Skip to content

P
PaddleClas

PaddlePaddle / PaddleClas
接近 2 年 前同步成功

通知 116
Star 4999
Fork 1114
P
PaddleClas
提交 22055c1d 编写于 作者: L lyuwenyu
浏览文件
操作

add pretrained arg

上级 6da86dd4
隐藏空白更改
内联 并排
Showing with 92 addition and 92 deletion
hubconf.py
浏览文件 @ 22055c1d
...@@ -54,10 +54,10 @@ _checkpoints = _load_pretrained_urls() ...@@ -54,10 +54,10 @@ _checkpoints = _load_pretrained_urls()
def AlexNet(**kwargs): def AlexNet(pretrained=False, **kwargs):
'''AlexNet '''AlexNet
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _alexnet.AlexNet(**kwargs) model = _alexnet.AlexNet(**kwargs)
if pretrained: if pretrained:
...@@ -69,10 +69,10 @@ def AlexNet(**kwargs): ...@@ -69,10 +69,10 @@ def AlexNet(**kwargs):
def VGG11(**kwargs): def VGG11(pretrained=False, **kwargs):
'''VGG11 '''VGG11
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _vgg.VGG11(**kwargs) model = _vgg.VGG11(**kwargs)
if pretrained: if pretrained:
...@@ -83,10 +83,10 @@ def VGG11(**kwargs): ...@@ -83,10 +83,10 @@ def VGG11(**kwargs):
return model return model
def VGG13(**kwargs): def VGG13(pretrained=False, **kwargs):
'''VGG13 '''VGG13
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _vgg.VGG13(**kwargs) model = _vgg.VGG13(**kwargs)
if pretrained: if pretrained:
...@@ -97,10 +97,10 @@ def VGG13(**kwargs): ...@@ -97,10 +97,10 @@ def VGG13(**kwargs):
return model return model
def VGG16(**kwargs): def VGG16(pretrained=False, **kwargs):
'''VGG16 '''VGG16
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _vgg.VGG16(**kwargs) model = _vgg.VGG16(**kwargs)
if pretrained: if pretrained:
...@@ -111,10 +111,10 @@ def VGG16(**kwargs): ...@@ -111,10 +111,10 @@ def VGG16(**kwargs):
return model return model
def VGG19(**kwargs): def VGG19(pretrained=False, **kwargs):
'''VGG19 '''VGG19
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _vgg.VGG19(**kwargs) model = _vgg.VGG19(**kwargs)
if pretrained: if pretrained:
...@@ -127,10 +127,10 @@ def VGG19(**kwargs): ...@@ -127,10 +127,10 @@ def VGG19(**kwargs):
def ResNet18(**kwargs): def ResNet18(pretrained=False, **kwargs):
'''ResNet18 '''ResNet18
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnet.ResNet18(**kwargs) model = _resnet.ResNet18(**kwargs)
if pretrained: if pretrained:
...@@ -141,10 +141,10 @@ def ResNet18(**kwargs): ...@@ -141,10 +141,10 @@ def ResNet18(**kwargs):
return model return model
def ResNet34(**kwargs): def ResNet34(pretrained=False, **kwargs):
'''ResNet34 '''ResNet34
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnet.ResNet34(**kwargs) model = _resnet.ResNet34(**kwargs)
if pretrained: if pretrained:
...@@ -155,10 +155,10 @@ def ResNet34(**kwargs): ...@@ -155,10 +155,10 @@ def ResNet34(**kwargs):
return model return model
def ResNet50(**kwargs): def ResNet50(pretrained=False, **kwargs):
'''ResNet50 '''ResNet50
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnet.ResNet50(**kwargs) model = _resnet.ResNet50(**kwargs)
if pretrained: if pretrained:
...@@ -169,10 +169,10 @@ def ResNet50(**kwargs): ...@@ -169,10 +169,10 @@ def ResNet50(**kwargs):
return model return model
def ResNet101(**kwargs): def ResNet101(pretrained=False, **kwargs):
'''ResNet101 '''ResNet101
