distillation

Step-by-Step

This document list steps of demo the usage of tensorflow distillation via Neural Compressor. This example can run on Intel CPUs and GPUs.

Note: Most of those models are both supported in Intel optimized TF 1.15.x and Intel optimized TF 2.x. Validated TensorFlow Version.

Prerequisite

1. Environment

Installation

Recommend python 3.8 or higher version.

# Install Intel® Neural Compressor
pip install neural-compressor

Install Tensorflow

pip install tensorflow

Install Intel Extension for Tensorflow

Running the model on Intel CPU(Optional to install ITEX)

Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for running the model on Intel CPUs.

pip install --upgrade intel-extension-for-tensorflow[cpu]

Note: The version compatibility of stock Tensorflow and ITEX can be checked here. Please make sure you have installed compatible Tensorflow and ITEX.

2. Prepare Dataset

TensorFlow models repo provides scripts and instructions to download. This example uses the raw ImageNet data. Therefore, users do not need to convert the data to TF Record format.

The data folder is expected to contain subfolders representing the classes to which its images belong.

Please arrange data in this way:
    dataset/class_1/xxx.png
    dataset/class_1/xxy.png
    dataset/class_1/xxz.png
    ...
    dataset/class_n/123.png
    dataset/class_n/nsdf3.png
    dataset/class_n/asd932_.png
Please put images of different categories into different folders.

Run

Run pretraining

bash run_distillation.sh --topology=mobilenet --teacher=densenet201 --dataset_location=/path/to/dataset --output_model=path/to/output_model

Note: --topology is the student model and --teacher is the teacher model.