This folder contains examples of quantizing a FP32 model with or without calibration and using the calibrated quantized for inference. Two pre-trained imagenet models are taken as examples for quantization. One is Resnet-152, and the other one is Inception with BatchNorm. The calibration dataset is the validation dataset for testing the pre-trained models.
Here are the details of the four files in this folder.
imagenet_gen_qsym.py This script provides an example of taking FP32 models and calibration dataset to generate calibrated quantized models. When launched for the first time, the script would download the user-specified model, either Resnet-152 or Inception, and calibration dataset into model and data folders, respectively. The generated quantized models can be found in the model folder.imagenet_inference.py This script is used for calculating the accuracy of FP32 models or quantized models on the validation dataset which was downloaded for calibration in imagenet_gen_qsym.py.launch_quantize.sh This is a shell script that generates various quantized models for Resnet-152 and Inception with BatchNorm with different configurations. Users can copy and paste the command from the script to the console to run model quantization for a specific configuration.launch_inference.sh This is a shell script that calculate the accuracies of all the quantized models generated by invoking launch_quantize.sh.NOTE: This example has only been tested on Linux systems.