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PyTorch SegNet

This document describes evaluation of optimized checkpoints for SegNet

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 1.23.

Additional Setup Dependencies

    pip install scikit-image

Experiment setup

    git clone https://github.com/quic/aimet-model-zoo.git
  • Append the repo location to your PYTHONPATH with the following:
    export PYTHONPATH=$PYTHONPATH:<path to aimet_model_zoo_path>

Dataset

This evaluation was designed for the CamVid dataset variant provided by SegNet authors repository.

  • Download and extract CamVid directory:
    wget https://github.com/alexgkendall/SegNet-Tutorial/archive/refs/heads/master.zip
    unzip master.zip && mv SegNet-Tutorial-master/CamVid . && rm -r SegNet-Tutorial-master

Model checkpoints and configuration

  • The SegNet model checkpoints can be downloaded from the Releases page.

Usage

To run evaluation with QuantSim in AIMET, use the following

    python3 aimet-model-zoo/aimet_zoo_torch/segnet/evaluator/segnet_quanteval.py \
        --dataset-path <path to CamVid dataset> \
        --model-config <configuration to be tested>

Available model configurations are:

  • segnet_w8a8
  • segnet_w4a8

Quantization Configuration

  • Weight quantization: 8 or 4 bits, per channel symmetric quantization
  • Bias parameters are not quantized
  • Activation quantization: 8 bits, asymmetric quantization
  • Model inputs are quantized
  • TF_enhanced was used as quantization scheme