[Relay][Frontend][Onnx] Compare against onnxruntime more consistently during testing #7300
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@masahi @mbrookhart can you guys let me know what you think of this PR? |
jwfromm
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Jan 17, 2021
| input_data = np.random.uniform(size=input_size).astype("int32") | ||
| verify_with_ort_with_inputs(onnx_model, [input_data]) | ||
| input_data = np.random.uniform(size=input_size).astype("float32") | ||
| verify_with_ort_with_inputs(onnx_model, [input_data], apply_softmax=True) |
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This is a fun one that I wanted to point out. Previously we were casting inputs to int32, however because they were generated with np.random.uniform they all were just being cast to 0. Using non-zero inputs caused some minor mismatch on outputs due to numerical instability but applying softmax (which torchvision models don't use by default) reduces the numerical difference well below our test threshold.
jwfromm
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Jan 17, 2021
masahi
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Jan 17, 2021
masahi
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Jan 18, 2021
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Jan 19, 2021
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thanks for the heroic effort 👍
mbrookhart
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Jan 19, 2021
TusharKanekiDey
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… during testing (apache#7300) Co-authored-by: Josh Fromm <[email protected]>
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… during testing (apache#7300) Co-authored-by: Josh Fromm <[email protected]>
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… during testing (apache#7300) Co-authored-by: Josh Fromm <[email protected]>
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I noticed that many of our onnx frontend tests compare to results produced by numpy rather than onnx itself. This is somewhat counterproductive because it makes assumptions about what onnx should do rather than checking what it actually does. I decided to go through all the tests in
test_forward.pyand make them use the newverify_with_orthelper function. This makes our testing suite more consistent and align more closely with their intention. In the process of making this conversion, I discovered many bugs with the importer that are also fixed in this PR. Although this PR might be a little painful to review due to its scope, I think that the result is an overall much cleaner and easier to maintain test suite.