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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())
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