Pointcept
'DefaultSegmentor is not in the models registry'
Experiment name: semseg-pt-v2m2-0-base Python interpreter dir: python Dataset: s3dis Config: semseg-pt-v2m2-0-base =========> CREATE EXP DIR <========= Experiment dir: /media/wsc/16B68EBDB68E9CBD/Han_PTv2/ptv2/PointTransformerV2-main/exp/s3dis/semseg-pt-v2m2-0-base Loading config in: configs/s3dis/semseg-pt-v2m2-0-base.py Running code in: exp/s3dis/semseg-pt-v2m2-0-base/code =========> RUN TASK <========= [2024-12-04 20:30:43,955 INFO defaults.py line 167 2199428] => Loading config ... [2024-12-04 20:30:43,955 INFO defaults.py line 169 2199428] Save path: exp/s3dis/semseg-pt-v2m2-0-base [2024-12-04 20:30:44,537 INFO defaults.py line 170 2199428] Config: weight = None resume = False evaluate = True test_only = False seed = 45629828 save_path = 'exp/s3dis/semseg-pt-v2m2-0-base' num_worker = 32 batch_size = 12 batch_size_val = None batch_size_test = 1 epoch = 3000 eval_epoch = 100 save_freq = None eval_metric = 'mIoU' sync_bn = False enable_amp = True empty_cache = False find_unused_parameters = False max_batch_points = 100000000.0 mix_prob = 0.8 param_dicts = None model = dict( type='DefaultSegmentor', backbone=dict( type='PT-v2m2', in_channels=6, num_classes=10, patch_embed_depth=2, patch_embed_channels=48, patch_embed_groups=6, patch_embed_neighbours=16, enc_depths=(2, 6, 2), enc_channels=(96, 192, 384), enc_groups=(12, 24, 48), enc_neighbours=(16, 16, 16), dec_depths=(1, 1, 1), dec_channels=(48, 96, 192), dec_groups=(6, 12, 24), dec_neighbours=(16, 16, 16), grid_sizes=(0.1, 0.2, 0.4), attn_qkv_bias=True, pe_multiplier=False, pe_bias=True, attn_drop_rate=0.0, drop_path_rate=0.3, enable_checkpoint=False, unpool_backend='interp'), criteria=[dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1)]) optimizer = dict(type='AdamW', lr=0.006, weight_decay=0.05) scheduler = dict(type='MultiStepLR', milestones=[0.6, 0.8], gamma=0.1) dataset_type = 'S3DISDataset' data_root = 'data/s3dis' data = dict( num_classes=10, ignore_index=-1, names=[ 'car', 'ground', 'groundline', 'highline', 'house', 'lowline', 'obstacle', 'road', 'tower', 'tree' ], train=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='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=0.04, hash_type='fnv', mode='train', keys=('coord', 'color', 'segment'), return_grid_coord=True), dict(type='SphereCrop', point_max=80000, mode='random'), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment'), feat_keys=['coord', 'color']) ], test_mode=False, loop=30), 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=0.04, hash_type='fnv', mode='train', keys=('coord', 'color', 'segment'), return_grid_coord=True), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment'), offset_keys_dict=dict(offset='coord'), feat_keys=['coord', 'color']) ], 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=0.04, hash_type='fnv', mode='test', keys=('coord', 'color'), return_grid_coord=True), crop=None, post_transform=[ dict(type='CenterShift', apply_z=False), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'index'), feat_keys=('coord', 'color')) ], aug_transform=[[{ 'type': 'RandomScale', 'scale': [0.9, 0.9] }], [{ 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomScale', 'scale': [1, 1] }], [{ 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomScale', 'scale': [1.1, 1.1] }], [{ 'type': 'RandomScale', 'scale': [0.9, 0.9] }, { 'type': 'RandomFlip', 'p': 1 }], [{ 'type': 'RandomScale', 'scale': [0.95, 0.95] }, { 'type': 'RandomFlip', 'p': 1 }], [{ 'type': 'RandomScale', 'scale': [1, 1] }, { 'type': 'RandomFlip', 'p': 1 }], [{ 'type': 'RandomScale', 'scale': [1.05, 1.05] }, { 'type': 'RandomFlip', 'p': 1 }], [{ 'type': 'RandomScale', 'scale': [1.1, 1.1] }, { 'type': 'RandomFlip', 'p': 1 }]]))) num_worker_per_gpu = 32 batch_size_per_gpu = 12 batch_size_val_per_gpu = 1
[2024-12-04 20:30:44,537 INFO defaults.py line 172 2199428] => Building model ...
Traceback (most recent call last):
File "exp/s3dis/semseg-pt-v2m2-0-base/code/tools/train.py", line 34, in
