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| author | mc-robinson <matthew.robinson@yale.edu> | 2019-03-24 19:17:00 -0700 |
|---|---|---|
| committer | Facebook Github Bot <facebook-github-bot@users.noreply.github.com> | 2019-03-24 19:22:14 -0700 |
| commit | 8bc5b867093f1b44f5168f3e41b1bdf0ba3e05a7 (patch) | |
| tree | a16ec7e849c73aeb589793b7b481925f9e88bcbf /test/test_nn.py | |
| parent | ca962f0f95185e76b1f43a4423d28986ee0da191 (diff) | |
| download | pytorch-8bc5b867093f1b44f5168f3e41b1bdf0ba3e05a7.tar.gz pytorch-8bc5b867093f1b44f5168f3e41b1bdf0ba3e05a7.tar.bz2 pytorch-8bc5b867093f1b44f5168f3e41b1bdf0ba3e05a7.zip | |
Added tensor size warning to F.mse_loss() (#18349)
Summary:
To address the issue of broadcasting giving the wrong result in `nn.MSELoss()` as mentioned here https://github.com/pytorch/pytorch/issues/16045 . In particular, the issue often arises when computing the loss between tensors with shapes (n, 1) and (n,)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18349
Differential Revision: D14594176
Pulled By: soumith
fbshipit-source-id: f23ae68a4bf42f3554ad7678a314ba2c7532a6db
Diffstat (limited to 'test/test_nn.py')
| -rw-r--r-- | test/test_nn.py | 12 |
1 files changed, 12 insertions, 0 deletions
diff --git a/test/test_nn.py b/test/test_nn.py index 897255c215..b93f850021 100644 --- a/test/test_nn.py +++ b/test/test_nn.py @@ -4363,6 +4363,18 @@ class TestNN(NNTestCase): def test_loss_equal_input_target_shape(self): self._test_loss_equal_input_target_shape(lambda x: x) + def test_mse_loss_size_warning(self): + i = torch.randn((10, 1), requires_grad=True) + t = torch.randn((10,)) + with warnings.catch_warnings(record=True) as w: + # Ensure warnings are being shown + warnings.simplefilter("always") + # Trigger Warning + F.mse_loss(i, t) + # Check warning occurs + self.assertEqual(len(w), 1) + self.assertIn('Please ensure they have the same size.', str(w[0])) + def test_nll_loss_mismatched_batch(self): x = torch.randn((10, 3), requires_grad=True) # t should have size (10,) |