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Change default_cfg names for senet to include the legacy and match model names

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This commit is contained in:
Ross Wightman 2020-08-08 09:01:46 -07:00
parent 6e9d6172c8
commit d5145fa4d5
2 changed files with 19 additions and 19 deletions
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2
tests/test_models.py
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@ -112,7 +112,7 @@ def test_model_default_cfgs(model_name, batch_size):
if 'GITHUB_ACTIONS' not in os.environ:
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models())
@pytest.mark.parametrize('model_name', list_models(pretrained=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_load_pretrained(model_name, batch_size):
"""Run a single forward pass with each model"""

36
timm/models/senet.py
View File

@ -36,25 +36,25 @@ def _cfg(url='', **kwargs):
default_cfgs = {
'senet154':
'legacy_senet154':
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth'),
'seresnet18': _cfg(
'legacy_seresnet18': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth',
interpolation='bicubic'),
'seresnet34': _cfg(
'legacy_seresnet34': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth'),
'seresnet50': _cfg(
'legacy_seresnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'),
'seresnet101': _cfg(
'legacy_seresnet101': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth'),
'seresnet152': _cfg(
'legacy_seresnet152': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth'),
'seresnext26_32x4d': _cfg(
'legacy_seresnext26_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth',
interpolation='bicubic'),
'seresnext50_32x4d':
'legacy_seresnext50_32x4d':
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth'),
'seresnext101_32x4d':
'legacy_seresnext101_32x4d':
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth'),
}
@ -408,35 +408,35 @@ def _create_senet(variant, pretrained=False, **kwargs):
def legacy_seresnet18(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBlock, layers=[2, 2, 2, 2], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet18', pretrained, **model_args)
return _create_senet('legacy_seresnet18', pretrained, **model_args)
@register_model
def legacy_seresnet34(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBlock, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet34', pretrained, **model_args)
return _create_senet('legacy_seresnet34', pretrained, **model_args)
@register_model
def legacy_seresnet50(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet50', pretrained, **model_args)
return _create_senet('legacy_seresnet50', pretrained, **model_args)
@register_model
def legacy_seresnet101(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet101', pretrained, **model_args)
return _create_senet('legacy_seresnet101', pretrained, **model_args)
@register_model
def legacy_seresnet152(pretrained=False, **kwargs):
model_args = dict(
block=SEResNetBottleneck, layers=[3, 8, 36, 3], groups=1, reduction=16, **kwargs)
return _create_senet('seresnet152', pretrained, **model_args)
return _create_senet('legacy_seresnet152', pretrained, **model_args)
@register_model
@ -444,25 +444,25 @@ def legacy_senet154(pretrained=False, **kwargs):
model_args = dict(
block=SEBottleneck, layers=[3, 8, 36, 3], groups=64, reduction=16,
downsample_kernel_size=3, downsample_padding=1, inplanes=128, input_3x3=True, **kwargs)
return _create_senet('senet154', pretrained, **model_args)
return _create_senet('legacy_senet154', pretrained, **model_args)
@register_model
def legacy_seresnext26_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[2, 2, 2, 2], groups=32, reduction=16, **kwargs)
return _create_senet('seresnext26_32x4d', pretrained, **model_args)
return _create_senet('legacy_seresnext26_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext50_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 6, 3], groups=32, reduction=16, **kwargs)
return _create_senet('seresnext50_32x4d', pretrained, **model_args)
return _create_senet('legacy_seresnext50_32x4d', pretrained, **model_args)
@register_model
def legacy_seresnext101_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=SEResNeXtBottleneck, layers=[3, 4, 23, 3], groups=32, reduction=16, **kwargs)
return _create_senet('seresnext101_32x4d', pretrained, **model_args)
return _create_senet('legacy_seresnext101_32x4d', pretrained, **model_args)