Use assertExpected on the segmentation tests #3287
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datumbox
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Jan 24, 2021
test/test_models.py
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| model.eval().to(device=dev) | ||
| input_shape = (1, 3, 300, 300) | ||
| input_shape = (1, 3, 64, 64) |
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Decreased significantly the size of input image and number of classes to reduce the expected file to less than 50kb.
datumbox
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Jan 24, 2021
test/test_models.py
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| self.check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(name, None)) | ||
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| if dev == torch.device("cuda"): | ||
| with torch.cuda.amp.autocast(): | ||
| out = model(x) | ||
| self.assertEqual(tuple(out["out"].shape), (1, 50, 300, 300)) | ||
| self.assertExpected(out["out"].cpu(), prec=0.1, strip_suffix=f"_{dev}") |
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Use same precision value as classification.
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Summary: * Modify segmentation tests compare against expected values. * Exclude flaky autocast tests. Reviewed By: datumbox Differential Revision: D26156369 fbshipit-source-id: be596799abe5ff68cd5f4bad6d64aad8451d1487 Co-authored-by: Francisco Massa <[email protected]>
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The current segmentation tests do not check against expected values but only check for output sizes. Thus it's very likely that breaking modifications on the segmentation models won't be detected. In this PR we modify the tests to compare against expected values, similarly to other model tests.