| 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 |
| }]]))) |
|
|