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnet.ResNet101(**kwargs) model = _resnet.ResNet101(**kwargs)
if pretrained: if pretrained:
...@@ -183,10 +183,10 @@ def ResNet101(**kwargs): ...@@ -183,10 +183,10 @@ def ResNet101(**kwargs):
return model return model
def ResNet152(**kwargs): def ResNet152(pretrained=False, **kwargs):
'''ResNet152 '''ResNet152
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnet.ResNet152(**kwargs) model = _resnet.ResNet152(**kwargs)
if pretrained: if pretrained:
...@@ -198,10 +198,10 @@ def ResNet152(**kwargs): ...@@ -198,10 +198,10 @@ def ResNet152(**kwargs):
def SqueezeNet1_0(**kwargs): def SqueezeNet1_0(pretrained=False, **kwargs):
'''SqueezeNet1_0 '''SqueezeNet1_0
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _squeezenet.SqueezeNet1_0(**kwargs) model = _squeezenet.SqueezeNet1_0(**kwargs)
if pretrained: if pretrained:
...@@ -212,10 +212,10 @@ def SqueezeNet1_0(**kwargs): ...@@ -212,10 +212,10 @@ def SqueezeNet1_0(**kwargs):
return model return model
def SqueezeNet1_1(**kwargs): def SqueezeNet1_1(pretrained=False, **kwargs):
'''SqueezeNet1_1 '''SqueezeNet1_1
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _squeezenet.SqueezeNet1_1(**kwargs) model = _squeezenet.SqueezeNet1_1(**kwargs)
if pretrained: if pretrained:
...@@ -228,10 +228,10 @@ def SqueezeNet1_1(**kwargs): ...@@ -228,10 +228,10 @@ def SqueezeNet1_1(**kwargs):
def DenseNet121(**kwargs): def DenseNet121(pretrained=False, **kwargs):
'''DenseNet121 '''DenseNet121
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _densenet.DenseNet121(**kwargs) model = _densenet.DenseNet121(**kwargs)
if pretrained: if pretrained:
...@@ -242,10 +242,10 @@ def DenseNet121(**kwargs): ...@@ -242,10 +242,10 @@ def DenseNet121(**kwargs):
return model return model
def DenseNet161(**kwargs): def DenseNet161(pretrained=False, **kwargs):
'''DenseNet161 '''DenseNet161
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _densenet.DenseNet161(**kwargs) model = _densenet.DenseNet161(**kwargs)
if pretrained: if pretrained:
...@@ -256,10 +256,10 @@ def DenseNet161(**kwargs): ...@@ -256,10 +256,10 @@ def DenseNet161(**kwargs):
return model return model
def DenseNet169(**kwargs): def DenseNet169(pretrained=False, **kwargs):
'''DenseNet169 '''DenseNet169
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _densenet.DenseNet169(**kwargs) model = _densenet.DenseNet169(**kwargs)
if pretrained: if pretrained:
...@@ -270,10 +270,10 @@ def DenseNet169(**kwargs): ...@@ -270,10 +270,10 @@ def DenseNet169(**kwargs):
return model return model
def DenseNet201(**kwargs): def DenseNet201(pretrained=False, **kwargs):
'''DenseNet201 '''DenseNet201
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _densenet.DenseNet201(**kwargs) model = _densenet.DenseNet201(**kwargs)
if pretrained: if pretrained:
...@@ -284,10 +284,10 @@ def DenseNet201(**kwargs): ...@@ -284,10 +284,10 @@ def DenseNet201(**kwargs):
return model return model
def DenseNet264(**kwargs): def DenseNet264(pretrained=False, **kwargs):
'''DenseNet264 '''DenseNet264
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _densenet.DenseNet264(**kwargs) model = _densenet.DenseNet264(**kwargs)
