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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/zoedepth/test_modeling_zoedepth.py
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@@ -253,7 +253,7 @@ class ZoeDepthModelIntegrationTest(unittest.TestCase):
[[1.0020, 1.0219, 1.0389], [1.0349, 1.0816, 1.1000], [1.0576, 1.1094, 1.1249]],
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_depth_estimation_multiple_heads(self):
image_processor = ZoeDepthImageProcessor.from_pretrained("Intel/zoedepth-nyu-kitti")
@@ -275,7 +275,7 @@ class ZoeDepthModelIntegrationTest(unittest.TestCase):
[[1.1571, 1.1438, 1.1783], [1.2163, 1.2036, 1.2320], [1.2688, 1.2461, 1.2734]],
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def check_target_size(
self,
@@ -301,7 +301,7 @@ class ZoeDepthModelIntegrationTest(unittest.TestCase):
out_l.unsqueeze(0).unsqueeze(1), size=img.size[::-1], mode="bicubic", align_corners=False
)
self.assertTrue((np.array(out_l.shape)[::-1] == np.array(img.size) * 2).all())
self.assertTrue(torch.allclose(out, out_l_reduced, rtol=2e-2))
torch.testing.assert_close(out, out_l_reduced, rtol=2e-2)
def check_post_processing_test(self, image_processor, images, model, pad_input=True, flip_aug=True):
inputs = image_processor(images=images, return_tensors="pt", do_pad=pad_input).to(torch_device)
@@ -323,7 +323,7 @@ class ZoeDepthModelIntegrationTest(unittest.TestCase):
for img, out, expected_slice in zip(images, outputs, expected_slices):
out = out["predicted_depth"]
self.assertTrue(img.size == out.shape[::-1])
self.assertTrue(torch.allclose(expected_slice, out[:3, :3], rtol=1e-3))
torch.testing.assert_close(expected_slice, out[:3, :3], rtol=1e-3)
self.check_target_size(image_processor, pad_input, images, outputs, raw_outputs, raw_outputs_flipped)