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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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14
tests/models/owlvit/test_modeling_owlvit.py
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@@ -819,7 +819,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[3.4613, 0.9403]], 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):
@@ -851,7 +851,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[3.6278, 0.8861]], 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, 626, 768))
self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)
@@ -868,7 +868,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.0680, 0.0422, 0.1347], [0.2071, 0.0450, 0.4146], [0.2000, 0.0418, 0.3476]]
).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 = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
query_image = prepare_img()
@@ -913,7 +913,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
[-1.9452, -3.1332, -3.1332, -3.1332],
]
)
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}
@@ -938,7 +938,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.0499, 0.0301, 0.0983], [0.2244, 0.0365, 0.4663], [0.1387, 0.0314, 0.1859]]
).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(
@@ -985,7 +985,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]]
).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)
# test post-processing
post_processed_output = processor.post_process_grounded_object_detection(outputs)
@@ -1028,7 +1028,7 @@ class OwlViTModelIntegrationTest(unittest.TestCase):
expected_slice_boxes = torch.tensor(
[[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]]
).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