if pretrained: if pretrained:
...@@ -299,10 +299,10 @@ def DenseNet264(**kwargs): ...@@ -299,10 +299,10 @@ def DenseNet264(**kwargs):
def InceptionV3(**kwargs): def InceptionV3(pretrained=False, **kwargs):
'''InceptionV3 '''InceptionV3
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _inception_v3.InceptionV3(**kwargs) model = _inception_v3.InceptionV3(**kwargs)
if pretrained: if pretrained:
...@@ -313,10 +313,10 @@ def InceptionV3(**kwargs): ...@@ -313,10 +313,10 @@ def InceptionV3(**kwargs):
return model return model
def InceptionV4(**kwargs): def InceptionV4(pretrained=False, **kwargs):
'''InceptionV4 '''InceptionV4
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _inception_v4.InceptionV4(**kwargs) model = _inception_v4.InceptionV4(**kwargs)
if pretrained: if pretrained:
...@@ -328,10 +328,10 @@ def InceptionV4(**kwargs): ...@@ -328,10 +328,10 @@ def InceptionV4(**kwargs):
def GoogLeNet(**kwargs): def GoogLeNet(pretrained=False, **kwargs):
'''GoogLeNet '''GoogLeNet
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _googlenet.GoogLeNet(**kwargs) model = _googlenet.GoogLeNet(**kwargs)
if pretrained: if pretrained:
...@@ -343,10 +343,10 @@ def GoogLeNet(**kwargs): ...@@ -343,10 +343,10 @@ def GoogLeNet(**kwargs):
def ShuffleNet(**kwargs): def ShuffleNet(pretrained=False, **kwargs):
'''ShuffleNet '''ShuffleNet
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _shufflenet_v2.ShuffleNet(**kwargs) model = _shufflenet_v2.ShuffleNet(**kwargs)
if pretrained: if pretrained:
...@@ -358,10 +358,10 @@ def ShuffleNet(**kwargs): ...@@ -358,10 +358,10 @@ def ShuffleNet(**kwargs):
def MobileNetV1(**kwargs): def MobileNetV1(pretrained=False, **kwargs):
'''MobileNetV1 '''MobileNetV1
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v1.MobileNetV1(**kwargs) model = _mobilenet_v1.MobileNetV1(**kwargs)
if pretrained: if pretrained:
...@@ -372,10 +372,10 @@ def MobileNetV1(**kwargs): ...@@ -372,10 +372,10 @@ def MobileNetV1(**kwargs):
return model return model
def MobileNetV1_x0_25(**kwargs): def MobileNetV1_x0_25(pretrained=False, **kwargs):
'''MobileNetV1_x0_25 '''MobileNetV1_x0_25
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v1.MobileNetV1_x0_25(**kwargs) model = _mobilenet_v1.MobileNetV1_x0_25(**kwargs)
if pretrained: if pretrained:
...@@ -386,10 +386,10 @@ def MobileNetV1_x0_25(**kwargs): ...@@ -386,10 +386,10 @@ def MobileNetV1_x0_25(**kwargs):
return model return model
def MobileNetV1_x0_5(**kwargs): def MobileNetV1_x0_5(pretrained=False, **kwargs):
'''MobileNetV1_x0_5 '''MobileNetV1_x0_5
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v1.MobileNetV1_x0_5(**kwargs) model = _mobilenet_v1.MobileNetV1_x0_5(**kwargs)
if pretrained: if pretrained:
...@@ -400,10 +400,10 @@ def MobileNetV1_x0_5(**kwargs): ...@@ -400,10 +400,10 @@ def MobileNetV1_x0_5(**kwargs):
return model return model
def MobileNetV1_x0_75(**kwargs): def MobileNetV1_x0_75(pretrained=False, **kwargs):
'''MobileNetV1_x0_75 '''MobileNetV1_x0_75
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v1.MobileNetV1_x0_75(**kwargs) model = _mobilenet_v1.MobileNetV1_x0_75(**kwargs)
