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| # dataset settings | |
| dataset_type = 'ImageNet' | |
| data_preprocessor = dict( | |
| num_classes=1000, | |
| # RGB format normalization parameters | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| # convert image from BGR to RGB | |
| to_rgb=True, | |
| ) | |
| bgr_mean = data_preprocessor['mean'][::-1] | |
| bgr_std = data_preprocessor['std'][::-1] | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict( | |
| type='RandAugment', | |
| policies='timm_increasing', | |
| num_policies=2, | |
| total_level=10, | |
| magnitude_level=9, | |
| magnitude_std=0.5, | |
| hparams=dict( | |
| pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')), | |
| dict( | |
| type='RandomErasing', | |
| erase_prob=0.25, | |
| mode='rand', | |
| min_area_ratio=0.02, | |
| max_area_ratio=1 / 3, | |
| fill_color=bgr_mean, | |
| fill_std=bgr_std), | |
| dict(type='PackClsInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='ResizeEdge', | |
| scale=233, | |
| edge='short', | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='CenterCrop', crop_size=224), | |
| dict(type='PackClsInputs') | |
| ] | |
| train_dataloader = dict( | |
| batch_size=64, | |
| num_workers=5, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root='data/imagenet', | |
| ann_file='meta/train.txt', | |
| data_prefix='train', | |
| pipeline=train_pipeline), | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| ) | |
| val_dataloader = dict( | |
| batch_size=64, | |
| num_workers=5, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root='data/imagenet', | |
| ann_file='meta/val.txt', | |
| data_prefix='val', | |
| pipeline=test_pipeline), | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| ) | |
| val_evaluator = dict(type='Accuracy', topk=(1, 5)) | |
| # If you want standard test, please manually configure the test dataset | |
| test_dataloader = val_dataloader | |
| test_evaluator = val_evaluator | |