66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
_base_ = [
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'../_base_/models/mobileone/mobileone_s3.py',
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'../_base_/datasets/imagenet_bs32_pil_resize.py',
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'../_base_/schedules/imagenet_bs256_coslr_coswd_300e.py',
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'../_base_/default_runtime.py'
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]
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# schedule settings
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optim_wrapper = dict(paramwise_cfg=dict(norm_decay_mult=0.))
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val_dataloader = dict(batch_size=256)
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test_dataloader = dict(batch_size=256)
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import copy # noqa: E402
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bgr_mean = _base_.data_preprocessor['mean'][::-1]
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base_train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', scale=224, backend='pillow'),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(
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type='RandAugment',
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policies='timm_increasing',
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num_policies=2,
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total_level=10,
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magnitude_level=7,
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magnitude_std=0.5,
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hparams=dict(pad_val=[round(x) for x in bgr_mean])),
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dict(type='PackClsInputs')
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]
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# modify start epoch RandomResizedCrop.scale to 160
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# and RA.magnitude_level * 0.3
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train_pipeline_1e = copy.deepcopy(base_train_pipeline)
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train_pipeline_1e[1]['scale'] = 160
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train_pipeline_1e[3]['magnitude_level'] *= 0.3
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_base_.train_dataloader.dataset.pipeline = train_pipeline_1e
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import copy # noqa: E402
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# modify 137 epoch's RandomResizedCrop.scale to 192
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# and RA.magnitude_level * 0.7
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train_pipeline_37e = copy.deepcopy(base_train_pipeline)
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train_pipeline_37e[1]['scale'] = 192
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train_pipeline_37e[3]['magnitude_level'] *= 0.7
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# modify 112 epoch's RandomResizedCrop.scale to 224
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# and RA.magnitude_level * 1.0
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train_pipeline_112e = copy.deepcopy(base_train_pipeline)
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train_pipeline_112e[1]['scale'] = 224
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train_pipeline_112e[3]['magnitude_level'] *= 1.0
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custom_hooks = [
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dict(
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type='SwitchRecipeHook',
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schedule=[
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dict(action_epoch=37, pipeline=train_pipeline_37e),
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dict(action_epoch=112, pipeline=train_pipeline_112e),
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]),
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dict(
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type='EMAHook',
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momentum=5e-4,
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priority='ABOVE_NORMAL',
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update_buffers=True)
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]
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