Fixes incorrectly rendered html MNASNet documentation due to docstring formatting (#3005)

* Fix MNASNet docstrings so they are rendered correctly

* Add dot after url link in models docstrings for consistency

torchvision/models/densenet.py

@@ -141,7 +141,7 @@ class _Transition(nn.Sequential):
 
 class DenseNet(nn.Module):
     r"""Densenet-BC model class, based on
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
 
     Args:
         growth_rate (int) - how many filters to add each layer (`k` in paper)
@@ -152,7 +152,7 @@ class DenseNet(nn.Module):
         drop_rate (float) - dropout rate after each dense layer
         num_classes (int) - number of classification classes
         memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
     """
 
     def __init__(
@@ -256,13 +256,13 @@ def _densenet(
 
 def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-121 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
         memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
     """
     return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
                      **kwargs)
@@ -270,13 +270,13 @@ def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any)
 
 def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-161 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
         memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
     """
     return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
                      **kwargs)
@@ -284,13 +284,13 @@ def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any)
 
 def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-169 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
         memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
     """
     return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
                      **kwargs)
@@ -298,13 +298,13 @@ def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any)
 
 def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-201 model from
-    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
         memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
-          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
+          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
     """
     return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
                      **kwargs)

torchvision/models/mnasnet.py

@@ -216,9 +216,10 @@ def _load_pretrained(model_name: str, model: nn.Module, progress: bool) -> None:
 
 
 def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
-    """MNASNet with depth multiplier of 0.5 from
+    r"""MNASNet with depth multiplier of 0.5 from
     `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
     <https://arxiv.org/pdf/1807.11626.pdf>`_.
+
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
@@ -230,9 +231,10 @@ def mnasnet0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -
 
 
 def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
-    """MNASNet with depth multiplier of 0.75 from
+    r"""MNASNet with depth multiplier of 0.75 from
     `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
     <https://arxiv.org/pdf/1807.11626.pdf>`_.
+
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
@@ -244,9 +246,10 @@ def mnasnet0_75(pretrained: bool = False, progress: bool = True, **kwargs: Any)
 
 
 def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
-    """MNASNet with depth multiplier of 1.0 from
+    r"""MNASNet with depth multiplier of 1.0 from
     `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
     <https://arxiv.org/pdf/1807.11626.pdf>`_.
+
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
@@ -258,9 +261,10 @@ def mnasnet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -
 
 
 def mnasnet1_3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MNASNet:
-    """MNASNet with depth multiplier of 1.3 from
+    r"""MNASNet with depth multiplier of 1.3 from
     `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"
     <https://arxiv.org/pdf/1807.11626.pdf>`_.
+
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr

torchvision/models/resnet.py

@@ -267,7 +267,7 @@ def _resnet(
 
 def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNet-18 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    `"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
@@ -279,7 +279,7 @@ def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNet-34 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    `"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
@@ -291,7 +291,7 @@ def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    `"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
@@ -303,7 +303,7 @@ def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNet-101 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    `"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
@@ -315,7 +315,7 @@ def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNet-152 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    `"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
@@ -327,7 +327,7 @@ def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNeXt-50 32x4d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+    `"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
@@ -341,7 +341,7 @@ def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: A
 
 def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""ResNeXt-101 32x8d model from
-    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+    `"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
@@ -355,7 +355,7 @@ def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs:
 
 def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""Wide ResNet-50-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+    `"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
@@ -373,7 +373,7 @@ def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: A
 
 def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
     r"""Wide ResNet-101-2 model from
-    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+    `"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

@@ -104,7 +104,7 @@ def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool
 
 def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     r"""VGG 11-layer model (configuration "A") from
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
+    `"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 +115,7 @@ def vgg11(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
 
 def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     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>`_
+    `"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
@@ -126,7 +126,7 @@ def vgg11_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     r"""VGG 13-layer model (configuration "B")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
+    `"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
@@ -137,7 +137,7 @@ def vgg13(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
 
 def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     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>`_
+    `"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
@@ -148,7 +148,7 @@ def vgg13_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     r"""VGG 16-layer model (configuration "D")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
+    `"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
@@ -159,7 +159,7 @@ def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
 
 def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     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>`_
+    `"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
@@ -170,7 +170,7 @@ def vgg16_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) ->
 
 def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     r"""VGG 19-layer model (configuration "E")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
+    `"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
@@ -181,7 +181,7 @@ def vgg19(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG
 
 def vgg19_bn(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:
     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>`_
+    `"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