AL/math/MaxPooling #407
AL/math/MaxPooling #407
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I have written some comments.
When I tried the example from the docs, I don't get the output that is written in the docs. So double check that.
And when I try to run the exact same example, but swapping numpy for torch, it doesn't work. Therefore I think that you should either make it work exactly the same for both cases, or write a clear example on what is different.
Related to that: for the torch version to work, the shapes have to be different from when running it with numpy. I can imagine that it might lead to problems when a pipeline has been created with numpy, and the user wants to run it with torch, and then gets a different result or that it doesn't work at all anymore. Maybe this is something to discuss in our next meeting.
| feature = math.MaxPooling(ksize=2) | ||
| pooled_image = feature.resolve(input_image) | ||
| self.assertTrue(np.all(pooled_image == [[6.0, 8.0]])) | ||
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| # Extending the test and setting the backend to torch | ||
| @unittest.skipUnless(TORCH_AVAILABLE, "PyTorch is not installed.") |
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I can see that you haven't created the layout like this. However, it looks different from how we have done it in test_features.py for example. I personally prefer the style we use in test_feature, as all tests belonging to one class are within the same "def test_....()"
Added docs, torch, tests for math.maxpooling.