Add pre-trained VGG models with batch normalization (#178)

Fixes #152

torchvision/models/__init__.py

@@ -29,27 +29,31 @@ PyTorch :mod:`torch.utils.model_zoo`. These can  constructed by passing
 
 ImageNet 1-crop error rates (224x224)
 
-======================== =============   =============
-Network                  Top-1 error     Top-5 error
-======================== =============   =============
-ResNet-18                30.24           10.92
-ResNet-34                26.70           8.58
-ResNet-50                23.85           7.13
-ResNet-101               22.63           6.44
-ResNet-152               21.69           5.94
-Inception v3             22.55           6.44
-AlexNet                  43.45           20.91
-VGG-11                   30.98           11.37
-VGG-13                   30.07           10.75
-VGG-16                   28.41           9.62
-VGG-19                   27.62           9.12
-SqueezeNet 1.0           41.90           19.58
-SqueezeNet 1.1           41.81           19.38
-Densenet-121             25.35           7.83
-Densenet-169             24.00           7.00
-Densenet-201             22.80           6.43
-Densenet-161             22.35           6.20
-======================== =============   =============
+================================  =============   =============
+Network                           Top-1 error     Top-5 error
+================================  =============   =============
+ResNet-18                         30.24           10.92
+ResNet-34                         26.70           8.58
+ResNet-50                         23.85           7.13
+ResNet-101                        22.63           6.44
+ResNet-152                        21.69           5.94
+Inception v3                      22.55           6.44
+AlexNet                           43.45           20.91
+VGG-11                            30.98           11.37
+VGG-13                            30.07           10.75
+VGG-16                            28.41           9.62
+VGG-19                            27.62           9.12
+VGG-11 with batch normalization   29.62           10.19
+VGG-13 with batch normalization   28.45           9.63
+VGG-16 with batch normalization   26.63           8.50
+VGG-19 with batch normalization   25.76           8.15
+SqueezeNet 1.0                    41.90           19.58
+SqueezeNet 1.1                    41.81           19.38
+Densenet-121                      25.35           7.83
+Densenet-169                      24.00           7.00
+Densenet-201                      22.80           6.43
+Densenet-161                      22.35           6.20
+================================  =============   =============
 
 
 .. _AlexNet: https://arxiv.org/abs/1404.5997

torchvision/models/vgg.py

@@ -14,6 +14,10 @@ model_urls = {
     'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
     'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
     'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
+    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
+    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
+    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
+    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
 }
 
 
@@ -91,9 +95,16 @@ def vgg11(pretrained=False, **kwargs):
     return model
 
 
-def vgg11_bn(**kwargs):
-    """VGG 11-layer model (configuration "A") with batch normalization"""
-    return VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
+def vgg11_bn(pretrained=False, **kwargs):
+    """VGG 11-layer model (configuration "A") with batch normalization
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['vgg11_bn']))
+    return model
 
 
 def vgg13(pretrained=False, **kwargs):
@@ -108,9 +119,16 @@ def vgg13(pretrained=False, **kwargs):
     return model
 
 
-def vgg13_bn(**kwargs):
-    """VGG 13-layer model (configuration "B") with batch normalization"""
-    return VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
+def vgg13_bn(pretrained=False, **kwargs):
+    """VGG 13-layer model (configuration "B") with batch normalization
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['vgg13_bn']))
+    return model
 
 
 def vgg16(pretrained=False, **kwargs):
@@ -125,9 +143,16 @@ def vgg16(pretrained=False, **kwargs):
     return model
 
 
-def vgg16_bn(**kwargs):
-    """VGG 16-layer model (configuration "D") with batch normalization"""
-    return VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
+def vgg16_bn(pretrained=False, **kwargs):
+    """VGG 16-layer model (configuration "D") with batch normalization
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['vgg16_bn']))
+    return model
 
 
 def vgg19(pretrained=False, **kwargs):
@@ -142,6 +167,13 @@ def vgg19(pretrained=False, **kwargs):
     return model
 
 
-def vgg19_bn(**kwargs):
-    """VGG 19-layer model (configuration 'E') with batch normalization"""
-    return VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
+def vgg19_bn(pretrained=False, **kwargs):
+    """VGG 19-layer model (configuration 'E') with batch normalization
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['vgg19_bn']))
+    return model