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Fix failing Owlv2ModelIntegrationTest & OwlViTModelIntegrationTest #43182

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Merged
vasqu merged 3 commits into from
Jan 12, 2026
Merged

Fix failing Owlv2ModelIntegrationTest & OwlViTModelIntegrationTest #43182

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7616793
Fix failing Owlv2ModelIntegrationTest & OwlViTModelIntegrationTest
Sai-Suraj-27 Jan 8, 2026
10733e6
Tolerance
Sai-Suraj-27 Jan 9, 2026
aa5a2cc
update expectations
vasqu Jan 9, 2026
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2 changes: 1 addition & 1 deletion tests/models/owlv2/test_modeling_owlv2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -735,7 +735,7 @@ def test_inference_interpolate_pos_encoding(self):
[-3.3644, -4.0717, -4.0717, -4.0717],
[-2.9425, -4.0717, -4.0717, -4.0717],
]
)
).to(torch_device)

torch.testing.assert_close(model.box_bias[:3, :4], expected_default_box_bias, rtol=1e-4, atol=1e-4)

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12 changes: 6 additions & 6 deletions tests/models/owlvit/test_modeling_owlvit.py
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Expand Up @@ -682,7 +682,7 @@ def test_inference_interpolate_pos_encoding(self):
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)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-2, atol=1e-2)
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@vasqu vasqu Jan 9, 2026

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The tolerance is a bit too high, do we really

  • need to change that many
  • have this high tol, i.e. is 1e-3 maybe enough instead

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@Sai-Suraj-27 Sai-Suraj-27 Jan 9, 2026

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have this high tol, i.e. is 1e-3 maybe enough instead

Yes, I thought of setting it to 1e-3 but the tests are failing with a difference of 0.004, ~0.002. So, went with 1e-2.

need to change that many

All tests are passing for me locally. So, i am not able to reproduce the failures (No A10 GPU 😅). So, I've made the change for all asserts among the failing tests. But yeah ideally i think some of them can be kept at 1e-3.

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@vasqu vasqu Jan 9, 2026

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Hmm, I will update the values in this PR myself then. Probably next week tho

Running on a10, rather keep correct values than update tols arbitrarily high

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@Sai-Suraj-27 Sai-Suraj-27 Jan 9, 2026

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Sure, Thank you!

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@vasqu vasqu Jan 9, 2026

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Ok, updated the values just now

cc @ydshieh if this is ok, the values only differ slightly, seems to be a GPU diff

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@vasqu vasqu Jan 12, 2026

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Discussed internally, merging 🫡


model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
query_image = prepare_img()
Expand Down Expand Up @@ -726,8 +726,8 @@ def test_inference_interpolate_pos_encoding(self):
[-2.3968, -3.1332, -3.1332, -3.1332],
[-1.9452, -3.1332, -3.1332, -3.1332],
]
)
torch.testing.assert_close(model.box_bias[:3, :4], expected_default_box_bias, rtol=1e-4, atol=1e-4)
).to(torch_device)
torch.testing.assert_close(model.box_bias[:3, :4], expected_default_box_bias, rtol=1e-2, atol=1e-2)

# Interpolate with any resolution size.
processor.image_processor.size = {"height": 1264, "width": 1024}
Expand All @@ -752,7 +752,7 @@ def test_inference_interpolate_pos_encoding(self):
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)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-2, atol=1e-2)

query_image = prepare_img()
inputs = processor(
Expand Down Expand Up @@ -799,7 +799,7 @@ def test_inference_object_detection(self):
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)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-2, atol=1e-2)

# test post-processing
post_processed_output = processor.post_process_grounded_object_detection(outputs)
Expand Down Expand Up @@ -842,7 +842,7 @@ def test_inference_one_shot_object_detection(self):
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)
torch.testing.assert_close(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-2, atol=1e-2)

@slow
@require_torch_accelerator
Expand Down
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