add tests for single scalar return from training #2587
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williamFalcon
changed the title
Codecov Report
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## master #2587 +/- ##
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Coverage 91% 91%
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Files 70 70
Lines 5755 5763 +8
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+ Hits 5243 5252 +9
+ Misses 512 511 -1 |
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Hello @williamFalcon! Thanks for updating this PR. There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻 Comment last updated at 2020-07-11 21:26:56 UTC |
williamFalcon
changed the title
Borda
reviewed
Jul 11, 2020
| self.training_step_end_called = True | ||
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| # make sure loss has the grad | ||
| assert isinstance(output, torch.Tensor) |
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this looks like a test, but why is it in DeterministicModel
well maybe rather ask why these tests are not part of the template?
Borda
reviewed
Jul 11, 2020
| assert len(trainer.progress_bar_metrics) == 0 | ||
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| # make sure training outputs what is expected | ||
| for batch_idx, batch in enumerate(model.train_dataloader()): |
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why you do not set directly?
batch_idx = 0
batch = model.train_dataloader()[0]
Borda
reviewed
Jul 11, 2020
| model.training_step_end = model.training_step_end_scalar | ||
| model.val_dataloader = None | ||
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| trainer = Trainer(fast_dev_run=True, weights_summary=None) |
Borda
reviewed
Jul 11, 2020
| assert not model.training_step_end_called | ||
| assert not model.training_epoch_end_called | ||
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| # make sure training outputs what is expected |
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all these asserts bellow seems to be the same in all three functions... rather wrap into single assert block, otherwise, it takes some time to check what is the difference...
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add tests to make sure a single scalar can be returned from train loop