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use torch.testing.assertclose instead to get more details about error in cis (#35659)

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* use torch.testing.assertclose instead to get more details about error in cis

* fix

* style

* test_all

* revert for I bert

* fixes and updates

* more image processing fixes

* more image processors

* fix mamba and co

* style

* less strick

* ok I won't be strict

* skip and be done

* up
This commit is contained in:
Arthur
2025-01-24 16:55:28 +01:00
committed by GitHub
parent 72d1a4cd53
commit b912f5ee43
255 changed files with 1048 additions and 969 deletions
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8
tests/models/patchtst/test_modeling_patchtst.py
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@@ -329,7 +329,7 @@ class PatchTSTModelIntegrationTests(unittest.TestCase):
[[[-0.0173]], [[-1.0379]], [[-0.1030]], [[0.3642]], [[0.1601]], [[-1.3136]], [[0.8780]]],
device=torch_device,
)
self.assertTrue(torch.allclose(output[0, :7, :1, :1], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(output[0, :7, :1, :1], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
# Publishing of pretrained weights are under internal review. Pretrained model is not yet downloadable.
def test_prediction_head(self):
@@ -349,7 +349,7 @@ class PatchTSTModelIntegrationTests(unittest.TestCase):
[[0.5142, 0.6928, 0.6118, 0.5724, -0.3735, -0.1336, -0.7124]],
device=torch_device,
)
self.assertTrue(torch.allclose(output[0, :1, :7], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(output[0, :1, :7], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
def test_prediction_generation(self):
model = PatchTSTForPrediction.from_pretrained("namctin/patchtst_etth1_forecast").to(torch_device)
@@ -367,7 +367,7 @@ class PatchTSTModelIntegrationTests(unittest.TestCase):
device=torch_device,
)
mean_prediction = outputs.sequences.mean(dim=1)
self.assertTrue(torch.allclose(mean_prediction[0, -1:], expected_slice, atol=TOLERANCE))
torch.testing.assert_close(mean_prediction[0, -1:], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
def test_regression_generation(self):
model = PatchTSTForRegression.from_pretrained("ibm/patchtst-etth1-regression-distribution").to(torch_device)
@@ -385,4 +385,4 @@ class PatchTSTModelIntegrationTests(unittest.TestCase):
device=torch_device,
)
mean_prediction = outputs.sequences.mean(dim=1)
self.assertTrue(torch.allclose(mean_prediction[-5:], expected_slice, rtol=TOLERANCE))
torch.testing.assert_close(mean_prediction[-5:], expected_slice, rtol=TOLERANCE)