.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_apps/lightning_classy_vision/interpret.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_apps_lightning_classy_vision_interpret.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_apps_lightning_classy_vision_interpret.py:


Model Interpretability App Example
=============================================

This is an example TorchX app that uses captum to analyze inputs to for model
interpretability purposes. It consumes the trained model from the trainer app
example and the preprocessed examples from the datapreproc app example. The
output is a series of images with integrated gradient attributions overlayed on
them.

See https://captum.ai/tutorials/CIFAR_TorchVision_Interpret for more info on
using captum.

.. GENERATED FROM PYTHON SOURCE LINES 21-131

.. code-block:: default


    import argparse
    import itertools
    import os.path
    import sys
    import tempfile
    from typing import List

    import fsspec
    import torch
    from data import TinyImageNetDataModule, download_data
    from model import TinyImageNetModel


    # FIXME: captum must be imported after torch otherwise it causes python to crash
    if True:
        import numpy as np
        from captum.attr import IntegratedGradients
        from captum.attr import visualization as viz


    def parse_args(argv: List[str]) -> argparse.Namespace:
        parser = argparse.ArgumentParser(description="example TorchX captum app")
        parser.add_argument(
            "--load_path",
            type=str,
            help="checkpoint path to load model weights from",
            required=True,
        )
        parser.add_argument(
            "--data_path",
            type=str,
            help="path to load the training data from",
            required=True,
        )
        parser.add_argument(
            "--output_path",
            type=str,
            help="path to place analysis results",
            required=True,
        )

        return parser.parse_args(argv)


    def convert_to_rgb(arr: torch.Tensor) -> np.ndarray:
        """
        This converts the image from a torch tensor with size (1, 1, 64, 64) to
        numpy array with size (64, 64, 3).
        """
        squeezed = arr.squeeze()
        stacked = torch.stack([squeezed, squeezed, squeezed], dim=2)
        print("img stats: ", stacked.count_nonzero(), stacked.mean())
        return stacked.numpy()


    def main(argv: List[str]) -> None:
        with tempfile.TemporaryDirectory() as tmpdir:
            args = parse_args(argv)

            # Init our model
            model = TinyImageNetModel()

            print(f"loading checkpoint: {args.load_path}...")
            model.load_from_checkpoint(checkpoint_path=args.load_path)

            # Download and setup the data module
            data_path = download_data(args.data_path, tmpdir)
            data = TinyImageNetDataModule(
                data_dir=data_path,
                batch_size=1,
            )

            ig = IntegratedGradients(model)

            data.setup("test")
            dataloader = data.test_dataloader()

            # process first 5 images
            for i, (input, label) in enumerate(itertools.islice(dataloader, 5)):
                print(f"analyzing example {i}")
                model.zero_grad()
                attr_ig, delta = ig.attribute(
                    input,
                    target=label,
                    baselines=input * 0,
                    return_convergence_delta=True,
                )

                if attr_ig.count_nonzero() == 0:
                    # Our toy model sometimes has no IG results.
                    print("skipping due to zero gradients")
                    continue

                fig, axis = viz.visualize_image_attr(
                    convert_to_rgb(attr_ig),
                    convert_to_rgb(input),
                    method="blended_heat_map",
                    sign="all",
                    show_colorbar=True,
                    title="Overlayed Integrated Gradients",
                )
                out_path = os.path.join(args.output_path, f"ig_{i}.png")
                print(f"saving heatmap to {out_path}")
                with fsspec.open(out_path, "wb") as f:
                    fig.savefig(f)


    if __name__ == "__main__":
        main(sys.argv[1:])


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_apps_lightning_classy_vision_interpret.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: interpret.py <interpret.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: interpret.ipynb <interpret.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_