63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# This is a BETA new format config file, and the usage may change recently.
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from mmengine.dataset import DefaultSampler
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from mmpretrain.datasets import (CenterCrop, ImageNet, LoadImageFromFile,
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PackInputs, RandomFlip, RandomResizedCrop,
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ResizeEdge)
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from mmpretrain.evaluation import Accuracy
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# dataset settings
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dataset_type = ImageNet
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data_preprocessor = dict(
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num_classes=1000,
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# RGB format normalization parameters
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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# convert image from BGR to RGB
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to_rgb=True,
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)
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train_pipeline = [
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dict(type=LoadImageFromFile),
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dict(type=RandomResizedCrop, scale=224),
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dict(type=RandomFlip, prob=0.5, direction='horizontal'),
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dict(type=PackInputs),
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]
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test_pipeline = [
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dict(type=LoadImageFromFile),
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dict(type=ResizeEdge, scale=256, edge='short'),
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dict(type=CenterCrop, crop_size=224),
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dict(type=PackInputs),
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]
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train_dataloader = dict(
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batch_size=32,
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num_workers=5,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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ann_file='meta/train.txt',
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data_prefix='train',
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pipeline=train_pipeline),
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sampler=dict(type=DefaultSampler, shuffle=True),
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)
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val_dataloader = dict(
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batch_size=32,
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num_workers=5,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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ann_file='meta/val.txt',
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data_prefix='val',
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pipeline=test_pipeline),
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sampler=dict(type=DefaultSampler, shuffle=False),
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)
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val_evaluator = dict(type=Accuracy, topk=(1, 5))
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# If you want standard test, please manually configure the test dataset
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test_dataloader = val_dataloader
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test_evaluator = val_evaluator
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