add pretrained arg

hubconf.py

@@ -54,10 +54,10 @@ _checkpoints = _load_pretrained_urls()
 
 
 
-def AlexNet(**kwargs):
+def AlexNet(pretrained=False, **kwargs):
     '''AlexNet
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _alexnet.AlexNet(**kwargs)
     if pretrained:
@@ -69,10 +69,10 @@ def AlexNet(**kwargs):
 
 
 
-def VGG11(**kwargs):
+def VGG11(pretrained=False, **kwargs):
     '''VGG11
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _vgg.VGG11(**kwargs)
     if pretrained:
@@ -83,10 +83,10 @@ def VGG11(**kwargs):
     return model
 
 
-def VGG13(**kwargs):
+def VGG13(pretrained=False, **kwargs):
     '''VGG13
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _vgg.VGG13(**kwargs)
     if pretrained:
@@ -97,10 +97,10 @@ def VGG13(**kwargs):
     return model
 
 
-def VGG16(**kwargs):
+def VGG16(pretrained=False, **kwargs):
     '''VGG16
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _vgg.VGG16(**kwargs)
     if pretrained:
@@ -111,10 +111,10 @@ def VGG16(**kwargs):
     return model
 
 
-def VGG19(**kwargs):
+def VGG19(pretrained=False, **kwargs):
     '''VGG19
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _vgg.VGG19(**kwargs)
     if pretrained:
@@ -127,10 +127,10 @@ def VGG19(**kwargs):
 
 
 
-def ResNet18(**kwargs):
+def ResNet18(pretrained=False, **kwargs):
     '''ResNet18
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnet.ResNet18(**kwargs)
     if pretrained:
@@ -141,10 +141,10 @@ def ResNet18(**kwargs):
     return model
 
 
-def ResNet34(**kwargs):
+def ResNet34(pretrained=False, **kwargs):
     '''ResNet34
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnet.ResNet34(**kwargs)
     if pretrained:
@@ -155,10 +155,10 @@ def ResNet34(**kwargs):
     return model
 
 
-def ResNet50(**kwargs):
+def ResNet50(pretrained=False, **kwargs):
     '''ResNet50
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnet.ResNet50(**kwargs)
     if pretrained:
@@ -169,10 +169,10 @@ def ResNet50(**kwargs):
     return model
 
 
-def ResNet101(**kwargs):
+def ResNet101(pretrained=False, **kwargs):
     '''ResNet101
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnet.ResNet101(**kwargs)
     if pretrained:
@@ -183,10 +183,10 @@ def ResNet101(**kwargs):
     return model
 
 
-def ResNet152(**kwargs):
+def ResNet152(pretrained=False, **kwargs):
     '''ResNet152
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnet.ResNet152(**kwargs)
     if pretrained:
@@ -198,10 +198,10 @@ def ResNet152(**kwargs):
 
 
 
-def SqueezeNet1_0(**kwargs):
+def SqueezeNet1_0(pretrained=False, **kwargs):
     '''SqueezeNet1_0
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _squeezenet.SqueezeNet1_0(**kwargs)
     if pretrained:
@@ -212,10 +212,10 @@ def SqueezeNet1_0(**kwargs):
     return model
 
 
-def SqueezeNet1_1(**kwargs):
+def SqueezeNet1_1(pretrained=False, **kwargs):
     '''SqueezeNet1_1
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _squeezenet.SqueezeNet1_1(**kwargs)
     if pretrained:
@@ -228,10 +228,10 @@ def SqueezeNet1_1(**kwargs):
 
 
 
-def DenseNet121(**kwargs):
+def DenseNet121(pretrained=False, **kwargs):
     '''DenseNet121
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _densenet.DenseNet121(**kwargs)
     if pretrained:
@@ -242,10 +242,10 @@ def DenseNet121(**kwargs):
     return model
 
 
-def DenseNet161(**kwargs):
+def DenseNet161(pretrained=False, **kwargs):
     '''DenseNet161
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _densenet.DenseNet161(**kwargs)
     if pretrained:
@@ -256,10 +256,10 @@ def DenseNet161(**kwargs):
     return model
 
 
-def DenseNet169(**kwargs):
+def DenseNet169(pretrained=False, **kwargs):
     '''DenseNet169
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _densenet.DenseNet169(**kwargs)
     if pretrained:
@@ -270,10 +270,10 @@ def DenseNet169(**kwargs):
     return model
 
 
-def DenseNet201(**kwargs):
+def DenseNet201(pretrained=False, **kwargs):
     '''DenseNet201
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _densenet.DenseNet201(**kwargs)
     if pretrained:
@@ -284,10 +284,10 @@ def DenseNet201(**kwargs):
     return model
 
 
-def DenseNet264(**kwargs):
+def DenseNet264(pretrained=False, **kwargs):
     '''DenseNet264
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _densenet.DenseNet264(**kwargs)
     if pretrained:
@@ -299,10 +299,10 @@ def DenseNet264(**kwargs):
 
 
 
