55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
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# dataset settings
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dataset_type = 'CUB'
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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='Resize', size=600),
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dict(type='RandomCrop', size=448),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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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='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', size=600),
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dict(type='CenterCrop', crop_size=448),
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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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data_root = 'data/CUB_200_2011/'
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data = dict(
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samples_per_gpu=8,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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ann_file=data_root + 'images.txt',
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image_class_labels_file=data_root + 'image_class_labels.txt',
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train_test_split_file=data_root + 'train_test_split.txt',
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data_prefix=data_root + 'images',
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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 + 'images.txt',
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image_class_labels_file=data_root + 'image_class_labels.txt',
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train_test_split_file=data_root + 'train_test_split.txt',
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data_prefix=data_root + 'images',
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test_mode=True,
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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 + 'images.txt',
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image_class_labels_file=data_root + 'image_class_labels.txt',
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train_test_split_file=data_root + 'train_test_split.txt',
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data_prefix=data_root + 'images',
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test_mode=True,
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pipeline=test_pipeline))
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evaluation = dict(
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interval=1, metric='accuracy',
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save_best='auto') # save the checkpoint with highest accuracy
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