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|
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dataset_type = 'WIDERFaceDataset' |
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data_root = 'data/WIDERFace/' |
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img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile', to_float32=True), |
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dict(type='LoadAnnotations', with_bbox=True), |
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dict( |
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type='PhotoMetricDistortion', |
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brightness_delta=32, |
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contrast_range=(0.5, 1.5), |
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saturation_range=(0.5, 1.5), |
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hue_delta=18), |
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dict( |
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type='Expand', |
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mean=img_norm_cfg['mean'], |
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to_rgb=img_norm_cfg['to_rgb'], |
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ratio_range=(1, 4)), |
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dict( |
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type='MinIoURandomCrop', |
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min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), |
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min_crop_size=0.3), |
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dict(type='Resize', img_scale=(300, 300), keep_ratio=False), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), |
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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=(300, 300), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=False), |
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dict(type='Normalize', **img_norm_cfg), |
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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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data = dict( |
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samples_per_gpu=60, |
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workers_per_gpu=2, |
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train=dict( |
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type='RepeatDataset', |
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times=2, |
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dataset=dict( |
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type=dataset_type, |
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ann_file=data_root + 'train.txt', |
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img_prefix=data_root + 'WIDER_train/', |
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min_size=17, |
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pipeline=train_pipeline)), |
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val=dict( |
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type=dataset_type, |
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ann_file=data_root + 'val.txt', |
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img_prefix=data_root + 'WIDER_val/', |
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pipeline=test_pipeline), |
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test=dict( |
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type=dataset_type, |
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ann_file=data_root + 'val.txt', |
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img_prefix=data_root + 'WIDER_val/', |
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pipeline=test_pipeline)) |
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