Add warning and note docs for scipy (#1842) (#1966)

docs/source/models.rst

@@ -71,7 +71,7 @@ This directory can be set using the `TORCH_MODEL_ZOO` environment variable. See
 Some models use modules which have different training and evaluation
 behavior, such as batch normalization. To switch between these modes, use
 ``model.train()`` or ``model.eval()`` as appropriate. See
-:meth:`~torch.nn.Module.train` or :meth:`~torch.nn.Module.eval` for details. 
+:meth:`~torch.nn.Module.train` or :meth:`~torch.nn.Module.eval` for details.
 
 All pre-trained models expect input images normalized in the same way,
 i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
@@ -183,11 +183,19 @@ Inception v3
 
 .. autofunction:: inception_v3
 
+.. note ::
+    This requires `scipy` to be installed
+
+
 GoogLeNet
 ------------
 
 .. autofunction:: googlenet
 
+.. note ::
+    This requires `scipy` to be installed
+
+
 ShuffleNet v2
 -------------
 

torchvision/datasets/caltech.py

@@ -10,6 +10,10 @@ from .utils import download_and_extract_archive, makedir_exist_ok, verify_str_ar
 class Caltech101(VisionDataset):
     """`Caltech 101 <http://www.vision.caltech.edu/Image_Datasets/Caltech101/>`_ Dataset.
 
+    .. warning::
+
+        This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
+
     Args:
         root (string): Root directory of dataset where directory
             ``caltech101`` exists or will be saved to if download is set to True.

torchvision/datasets/svhn.py

@@ -13,6 +13,10 @@ class SVHN(VisionDataset):
     we assign the label `0` to the digit `0` to be compatible with PyTorch loss functions which
     expect the class labels to be in the range `[0, C-1]`
 
+    .. warning::
+
+        This class needs `scipy <https://docs.scipy.org/doc/>`_ to load data from `.mat` format.
+
     Args:
         root (string): Root directory of dataset where directory
             ``SVHN`` exists.

torchvision/models/googlenet.py

@@ -103,7 +103,7 @@ class GoogLeNet(nn.Module):
         else:
             self.aux1 = None
             self.aux2 = None
-            
+
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.dropout = nn.Dropout(0.2)
         self.fc = nn.Linear(1024, num_classes)