40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
_base_ = [
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'../_base_/models/efficientnet_b8.py',
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'../_base_/datasets/imagenet_bs32.py',
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'../_base_/schedules/imagenet_bs256.py',
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'../_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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mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='RandomResizedCrop',
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size=672,
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efficientnet_style=True,
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interpolation='bicubic'),
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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(
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type='CenterCrop',
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crop_size=672,
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efficientnet_style=True,
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interpolation='bicubic'),
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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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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))
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