[extractor] fixed the docstrings

bob/ip/pytorch_extractor/CNN8.py

@@ -9,8 +9,30 @@ from bob.learn.pytorch.architectures import CNN8
 from bob.bio.base.extractor import Extractor
 
 class CNN8Extractor(Extractor):
+  """ The class implementing the feature extraction of CASIA-Net embeddings.
+
+  Attributes
+  ----------
+  network: :py:class:`torch.nn.Module`
+      The network architecture
+  to_tensor: :py:mod:`torchvision.transforms`
+      The transform from numpy.array to torch.Tensor
+  norm: :py:mod:`torchvision.transforms`
+      The transform to normalize the input 
+
+  """
   
   def __init__(self, model_file=None, num_classes=10575):
+    """ Init method
+
+    Parameters
+    ----------
+    model_file: str
+        The path of the trained network to load
+    drop_rate: float
+        The number of classes.
+    
+    """
 
     Extractor.__init__(self, skip_extractor_training=True)
     
@@ -30,20 +52,20 @@ class CNN8Extractor(Extractor):
     self.norm = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
 
   def __call__(self, image):
-    """__call__(image) -> feature
-
-    Extract features from an image
-
-    **Parameters:**
+    """ Extract features from an image
 
+    Parameters
+    ----------
     image : 3D :py:class:`numpy.ndarray` (floats)
       The image to extract the features from. Its size must be 3x128x128
 
-    **Returns:**
-
+    Returns
+    -------
     feature : 2D :py:class:`numpy.ndarray` (floats)
-      The extracted features as a 1d array of size 512 
+      The extracted features as a 1d array of size 320 
+    
     """
+   
     input_image = numpy.rollaxis(numpy.rollaxis(image, 2),2)
     input_image = self.to_tensor(input_image)
     input_image = self.norm(input_image)

bob/ip/pytorch_extractor/CasiaNet.py

@@ -15,9 +15,9 @@ class CasiaNetExtractor(Extractor):
   ----------
   network: :py:class:`torch.nn.Module`
       The network architecture
-  to_tensor: :py:class:`torchvision.transforms`
+  to_tensor: :py:mod:`torchvision.transforms`
       The transform from numpy.array to torch.Tensor
-  norm: :py:class:`torchvision.transforms`
+  norm: :py:mod:`torchvision.transforms`
       The transform to normalize the input 
 
   """
@@ -61,10 +61,11 @@ class CasiaNetExtractor(Extractor):
 
     Returns
     -------
-
     feature : 2D :py:class:`numpy.ndarray` (floats)
       The extracted features as a 1d array of size 320 
+    
     """
+   
     input_image = numpy.rollaxis(numpy.rollaxis(image, 2),2)
     input_image = self.to_tensor(input_image)
     input_image = self.norm(input_image)