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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)

4
tests/models/convnext/test_modeling_convnext.py
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@@ -286,9 +286,9 @@ class ConvNextModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.0260, -0.4739, 0.1911]).to(torch_device)
expected_slice = torch.tensor([-0.0261, -0.4739, 0.1910]).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4)
@require_torch

8
tests/models/cvt/test_modeling_cvt.py
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@@ -185,6 +185,10 @@ class CvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
def test_model_get_set_embeddings(self):
pass
# Larger differences on A10 than T4
def test_batching_equivalence(self, atol=2e-4, rtol=2e-4):
super().test_batching_equivalence(atol=atol, rtol=rtol)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@@ -265,6 +269,6 @@ class CvtModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([0.9285, 0.9015, -0.3150]).to(torch_device)
expected_slice = torch.tensor([0.9287, 0.9016, -0.3152]).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4)

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,
)

28
tests/models/dab_detr/test_modeling_dab_detr.py
View File

@@ -787,7 +787,11 @@ class DabDetrModelIntegrationTests(unittest.TestCase):
expected_shape = torch.Size((1, 300, 256))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.4879, -0.2594, 0.4524], [-0.4997, -0.4258, 0.4329], [-0.8220, -0.4996, 0.0577]]
[
[-0.4878, -0.2593, 0.4521],
[-0.4999, -0.4257, 0.4326],
[-0.8220, -0.4997, 0.0578],
]
).to(torch_device)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=2e-4, rtol=2e-4)
@@ -806,26 +810,34 @@ class DabDetrModelIntegrationTests(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(
[[-10.1765, -5.5243, -8.9324], [-9.8138, -5.6721, -7.5161], [-10.3054, -5.6081, -8.5931]]
[
[-10.1764, -5.5247, -8.9324],
[-9.8137, -5.6730, -7.5163],
[-10.3056, -5.6075, -8.5935],
]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, atol=3e-4, rtol=3e-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(
[[0.3708, 0.3000, 0.2753], [0.5211, 0.6125, 0.9495], [0.2897, 0.6730, 0.5459]]
[
[0.3708, 0.3000, 0.2754],
[0.5211, 0.6126, 0.9494],
[0.2897, 0.6731, 0.5460],
]
).to(torch_device)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4, rtol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=3e-4, rtol=3e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.8732, 0.8563, 0.8554, 0.6079, 0.5896]).to(torch_device)
expected_scores = torch.tensor([0.8732, 0.8563, 0.8554, 0.6080, 0.5895]).to(torch_device)
expected_labels = [17, 75, 17, 75, 63]
expected_boxes = torch.tensor([14.6970, 49.3892, 320.5165, 469.2765]).to(torch_device)
expected_boxes = torch.tensor([14.6931, 49.3886, 320.5176, 469.2762]).to(torch_device)
self.assertEqual(len(results["scores"]), 5)
torch.testing.assert_close(results["scores"], expected_scores, atol=1e-4, rtol=1e-4)
torch.testing.assert_close(results["scores"], expected_scores, atol=3e-4, rtol=3e-4)
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
torch.testing.assert_close(results["boxes"][0, :], expected_boxes, atol=1e-4, rtol=1e-4)
torch.testing.assert_close(results["boxes"][0, :], expected_boxes, atol=3e-4, rtol=3e-4)

51
tests/models/deformable_detr/test_modeling_deformable_detr.py
View File

@@ -677,30 +677,38 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_logits = torch.tensor(
[[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]
[
[-9.6644, -4.3434, -5.8707],
[-9.7035, -3.8503, -5.0721],
[-10.5633, -5.3387, -7.5119],
]
).to(torch_device)
expected_boxes = torch.tensor(
[[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]
[
[0.8693, 0.2290, 0.2492],
[0.3150, 0.5489, 0.5845],
[0.5563, 0.7580, 0.8518],
]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_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)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_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([0.7999, 0.7894, 0.6331, 0.4720, 0.4382]).to(torch_device)
expected_scores = torch.tensor([0.7999, 0.7895, 0.6332, 0.4719, 0.4382]).to(torch_device)
expected_labels = [17, 17, 75, 75, 63]
expected_slice_boxes = torch.tensor([16.5028, 52.8390, 318.2544, 470.7841]).to(torch_device)
expected_slice_boxes = torch.tensor([16.4960, 52.8387, 318.2565, 470.7831]).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)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=2e-4, atol=2e-4)
def test_inference_object_detection_head_with_box_refine_two_stage(self):
model = DeformableDetrForObjectDetection.from_pretrained(
@@ -720,17 +728,25 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_logits = torch.tensor(
[[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]
[
[-6.7112, -4.3216, -6.3781],
[-8.9035, -6.1738, -6.7249],
[-6.9314, -4.4736, -6.2303],
]
).to(torch_device)
expected_boxes = torch.tensor(
[[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]
[
[0.2582, 0.5499, 0.4683],
[0.7652, 0.9084, 0.4884],
[0.5490, 0.2763, 0.0564],
]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_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)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=2e-4, atol=2e-4)
@require_torch_accelerator
def test_inference_object_detection_head_equivalence_cpu_accelerator(self):
@@ -753,10 +769,15 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
gpu_outputs = model(pixel_values.to(torch_device), pixel_mask.to(torch_device))
# 3. assert equivalence
# (on A10, the differences get larger than on T4)
for key in cpu_outputs.keys():
assert torch.allclose(cpu_outputs[key], gpu_outputs[key].cpu(), atol=1e-4)
torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), atol=2e-2, rtol=2e-2)
expected_logits = torch.tensor(
[[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]
[
[-9.9051, -4.2541, -6.4852],
[-9.6947, -4.0854, -6.8033],
[-10.0665, -5.8470, -7.7003],
]
)
assert torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=1e-4)
assert torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=2e-4)

