cleanup headings from pandoc

Author: Thomas Grainger

README.rst

@@ -201,7 +201,7 @@ Transforms are common image transforms. They can be chained together
 using ``transforms.Compose``
 
 ``transforms.Compose``
-~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^
 
 One can compose several transforms together. For example.
 
@@ -216,10 +216,10 @@ One can compose several transforms together. For example.
     ])
 
 Transforms on PIL.Image
------------------------
+~~~~~~~~~~~~~~~~~~~~~~~
 
 ``Scale(size, interpolation=Image.BILINEAR)``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Rescales the input PIL.Image to the given 'size'. 'size' will be the
 size of the smaller edge.
@@ -229,14 +229,14 @@ height / width, size) - size: size of the smaller edge - interpolation:
 Default: PIL.Image.BILINEAR
 
 ``CenterCrop(size)`` - center-crops the image to the given size
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Crops the given PIL.Image at the center to have a region of the given
 size. size can be a tuple (target\_height, target\_width) or an integer,
 in which case the target will be of a square shape (size, size)
 
 ``RandomCrop(size, padding=0)``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Crops the given PIL.Image at a random location to have a region of the
 given size. size can be a tuple (target\_height, target\_width) or an
@@ -245,13 +245,13 @@ If ``padding`` is non-zero, then the image is first zero-padded on each
 side with ``padding`` pixels.
 
 ``RandomHorizontalFlip()``
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Randomly horizontally flips the given PIL.Image with a probability of
 0.5
 
 ``RandomSizedCrop(size, interpolation=Image.BILINEAR)``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the
 original size and and a random aspect ratio of 3/4 to 4/3 of the
@@ -261,23 +261,23 @@ This is popularly used to train the Inception networks - size: size of
 the smaller edge - interpolation: Default: PIL.Image.BILINEAR
 
 ``Pad(padding, fill=0)``
-~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^
 
 Pads the given image on each side with ``padding`` number of pixels, and
 the padding pixels are filled with pixel value ``fill``. If a ``5x5``
 image is padded with ``padding=1`` then it becomes ``7x7``
 
 Transforms on torch.\*Tensor
-----------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 ``Normalize(mean, std)``
-~~~~~~~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^
 
 Given mean: (R, G, B) and std: (R, G, B), will normalize each channel of
 the torch.\*Tensor, i.e. channel = (channel - mean) / std
 
 Conversion Transforms
----------------------
+~~~~~~~~~~~~~~~~~~~~~
 
 -  ``ToTensor()`` - Converts a PIL.Image (RGB) or numpy.ndarray (H x W x
    C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W)
@@ -287,10 +287,10 @@ Conversion Transforms
    shape H x W x C to a PIL.Image of range [0, 255]
 
 Generic Transofrms
-------------------
+~~~~~~~~~~~~~~~~~~
 
 ``Lambda(lambda)``
-~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^
 
 Given a Python lambda, applies it to the input ``img`` and returns it.
 For example: