InverseForm.md

Pytorch InverseForm (Semantic Segmentation)

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.

Install dependencies

   python -m pip install runx
   python -m pip install fire
   python -m pip install scikit-image

Additional setup

Append the repo location to your PYTHONPATH with the following:

export PYTHONPATH=$PYTHONPATH:<path to parent of aimet_model_zoo>

Dataset

Benchmark dataset can be downloaded from here:

• Cityscapes

---

Usage

To run evaluation with QuantSim in AIMET, use the following

python3  aimet_zoo_torch/inverseform/evaluators/inverseform_quanteval.py \
                --model-config <configuration to be tested> \
                --dataset-path <path to directory containing CityScapes> \
                --batch-size  <batch size as an integer value, defaults to 2> \

Available model configurations are:

• hrnet_16_slim_if
• ocrnet_48_if

---

Model checkpoints and configuration

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

---

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 as quantization scheme
• Cross layer equalization and Adaround have been applied on optimized checkpoint