Add references to VGG and Resnet variants (#1088)

* Add paper references to VGG

* Add paper references to ResNet and its variants

torchvision/models/resnet.py

@@ -219,7 +219,8 @@ def _resnet(arch, block, layers, pretrained, progress, **kwargs):
 
 
 def resnet18(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNet-18 model.
+    r"""ResNet-18 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -230,7 +231,8 @@ def resnet18(pretrained=False, progress=True, **kwargs):
 
 
 def resnet34(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNet-34 model.
+    r"""ResNet-34 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -241,7 +243,8 @@ def resnet34(pretrained=False, progress=True, **kwargs):
 
 
 def resnet50(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNet-50 model.
+    r"""ResNet-50 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -252,7 +255,8 @@ def resnet50(pretrained=False, progress=True, **kwargs):
 
 
 def resnet101(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNet-101 model.
+    r"""ResNet-101 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -263,7 +267,8 @@ def resnet101(pretrained=False, progress=True, **kwargs):
 
 
 def resnet152(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNet-152 model.
+    r"""ResNet-152 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -274,7 +279,8 @@ def resnet152(pretrained=False, progress=True, **kwargs):
 
 
 def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNeXt-50 32x4d model.
+    r"""ResNeXt-50 32x4d model from
+    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -287,7 +293,8 @@ def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
 
 
 def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
-    """Constructs a ResNeXt-101 32x8d model.
+    r"""ResNeXt-101 32x8d model from
+    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -300,7 +307,8 @@ def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
 
 
 def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
-    """Constructs a Wide ResNet-50-2 model.
+    r"""Wide ResNet-50-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
 
     The model is the same as ResNet except for the bottleneck number of channels
     which is twice larger in every block. The number of channels in outer 1x1
@@ -317,7 +325,8 @@ def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
 
 
 def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
-    """Constructs a Wide ResNet-101-2 model.
+    r"""Wide ResNet-101-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
 
     The model is the same as ResNet except for the bottleneck number of channels
     which is twice larger in every block. The number of channels in outer 1x1

torchvision/models/vgg.py

@@ -95,7 +95,8 @@ def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
 
 
 def vgg11(pretrained=False, progress=True, **kwargs):
-    """VGG 11-layer model (configuration "A")
+    r"""VGG 11-layer model (configuration "A") from
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -105,7 +106,8 @@ def vgg11(pretrained=False, progress=True, **kwargs):
 
 
 def vgg11_bn(pretrained=False, progress=True, **kwargs):
-    """VGG 11-layer model (configuration "A") with batch normalization
+    r"""VGG 11-layer model (configuration "A") with batch normalization
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -115,7 +117,8 @@ def vgg11_bn(pretrained=False, progress=True, **kwargs):
 
 
 def vgg13(pretrained=False, progress=True, **kwargs):
-    """VGG 13-layer model (configuration "B")
+    r"""VGG 13-layer model (configuration "B")
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -125,7 +128,8 @@ def vgg13(pretrained=False, progress=True, **kwargs):
 
 
 def vgg13_bn(pretrained=False, progress=True, **kwargs):
-    """VGG 13-layer model (configuration "B") with batch normalization
+    r"""VGG 13-layer model (configuration "B") with batch normalization
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -135,7 +139,8 @@ def vgg13_bn(pretrained=False, progress=True, **kwargs):
 
 
 def vgg16(pretrained=False, progress=True, **kwargs):
-    """VGG 16-layer model (configuration "D")
+    r"""VGG 16-layer model (configuration "D")
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -145,7 +150,8 @@ def vgg16(pretrained=False, progress=True, **kwargs):
 
 
 def vgg16_bn(pretrained=False, progress=True, **kwargs):
-    """VGG 16-layer model (configuration "D") with batch normalization
+    r"""VGG 16-layer model (configuration "D") with batch normalization
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -155,7 +161,8 @@ def vgg16_bn(pretrained=False, progress=True, **kwargs):
 
 
 def vgg19(pretrained=False, progress=True, **kwargs):
-    """VGG 19-layer model (configuration "E")
+    r"""VGG 19-layer model (configuration "E")
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -165,7 +172,8 @@ def vgg19(pretrained=False, progress=True, **kwargs):
 
 
 def vgg19_bn(pretrained=False, progress=True, **kwargs):
-    """VGG 19-layer model (configuration 'E') with batch normalization
+    r"""VGG 19-layer model (configuration 'E') with batch normalization
+    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet