Hello there! We are at the end of GSoC! Week 12 was the last week where we’d do any major coding. We still have the next week where we do a submission (and a final check-in?), so let me keep it short and tell you what I worked on this week and what I got stuck on.
This week I focused on my two open PR’s - the text PR and the PyTorch PR.
For the text PR, I did a couple of things:
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I updated the image tutorial and image tests with new features introduced in this PR, such as explanation “modifiers”.
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I fleshed out (padded with comments) the text tutorial so that it looks nice.
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I opened issues on GitHub for some TODO’s and FIXME’s.
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One of the mentors reviewed the PR so I made any appropriate changes, including:
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API changes - instead of letting an argument take multiple types, have separate arguments for each type.
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An interesting change - when trying to choose a hidden layer for Grad-CAM, we will now default to one of the latter layers (closer to output). This is closer to how Grad-CAM is defined (since picking earlier layers would be like using some other gradient method for explanations).
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One interesting issue that arose as a result of the above change (defaulting to “higher level” layers) was that my integration tests broke and the tutorial explanations didn’t look as good. With the guidance of my mentor I resolved the failing tests by explicitly picking a layer that works. For the tutorials I made a comment that using earlier layers gave better results. Interesting issues with text!
Next for the PyTorch PR I did the following:
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Added code that can extract an image from an input tensor (a simple transform in torchvision), like what we do for Keras.
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Added a very short image tutorial.
I got stuck on the text part of PyTorch because my test model was quite large (large word embeddings). It looks like I will have to train my own embedding layer with a small vocabulary.
What’s next
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Final submission coming soon.
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Slowly work on getting text and pytorch PR’s merged as a regular open source contributor!
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Text PR still has some TODO’s and reviews to resolve.
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PyTorch PR needs a text tutorial, integration tests, and unit tests. We’ll scale down so hopefully this doesn’t take too much time.
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That’s all the technical details! I think we have one more blog next week, so I can talk about GSoC in broader terms then?
Thanks for reading once again!
Tomas Baltrunas