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remove unused type: ignore directives (#60006)

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Summary:
During development it is common practice to put `type: ignore` comments on lines that are correct, but `mypy` doesn't recognize this. This often stems from the fact, that the used `mypy` version wasn't able to handle the used pattern.

With every new release `mypy` gets better at handling complex code. In addition to fix all the previously accepted but now failing patterns, we should also revisit all `type: ignore` comments to see if they are still needed or not. Fortunately, we don't need to do it manually: by adding `warn_unused_ignores = True` to the configuration, `mypy` will error out in case it encounters an `type: ignore` that is no longer needed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/60006

Reviewed By: jbschlosser, malfet

Differential Revision: D29133237

Pulled By: albanD

fbshipit-source-id: 41e82edc5cd5affa7ccedad044b59b94dad4425a
This commit is contained in:
Philip Meier
2021-06-18 07:22:22 -07:00
committed by Facebook GitHub Bot
parent 7c29ca7f2b
commit d5988c5eca
37 changed files with 108 additions and 93 deletions
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12
torch/utils/data/distributed.py
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@ -78,17 +78,15 @@ class DistributedSampler(Sampler[T_co]):
self.drop_last = drop_last
# If the dataset length is evenly divisible by # of replicas, then there
# is no need to drop any data, since the dataset will be split equally.
if self.drop_last and len(self.dataset) % self.num_replicas != 0: # type: ignore[arg-type]
if self.drop_last and len(self.dataset) % self.num_replicas != 0:
# Split to nearest available length that is evenly divisible.
# This is to ensure each rank receives the same amount of data when
# using this Sampler.
self.num_samples = math.ceil(
# `type:ignore` is required because Dataset cannot provide a default __len__
# see NOTE in pytorch/torch/utils/data/sampler.py
(len(self.dataset) - self.num_replicas) / self.num_replicas # type: ignore[arg-type]
(len(self.dataset) - self.num_replicas) / self.num_replicas
)
else:
self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore[arg-type]
self.num_samples = math.ceil(len(self.dataset) / self.num_replicas)
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.seed = seed
@ -98,9 +96,9 @@ class DistributedSampler(Sampler[T_co]):
# deterministically shuffle based on epoch and seed
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist() # type: ignore[arg-type]
indices = torch.randperm(len(self.dataset), generator=g).tolist()
else:
indices = list(range(len(self.dataset))) # type: ignore[arg-type]
indices = list(range(len(self.dataset)))
if not self.drop_last:
# add extra samples to make it evenly divisible