Added tensor transforms jupyter notebook (pytorch#2730)

* [WIP] Added scriptable transforms python example

* Replaced script file with jupyter notebook

* Updated readme

* Updates according to review
+ updated docstrings

* Updated notebook and docstring according to the review

* torch script -> torchscript

docs/source/transforms.rst

@@ -14,6 +14,24 @@ All transformations accept PIL Image, Tensor Image or batch of Tensor Images as
 Tensor Images is a tensor of ``(B, C, H, W)`` shape, where ``B`` is a number of images in the batch. Deterministic or
 random transformations applied on the batch of Tensor Images identically transform all the images of the batch.
 
+.. warning::
+
+    Since v0.8.0 all random transformations are using torch default random generator to sample random parameters.
+    It is a backward compatibility breaking change and user should set the random state as following:
+
+    .. code:: python
+
+        # Previous versions
+        # import random
+        # random.seed(12)
+
+        # Now
+        import torch
+        torch.manual_seed(17)
+
+    Please, keep in mind that the same seed for torch random generator and Python random generator will not
+    produce the same results.
+
 
 Scriptable transforms
 ---------------------
@@ -33,6 +51,7 @@ Make sure to use only scriptable transformations, i.e. that work with ``torch.Te
 
 For any custom transformations to be used with ``torch.jit.script``, they should be derived from ``torch.nn.Module``.
 
+
 Compositions of transforms
 --------------------------
 

examples/python/README.md

@@ -0,0 +1,13 @@
+# Python examples
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
+[Examples of Tensor Images transformations](https://github.com/pytorch/vision/blob/master/examples/python/tensor_transforms.ipynb)
+
+Prior to v0.8.0, transforms in torchvision have traditionally been PIL-centric and presented multiple limitations due to 
+that. Now, since v0.8.0, transforms implementations are Tensor and PIL compatible and we can achieve the following new 
+features:
+- transform multi-band torch tensor images (with more than 3-4 channels) 
+- torchscript transforms together with your model for deployment
+- support for GPU acceleration
+- batched transformation such as for videos
+- read and decode data directly as torch tensor with torchscript support (for PNG and JPEG image formats)