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Update Captum tutorial dependencies for Captum 0.8.0 #3868

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Update Captum tutorial dependencies for Captum 0.8.0 #3868

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29 changes: 17 additions & 12 deletions beginner_source/introyt/captumyt.py
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Expand Up @@ -48,7 +48,7 @@
the review is an example of feature attribution.
- **Layer Attribution** examines the activity of a model’s hidden layer
subsequent to a particular input. Examining the spatially-mapped
output of a convolutional layer in response to an input image in an
output of a convolutional layer in response to an input image is an
example of layer attribution.
- **Neuron Attribution** is analagous to layer attribution, but focuses
on the activity of a single neuron.
Expand Down Expand Up @@ -97,14 +97,13 @@

Before you get started, you need to have a Python environment with:

- Python version 3.6 or higher
- Python version 3.9.0 or higher
- For the Captum Insights example, Flask 1.1 or higher and Flask-Compress
(the latest version is recommended)
- PyTorch version 1.2 or higher (the latest version is recommended)
- TorchVision version 0.6 or higher (the latest version is recommended)
- Captum (the latest version is recommended)
- Matplotlib version 3.3.4, since Captum currently uses a Matplotlib
function whose arguments have been renamed in later versions
- PyTorch version 1.10.0 or higher
- TorchVision version 0.11.0 or higher
- Captum version 0.8.0, as Captum Insights was retired after this version
and is no longer supported
- Matplotlib version 3.5.0 or higher

To install Captum in an Anaconda or pip virtual environment, use the
appropriate command for your environment below:
Expand All @@ -113,13 +112,13 @@

.. code-block:: sh

conda install pytorch torchvision captum flask-compress matplotlib=3.3.4 -c pytorch
conda install "pytorch>=1.10.0" "torchvision>=0.11.0" "captum=0.8.0" "matplotlib>=3.5.0" flask-compress ipywidgets -c pytorch

With ``pip``:

.. code-block:: sh

pip install torch torchvision captum matplotlib==3.3.4 Flask-Compress
pip install "torch>=1.10.0" "torchvision>=0.11.0" "captum==0.8.0" "matplotlib>=3.5.0" flask-compress ipywidgets

Restart this notebook in the environment you set up, and you’re ready to
go!
Expand Down Expand Up @@ -266,8 +265,10 @@
attributions_ig = integrated_gradients.attribute(input_img, target=pred_label_idx, n_steps=200)

# Show the original image for comparison
_ = viz.visualize_image_attr(None, np.transpose(transformed_img.squeeze().cpu().detach().numpy(), (1,2,0)),
method="original_image", title="Original Image")
_ = viz.visualize_image_attr(np.ones_like(np.transpose(transformed_img.squeeze().cpu().detach().numpy(), (1,2,0))),
np.transpose(transformed_img.squeeze().cpu().detach().numpy(), (1,2,0)),
method="original_image",
title="Original Image")

default_cmap = LinearSegmentedColormap.from_list('custom blue',
[(0, '#ffffff'),
Expand Down Expand Up @@ -495,5 +496,9 @@ def full_img_transform(input):
# model’s predictions with associated probabilities, and view heatmaps of
# the attribution compared with the original image.
#
# If you are not using a Jupyter Notebook, you can use
# ``visualizer.serve(debug=True)`` instead. This starts a local web server
# and prints a URL that you can open in your browser.
#

visualizer.render()