Fix docstring formatting issues (#2049)

Summary: Fix docstring formatting issues

Reviewed By: fmassa

Differential Revision: D20736644

fbshipit-source-id: 78f66045cfd4c84cb35ca84a1e1fa6aadcd50642
Co-authored-by: Patrick Labatut <plabatut@fb.com>

torchvision/__init__.py

@@ -51,9 +51,9 @@ def set_video_backend(backend):
         backend (string): Name of the video backend. one of {'pyav', 'video_reader'}.
             The :mod:`pyav` package uses the 3rd party PyAv library. It is a Pythonic
             binding for the FFmpeg libraries.
-            The :mod:`video_reader` package includes a native c++ implementation on
+            The :mod:`video_reader` package includes a native C++ implementation on
             top of FFMPEG libraries, and a python API of TorchScript custom operator.
-                It is generally decoding faster than pyav, but perhaps is less robust.
+            It is generally decoding faster than :mod:`pyav`, but perhaps is less robust.
     """
     global _video_backend
     if backend not in ["pyav", "video_reader"]:

torchvision/transforms/functional.py

@@ -349,6 +349,7 @@ def crop(img, top, left, height, width):
         left (int): Horizontal component of the top left corner of the crop box.
         height (int): Height of the crop box.
         width (int): Width of the crop box.
+
     Returns:
         PIL Image: Cropped image.
     """
@@ -554,7 +555,8 @@ def five_crop(img, size):
 
 
 def ten_crop(img, size, vertical_flip=False):
-    r"""Crop the given PIL Image into four corners and the central crop plus the
+    """Generate ten cropped images from the given PIL Image.
+    Crop the given PIL Image into four corners and the central crop plus the
     flipped version of these (horizontal flipping is used by default).
 
     .. Note::
@@ -569,8 +571,8 @@ def ten_crop(img, size, vertical_flip=False):
 
     Returns:
         tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
-                Corresponding top left, top right, bottom left, bottom right and center crop
-                and same for the flipped image.
+            Corresponding top left, top right, bottom left, bottom right and
+            center crop and same for the flipped image.
     """
     if isinstance(size, numbers.Number):
         size = (int(size), int(size))

torchvision/transforms/transforms.py

@@ -641,7 +641,7 @@ class RandomResizedCrop(object):
         width, height = _get_image_size(img)
         area = height * width
 
-        for attempt in range(10):
+        for _ in range(10):
             target_area = random.uniform(*scale) * area
             log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
             aspect_ratio = math.exp(random.uniform(*log_ratio))
@@ -1150,8 +1150,8 @@ class Grayscale(object):
 
     Returns:
         PIL Image: Grayscale version of the input.
-        - If num_output_channels == 1 : returned image is single channel
-        - If num_output_channels == 3 : returned image is 3 channel with r == g == b
+         - If ``num_output_channels == 1`` : returned image is single channel
+         - If ``num_output_channels == 3`` : returned image is 3 channel with r == g == b
 
     """
 
@@ -1208,8 +1208,8 @@ class RandomGrayscale(object):
 
 class RandomErasing(object):
     """ Randomly selects a rectangle region in an image and erases its pixels.
-        'Random Erasing Data Augmentation' by Zhong et al.
-        See https://arxiv.org/pdf/1708.04896.pdf
+    'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/pdf/1708.04896.pdf
+
     Args:
          p: probability that the random erasing operation will be performed.
          scale: range of proportion of erased area against input image.
@@ -1222,6 +1222,7 @@ class RandomErasing(object):
 
     Returns:
         Erased Image.
+
     # Examples:
         >>> transform = transforms.Compose([
         >>>   transforms.RandomHorizontalFlip(),
@@ -1261,7 +1262,7 @@ class RandomErasing(object):
         img_c, img_h, img_w = img.shape
         area = img_h * img_w
 
-        for attempt in range(10):
+        for _ in range(10):
             erase_area = random.uniform(scale[0], scale[1]) * area
             aspect_ratio = random.uniform(ratio[0], ratio[1])
 

torchvision/utils.py

@@ -95,7 +95,7 @@ def save_image(tensor, fp, nrow=8, padding=2,
     Args:
         tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
             saves the tensor as a grid of images by calling ``make_grid``.
-        fp - A filename(string) or file object
+        fp (string or file object): A filename or a file object
         format(Optional):  If omitted, the format to use is determined from the filename extension.
             If a file object was used instead of a filename, this parameter should always be used.
         **kwargs: Other arguments are documented in ``make_grid``.