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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# 🎛 Custom convolutions\n", | ||
| "\n", | ||
| "In this notebook, overriding the convolution layers operation is demonstrated using [kernex](https://github.com/ASEM000/kernex/blob/main/README.md). By defining only the kernel operation, the layer can be used in the same way as the original layer and parameter initialization/shape checking is handled automatically." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Direct convolution\n", | ||
| "\n", | ||
| "This example demonstrates how to recreate the convolution operation using the `kernex` library. `kernex` offers function transformation similar to `jax.vmap`, that wraps a kernel operation (e.g. `lambda input,kernel: sum(input*kernel)`) and returns a function that works on array views." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install git+https://github.com/ASEM000/serket --quiet\n", | ||
| "!pip install kernex --quiet" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import kernex as kex # for stencil operations like convolutions\n", | ||
| "import serket as sk\n", | ||
| "import jax\n", | ||
| "import jax.random as jr\n", | ||
| "import jax.numpy as jnp\n", | ||
| "import numpy.testing as npt\n", | ||
| "\n", | ||
| "\n", | ||
| "def my_conv(\n", | ||
| " input: jax.Array,\n", | ||
| " weight: jax.Array,\n", | ||
| " bias: jax.Array | None,\n", | ||
| " strides: tuple[int, ...],\n", | ||
| " padding: tuple[tuple[int, int], ...],\n", | ||
| " dilation: tuple[int, ...],\n", | ||
| " groups: int,\n", | ||
| " mask: jax.Array | None,\n", | ||
| "):\n", | ||
| " # same function signature as serket.nn.conv_nd\n", | ||
| " del mask #\n", | ||
| " del dilation # for simplicity\n", | ||
| " del groups # for simplicity\n", | ||
| " _, in_features, *kernel_size = weight.shape\n", | ||
| "\n", | ||
| " @kex.kmap(\n", | ||
| " kernel_size=(in_features, *kernel_size),\n", | ||
| " strides=(1, *strides),\n", | ||
| " padding=((0, 0), *padding),\n", | ||
| " )\n", | ||
| " def conv_func(input, weight):\n", | ||
| " # define the kernel operation\n", | ||
| " return jnp.sum(input * weight)\n", | ||
| "\n", | ||
| " # vectorize over the out_features of the weight\n", | ||
| " out = jax.vmap(conv_func, in_axes=(None, 0))(input, weight)\n", | ||
| " # squeeze out the vmapped axis\n", | ||
| " out = jnp.squeeze(out, axis=1)\n", | ||
| " return out + bias if bias is not None else out\n", | ||
| "\n", | ||
| "\n", | ||
| "class CustomConv2D(sk.nn.Conv2D):\n", | ||
| " # override the conv_op\n", | ||
| " conv_op = my_conv\n", | ||
| "\n", | ||
| "\n", | ||
| "k1, k2 = jr.split(jr.PRNGKey(0), 2)\n", | ||
| "\n", | ||
| "basic_conv = sk.nn.Conv2D(\n", | ||
| " in_features=1,\n", | ||
| " out_features=2,\n", | ||
| " kernel_size=3,\n", | ||
| " bias_init=None,\n", | ||
| " key=k1,\n", | ||
| ")\n", | ||
| "\n", | ||
| "custom_conv = CustomConv2D(\n", | ||
| " in_features=1,\n", | ||
| " out_features=2,\n", | ||
| " kernel_size=3,\n", | ||
| " bias_init=None,\n", | ||
| " key=k1,\n", | ||
| ")\n", | ||
| "\n", | ||
| "# channel-first input\n", | ||
| "input = jr.uniform(k2, shape=(1, 10, 10))\n", | ||
| "\n", | ||
| "npt.assert_allclose(\n", | ||
| " basic_conv(input),\n", | ||
| " custom_conv(input),\n", | ||
| " atol=1e-6,\n", | ||
| ")\n", | ||
| "# lets check gradients\n", | ||
| "npt.assert_allclose(\n", | ||
| " jax.grad(lambda x: basic_conv(x).sum())(input),\n", | ||
| " jax.grad(lambda x: custom_conv(x).sum())(input),\n", | ||
| " atol=1e-6,\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Depthwise convolution\n", | ||
| "\n", | ||
| "Similar to the above example, For recreating depthwise convolution, the only addition is to add vectorize the kernel operation over the channels dimension using `jax.vmap`" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import kernex as kex # for stencil operations like convolutions\n", | ||
| "import jax\n", | ||
| "import jax.random as jr\n", | ||
| "import jax.numpy as jnp\n", | ||
| "import numpy.testing as npt\n", | ||
| "\n", | ||
| "\n", | ||
| "def my_depthwise_conv(\n", | ||
| " input: jax.Array,\n", | ||
| " weight: jax.Array,\n", | ||
| " bias: jax.Array | None,\n", | ||
| " strides: tuple[int, ...],\n", | ||
| " padding: tuple[tuple[int, int], ...],\n", | ||
| " mask: jax.Array | None,\n", | ||
| "):\n", | ||
| " # same function signature as serket.nn.depthwise_conv_nd\n", | ||
| " del mask #\n", | ||
| " _, _, *kernel_size = weight.shape\n", | ||
| "\n", | ||
| " @jax.vmap # <- vectorize over the input channels\n", | ||
| " @kex.kmap(\n", | ||
| " kernel_size=tuple(kernel_size),\n", | ||
| " strides=strides,\n", | ||
| " padding=padding,\n", | ||
| " )\n", | ||
| " def conv_func(input, weight):\n", | ||
| " # define the kernel operation\n", | ||
| " return jnp.sum(input * weight)\n", | ||
| "\n", | ||
| " # vectorize over the output channels (filters)\n", | ||
| " out = jax.vmap(conv_func, in_axes=(None, 0))(input, weight)\n", | ||
| " out = jnp.squeeze(out, axis=1) # squeeze out the vmapped axis\n", | ||
| " return out + bias if bias is not None else out\n", | ||
| "\n", | ||
| "\n", | ||
| "class CustomDepthwiseConv2D(sk.nn.DepthwiseConv2D):\n", | ||
| " # override the conv_op\n", | ||
| " conv_op = my_depthwise_conv\n", | ||
| "\n", | ||
| "\n", | ||
| "k1, k2 = jr.split(jr.PRNGKey(0), 2)\n", | ||
| "\n", | ||
| "basic_conv = sk.nn.DepthwiseConv2D(\n", | ||
| " in_features=1,\n", | ||
| " depth_multiplier=2,\n", | ||
| " kernel_size=3,\n", | ||
| " bias_init=None,\n", | ||
| " key=k1,\n", | ||
| ")\n", | ||
| "\n", | ||
| "custom_conv = CustomDepthwiseConv2D(\n", | ||
| " in_features=1,\n", | ||
| " depth_multiplier=2,\n", | ||
| " kernel_size=3,\n", | ||
| " bias_init=None,\n", | ||
| " key=k1,\n", | ||
| ")\n", | ||
| "\n", | ||
| "# channel-first input\n", | ||
| "input = jr.uniform(k2, shape=(1, 10, 10))\n", | ||
| "\n", | ||
| "npt.assert_allclose(\n", | ||
| " basic_conv(input),\n", | ||
| " custom_conv(input),\n", | ||
| " atol=1e-6,\n", | ||
| ")\n", | ||
| "# lets check gradients\n", | ||
| "npt.assert_allclose(\n", | ||
| " jax.grad(lambda x: basic_conv(x).sum())(input),\n", | ||
| " jax.grad(lambda x: custom_conv(x).sum())(input),\n", | ||
| " atol=1e-6,\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Positive kernel convolution\n", | ||
| "\n", | ||
| "In this example, a custom convolution operation is defined. As a toy examaple the operation will only multiply weight values\n", | ||
| "that are not zero." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "(2, 10, 10)" | ||
| ] | ||
| }, | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import kernex as kex # for stencil operations like convolutions\n", | ||
| "import serket as sk\n", | ||
| "import jax\n", | ||
| "import jax.random as jr\n", | ||
| "import jax.numpy as jnp\n", | ||
| "import numpy.testing as npt\n", | ||
| "\n", | ||
| "\n", | ||
| "def my_custom_conv(\n", | ||
| " input: jax.Array,\n", | ||
| " weight: jax.Array,\n", | ||
| " bias: jax.Array | None,\n", | ||
| " strides: tuple[int, ...],\n", | ||
| " padding: tuple[tuple[int, int], ...],\n", | ||
| " dilation: tuple[int, ...],\n", | ||
| " groups: int,\n", | ||
| " mask: jax.Array | None,\n", | ||
| "):\n", | ||
| " # same function signature as serket.nn.conv_nd\n", | ||
| " del mask #\n", | ||
| " del dilation # for simplicity\n", | ||
| " del groups # for simplicity\n", | ||
| " _, in_features, *kernel_size = weight.shape\n", | ||
| "\n", | ||
| " @kex.kmap(\n", | ||
| " kernel_size=(in_features, *kernel_size),\n", | ||
| " strides=(1, *strides),\n", | ||
| " padding=((0, 0), *padding),\n", | ||
| " )\n", | ||
| " def conv_func(input, weight):\n", | ||
| " # define a custom kernel operation\n", | ||
| " # that only multiplies the input with the weight\n", | ||
| " # if the weight is positive\n", | ||
| " return jnp.sum(input * jnp.where(weight < 0, 0, weight))\n", | ||
| "\n", | ||
| " # vectorize over the out_features of the weight\n", | ||
| " out = jax.vmap(conv_func, in_axes=(None, 0))(input, weight)\n", | ||
| " # squeeze out the vmapped axis\n", | ||
| " out = jnp.squeeze(out, axis=1)\n", | ||
| " return out + bias if bias is not None else out\n", | ||
| "\n", | ||
| "\n", | ||
| "class CustomConv2D(sk.nn.Conv2D):\n", | ||
| " # override the conv_op\n", | ||
| " conv_op = my_custom_conv\n", | ||
| "\n", | ||
| "\n", | ||
| "k1, k2 = jr.split(jr.PRNGKey(0), 2)\n", | ||
| "\n", | ||
| "\n", | ||
| "custom_conv = CustomConv2D(\n", | ||
| " in_features=1,\n", | ||
| " out_features=2,\n", | ||
| " kernel_size=3,\n", | ||
| " bias_init=None,\n", | ||
| " key=k1,\n", | ||
| ")\n", | ||
| "\n", | ||
| "# channel-first input\n", | ||
| "input = jr.uniform(k2, shape=(1, 10, 10))\n", | ||
| "\n", | ||
| "basic_conv(input).shape" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "py311", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.0" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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