-def InceptionV3(**kwargs):
+def InceptionV3(pretrained=False, **kwargs):
     '''InceptionV3
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _inception_v3.InceptionV3(**kwargs)
     if pretrained:
@@ -313,10 +313,10 @@ def InceptionV3(**kwargs):
     return model
 
 
-def InceptionV4(**kwargs):
+def InceptionV4(pretrained=False, **kwargs):
     '''InceptionV4
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _inception_v4.InceptionV4(**kwargs)
     if pretrained:
@@ -328,10 +328,10 @@ def InceptionV4(**kwargs):
 
 
 
-def GoogLeNet(**kwargs):
+def GoogLeNet(pretrained=False, **kwargs):
     '''GoogLeNet
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _googlenet.GoogLeNet(**kwargs)
     if pretrained:
@@ -343,10 +343,10 @@ def GoogLeNet(**kwargs):
 
 
 
-def ShuffleNet(**kwargs):
+def ShuffleNet(pretrained=False, **kwargs):
     '''ShuffleNet
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _shufflenet_v2.ShuffleNet(**kwargs)
     if pretrained:
@@ -358,10 +358,10 @@ def ShuffleNet(**kwargs):
 
 
 
-def MobileNetV1(**kwargs):
+def MobileNetV1(pretrained=False, **kwargs):
     '''MobileNetV1
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v1.MobileNetV1(**kwargs)
     if pretrained:
@@ -372,10 +372,10 @@ def MobileNetV1(**kwargs):
     return model
 
 
-def MobileNetV1_x0_25(**kwargs):
+def MobileNetV1_x0_25(pretrained=False, **kwargs):
     '''MobileNetV1_x0_25
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v1.MobileNetV1_x0_25(**kwargs)
     if pretrained:
@@ -386,10 +386,10 @@ def MobileNetV1_x0_25(**kwargs):
     return model
 
 
-def MobileNetV1_x0_5(**kwargs):
+def MobileNetV1_x0_5(pretrained=False, **kwargs):
     '''MobileNetV1_x0_5
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v1.MobileNetV1_x0_5(**kwargs)
     if pretrained:
@@ -400,10 +400,10 @@ def MobileNetV1_x0_5(**kwargs):
     return model
 
 
-def MobileNetV1_x0_75(**kwargs):
+def MobileNetV1_x0_75(pretrained=False, **kwargs):
     '''MobileNetV1_x0_75
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v1.MobileNetV1_x0_75(**kwargs)
     if pretrained:
@@ -414,10 +414,10 @@ def MobileNetV1_x0_75(**kwargs):
     return model
 
 
-def MobileNetV2_x0_25(**kwargs):
+def MobileNetV2_x0_25(pretrained=False, **kwargs):
     '''MobileNetV2_x0_25
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v2.MobileNetV2_x0_25(**kwargs)
     if pretrained:
@@ -428,10 +428,10 @@ def MobileNetV2_x0_25(**kwargs):
     return model
 
 
-def MobileNetV2_x0_5(**kwargs):
+def MobileNetV2_x0_5(pretrained=False, **kwargs):
     '''MobileNetV2_x0_5
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v2.MobileNetV2_x0_5(**kwargs)
     if pretrained:
@@ -442,10 +442,10 @@ def MobileNetV2_x0_5(**kwargs):
     return model
 
 
-def MobileNetV2_x0_75(**kwargs):
+def MobileNetV2_x0_75(pretrained=False, **kwargs):
     '''MobileNetV2_x0_75
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v2.MobileNetV2_x0_75(**kwargs)
     if pretrained:
@@ -456,10 +456,10 @@ def MobileNetV2_x0_75(**kwargs):
     return model
 
 
-def MobileNetV2_x1_5(**kwargs):
+def MobileNetV2_x1_5(pretrained=False, **kwargs):
     '''MobileNetV2_x1_5
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v2.MobileNetV2_x1_5(**kwargs)
     if pretrained:
@@ -470,10 +470,10 @@ def MobileNetV2_x1_5(**kwargs):
     return model
 
 
-def MobileNetV2_x2_0(**kwargs):
+def MobileNetV2_x2_0(pretrained=False, **kwargs):
     '''MobileNetV2_x2_0
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v2.MobileNetV2_x2_0(**kwargs)
     if pretrained:
@@ -484,10 +484,10 @@ def MobileNetV2_x2_0(**kwargs):
     return model
 
 
-def MobileNetV3_large_x0_35(**kwargs):
+def MobileNetV3_large_x0_35(pretrained=False, **kwargs):
     '''MobileNetV3_large_x0_35
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_large_x0_35(**kwargs)
     if pretrained:
@@ -498,10 +498,10 @@ def MobileNetV3_large_x0_35(**kwargs):
     return model
 
 
-def MobileNetV3_large_x0_5(**kwargs):
+def MobileNetV3_large_x0_5(pretrained=False, **kwargs):
     '''MobileNetV3_large_x0_5
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_large_x0_5(**kwargs)
     if pretrained:
@@ -512,10 +512,10 @@ def MobileNetV3_large_x0_5(**kwargs):
     return model
 