if pretrained: if pretrained:
...@@ -414,10 +414,10 @@ def MobileNetV1_x0_75(**kwargs): ...@@ -414,10 +414,10 @@ def MobileNetV1_x0_75(**kwargs):
return model return model
def MobileNetV2_x0_25(**kwargs): def MobileNetV2_x0_25(pretrained=False, **kwargs):
'''MobileNetV2_x0_25 '''MobileNetV2_x0_25
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v2.MobileNetV2_x0_25(**kwargs) model = _mobilenet_v2.MobileNetV2_x0_25(**kwargs)
if pretrained: if pretrained:
...@@ -428,10 +428,10 @@ def MobileNetV2_x0_25(**kwargs): ...@@ -428,10 +428,10 @@ def MobileNetV2_x0_25(**kwargs):
return model return model
def MobileNetV2_x0_5(**kwargs): def MobileNetV2_x0_5(pretrained=False, **kwargs):
'''MobileNetV2_x0_5 '''MobileNetV2_x0_5
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v2.MobileNetV2_x0_5(**kwargs) model = _mobilenet_v2.MobileNetV2_x0_5(**kwargs)
if pretrained: if pretrained:
...@@ -442,10 +442,10 @@ def MobileNetV2_x0_5(**kwargs): ...@@ -442,10 +442,10 @@ def MobileNetV2_x0_5(**kwargs):
return model return model
def MobileNetV2_x0_75(**kwargs): def MobileNetV2_x0_75(pretrained=False, **kwargs):
'''MobileNetV2_x0_75 '''MobileNetV2_x0_75
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v2.MobileNetV2_x0_75(**kwargs) model = _mobilenet_v2.MobileNetV2_x0_75(**kwargs)
if pretrained: if pretrained:
...@@ -456,10 +456,10 @@ def MobileNetV2_x0_75(**kwargs): ...@@ -456,10 +456,10 @@ def MobileNetV2_x0_75(**kwargs):
return model return model
def MobileNetV2_x1_5(**kwargs): def MobileNetV2_x1_5(pretrained=False, **kwargs):
'''MobileNetV2_x1_5 '''MobileNetV2_x1_5
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v2.MobileNetV2_x1_5(**kwargs) model = _mobilenet_v2.MobileNetV2_x1_5(**kwargs)
if pretrained: if pretrained:
...@@ -470,10 +470,10 @@ def MobileNetV2_x1_5(**kwargs): ...@@ -470,10 +470,10 @@ def MobileNetV2_x1_5(**kwargs):
return model return model
def MobileNetV2_x2_0(**kwargs): def MobileNetV2_x2_0(pretrained=False, **kwargs):
'''MobileNetV2_x2_0 '''MobileNetV2_x2_0
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v2.MobileNetV2_x2_0(**kwargs) model = _mobilenet_v2.MobileNetV2_x2_0(**kwargs)
if pretrained: if pretrained:
...@@ -484,10 +484,10 @@ def MobileNetV2_x2_0(**kwargs): ...@@ -484,10 +484,10 @@ def MobileNetV2_x2_0(**kwargs):
return model return model
def MobileNetV3_large_x0_35(**kwargs): def MobileNetV3_large_x0_35(pretrained=False, **kwargs):
'''MobileNetV3_large_x0_35 '''MobileNetV3_large_x0_35
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_large_x0_35(**kwargs) model = _mobilenet_v3.MobileNetV3_large_x0_35(**kwargs)
if pretrained: if pretrained:
...@@ -498,10 +498,10 @@ def MobileNetV3_large_x0_35(**kwargs): ...@@ -498,10 +498,10 @@ def MobileNetV3_large_x0_35(**kwargs):
return model return model
def MobileNetV3_large_x0_5(**kwargs): def MobileNetV3_large_x0_5(pretrained=False, **kwargs):
'''MobileNetV3_large_x0_5 '''MobileNetV3_large_x0_5
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_large_x0_5(**kwargs) model = _mobilenet_v3.MobileNetV3_large_x0_5(**kwargs)
if pretrained: if pretrained:
