.4168/0.5167
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_178 - trash bin Result: iou/accuracy 0.4187/0.6079
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_179 - dumbbell Result: iou/accuracy 0.8231/0.9822
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_180 - stair rail Result: iou/accuracy 0.5044/0.5994
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_181 - tube Result: iou/accuracy 0.2023/0.3109
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_182 - bathroom cabinet Result: iou/accuracy 0.0938/0.1731
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_183 - cd case Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_184 - closet rod Result: iou/accuracy 0.2890/0.9056
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_185 - coffee kettle Result: iou/accuracy 0.3411/0.7699
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_186 - structure Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_187 - shower head Result: iou/accuracy 0.2802/0.3198
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_188 - keyboard piano Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_189 - case of water bottles Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_190 - coat rack Result: iou/accuracy 0.1179/0.3772
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_191 - storage organizer Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_192 - folded chair Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_193 - fire alarm Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_194 - power strip Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_195 - calendar Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_196 - poster Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_197 - potted plant Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_198 - luggage Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,556 INFO test.py line 317 25394] Class_199 - mattress Result: iou/accuracy 0.0000/0.0000
+[2024-10-13 17:08:07,557 INFO test.py line 325 25394] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
diff --git a/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..052dcb4e8a94010df2ff14ddfe814d95cd554997
--- /dev/null
+++ b/scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log
@@ -0,0 +1,3 @@
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