 
-def MobileNetV3_large_x0_75(**kwargs):
+def MobileNetV3_large_x0_75(pretrained=False, **kwargs):
     '''MobileNetV3_large_x0_75
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_large_x0_75(**kwargs)
     if pretrained:
@@ -526,10 +526,10 @@ def MobileNetV3_large_x0_75(**kwargs):
     return model
 
 
-def MobileNetV3_large_x1_0(**kwargs):
+def MobileNetV3_large_x1_0(pretrained=False, **kwargs):
     '''MobileNetV3_large_x1_0
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_large_x1_0(**kwargs)
     if pretrained:
@@ -540,10 +540,10 @@ def MobileNetV3_large_x1_0(**kwargs):
     return model
 
 
-def MobileNetV3_large_x1_25(**kwargs):
+def MobileNetV3_large_x1_25(pretrained=False, **kwargs):
     '''MobileNetV3_large_x1_25
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_large_x1_25(**kwargs)
     if pretrained:
@@ -554,10 +554,10 @@ def MobileNetV3_large_x1_25(**kwargs):
     return model
 
 
-def MobileNetV3_small_x0_35(**kwargs):
+def MobileNetV3_small_x0_35(pretrained=False, **kwargs):
     '''MobileNetV3_small_x0_35
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_small_x0_35(**kwargs)
     if pretrained:
@@ -568,10 +568,10 @@ def MobileNetV3_small_x0_35(**kwargs):
     return model
 
 
-def MobileNetV3_small_x0_5(**kwargs):
+def MobileNetV3_small_x0_5(pretrained=False, **kwargs):
     '''MobileNetV3_small_x0_5
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_small_x0_5(**kwargs)
     if pretrained:
@@ -582,10 +582,10 @@ def MobileNetV3_small_x0_5(**kwargs):
     return model
 
 
-def MobileNetV3_small_x0_75(**kwargs):
+def MobileNetV3_small_x0_75(pretrained=False, **kwargs):
     '''MobileNetV3_small_x0_75
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_small_x0_75(**kwargs)
     if pretrained:
@@ -596,10 +596,10 @@ def MobileNetV3_small_x0_75(**kwargs):
     return model
 
 
-def MobileNetV3_small_x1_0(**kwargs):
+def MobileNetV3_small_x1_0(pretrained=False, **kwargs):
     '''MobileNetV3_small_x1_0
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_small_x1_0(**kwargs)
     if pretrained:
@@ -610,10 +610,10 @@ def MobileNetV3_small_x1_0(**kwargs):
     return model
 
 
-def MobileNetV3_small_x1_25(**kwargs):
+def MobileNetV3_small_x1_25(pretrained=False, **kwargs):
     '''MobileNetV3_small_x1_25
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _mobilenet_v3.MobileNetV3_small_x1_25(**kwargs)
     if pretrained:
@@ -625,10 +625,10 @@ def MobileNetV3_small_x1_25(**kwargs):
 
 
 
-def ResNeXt101_32x4d(**kwargs):
+def ResNeXt101_32x4d(pretrained=False, **kwargs):
     '''ResNeXt101_32x4d
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnext.ResNeXt101_32x4d(**kwargs)
     if pretrained:
@@ -639,10 +639,10 @@ def ResNeXt101_32x4d(**kwargs):
     return model
 
 
-def ResNeXt101_64x4d(**kwargs):
+def ResNeXt101_64x4d(pretrained=False, **kwargs):
     '''ResNeXt101_64x4d
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnext.ResNeXt101_64x4d(**kwargs)
     if pretrained:
@@ -653,10 +653,10 @@ def ResNeXt101_64x4d(**kwargs):
     return model
 
 
-def ResNeXt152_32x4d(**kwargs):
+def ResNeXt152_32x4d(pretrained=False, **kwargs):
     '''ResNeXt152_32x4d
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnext.ResNeXt152_32x4d(**kwargs)
     if pretrained:
@@ -667,10 +667,10 @@ def ResNeXt152_32x4d(**kwargs):
     return model
 
 
-def ResNeXt152_64x4d(**kwargs):
+def ResNeXt152_64x4d(pretrained=False, **kwargs):
     '''ResNeXt152_64x4d
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnext.ResNeXt152_64x4d(**kwargs)
     if pretrained:
@@ -681,10 +681,10 @@ def ResNeXt152_64x4d(**kwargs):
     return model
 
 
-def ResNeXt50_32x4d(**kwargs):
+def ResNeXt50_32x4d(pretrained=False, **kwargs):
     '''ResNeXt50_32x4d
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnext.ResNeXt50_32x4d(**kwargs)
     if pretrained:
@@ -695,10 +695,10 @@ def ResNeXt50_32x4d(**kwargs):
     return model
 
 
-def ResNeXt50_64x4d(**kwargs):
+def ResNeXt50_64x4d(pretrained=False, **kwargs):
     '''ResNeXt50_64x4d
     '''
-    pretrained = kwargs.pop('pretrained', False)
+    # pretrained = kwargs.pop('pretrained', False)
 
     model = _resnext.ResNeXt50_64x4d(**kwargs)
     if pretrained: