79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
dataset_type = 'FaceOccludedDataset'
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data_root = 'data/occlusion-aware-face-dataset'
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crop_size = (512, 512)
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', img_scale=(512, 512)),
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dict(type='RandomFlip', prob=0.5),
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dict(type='RandomRotate', degree=(-30, 30), prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(512, 512),
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img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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flip=True,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='ResizeToMultiple', size_divisor=32),
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dict(type='RandomFlip'),
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dict(
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type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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])
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]
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dataset_train_A = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='NatOcc_hand_sot/img',
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ann_dir='NatOcc_hand_sot/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_train_B = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='NatOcc_object/img',
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ann_dir='NatOcc_object/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_train_C = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='RandOcc/img',
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ann_dir='RandOcc/mask',
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split='train.txt',
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pipeline=train_pipeline)
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dataset_valid = dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='RealOcc/image',
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ann_dir='RealOcc/mask',
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split='RealOcc/split/val.txt',
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pipeline=test_pipeline)
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=[dataset_train_A, dataset_train_B, dataset_train_C],
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val=dataset_valid)
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