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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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204
tests/kit/model_zoo/torchvision/torchvision.py
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@@ -1,5 +1,3 @@
from collections import namedtuple
import torch
import torchvision
import torchvision.models as tm
@@ -29,103 +27,133 @@ def swin_s():
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=[7, 7],
stochastic_depth_prob=0, # it is originally 0.2, but we set it to 0 to make it deterministic
stochastic_depth_prob=0, # it is originally 0.2, but we set it to 0 to make it deterministic
weights=weights,
progress=progress,
)
# special output transform fn
google_net_output_transform_fn = lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.GoogLeNetOutputs
) else dict(output=x)
swin_s_output_output_transform_fn = lambda x: {f'output{idx}': val
for idx, val in enumerate(x)} if isinstance(x, tuple) else dict(output=x)
inception_v3_output_transform_fn = lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.InceptionOutputs
) else dict(output=x)
google_net_output_transform_fn = (
lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.GoogLeNetOutputs) else dict(output=x)
)
swin_s_output_output_transform_fn = (
lambda x: {f"output{idx}": val for idx, val in enumerate(x)} if isinstance(x, tuple) else dict(output=x)
)
inception_v3_output_transform_fn = (
lambda x: dict(output=sum(x)) if isinstance(x, torchvision.models.InceptionOutputs) else dict(output=x)
)
model_zoo.register(name='torchvision_alexnet',
model_fn=tm.alexnet,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_densenet121',
model_fn=tm.densenet121,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_efficientnet_b0',
model_fn=tm.efficientnet_b0,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_stochastic_depth_prob=True))
model_zoo.register(name='torchvision_googlenet',
model_fn=tm.googlenet,
data_gen_fn=data_gen_fn,
output_transform_fn=google_net_output_transform_fn)
model_zoo.register(name='torchvision_inception_v3',
model_fn=tm.inception_v3,
data_gen_fn=inception_v3_data_gen_fn,
output_transform_fn=inception_v3_output_transform_fn)
model_zoo.register(name='torchvision_mobilenet_v2',
model_fn=tm.mobilenet_v2,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_mobilenet_v3_small',
model_fn=tm.mobilenet_v3_small,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_mnasnet0_5',
model_fn=tm.mnasnet0_5,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_resnet18',
model_fn=tm.resnet18,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_regnet_x_16gf',
model_fn=tm.regnet_x_16gf,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_resnext50_32x4d',
model_fn=tm.resnext50_32x4d,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_shufflenet_v2_x0_5',
model_fn=tm.shufflenet_v2_x0_5,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_squeezenet1_0',
model_fn=tm.squeezenet1_0,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(
name="torchvision_alexnet", model_fn=tm.alexnet, data_gen_fn=data_gen_fn, output_transform_fn=output_transform_fn
)
model_zoo.register(
name="torchvision_densenet121",
model_fn=tm.densenet121,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_efficientnet_b0",
model_fn=tm.efficientnet_b0,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_stochastic_depth_prob=True),
)
model_zoo.register(
name="torchvision_googlenet",
model_fn=tm.googlenet,
data_gen_fn=data_gen_fn,
output_transform_fn=google_net_output_transform_fn,
)
model_zoo.register(
name="torchvision_inception_v3",
model_fn=tm.inception_v3,
data_gen_fn=inception_v3_data_gen_fn,
output_transform_fn=inception_v3_output_transform_fn,
)
model_zoo.register(
name="torchvision_mobilenet_v2",
model_fn=tm.mobilenet_v2,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_mobilenet_v3_small",
model_fn=tm.mobilenet_v3_small,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_mnasnet0_5",
model_fn=tm.mnasnet0_5,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_resnet18", model_fn=tm.resnet18, data_gen_fn=data_gen_fn, output_transform_fn=output_transform_fn
)
model_zoo.register(
name="torchvision_regnet_x_16gf",
model_fn=tm.regnet_x_16gf,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_resnext50_32x4d",
model_fn=tm.resnext50_32x4d,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_shufflenet_v2_x0_5",
model_fn=tm.shufflenet_v2_x0_5,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_squeezenet1_0",
model_fn=tm.squeezenet1_0,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(name='torchvision_vgg11',
model_fn=tm.vgg11,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_wide_resnet50_2',
model_fn=tm.wide_resnet50_2,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(
name="torchvision_vgg11", model_fn=tm.vgg11, data_gen_fn=data_gen_fn, output_transform_fn=output_transform_fn
)
model_zoo.register(
name="torchvision_wide_resnet50_2",
model_fn=tm.wide_resnet50_2,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
if version.parse(torchvision.__version__) >= version.parse('0.12.0'):
model_zoo.register(name='torchvision_vit_b_16',
model_fn=tm.vit_b_16,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn)
model_zoo.register(name='torchvision_convnext_base',
model_fn=tm.convnext_base,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_stochastic_depth_prob=True))
if version.parse(torchvision.__version__) >= version.parse('0.13.0'):
if version.parse(torchvision.__version__) >= version.parse("0.12.0"):
model_zoo.register(
name='torchvision_swin_s',
name="torchvision_vit_b_16",
model_fn=tm.vit_b_16,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="torchvision_convnext_base",
model_fn=tm.convnext_base,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_stochastic_depth_prob=True),
)
if version.parse(torchvision.__version__) >= version.parse("0.13.0"):
model_zoo.register(
name="torchvision_swin_s",
model_fn=swin_s,
data_gen_fn=data_gen_fn,
output_transform_fn=swin_s_output_output_transform_fn,
)
model_zoo.register(name='torchvision_efficientnet_v2_s',
model_fn=tm.efficientnet_v2_s,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_stochastic_depth_prob=True))
model_zoo.register(
name="torchvision_efficientnet_v2_s",
model_fn=tm.efficientnet_v2_s,
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
model_attribute=ModelAttribute(has_stochastic_depth_prob=True),
)