54
tests/models/detr/test_modeling_detr.py
View File

@@ -586,9 +586,13 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
expected_shape = torch.Size((1, 100, 256))
assert outputs.last_hidden_state.shape == expected_shape
expected_slice = torch.tensor(
[[0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826]]
[
[0.0622, -0.5142, -0.4034],
[-0.7628, -0.4935, -1.7153],
[-0.4751, -0.6386, -0.7818],
]
).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 = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(torch_device)
@@ -606,16 +610,24 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor(
[[-19.1194, -0.0893, -11.0154], [-17.3640, -1.8035, -14.0219], [-20.0461, -0.5837, -11.1060]]
[
[-19.1211, -0.0881, -11.0188],
[-17.3641, -1.8045, -14.0229],
[-20.0415, -0.5833, -11.1005],
]
).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(
[[0.4433, 0.5302, 0.8853], [0.5494, 0.2517, 0.0529], [0.4998, 0.5360, 0.9956]]
[
[0.4433, 0.5302, 0.8852],
[0.5494, 0.2517, 0.0529],
[0.4998, 0.5360, 0.9955],
]
).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(
@@ -623,12 +635,12 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
)[0]
expected_scores = torch.tensor([0.9982, 0.9960, 0.9955, 0.9988, 0.9987]).to(torch_device)
expected_labels = [75, 75, 63, 17, 17]
expected_slice_boxes = torch.tensor([40.1633, 70.8115, 175.5471, 117.9841]).to(torch_device)
expected_slice_boxes = torch.tensor([40.1615, 70.8090, 175.5476, 117.9810]).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)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=2e-4, atol=2e-4)
def test_inference_panoptic_segmentation_head(self):
model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic").to(torch_device)
@@ -646,23 +658,27 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor(
[[-18.1565, -1.7568, -13.5029], [-16.8888, -1.4138, -14.1028], [-17.5709, -2.5080, -11.8654]]
[
[-18.1523, -1.7592, -13.5019],
[-16.8866, -1.4139, -14.1025],
[-17.5735, -2.5090, -11.8666],
]
).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(
[[0.5344, 0.1789, 0.9285], [0.4420, 0.0572, 0.0875], [0.6630, 0.6887, 0.1017]]
[[0.5344, 0.1790, 0.9284], [0.4421, 0.0571, 0.0875], [0.6632, 0.6886, 0.1015]]
).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)
expected_shape_masks = torch.Size((1, model.config.num_queries, 200, 267))
self.assertEqual(outputs.pred_masks.shape, expected_shape_masks)
expected_slice_masks = torch.tensor(
[[-7.7558, -10.8788, -11.9797], [-11.8881, -16.4329, -17.7451], [-14.7316, -19.7383, -20.3004]]
[[-7.8408, -11.0104, -12.1279], [-12.0299, -16.6498, -17.9806], [-14.8995, -19.9940, -20.5646]]
).to(torch_device)
torch.testing.assert_close(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, rtol=2e-3, atol=2e-3)
# verify postprocessing
results = image_processor.post_process_panoptic_segmentation(
@@ -674,7 +690,7 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
torch_device
)
expected_number_of_segments = 5
expected_first_segment = {"id": 1, "label_id": 17, "was_fused": False, "score": 0.994097}
expected_first_segment = {"id": 1, "label_id": 17, "was_fused": False, "score": 0.9941}
number_of_unique_segments = len(torch.unique(results["segmentation"]))
self.assertTrue(
@@ -716,6 +732,10 @@ class DetrModelIntegrationTests(unittest.TestCase):
expected_shape = torch.Size((1, 100, 256))
assert outputs.last_hidden_state.shape == expected_shape
expected_slice = torch.tensor(
[[0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826]]
[
[0.0622, -0.5142, -0.4034],
[-0.7628, -0.4935, -1.7153],
[-0.4751, -0.6386, -0.7818],
]
).to(torch_device)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)