README.md

Post-Training Quantization of Resnet18 PyTorch Model exported to torch.fx.GraphModule

This example demonstrates how to use Post-Training Quantization API from Neural Network Compression Framework (NNCF) to quantize PyTorch models exported to torch.fx.GraphModule on the example of Resnet18 post-training quantization, pretrained on Tiny ImageNet-200 dataset.

The example includes the following steps:

• Loading the Tiny ImageNet-200 dataset (~237 Mb) and the Resnet18 PyTorch model pretrained on this dataset.
• Exporting model to torch.fx.GraphModule by torch.export.export function.
• Quantizing the model using NNCF Post-Training Quantization algorithm.
• Output of the following characteristics of the quantized model:
  • Accuracy drop of the quantized model (INT8) over the pre-trained model (FP32)
  • Performance speed up of the quantized model (INT8)

Install requirements

At this point it is assumed that you have already installed NNCF. You can find information on installation NNCF here.

To work with the example you should install the corresponding Python package dependencies:

pip install -r requirements.txt

Run Example

It's pretty simple. The example does not require additional preparation. It will do the preparation itself, such as loading the dataset and model, etc.

python main.py