fixed docstrings

bob/learn/pytorch/datasets/casia_webface.py

@@ -23,9 +23,9 @@ class CasiaWebFaceDataset(Dataset):
     The path to the data
   transform : `torchvision.transforms`
     The transform(s) to apply to the face images
-  data_files : list of str
+  data_files : list of :obj:`str`
     The list of data files
-  id_labels : list of int
+  id_labels : list of :obj:`int`
     The list of identities, for each data file
 
   """
@@ -108,11 +108,11 @@ class CasiaDataset(Dataset):
     The path to the data
   transform : `torchvision.transforms`
     The transform(s) to apply to the face images
-  data_files: list of str
+  data_files: list of :obj:`str`
     The list of data files
-  id_labels : list of int
+  id_labels : list of :obj:`int`
     The list of identities, for each data file
-  pose_labels : list of int
+  pose_labels : list of :obj:`int`
     The list containing the pose labels 
 
   """

bob/learn/pytorch/trainers/CNNTrainer.py

@@ -24,7 +24,7 @@ class CNNTrainer(object):
     The network to train
   batch_size: int
     The size of your minibatch
-  use_gpu: boolean
+  use_gpu: bool
     If you would like to use the gpu
   verbosity_level: int
     The level of verbosity output to stdout
@@ -40,7 +40,7 @@ class CNNTrainer(object):
       The network to train
     batch_size: int
       The size of your minibatch
-    use_gpu: boolean
+    use_gpu: bool
       If you would like to use the gpu
     verbosity_level: int
       The level of verbosity output to stdout

bob/learn/pytorch/trainers/ConditionalGANTrainer.py

@@ -22,7 +22,7 @@ class ConditionalGANTrainer(object):
     The generator network
   discriminator : :py:class:`torch.nn.Module`
     The discriminator network
-  image_size: list of int
+  image_size: list of :obj:`int`
     The size of the images in this format: [channels,height, width]
   batch_size: int
     The size of your minibatch
@@ -49,7 +49,7 @@ class ConditionalGANTrainer(object):
       The generator network
     netD : :py:class:`torch.nn.Module`
       The discriminator network
-    image_size: list of int
+    image_size: list of :obj:`int`
       The size of the images in this format: [channels,height, width]
     batch_size: int
       The size of your minibatch