Add documentation for ShuffleNet plus minor doc fixes (#932)

docs/source/models.rst

@@ -171,7 +171,10 @@ GoogLeNet
 ShuffleNet v2
 -------------
 
-.. autofunction:: shufflenet
+.. autofunction:: shufflenet_v2_x0_5
+.. autofunction:: shufflenet_v2_x1_0
+.. autofunction:: shufflenet_v2_x1_5
+.. autofunction:: shufflenet_v2_x2_0
 
 MobileNet v2
 -------------

torchvision/datasets/cityscapes.py

@@ -27,6 +27,7 @@ class Cityscapes(VisionDataset):
         Get semantic segmentation target
 
         .. code-block:: python
+
             dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
                                  target_type='semantic')
 
@@ -35,6 +36,7 @@ class Cityscapes(VisionDataset):
         Get multiple targets
 
         .. code-block:: python
+
             dataset = Cityscapes('./data/cityscapes', split='train', mode='fine',
                                  target_type=['instance', 'color', 'polygon'])
 
@@ -43,6 +45,7 @@ class Cityscapes(VisionDataset):
         Validate on the "coarse" set
 
         .. code-block:: python
+
             dataset = Cityscapes('./data/cityscapes', split='val', mode='coarse',
                                  target_type='semantic')
 

torchvision/models/shufflenetv2.py

@@ -146,20 +146,56 @@ def _shufflenetv2(arch, pretrained, progress, *args, **kwargs):
 
 
 def shufflenet_v2_x0_5(pretrained=False, progress=True, **kwargs):
+    """
+    Constructs a ShuffleNetV2 with 0.5x output channels, as described in
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
+    <https://arxiv.org/abs/1807.11164>`_.
+
+    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
+    """
     return _shufflenetv2('shufflenetv2_x0.5', pretrained, progress,
                          [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
 
 
 def shufflenet_v2_x1_0(pretrained=False, progress=True, **kwargs):
+    """
+    Constructs a ShuffleNetV2 with 1.0x output channels, as described in
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
+    <https://arxiv.org/abs/1807.11164>`_.
+
+    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
+    """
     return _shufflenetv2('shufflenetv2_x1.0', pretrained, progress,
                          [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
 
 
 def shufflenet_v2_x1_5(pretrained=False, progress=True, **kwargs):
+    """
+    Constructs a ShuffleNetV2 with 1.5x output channels, as described in
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
+    <https://arxiv.org/abs/1807.11164>`_.
+
+    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
+    """
     return _shufflenetv2('shufflenetv2_x1.5', pretrained, progress,
                          [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
 
 
 def shufflenet_v2_x2_0(pretrained=False, progress=True, **kwargs):
+    """
+    Constructs a ShuffleNetV2 with 2.0x output channels, as described in
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
+    <https://arxiv.org/abs/1807.11164>`_.
+
+    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
+    """
     return _shufflenetv2('shufflenetv2_x2.0', pretrained, progress,
                          [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)

torchvision/transforms/transforms.py

@@ -787,6 +787,7 @@ class LinearTransformation(object):
         whitening transformation: Suppose X is a column vector zero-centered data.
         Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X),
         perform SVD on this matrix and pass it as transformation_matrix.
+
     Args:
         transformation_matrix (Tensor): tensor [D x D], D = C x H x W
         mean_vector (Tensor): tensor [D], D = C x H x W