| weight = None | |
| resume = False | |
| evaluate = True | |
| test_only = False | |
| seed = 1023306 | |
| save_path = 'exp/scannet200/semseg-pt-v3m1-0-base' | |
| 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 | |
| find_unused_parameters = False | |
| mix_prob = 0.8 | |
| param_dicts = [dict(keyword='block', lr=0.0006)] | |
| 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='DefaultTrainer') | |
| 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='DefaultSegmentorV2', | |
| num_classes=200, | |
| backbone_out_channels=64, | |
| 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, 512), | |
| enc_num_head=(2, 4, 8, 16, 32), | |
| enc_patch_size=(1024, 1024, 1024, 1024, 1024), | |
| dec_depths=(2, 2, 2, 2), | |
| dec_channels=(64, 64, 128, 256), | |
| dec_num_head=(4, 4, 8, 16), | |
| 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=False, | |
| pdnorm_ln=False, | |
| 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) | |
| ]) | |
| 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 = 'ScanNet200Dataset' | |
| data_root = 'data/scannet' | |
| 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='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='ToTensor'), | |
| dict( | |
| type='Collect', | |
| keys=('coord', 'grid_coord', 'segment'), | |
| feat_keys=('color', 'normal')) | |
| ], | |
| test_mode=False, | |
| loop=8), | |
| val=dict( | |
| type='ScanNet200Dataset', | |
| 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='Collect', | |
| keys=('coord', 'grid_coord', 'segment'), | |
| 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='ToTensor'), | |
| dict( | |
| type='Collect', | |
| keys=('coord', 'grid_coord', 'index'), | |
| 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 | |
| }]]))) | |