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mobileone-s2_8xb32_in1k.py
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mobileone-s2_8xb32_in1k.py
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_base_ = [
'../_base_/models/mobileone/mobileone_s2.py',
'../_base_/datasets/imagenet_bs32_pil_resize.py',
'../_base_/schedules/imagenet_bs256_coslr_coswd_300e.py',
'../_base_/default_runtime.py'
]
# schedule settings
optim_wrapper = dict(paramwise_cfg=dict(norm_decay_mult=0.))
val_dataloader = dict(batch_size=256)
test_dataloader = dict(batch_size=256)
import copy # noqa: E402
bgr_mean = _base_.data_preprocessor['mean'][::-1]
base_train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=7,
magnitude_std=0.5,
hparams=dict(pad_val=[round(x) for x in bgr_mean])),
dict(type='PackInputs')
]
# modify start epoch RandomResizedCrop.scale to 160
# and RA.magnitude_level * 0.3
train_pipeline_1e = copy.deepcopy(base_train_pipeline)
train_pipeline_1e[1]['scale'] = 160
train_pipeline_1e[3]['magnitude_level'] *= 0.3
_base_.train_dataloader.dataset.pipeline = train_pipeline_1e
import copy # noqa: E402
# modify 137 epoch's RandomResizedCrop.scale to 192
# and RA.magnitude_level * 0.7
train_pipeline_37e = copy.deepcopy(base_train_pipeline)
train_pipeline_37e[1]['scale'] = 192
train_pipeline_37e[3]['magnitude_level'] *= 0.7
# modify 112 epoch's RandomResizedCrop.scale to 224
# and RA.magnitude_level * 1.0
train_pipeline_112e = copy.deepcopy(base_train_pipeline)
train_pipeline_112e[1]['scale'] = 224
train_pipeline_112e[3]['magnitude_level'] *= 1.0
custom_hooks = [
dict(
type='SwitchRecipeHook',
schedule=[
dict(action_epoch=37, pipeline=train_pipeline_37e),
dict(action_epoch=112, pipeline=train_pipeline_112e),
]),
dict(
type='EMAHook',
momentum=5e-4,
priority='ABOVE_NORMAL',
update_buffers=True)
]