...@@ -512,10 +512,10 @@ def MobileNetV3_large_x0_5(**kwargs): ...@@ -512,10 +512,10 @@ def MobileNetV3_large_x0_5(**kwargs):
return model return model
def MobileNetV3_large_x0_75(**kwargs): def MobileNetV3_large_x0_75(pretrained=False, **kwargs):
'''MobileNetV3_large_x0_75 '''MobileNetV3_large_x0_75
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_large_x0_75(**kwargs) model = _mobilenet_v3.MobileNetV3_large_x0_75(**kwargs)
if pretrained: if pretrained:
...@@ -526,10 +526,10 @@ def MobileNetV3_large_x0_75(**kwargs): ...@@ -526,10 +526,10 @@ def MobileNetV3_large_x0_75(**kwargs):
return model return model
def MobileNetV3_large_x1_0(**kwargs): def MobileNetV3_large_x1_0(pretrained=False, **kwargs):
'''MobileNetV3_large_x1_0 '''MobileNetV3_large_x1_0
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_large_x1_0(**kwargs) model = _mobilenet_v3.MobileNetV3_large_x1_0(**kwargs)
if pretrained: if pretrained:
...@@ -540,10 +540,10 @@ def MobileNetV3_large_x1_0(**kwargs): ...@@ -540,10 +540,10 @@ def MobileNetV3_large_x1_0(**kwargs):
return model return model
def MobileNetV3_large_x1_25(**kwargs): def MobileNetV3_large_x1_25(pretrained=False, **kwargs):
'''MobileNetV3_large_x1_25 '''MobileNetV3_large_x1_25
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_large_x1_25(**kwargs) model = _mobilenet_v3.MobileNetV3_large_x1_25(**kwargs)
if pretrained: if pretrained:
...@@ -554,10 +554,10 @@ def MobileNetV3_large_x1_25(**kwargs): ...@@ -554,10 +554,10 @@ def MobileNetV3_large_x1_25(**kwargs):
return model return model
def MobileNetV3_small_x0_35(**kwargs): def MobileNetV3_small_x0_35(pretrained=False, **kwargs):
'''MobileNetV3_small_x0_35 '''MobileNetV3_small_x0_35
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_small_x0_35(**kwargs) model = _mobilenet_v3.MobileNetV3_small_x0_35(**kwargs)
if pretrained: if pretrained:
...@@ -568,10 +568,10 @@ def MobileNetV3_small_x0_35(**kwargs): ...@@ -568,10 +568,10 @@ def MobileNetV3_small_x0_35(**kwargs):
return model return model
def MobileNetV3_small_x0_5(**kwargs): def MobileNetV3_small_x0_5(pretrained=False, **kwargs):
'''MobileNetV3_small_x0_5 '''MobileNetV3_small_x0_5
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_small_x0_5(**kwargs) model = _mobilenet_v3.MobileNetV3_small_x0_5(**kwargs)
if pretrained: if pretrained:
...@@ -582,10 +582,10 @@ def MobileNetV3_small_x0_5(**kwargs): ...@@ -582,10 +582,10 @@ def MobileNetV3_small_x0_5(**kwargs):
return model return model
def MobileNetV3_small_x0_75(**kwargs): def MobileNetV3_small_x0_75(pretrained=False, **kwargs):
'''MobileNetV3_small_x0_75 '''MobileNetV3_small_x0_75
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_small_x0_75(**kwargs) model = _mobilenet_v3.MobileNetV3_small_x0_75(**kwargs)
if pretrained: if pretrained:
...@@ -596,10 +596,10 @@ def MobileNetV3_small_x0_75(**kwargs): ...@@ -596,10 +596,10 @@ def MobileNetV3_small_x0_75(**kwargs):
return model return model
def MobileNetV3_small_x1_0(**kwargs): def MobileNetV3_small_x1_0(pretrained=False, **kwargs):
'''MobileNetV3_small_x1_0 '''MobileNetV3_small_x1_0
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_small_x1_0(**kwargs) model = _mobilenet_v3.MobileNetV3_small_x1_0(**kwargs)
