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HRNet_Image_Classification.md

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Pytorch HRNet-W32 for image classification

Environment setup

Setup AI Model Efficiency Toolkit

Please install and setup AIMET before proceeding further. This model was tested with the torch_gpu variant of AIMET 1.25.0.

  • Add AIMET Model Zoo and the HRNet Lib to your pythonpath
export PYTHONPATH=$PYTHONPATH:<aimet_model_zoo_path>

Dependencies

  • Makes sure additional dependencies pyyaml, yacs are installed: 
    pip install pyyaml
pip install 'yacs>=0.1.5'

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 validation dataset is needed for this evaluation). 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
  │   ├── ......
  ├── ......

Model checkpoints and configuration

  • The original HRNet-W32 checkpoint can be downloaded from links provided at HRNet-W32-C.
  • Optimized HRNet checkpoint can be downloaded from the Releases page.
  • The Quantization Simulation (Quantsim) Configuration file can be downloaded from here: default_config_per_channel.json (Please see this page for more information on this file).
  • Downloading optimized checkpoints and quantization configuration file are also handled through evaluation script.

Usage

To run evaluation with QuantSim in AIMET, use the following

python hrnet_image_classification_quanteval.py \
	--model-config <model configuation to be tested> \
	--dataset-path <path imagenet dataset> \
        --use-cuda <whether to compute on GPU or CPU>

Available model configurations are:

  • hrnet_w32_w8a8

Quantization Configuration

  • Weight quantization: 8 bits per tensor symmetric quantization
  • Bias parameters are not quantized
  • Activation quantization: 8 bits asymmetric quantization
  • Model inputs are quantized
  • TF_enhanced was used for quantization scheme
  • Autoquant have been applied on optimized checkpoint
  • 2 images per class (1000 classes) from ImageNet dataset are used as calibration dataset