SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Update expected values (after switching to A10) (#39157)

Browse Source 
* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* empty

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2025-07-01 20:54:31 +02:00
committed by GitHub
parent ab59cc27fe
commit 4c1715b610
7 changed files with 138 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
tests/models/d_fine/test_modeling_d_fine.py
View File

@@ -758,6 +758,7 @@ def prepare_img():
@require_torch
@require_vision
@slow
class DFineModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
@@ -778,37 +779,38 @@ class DFineModelIntegrationTest(unittest.TestCase):
expected_logits = torch.tensor(
[
[-3.8097816, -4.7724586, -5.994499],
[-5.2974715, -9.499067, -6.1653666],
[-5.3502765, -3.9530406, -6.3630295],
[-3.8221, -4.7679, -6.0063],
[-5.2994, -9.5009, -6.1697],
[-5.3103, -3.8005, -6.2972],
]
).to(torch_device)
expected_boxes = torch.tensor(
[
[0.7677696, 0.41479152, 0.46441072],
[0.16912134, 0.19869131, 0.2123824],
[0.2581653, 0.54818195, 0.47512347],
[0.7678, 0.4148, 0.4644],
[0.1691, 0.1987, 0.2124],
[0.2582, 0.5482, 0.4751],
]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=1e-4, rtol=1e-4)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=2e-4, rtol=2e-4)
expected_shape_boxes = torch.Size((1, 300, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4, rtol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=2e-4, rtol=2e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.0, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.9642, 0.9542, 0.9536, 0.8548], device=torch_device)
expected_scores = torch.tensor([0.9616, 0.9541, 0.9541, 0.8551], device=torch_device)
expected_labels = [15, 65, 15, 57]
expected_slice_boxes = torch.tensor(
[
[1.3186283e01, 5.4130211e01, 3.1726535e02, 4.7212445e02],
[4.0275269e01, 7.2975174e01, 1.7620003e02, 1.1776848e02],
[3.4276117e02, 2.3427944e01, 6.3998401e02, 3.7477191e02],
[5.8418274e-01, 1.1794567e00, 6.3933154e02, 4.7485995e02],
[1.3358e01, 5.4123e01, 3.1726e02, 4.7222e02],
[4.0274e01, 7.2972e01, 1.7620e02, 1.1777e02],
[3.4270e02, 2.3427e01, 6.3998e02, 3.7476e02],
[5.7796e-01, 1.1773e00, 6.3933e02, 4.7486e02],
],
device=torch_device,
)