Full integer post training quant colab.
PiperOrigin-RevId: 248605293
This commit is contained in:
Suharsh Sivakumar
TensorFlower Gardener
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76ff5541a3
commit
1bb6236372
651
tensorflow/lite/tutorials/full_integer_post_training_quant.ipynb
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651
tensorflow/lite/tutorials/full_integer_post_training_quant.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "full-integer-post-training-quant.ipynb",
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"version": "0.3.2",
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"provenance": [],
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"private_outputs": true,
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"collapsed_sections": [],
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"toc_visible": true
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},
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"kernelspec": {
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"display_name": "Python 2",
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"name": "python2"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "6Y8E0lw5eYWm"
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},
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"source": [
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"# Full Integer Post Training Quantization"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "CIGrZZPTZVeO"
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},
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"source": [
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"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/tutorials/post_training_quant.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tutorials/post_training_quant.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "BTC1rDAuei_1"
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},
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"source": [
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"## Overview\n",
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"\n",
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"[TensorFlow Lite](https://www.tensorflow.org/lite/) now supports\n",
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"converting an entire model (weights and activations) to 8-bit during model conversion from TensorFlow to TensorFlow Lite's flat buffer format. This results in a 4x reduction in model size and a 3 to 4x performance improvement on CPU performance. In addition, this fully quantized model can be consumed by integer-only hardware accelerators.\n",
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"\n",
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"In contrast to [post-training \"on-the-fly\" quantization](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/tutorials/post_training_quant.ipynb)\n",
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", which only stores weights as 8-bit ints, in this technique all weights *and* activations are quantized statically during model conversion.\n",
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"\n",
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"In this tutorial, we train an MNIST model from scratch, check its accuracy in TensorFlow, and then convert the saved model into a Tensorflow Lite flatbuffer\n",
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"with full quantization. We finally check the\n",
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"accuracy of the converted model and compare it to the original saved model. We\n",
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"run the training script [mnist.py](https://github.com/tensorflow/models/blob/master/official/mnist/mnist.py) from\n",
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"[Tensorflow official MNIST tutorial](https://github.com/tensorflow/models/tree/master/official/mnist).\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "2XsEP17Zelz9"
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},
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"source": [
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"## Building an MNIST model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "dDqqUIZjZjac"
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},
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"source": [
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"### Setup"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "gyqAw1M9lyab",
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"colab": {}
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},
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"source": [
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"! pip uninstall -y tensorflow\n",
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"! pip install -U tf-nightly"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "WsN6s5L1ieNl",
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"colab": {}
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},
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"source": [
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"import tensorflow as tf\n",
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"tf.enable_eager_execution()"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "00U0taBoe-w7",
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"colab": {}
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},
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"source": [
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"! git clone --depth 1 https://github.com/tensorflow/models"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "4XZPtSh-fUOc",
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"colab": {}
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},
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"source": [
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"import sys\n",
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"import os\n",
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"\n",
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"if sys.version_info.major >= 3:\n",
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" import pathlib\n",
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"else:\n",
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" import pathlib2 as pathlib\n",
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"\n",
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"# Add `models` to the python path.\n",
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"models_path = os.path.join(os.getcwd(), \"models\")\n",
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"sys.path.append(models_path)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "eQ6Q0qqKZogR"
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},
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"source": [
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"### Train and export the model"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "eMsw_6HujaqM",
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"colab": {}
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},
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"source": [
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"saved_models_root = \"/tmp/mnist_saved_model\""
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "hWSAjQWagIHl",
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"colab": {}
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},
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"source": [
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"# The above path addition is not visible to subprocesses, add the path for the subprocess as well.\n",
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"# Note: channels_last is required here or the conversion may fail. \n",
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"!PYTHONPATH={models_path} python models/official/mnist/mnist.py --train_epochs=1 --export_dir {saved_models_root} --data_format=channels_last"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "5NMaNZQCkW9X"
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},
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"source": [
