fixes typing in pytorch_lightning/callbacks/stochastic_weight_avg.py #13685
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donlapark
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otaj
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Jul 18, 2022
…m:donlapark/lightning into fix_typing_callback_stochastic_weight_avg merge with prior changes
Borda
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Codecov Report
@@ Coverage Diff @@
## master #13685 +/- ##
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- Coverage 86% 74% -12%
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Files 327 330 +3
Lines 25505 27641 +2136
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- Hits 21903 20475 -1428
- Misses 3602 7166 +3564 |
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carmocca
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| module.running_mean, # type: ignore[arg-type] | ||
| device=pl_module.device, | ||
| dtype=module.running_mean.dtype, # type: ignore[union-attr] | ||
| ) | ||
| module.running_var = torch.ones_like( | ||
| module.running_var, device=pl_module.device, dtype=module.running_var.dtype | ||
| module.running_var, # type: ignore[arg-type] | ||
| device=pl_module.device, | ||
| dtype=module.running_var.dtype, # type: ignore[union-attr] |
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IMO these shouldn't have been ignored, but instead added assertions that running_{mean,var} are not None
| module.momentum = None | ||
| module.num_batches_tracked *= 0 | ||
| module.momentum = None # type: ignore[assignment] | ||
| module.num_batches_tracked *= 0 # type: ignore[assignment, operator] |
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Same comment here about asserting that it's not None
| anneal_epochs=self._annealing_epochs, | ||
| anneal_strategy=self._annealing_strategy, | ||
| last_epoch=trainer.max_epochs if self._annealing_strategy == "cos" else -1, | ||
| self._swa_scheduler: _LRScheduler = cast( |
Contributor
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The type annotation is redundant when you cast
Suggested change
| self._swa_scheduler: _LRScheduler = cast( | |
| self._swa_scheduler = cast( |
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What does this PR do?
Fixes typing in pytorch in
pytorch_lightning/callbacks/stochastic_weight_avg.pyfrom #13445.Note:
isinstance(..., Callable)throwing an arg-type error (isinstance(..., collections.abc.Callable) complains about "_SpecialForm" python/mypy#6864).self._swa_epoch_startis afloat, it is expected to be converted tointby a call toon_fit_start(). However,mypydoes not see this and throws in a return-value error.torch.optim.swa_utils.SWALRhas not been type-annotated yet, so the type of the argument ofswa_lris automatically set tofloat._BatchNorm.running_mean,_BatchNorm.running_varand_BatchNorm.num_batches_trackedhave typesOptional[Tensor]. These attributes are initialized to be zero tensors viaself.reset_parameters()butmypydoes not see this.Does your PR introduce any breaking changes? If yes, please list them.
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