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https://github.com/open-mmlab/mmpretrain.git
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d56170a734
* add config for resnest test * fix config * add label smoothing * add memcached * minor fix * fix bug * fix config * add config * minor fix * fix configs * use EResize * change interpolation * add more configs * add docsting * add unittest * remove unnecessary changes * minor fix * add more docstring * fix linting * add efficient backbone * add config * add Edge Residual * fix bug * remove unnecessary files * refactor * add resize in crop to ensure crop size is output size * fix bug and add comments * test * fix * add more configs * add more configs * add more configs * fix bug * add model zoo * fix * reorganize code * add edge tpu * add edge tpu converter * rename * update readme * reorganize code and config * Rename configs of EfficientNet, and add metafile & model_zoo * Remove `backend='pillow'` * Add comments about EfficientNet-EdgeTPU * Rename the convert tool of EfficientNet. * Refactor EfficientNet and update docstring. * Update EfficientNet-EdgeTPU config * Fix unit tests Co-authored-by: lixinran <lixr423@outlook.com> Co-authored-by: lixinran <lixinran@sensetime.com> Co-authored-by: mzr1996 <mzr1996@163.com>
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
_base_ = [
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'../_base_/models/efficientnet_b0.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=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], 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=224,
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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=224,
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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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