| 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()) | |