Filling up readme

README.md

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+# Overview
+
+`PixelShuffle`/`PixelUnshuffle` in PyTorch and `depth_to_space`/`space_to_depth` in TensorFlow are very similar, but they are not numerically identical unless the upsampled image contains a single channel. This can be easyly verified by hand: while the tensor dimensions match between the PyTorch and TensorFlow worlds, the output channels do not follow the same order.
+
+However, in some deployment setups, there might be performance benefits to using the space-to-depth/depth-to-space variant. For example, at the moment of writing, [Android NN API](https://developer.android.com/ndk/guides/neuralnetworks) only supports depth-to-space and space-to-depth.
+
+This repository provides a unit-tested space-to-depth/depth-to-space implementation for PyTorch, supporting conversion to [SpaceToDepth](http://www.xavierdupre.fr/app/mlprodict/helpsphinx/onnxops/onnx__SpaceToDepth.html)/[DepthToSpace](http://www.xavierdupre.fr/app/mlprodict/helpsphinx/onnxops/onnx__DepthToSpace.html) ONNX ops when exporting the model as an ONNX graph for further deployment.
+
+The provided implementation follows channels-first PyTorch standard, allowing for arbitrary number of outer dimensions, i.e. it supports tensors of `[..., C, H, W]` layouts.
+
+# Installation
+
+```
+python3 -m pip install s2d2s
+```
+
+# Usage
+
+Both functional implementations and `nn.Module`s are available and can be used as follows:
+
+```
+from s2d2s import space_to_depth, depth_to_space
+
+y = space_to_depth(x, 2)
+```
+
+```
+from s2d2s import SpaceToDepth, DepthToSpace
+
+module = SpaceToDepth(2)
+y = module(x)
+```