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| # Copyright (c) OpenMMLab. All rights reserved. | |
| # This is a BETA new format config file, and the usage may change recently. | |
| from mmengine.dataset import DefaultSampler | |
| from mmpretrain.datasets import (AutoAugment, CenterCrop, ImageNet, | |
| LoadImageFromFile, PackInputs, RandomErasing, | |
| RandomFlip, RandomResizedCrop, ResizeEdge) | |
| from mmpretrain.evaluation import Accuracy | |
| # 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'), | |
| dict(type=RandomFlip, prob=0.5, direction='horizontal'), | |
| dict( | |
| type=AutoAugment, | |
| policies='imagenet', | |
| hparams=dict(pad_val=[round(x) for x in bgr_mean])), | |
| dict( | |
| type=RandomErasing, | |
| erase_prob=0.2, | |
| mode='rand', | |
| min_area_ratio=0.02, | |
| max_area_ratio=1 / 3, | |
| fill_color=bgr_mean, | |
| fill_std=bgr_std), | |
| dict(type=PackInputs), | |
| ] | |
| test_pipeline = [ | |
| dict(type=LoadImageFromFile), | |
| dict(type=ResizeEdge, scale=256, edge='short', backend='pillow'), | |
| dict(type=CenterCrop, crop_size=224), | |
| dict(type=PackInputs), | |
| ] | |
| train_dataloader = dict( | |
| batch_size=128, | |
| num_workers=5, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root='data/imagenet', | |
| split='train', | |
| pipeline=train_pipeline), | |
| sampler=dict(type=DefaultSampler, shuffle=True), | |
| ) | |
| val_dataloader = dict( | |
| batch_size=128, | |
| num_workers=5, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root='data/imagenet', | |
| split='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 | |