52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
_base_ = [
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'../_base_/models/regnet/regnetx_1.6gf.py',
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'../_base_/datasets/imagenet_bs32.py',
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'../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
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]
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# dataset settings
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dataset_type = 'ImageNet'
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img_norm_cfg = dict(
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# The mean and std are used in PyCls when training RegNets
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mean=[103.53, 116.28, 123.675],
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std=[57.375, 57.12, 58.395],
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to_rgb=False)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', size=224),
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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=(256, -1)),
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dict(type='CenterCrop', crop_size=224),
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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 = dict(
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samples_per_gpu=32,
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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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data_prefix='data/imagenet/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline),
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test=dict(
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# replace `data/val` with `data/test` for standard test
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='accuracy')
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