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"For the example, we only trained the model for a single epoch, so it only trains to ~96% accuracy.\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "xl8_fzVAZwOh"
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},
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"source": [
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"### Convert to a TensorFlow Lite model\n",
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"\n",
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"The `savedmodel` directory is named with a timestamp. Select the most recent one: "
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "Xp5oClaZkbtn",
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"colab": {}
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},
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"source": [
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"saved_model_dir = str(sorted(pathlib.Path(saved_models_root).glob(\"*\"))[-1])\n",
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"saved_model_dir"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "AT8BgkKmljOy"
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},
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"source": [
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"Using the [Python `TFLiteConverter`](https://www.tensorflow.org/lite/convert/python_api), the saved model can be converted into a TensorFlow Lite model.\n",
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"\n",
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"First load the model using the `TFLiteConverter`:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "_i8B2nDZmAgQ",
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"colab": {}
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},
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"source": [
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"import tensorflow as tf\n",
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"tf.enable_eager_execution()\n",
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"tf.logging.set_verbosity(tf.logging.DEBUG)\n",
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"\n",
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"converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
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"tflite_model = converter.convert()"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
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"colab_type": "text",
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"id": "F2o2ZfF0aiCx"
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},
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"source": [
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"Write it out to a `.tflite` file:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "vptWZq2xnclo",
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"colab": {}
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},
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"source": [
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"tflite_models_dir = pathlib.Path(\"/tmp/mnist_tflite_models/\")\n",
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"tflite_models_dir.mkdir(exist_ok=True, parents=True)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
|
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"colab_type": "code",
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"id": "Ie9pQaQrn5ue",
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"colab": {}
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},
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"source": [
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"tflite_model_file = tflite_models_dir/\"mnist_model.tflite\"\n",
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"tflite_model_file.write_bytes(tflite_model)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "7BONhYtYocQY"
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},
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"source": [
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"To instead quantize the model on export, first set the `optimizations` flag to optimize for size:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
|
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"colab_type": "code",
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"id": "HEZ6ET1AHAS3",
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"colab": {}
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},
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"source": [
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"tf.logging.set_verbosity(tf.logging.INFO)\n",
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"converter.optimizations = [tf.lite.Optimize.DEFAULT]"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "rTe8avZJHMDO",
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"colab_type": "text"
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},
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"source": [
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"Now, construct and provide a representative dataset, this is used to get the dynamic range of activations."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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||||
"id": "FiwiWU3gHdkW",
|
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"mnist_train, _ = tf.keras.datasets.mnist.load_data()\n",
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"images = tf.cast(mnist_train[0], tf.float32)/255.0\n",
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"mnist_ds = tf.data.Dataset.from_tensor_slices((images)).batch(1)\n",
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"def representative_data_gen():\n",
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" for input_value in mnist_ds.take(100):\n",
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" yield [input_value]\n",
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"\n",
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"converter.representative_dataset = representative_data_gen"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xW84iMYjHd9t",
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"colab_type": "text"
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},
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"source": [
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"Finally, convert the model like usual. Note, by default the converted model will still use float input and outputs for invocation convenience."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "yuNfl3CoHNK3",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"tflite_quant_model = converter.convert()\n",
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"tflite_model_quant_file = tflite_models_dir/\"mnist_model_quant.tflite\"\n",
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"tflite_model_quant_file.write_bytes(tflite_quant_model)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
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"colab_type": "text",
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"id": "PhMmUTl4sbkz"
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},
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"source": [
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"Note how the resulting file is approximately `1/4` the size."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
|
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"colab_type": "code",
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"id": "JExfcfLDscu4",
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"colab": {}
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},
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"source": [
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"!ls -lh {tflite_models_dir}"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
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"colab_type": "text",
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"id": "L8lQHMp_asCq"
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},
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"source": [
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"## Run the TensorFlow Lite models"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
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"colab_type": "text",
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"id": "-5l6-ciItvX6"
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},
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"source": [
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"We can run the TensorFlow Lite model using the Python TensorFlow Lite\n",
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"Interpreter. \n",
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"\n",
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"### Load the test data\n",
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"\n",
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"First, let's load the MNIST test data to feed to the model:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
|
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"colab_type": "code",
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"id": "eTIuU07NuKFL",
|
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"colab": {}
|
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},
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"source": [
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"import numpy as np\n",
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"_, mnist_test = tf.keras.datasets.mnist.load_data()\n",
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"images, labels = tf.cast(mnist_test[0], tf.float32)/255.0, mnist_test[1]\n",
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"\n",
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"mnist_ds = tf.data.Dataset.from_tensor_slices((images, labels)).batch(1)"
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],
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"execution_count": 0,
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"outputs": []
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},
|
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{
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"cell_type": "markdown",
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"metadata": {
|
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"colab_type": "text",
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"id": "Ap_jE7QRvhPf"
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},
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"source": [
|
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"### Load the model into the interpreters"
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]
|
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},
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{
|
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"cell_type": "code",
|
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"metadata": {
|
||||
"colab_type": "code",
|
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"id": "Jn16Rc23zTss",
|
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"colab": {}
|
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},
|
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"source": [
|
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"interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))\n",
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"interpreter.allocate_tensors()"
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],
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"execution_count": 0,
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"outputs": []
|
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},
|
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{
|
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"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "J8Pztk1mvNVL",
|
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"colab": {}
|
||||
},
|
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"source": [
|
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"interpreter_quant = tf.lite.Interpreter(model_path=str(tflite_model_quant_file))\n",
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"interpreter_quant.allocate_tensors()"
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],
|
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"execution_count": 0,
|
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"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "2opUt_JTdyEu"
|
||||
},
|
||||
"source": [
|
||||
"### Test the models on one image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "AKslvo2kwWac",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"for img, label in mnist_ds:\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
"interpreter.set_tensor(interpreter.get_input_details()[0][\"index\"], img)\n",
|
||||
"interpreter.invoke()\n",
|
||||
"predictions = interpreter.get_tensor(\n",
|
||||
" interpreter.get_output_details()[0][\"index\"])"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "XZClM2vo3_bm",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"import matplotlib.pylab as plt\n",
|
||||
"\n",
|
||||
"plt.imshow(img[0])\n",
|
||||
"template = \"True:{true}, predicted:{predict}\"\n",
|
||||
"_ = plt.title(template.format(true= str(label[0].numpy()),\n",
|
||||
" predict=str(predictions[0])))\n",
|
||||
"plt.grid(False)"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "3gwhv4lKbYZ4",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"interpreter_quant.set_tensor(\n",
|
||||
" interpreter_quant.get_input_details()[0][\"index\"], img)\n",
|
||||
"interpreter_quant.invoke()\n",
|
||||
"predictions = interpreter_quant.get_tensor(\n",
|
||||
" interpreter_quant.get_output_details()[0][\"index\"])"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "CIH7G_MwbY2x",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"plt.imshow(img[0])\n",
|
||||
"template = \"True:{true}, predicted:{predict}\"\n",
|
||||
"_ = plt.title(template.format(true= str(label[0].numpy()),\n",
|
||||
" predict=str(predictions[0])))\n",
|
||||
"plt.grid(False)"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "LwN7uIdCd8Gw"
|
||||
},
|
||||
"source": [
|
||||
"### Evaluate the models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "05aeAuWjvjPx",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"def eval_model(interpreter, mnist_ds):\n",
|
||||
" total_seen = 0\n",
|
||||
" num_correct = 0\n",
|
||||
"\n",
|
||||
" input_index = interpreter.get_input_details()[0][\"index\"]\n",
|
||||
" output_index = interpreter.get_output_details()[0][\"index\"]\n",
|
||||
" for img, label in mnist_ds:\n",
|
||||
" total_seen += 1\n",
|
||||
" interpreter.set_tensor(input_index, img)\n",
|
||||
" interpreter.invoke()\n",
|
||||
" predictions = interpreter.get_tensor(output_index)\n",
|
||||
" if predictions == label.numpy():\n",
|
||||
" num_correct += 1\n",
|
||||
"\n",
|
||||
" if total_seen % 500 == 0:\n",
|
||||
" print(\"Accuracy after %i images: %f\" %\n",
|
||||
" (total_seen, float(num_correct) / float(total_seen)))\n",
|
||||
"\n",
|
||||
" return float(num_correct) / float(total_seen)"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "T5mWkSbMcU5z",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"print(eval_model(interpreter, mnist_ds))"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "Km3cY9ry8ZlG"
|
||||
},
|
||||
"source": [
|
||||
"We can repeat the evaluation on the fully quantized model to obtain:\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"colab_type": "code",
|
||||
"id": "-9cnwiPp6EGm",
|
||||
"colab": {}
|
||||
},
|
||||
"source": [
|
||||
"# NOTE: Colab runs on server CPUs. At the time of writing this, TensorFlow Lite\n",
|
||||
"# doesn't have super optimized server CPU kernels. For this reason this may be\n",
|
||||
"# slower than the above float interpreter. But for mobile CPUs, considerable\n",
|
||||
"# speedup can be observed.\n",
|
||||
"print(eval_model(interpreter_quant, mnist_ds))\n"
|
||||
],
|
||||
"execution_count": 0,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "L7lfxkor8pgv"
|
||||
},
|
||||
"source": [
|
||||
"In this example, we have fully quantized a model with no difference in the accuracy."
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user