Using system generated seed in RandomSampler #1441
Conversation
facebook-github-bot
added
the
CLA Signed
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/data/1441
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 14fa418 with merge base fe6b405 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
| seed = 1 | ||
| torch.manual_seed(seed) | ||
| dl3 = StatefulDataLoader(self.dataset, batch_size=1, shuffle=True) | ||
| data_dl3 = [] |
There was a problem hiding this comment.
Can we call this results3 ? and similarly results1 and results2 above.
|
|
||
| seed = 1 | ||
| torch.manual_seed(seed) | ||
| dl3 = StatefulDataLoader(self.dataset, batch_size=1, shuffle=True) |
There was a problem hiding this comment.
We can rename dl3 to dataloader3. ditto for other dataloader variables.
| ) | ||
|
|
||
| def test_seed_replicability(self): | ||
|
|
||
| seed = 0 |
There was a problem hiding this comment.
Instead of checking for specific seeds 0 and 1, we can generalize it to two randomly generated seeds.
And also add a assert to ensure both seeds are not equal.
| data_source: Sized, | ||
| replacement: bool = False, | ||
| num_samples: Optional[int] = None, | ||
| generator=None, | ||
| ): | ||
| if generator is None: | ||
| # Ensure that underlying sampler has something repeatable |
There was a problem hiding this comment.
let's remove or update this comment
Currently we are fixing the seed for
generatorinRandomSampleras 1.This leads to the generator not changing even when torch.manual_seed() seed is changed.
For the RandomSampler in
torch.utils.data.sampler, they useseed = int(torch.empty((), dtype=torch.int64).random_().item()). Using the same here.Fixes #1440