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 <[email protected]>

Author: fmassa

torchvision/__init__.py

@@ -50,10 +50,10 @@ def set_video_backend(backend):
     Args:
         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
-                top of FFMPEG libraries, and a python API of TorchScript custom operator.
-                It is generally decoding faster than pyav, but perhaps is less robust.
+            binding for the FFmpeg libraries.
+            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 :mod:`pyav`, but perhaps is less robust.
     """
     global _video_backend
     if backend not in ["pyav", "video_reader"]:

torchvision/transforms/functional.py

@@ -342,13 +342,14 @@ def pad(img, padding, fill=0, padding_mode='constant'):
 
 def crop(img, top, left, height, width):
     """Crop the given PIL Image.
-    
+
     Args:
         img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
         top (int): Vertical component of the top left corner of the crop box.
         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.
     """
@@ -361,13 +362,13 @@ def crop(img, top, left, height, width):
 def center_crop(img, output_size):
     """Crop the given PIL Image and resize it to desired size.
 
-        Args:
-            img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
-            output_size (sequence or int): (height, width) of the crop box. If int,
-                it is used for both directions
-        Returns:
-            PIL Image: Cropped image.
-        """
+    Args:
+        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
+        output_size (sequence or int): (height, width) of the crop box. If int,
+            it is used for both directions
+    Returns:
+        PIL Image: Cropped image.
+    """
     if isinstance(output_size, numbers.Number):
         output_size = (int(output_size), int(output_size))
     image_width, image_height = img.size
@@ -554,23 +555,24 @@ 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
-        flipped version of these (horizontal flipping is used by default).
+    """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::
         This transform returns a tuple of images and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
     Args:
-       size (sequence or int): Desired output size of the crop. If size is an
+        size (sequence or int): Desired output size of the crop. If size is an
             int instead of sequence like (h, w), a square crop (size, size) is
             made.
-       vertical_flip (bool): Use vertical flipping instead of horizontal
+        vertical_flip (bool): Use vertical flipping instead of horizontal
 
     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.
+        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.
     """
     if isinstance(size, numbers.Number):
         size = (int(size), int(size))

torchvision/transforms/transforms.py

@@ -641,7 +641,7 @@ def get_params(img, scale, ratio):
         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 @@ def __repr__(self):
 
 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.
@@ -1222,12 +1222,13 @@ class RandomErasing(object):
 
     Returns:
         Erased Image.
+
     # Examples:
         >>> transform = transforms.Compose([
-        >>> transforms.RandomHorizontalFlip(),
-        >>> transforms.ToTensor(),
-        >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
-        >>> transforms.RandomErasing(),
+        >>>   transforms.RandomHorizontalFlip(),
+        >>>   transforms.ToTensor(),
+        >>>   transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        >>>   transforms.RandomErasing(),
         >>> ])
     """
 
@@ -1261,7 +1262,7 @@ def get_params(img, scale, ratio, value=0):
         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``.