Parameterize some metric tests #1683
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vfdev-5
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Feb 22, 2021
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Thanks for experimenting with parametrize @Moh-Yakoub
IMO, this is still something to think about on how to use it properly. I left my comments on the PR.
| with pytest.raises(ValueError, match=r"If recall argument is provided, output_transform should be None"): | ||
| r = Recall(average=False) | ||
| Fbeta(1.0, recall=r, output_transform=lambda x: x) | ||
| @pytest.mark.parametrize( |
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I'm not sure about this usage, it makes thing more cumbersome
| ), f"{type(loss._sum.device)}:{loss._sum.device} vs {type(metric_device)}:{metric_device}" | ||
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| def test_sum_detached(): |
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Why did you remove this test ?
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| def test_compute_on_criterion(): | ||
| loss = Loss(nn.NLLLoss()) | ||
| @pytest.mark.parametrize("loss_function", [nll_loss, nn.NLLLoss()]) |
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Fixes #1469
Description:
Parameterize some metric tests.
I started with couple of test cases that had obvious use of test parameterization. Will refactor more metric tests in later PRs
Check list: