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| # dataset settings | |
| dataset_type = 'ImageNet' | |
| img_norm_cfg = dict( | |
| mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255], | |
| std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255], | |
| to_rgb=True) | |
| image_size = 384 | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| size=image_size, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), | |
| # dict( | |
| # type='RandAugment', | |
| # policies={{_base_.rand_increasing_policies}}, | |
| # num_policies=2, | |
| # total_level=10, | |
| # magnitude_level=9, | |
| # magnitude_std=0.5, | |
| # hparams=dict( | |
| # pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], | |
| # interpolation='bicubic')), | |
| dict( | |
| type='RandomErasing', | |
| erase_prob=0.25, | |
| mode='rand', | |
| min_area_ratio=0.02, | |
| max_area_ratio=1 / 3, | |
| fill_color=img_norm_cfg['mean'][::-1], | |
| fill_std=img_norm_cfg['std'][::-1]), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='ToTensor', keys=['gt_label']), | |
| dict(type='Collect', keys=['img', 'gt_label']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='Resize', | |
| size=(image_size, -1), | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='CenterCrop', crop_size=image_size), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ] | |
| data = dict( | |
| samples_per_gpu=64, | |
| workers_per_gpu=8, | |
| train=dict( | |
| type=dataset_type, | |
| data_prefix='data/imagenet/train', | |
| pipeline=train_pipeline), | |
| val=dict( | |
| type=dataset_type, | |
| data_prefix='data/imagenet/val', | |
| ann_file='data/imagenet/meta/val.txt', | |
| pipeline=test_pipeline), | |
| test=dict( | |
| # replace `data/val` with `data/test` for standard test | |
| type=dataset_type, | |
| data_prefix='data/imagenet/val', | |
| ann_file='data/imagenet/meta/val.txt', | |
| pipeline=test_pipeline)) | |
| evaluation = dict(interval=10, metric='accuracy') | |