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RegNet (Classification)

This document describes evaluation of optimized checkpoints for Regnet_x_3_2gf

Environment Setup

Setup AI Model Efficiency Toolkit (AIMET)

Please install and setup AIMET before proceeding further. This model was tested with the torch_gpu variant of AIMET 1.24.

Additional Setup Dependencies

sudo -H pip install torchvision==0.11.2 --no-deps
sudo -H chmod 777 -R <path_to_python_package>/dist-packages/*

Obtain the Original Model for Comparison

  • Pytorch Torchvision hub instances of Regnet_x_3_2gf are used as reference FP32 models. These instances are optimized using AIMET to obtain quantized optimized checkpoints.

Experiment setup

git clone https://github.com/quic/aimet-model-zoo.git

export PYTHONPATH=$PYTHONPATH:<path to parent>/aimet-model-zoo

Dataset

This evaluation was designed for the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012), which can be obtained from: http://www.image-net.org/
The dataset directory is expected to have 3 subdirectories: train, valid, and test (only the valid test is used, hence if the other subdirectories are missing that is ok). Each of the {train, valid, test} directories is then expected to have 1000 subdirectories, each containing the images from the 1000 classes present in the ILSVRC2012 dataset, such as in the example below:

  train/
  ├── n01440764
  │   ├── n01440764_10026.JPEG
  │   ├── n01440764_10027.JPEG
  │   ├── ......
  ├── ......
  val/
  ├── n01440764
  │   ├── ILSVRC2012_val_00000293.JPEG
  │   ├── ILSVRC2012_val_00002138.JPEG
  │   ├── ......
  ├── ......

Usage

To run evaluation with QuantSim in AIMET, use the following

python regnet_quanteval.py\
  --model-config <configuration to be tested> \
  --dataset-path <path to ImageNet dataset> \
  --use-cuda <whether to run on GPU or cpu>

Available model configurations are:

  • regnet_x_3_2gf_w8a8

Quantization Configuration

W8A8 optimization

The following configuration has been used for the above models for INT8 quantization:

  • Weight quantization: 8 bits, symmetric quantization
  • Bias parameters are not quantized
  • Activation quantization: 8 bits, asymmetric quantization
  • Model inputs are quantized
  • 2000 images from the calibration dataset were used for computing encodings
  • TF_enhanced was used as quantization scheme
  • Cross layer equalization and Adaround in per channel mode has been applied for all the models to get the best INT8 optimized checkpoint

W4A8 optimization

The following configuration has been used for the above models for INT4 quantization:

  • Weight quantization: 4 bits, symmetric quantization
  • Bias parameters are not quantized
  • Activation quantization: 8 bits, asymmetric quantization
  • Model inputs are quantized
  • 2000 images from the calibration dataset were used for computing encodings
  • TF_enhanced was used as quantization scheme
  • Cross layer equalization and Adaround in per channel mode has been applied for all the models to get the best INT4 optimized checkpoint