The GitHub repository has an examples/ folder which contains two Python scripts to show how to apply the methods in this library on a PyTorch model. The used dataset and neural network is “testnet”, a simple generated segmentation dataset using the U-Net architecture. See Testnet for more information.

Run the examples

  • Install interpret-segmentation into a pip virtualenv or anaconda environment
  • Clone GitHub repository: git clone https://github.com/andef4/interpret-segmentation
  • Install additional dependencies: pip install scikit-image requests / conda install scikit-image requests

The example uses the “testnet” dataset, you can download the dataset and a pretrained model by running the examples/testnet/download.py script. Alternatively, you can generate and train the dataset yourself with the examples/testnet/generate.py and examples/testnet/train.py scripts.

The run one of the example scripts:

  • python3 examples/hdm.py
  • python3 examples/rise.py

Both scripts generate PNG visualizations in the examples directory. The runtime of the scripts are around 30-60 seconds on a current generation high-end graphics card (GeForce 1080 Ti/RTX 2080).