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

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---------

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
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31
tests/models/conditional_detr/test_modeling_conditional_detr.py
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@@ -570,9 +570,14 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
expected_shape = torch.Size((1, 300, 256))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[0.4222, 0.7471, 0.8760], [0.6395, -0.2729, 0.7127], [-0.3090, 0.7642, 0.9529]]
[
[0.4223, 0.7474, 0.8760],
[0.6397, -0.2727, 0.7126],
[-0.3089, 0.7643, 0.9529],
]
).to(torch_device)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
def test_inference_object_detection_head(self):
model = ConditionalDetrForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50").to(
@@ -592,26 +597,34 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor(
[[-10.4372, -5.7558, -8.6764], [-10.5410, -5.8704, -8.0590], [-10.6827, -6.3469, -8.3923]]
[
[-10.4371, -5.7565, -8.6765],
[-10.5413, -5.8700, -8.0589],
[-10.6824, -6.3477, -8.3927],
]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4)
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
expected_slice_boxes = torch.tensor(
[[0.7733, 0.6576, 0.4496], [0.5171, 0.1184, 0.9094], [0.8846, 0.5647, 0.2486]]
[
[0.7733, 0.6576, 0.4496],
[0.5171, 0.1184, 0.9095],
[0.8846, 0.5647, 0.2486],
]
).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=2e-4, atol=2e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.8330, 0.8313, 0.8039, 0.6829, 0.5355]).to(torch_device)
expected_scores = torch.tensor([0.8330, 0.8315, 0.8039, 0.6829, 0.5354]).to(torch_device)
expected_labels = [75, 17, 17, 75, 63]
expected_slice_boxes = torch.tensor([38.3089, 72.1022, 177.6293, 118.4512]).to(torch_device)
expected_slice_boxes = torch.tensor([38.3109, 72.1002, 177.6301, 118.4511]).to(torch_device)
self.assertEqual(len(results["scores"]), 5)
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(results["scores"], expected_scores, rtol=2e-4, atol=2e-4)
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes)