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PIX is an image processing library in JAX, for JAX.
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# PIX <img src="https://raw.githubusercontent.com/google-deepmind/dm_pix/master/dm_pix/images/pix_logo.png" title="PIX logo" height=200 width=200> PIX is an image processing library in [JAX], for [JAX]. [](https://github.com/deepmind/dm_pix/actions/workflows/ci.yml) [](https://dm-pix.readthedocs.io/en/latest/?badge=latest) [](https://pypi.org/project/dm-pix/) ## Overview [JAX] is a library resulting from the union of [Autograd] and [XLA] for high-performance machine learning research. It provides [NumPy], [SciPy], automatic differentiation and first-class GPU/TPU support. PIX is a library built on top of JAX with the goal of providing image processing functions and tools to JAX in a way that they can be optimised and parallelised through [`jax.jit`][jit], [`jax.vmap`][vmap] and [`jax.pmap`][pmap]. ## Installation PIX is written in pure Python, but depends on C++ code via JAX. Because JAX installation is different depending on your CUDA version, PIX does not list JAX as a dependency in [`pyproject.toml`], although it is technically listed for reference, but commented. First, follow [JAX installation instructions] to install JAX with the relevant accelerator support. Then, install PIX using `pip`: ```bash $ pip install dm-pix ``` ## Quickstart To use `PIX`, you just need to `import dm_pix as pix` and use it right away! For example, let's assume to have loaded the JAX logo (available in `examples/assets/jax_logo.jpg`) in a variable called `image` and we want to flip it left to right. ![JAX logo] All it's needed is the following code! ```python import dm_pix as pix # Load an image into a NumPy array with your preferred library. image = load_image() flip_left_right_image = pix.flip_left_right(image) ``` And here is the result! ![JAX logo left-right] All the functions in PIX can be [`jax.jit`][jit]ed, [`jax.vmap`][vmap]ed and [`jax.pmap`][pmap]ed, so all the following functions can take advantage of optimization and parallelization. ```python import dm_pix as pix import jax # Load an image into a NumPy array with your preferred library. image = load_image() # Vanilla Python function. flip_left_right_image = pix.flip_left_right(image) # `jax.jit`ed function. flip_left_right_image = jax.jit(pix.flip_left_right)(image) # Assuming to have a single device, like a CPU or a single GPU, we add a # single leading dimension for using `image` with the parallelized or # the multi-device parallelization version of `pix.flip_left_right`. # To know more, please refer to JAX documentation of `jax.vmap` and `jax.pmap`. image = image[np.newaxis, ...] # `jax.vmap`ed function. flip_left_right_image = jax.vmap(pix.flip_left_right)(image) # `jax.pmap`ed function. flip_left_right_image = jax.pmap(pix.flip_left_right)(image) ``` You can check it yourself that the result from the four versions of `pix.flip_left_right` is the same (up to the accelerator floating point accuracy)! ## Examples We have a few examples in the [`examples/`] folder. They are not much more involved then the previous example, but they may be a good starting point for you! ## Testing We provide a suite of tests to help you both testing your development environment and to know more about the library itself! All test files have `_test` suffix, and can be executed using `pytest`. If you already have PIX installed, you just need to install some extra dependencies and run `pytest` as follows: ```bash $ pip install -e ".[test]" $ python -m pytest [-n <NUMCPUS>] dm_pix ``` If you want an isolated virtual environment, you just need to run our utility `bash` script as follows: ```bash $ .
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:37dd8852fb36b1fd, topic:jax, readme:autograd, readme:automatic differentiation
matched fp:37dd8852fb36b1fd, topic:computer-vision