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_base_ = ["../_base_/default_runtime.py"] |
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batch_size = 12 |
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num_worker = 24 |
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mix_prob = 0.8 |
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empty_cache = False |
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enable_amp = True |
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model = dict( |
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type="DefaultSegmentorV2", |
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num_classes=21, |
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backbone_out_channels=64, |
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backbone=dict( |
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type="PT-v3m1", |
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in_channels=6, |
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order=("z", "z-trans", "hilbert", "hilbert-trans"), |
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stride=(2, 2, 2, 2), |
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enc_depths=(2, 2, 2, 6, 2), |
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enc_channels=(32, 64, 128, 256, 512), |
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enc_num_head=(2, 4, 8, 16, 32), |
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enc_patch_size=(1024, 1024, 1024, 1024, 1024), |
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dec_depths=(2, 2, 2, 2), |
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dec_channels=(64, 64, 128, 256), |
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dec_num_head=(4, 4, 8, 16), |
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dec_patch_size=(1024, 1024, 1024, 1024), |
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mlp_ratio=4, |
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qkv_bias=True, |
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qk_scale=None, |
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attn_drop=0.0, |
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proj_drop=0.0, |
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drop_path=0.3, |
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shuffle_orders=True, |
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pre_norm=True, |
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enable_rpe=False, |
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enable_flash=True, |
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upcast_attention=False, |
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upcast_softmax=False, |
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cls_mode=False, |
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pdnorm_bn=False, |
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pdnorm_ln=False, |
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pdnorm_decouple=True, |
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pdnorm_adaptive=False, |
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pdnorm_affine=True, |
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pdnorm_conditions=("ScanNet", "S3DIS", "Structured3D"), |
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), |
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criteria=[ |
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dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1), |
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dict(type="LovaszLoss", mode="multiclass", loss_weight=1.0, ignore_index=-1), |
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], |
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) |
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epoch = 800 |
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optimizer = dict(type="AdamW", lr=0.006, weight_decay=0.05) |
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scheduler = dict( |
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type="OneCycleLR", |
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max_lr=[0.006, 0.0006], |
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pct_start=0.05, |
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anneal_strategy="cos", |
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div_factor=10.0, |
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final_div_factor=1000.0, |
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) |
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param_dicts = [dict(keyword="block", lr=0.0006)] |
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dataset_type = "DefaultDataset" |
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data_root = "data/matterport3d" |
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data = dict( |
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num_classes=21, |
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ignore_index=-1, |
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names=( |
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"wall", |
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"floor", |
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"cabinet", |
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"bed", |
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"chair", |
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"sofa", |
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"table", |
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"door", |
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"window", |
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"bookshelf", |
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"picture", |
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"counter", |
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"desk", |
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"curtain", |
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"refrigerator", |
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"shower curtain", |
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"toilet", |
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"sink", |
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"bathtub", |
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"other", |
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"ceiling", |
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), |
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train=dict( |
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type=dataset_type, |
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split="train", |
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data_root=data_root, |
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transform=[ |
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dict(type="CenterShift", apply_z=True), |
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dict( |
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type="RandomDropout", dropout_ratio=0.2, dropout_application_ratio=0.2 |
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), |
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dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=0.5), |
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dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=0.5), |
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dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=0.5), |
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dict(type="RandomScale", scale=[0.9, 1.1]), |
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dict(type="RandomFlip", p=0.5), |
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dict(type="RandomJitter", sigma=0.005, clip=0.02), |
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dict(type="ElasticDistortion", distortion_params=[[0.2, 0.4], [0.8, 1.6]]), |
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dict(type="ChromaticAutoContrast", p=0.2, blend_factor=None), |
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dict(type="ChromaticTranslation", p=0.95, ratio=0.05), |
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dict(type="ChromaticJitter", p=0.95, std=0.05), |
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dict( |
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type="GridSample", |
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grid_size=0.02, |
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hash_type="fnv", |
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mode="train", |
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return_grid_coord=True, |
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), |
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dict(type="SphereCrop", point_max=102400, mode="random"), |
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dict(type="CenterShift", apply_z=False), |
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dict(type="NormalizeColor"), |
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dict(type="ToTensor"), |
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dict( |
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type="Collect", |
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keys=("coord", "grid_coord", "segment"), |
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feat_keys=("color", "normal"), |
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), |
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], |
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test_mode=False, |
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), |
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val=dict( |
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type=dataset_type, |
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split="val", |
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data_root=data_root, |
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transform=[ |
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dict(type="CenterShift", apply_z=True), |
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dict( |
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type="GridSample", |
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grid_size=0.02, |
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hash_type="fnv", |
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mode="train", |
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return_grid_coord=True, |
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), |
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dict(type="CenterShift", apply_z=False), |
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dict(type="NormalizeColor"), |
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dict(type="ToTensor"), |
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dict( |
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type="Collect", |
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keys=("coord", "grid_coord", "segment"), |
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feat_keys=("color", "normal"), |
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), |
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], |
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test_mode=False, |
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), |
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test=dict( |
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type=dataset_type, |
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split="val", |
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data_root=data_root, |
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transform=[ |
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dict(type="CenterShift", apply_z=True), |
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dict(type="NormalizeColor"), |
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], |
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test_mode=True, |
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test_cfg=dict( |
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voxelize=dict( |
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type="GridSample", |
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grid_size=0.02, |
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hash_type="fnv", |
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mode="test", |
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keys=("coord", "color", "normal"), |
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return_grid_coord=True, |
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), |
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crop=None, |
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post_transform=[ |
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dict(type="CenterShift", apply_z=False), |
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dict(type="ToTensor"), |
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dict( |
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type="Collect", |
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keys=("coord", "grid_coord", "index"), |
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feat_keys=("color", "normal"), |
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), |
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], |
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aug_transform=[ |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[0], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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) |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[1 / 2], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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) |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[1], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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) |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[3 / 2], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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) |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[0], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[0.95, 0.95]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[1 / 2], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[0.95, 0.95]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[1], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[0.95, 0.95]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[3 / 2], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[0.95, 0.95]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[0], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[1.05, 1.05]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[1 / 2], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[1.05, 1.05]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[1], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[1.05, 1.05]), |
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], |
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[ |
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dict( |
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type="RandomRotateTargetAngle", |
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angle=[3 / 2], |
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axis="z", |
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center=[0, 0, 0], |
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p=1, |
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), |
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dict(type="RandomScale", scale=[1.05, 1.05]), |
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], |
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[dict(type="RandomFlip", p=1)], |
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], |
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), |
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), |
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) |
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