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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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18
tests/models/owlv2/test_modeling_owlv2.py
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@@ -826,7 +826,7 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[-6.2229, -8.2601]], device=torch_device)
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
@slow
def test_inference_interpolate_pos_encoding(self):
@@ -858,7 +858,7 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[-6.2520, -8.2970]], device=torch_device)
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
expected_shape = torch.Size((1, 4097, 768))
self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)
@@ -874,7 +874,7 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.2407, 0.0553, 0.4636], [0.1082, 0.0494, 0.1861], [0.2459, 0.0527, 0.4398]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
model = Owlv2ForObjectDetection.from_pretrained(model_name).to(torch_device)
query_image = prepare_img()
@@ -920,7 +920,7 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
]
)
self.assertTrue(torch.allclose(model.box_bias[:3, :4], expected_default_box_bias, atol=1e-4))
torch.testing.assert_close(model.box_bias[:3, :4], expected_default_box_bias, rtol=1e-4, atol=1e-4)
# Interpolate with any resolution size.
processor.image_processor.size = {"height": 1264, "width": 1024}
@@ -945,7 +945,7 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.2438, 0.0945, 0.4675], [0.1361, 0.0431, 0.2406], [0.2465, 0.0428, 0.4429]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
query_image = prepare_img()
inputs = processor(
@@ -992,13 +992,11 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
expected_slice_logits = torch.tensor(
[[-21.413497, -21.612638], [-19.008193, -19.548841], [-20.958896, -21.382694]]
).to(torch_device)
resulted_slice_logits = outputs.logits[0, :3, :3]
max_diff = torch.max(torch.abs(resulted_slice_logits - expected_slice_logits)).item()
self.assertLess(max_diff, 3e-4)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=1e-4, atol=1e-4)
expected_slice_boxes = torch.tensor(
[[0.241309, 0.051896, 0.453267], [0.139474, 0.045701, 0.250660], [0.233022, 0.050479, 0.427671]],
).to(torch_device)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
resulted_slice_boxes = outputs.pred_boxes[0, :3, :3]
max_diff = torch.max(torch.abs(resulted_slice_boxes - expected_slice_boxes)).item()
self.assertLess(max_diff, 3e-4)
@@ -1044,7 +1042,7 @@ class Owlv2ModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.2413, 0.0519, 0.4533], [0.1395, 0.0457, 0.2507], [0.2330, 0.0505, 0.4277]],
).to(torch_device)
self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
torch.testing.assert_close(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
@slow
@require_torch_accelerator