if pretrained: if pretrained:
...@@ -610,10 +610,10 @@ def MobileNetV3_small_x1_0(**kwargs): ...@@ -610,10 +610,10 @@ def MobileNetV3_small_x1_0(**kwargs):
return model return model
def MobileNetV3_small_x1_25(**kwargs): def MobileNetV3_small_x1_25(pretrained=False, **kwargs):
'''MobileNetV3_small_x1_25 '''MobileNetV3_small_x1_25
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _mobilenet_v3.MobileNetV3_small_x1_25(**kwargs) model = _mobilenet_v3.MobileNetV3_small_x1_25(**kwargs)
if pretrained: if pretrained:
...@@ -625,10 +625,10 @@ def MobileNetV3_small_x1_25(**kwargs): ...@@ -625,10 +625,10 @@ def MobileNetV3_small_x1_25(**kwargs):
def ResNeXt101_32x4d(**kwargs): def ResNeXt101_32x4d(pretrained=False, **kwargs):
'''ResNeXt101_32x4d '''ResNeXt101_32x4d
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnext.ResNeXt101_32x4d(**kwargs) model = _resnext.ResNeXt101_32x4d(**kwargs)
if pretrained: if pretrained:
...@@ -639,10 +639,10 @@ def ResNeXt101_32x4d(**kwargs): ...@@ -639,10 +639,10 @@ def ResNeXt101_32x4d(**kwargs):
return model return model
def ResNeXt101_64x4d(**kwargs): def ResNeXt101_64x4d(pretrained=False, **kwargs):
'''ResNeXt101_64x4d '''ResNeXt101_64x4d
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnext.ResNeXt101_64x4d(**kwargs) model = _resnext.ResNeXt101_64x4d(**kwargs)
if pretrained: if pretrained:
...@@ -653,10 +653,10 @@ def ResNeXt101_64x4d(**kwargs): ...@@ -653,10 +653,10 @@ def ResNeXt101_64x4d(**kwargs):
return model return model
def ResNeXt152_32x4d(**kwargs): def ResNeXt152_32x4d(pretrained=False, **kwargs):
'''ResNeXt152_32x4d '''ResNeXt152_32x4d
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnext.ResNeXt152_32x4d(**kwargs) model = _resnext.ResNeXt152_32x4d(**kwargs)
if pretrained: if pretrained:
...@@ -667,10 +667,10 @@ def ResNeXt152_32x4d(**kwargs): ...@@ -667,10 +667,10 @@ def ResNeXt152_32x4d(**kwargs):
return model return model
def ResNeXt152_64x4d(**kwargs): def ResNeXt152_64x4d(pretrained=False, **kwargs):
'''ResNeXt152_64x4d '''ResNeXt152_64x4d
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnext.ResNeXt152_64x4d(**kwargs) model = _resnext.ResNeXt152_64x4d(**kwargs)
if pretrained: if pretrained:
...@@ -681,10 +681,10 @@ def ResNeXt152_64x4d(**kwargs): ...@@ -681,10 +681,10 @@ def ResNeXt152_64x4d(**kwargs):
return model return model
def ResNeXt50_32x4d(**kwargs): def ResNeXt50_32x4d(pretrained=False, **kwargs):
'''ResNeXt50_32x4d '''ResNeXt50_32x4d
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnext.ResNeXt50_32x4d(**kwargs) model = _resnext.ResNeXt50_32x4d(**kwargs)
if pretrained: if pretrained:
...@@ -695,10 +695,10 @@ def ResNeXt50_32x4d(**kwargs): ...@@ -695,10 +695,10 @@ def ResNeXt50_32x4d(**kwargs):
return model return model
def ResNeXt50_64x4d(**kwargs): def ResNeXt50_64x4d(pretrained=False, **kwargs):
'''ResNeXt50_64x4d '''ResNeXt50_64x4d
''' '''
pretrained = kwargs.pop('pretrained', False) # pretrained = kwargs.pop('pretrained', False)
model = _resnext.ResNeXt50_64x4d(**kwargs) model = _resnext.ResNeXt50_64x4d(**kwargs)
if pretrained: if pretrained:
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或