| weight = None | |
| resume = False | |
| evaluate = True | |
| test_only = False | |
| seed = 36123202 | |
| save_path = 'exp/s3dis/semseg-pt-v3m1-1-ppt-extreme' | |
| num_worker = 48 | |
| batch_size = 24 | |
| batch_size_val = None | |
| batch_size_test = None | |
| epoch = 100 | |
| eval_epoch = 100 | |
| sync_bn = False | |
| enable_amp = True | |
| empty_cache = 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) | |
| model = dict( | |
| type='PPT-v1m1', | |
| backbone=dict( | |
| type='PT-v3m1', | |
| in_channels=6, | |
| order=('z', 'z-trans', 'hilbert', 'hilbert-trans'), | |
| stride=(2, 2, 2, 2), | |
| enc_depths=(2, 2, 2, 6, 2), | |
| enc_channels=(32, 64, 128, 256, 384), | |
| enc_num_head=(2, 4, 8, 16, 24), | |
| enc_patch_size=(128, 128, 128, 128, 128), | |
| dec_depths=(2, 2, 2, 2), | |
| dec_channels=(64, 64, 128, 256), | |
| dec_num_head=(4, 4, 8, 16), | |
| dec_patch_size=(128, 128, 128, 128), | |
| 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=True, | |
| enable_flash=False, | |
| upcast_attention=True, | |
| upcast_softmax=True, | |
| cls_mode=False, | |
| pdnorm_bn=True, | |
| pdnorm_ln=True, | |
| pdnorm_decouple=True, | |
| pdnorm_adaptive=False, | |
| pdnorm_affine=True, | |
| pdnorm_conditions=('ScanNet', 'S3DIS', 'Structured3D')), | |
| 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=('Structured3D', 'ScanNet', 'S3DIS'), | |
| template='[x]', | |
| clip_model='ViT-B/16', | |
| 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)), | |
| backbone_mode=False) | |
| 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=13, | |
| ignore_index=-1, | |
| names=[ | |
| 'ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door', | |
| 'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter' | |
| ], | |
| train=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='Structured3DDataset', | |
| split=['train', 'val', '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='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=204800, 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=4), | |
| 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='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='ScanNet')), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='Collect', | |
| keys=('coord', 'grid_coord', 'segment', 'condition'), | |
| feat_keys=('color', 'normal')) | |
| ], | |
| test_mode=False, | |
| loop=2), | |
| dict( | |
| type='S3DISDataset', | |
| split=('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6'), | |
| data_root='data/s3dis', | |
| 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='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=0.6, mode='random'), | |
| dict(type='SphereCrop', point_max=204800, mode='random'), | |
| dict(type='CenterShift', apply_z=False), | |
| dict(type='NormalizeColor'), | |
| dict(type='Add', keys_dict=dict(condition='S3DIS')), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='Collect', | |
| keys=('coord', 'grid_coord', 'segment', 'condition'), | |
| feat_keys=('color', 'normal')) | |
| ], | |
| test_mode=False, | |
| loop=1) | |
| ], | |
| loop=1), | |
| val=dict( | |
| type='S3DISDataset', | |
| split='Area_5', | |
| data_root='data/s3dis', | |
| transform=[ | |
| dict(type='CenterShift', apply_z=True), | |
| dict( | |
| type='Copy', | |
| keys_dict=dict(coord='origin_coord', | |
| segment='origin_segment')), | |
| 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='S3DIS')), | |
| dict( | |
| type='Collect', | |
| keys=('coord', 'grid_coord', 'origin_coord', 'segment', | |
| 'origin_segment', 'condition'), | |
| offset_keys_dict=dict( | |
| offset='coord', origin_offset='origin_coord'), | |
| feat_keys=('color', 'normal')) | |
| ], | |
| test_mode=False), | |
| test=dict( | |
| type='S3DISDataset', | |
| split='Area_5', | |
| data_root='data/s3dis', | |
| 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='S3DIS')), | |
| 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 | |
| }]]))) | |