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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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52
tests/models/grounding_dino/test_image_processing_grounding_dino.py
View File

@@ -193,10 +193,10 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
self.assertEqual(results[0]["scores"].shape, (self.image_processor_tester.num_queries,))
expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220])
self.assertTrue(torch.allclose(results[0]["scores"], expected_scores, atol=1e-4))
torch.testing.assert_close(results[0]["scores"], expected_scores, rtol=1e-4, atol=1e-4)
expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947])
self.assertTrue(torch.allclose(results[0]["boxes"][0], expected_box_slice, atol=1e-4))
torch.testing.assert_close(results[0]["boxes"][0], expected_box_slice, rtol=1e-4, atol=1e-4)
@slow
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_detection_annotations with DeformableDetr->GroundingDino
@@ -218,31 +218,31 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->GroundingDino
@@ -309,8 +309,8 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
[0.5790, 0.4115, 0.3430, 0.7161],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
@@ -361,8 +361,8 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)
@slow
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_panoptic_annotations with DeformableDetr->GroundingDino
@@ -386,35 +386,35 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify masks
expected_masks_sum = 822873
relative_error = torch.abs(encoding["labels"][0]["masks"].sum() - expected_masks_sum) / expected_masks_sum
self.assertTrue(relative_error < 1e-3)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->GroundingDino
@@ -485,8 +485,8 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
[0.2997, 0.2994, 0.5994, 0.5987],
]
)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
@@ -538,8 +538,8 @@ class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessin
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->GroundingDino
def test_max_width_max_height_resizing_and_pad_strategy(self):

24
tests/models/grounding_dino/test_modeling_grounding_dino.py
View File

@@ -645,11 +645,11 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
[[-4.8913, -0.1900, -0.2161], [-4.9653, -0.3719, -0.3950], [-5.9599, -3.3765, -3.3104]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-3))
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
# verify postprocessing
results = processor.image_processor.post_process_object_detection(
@@ -659,8 +659,8 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
expected_slice_boxes = torch.tensor([344.8143, 23.1796, 637.4004, 373.8295]).to(torch_device)
self.assertEqual(len(results["scores"]), 2)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-3))
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes, atol=1e-2))
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
# verify grounded postprocessing
expected_labels = ["a cat", "a cat"]
@@ -672,8 +672,8 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
target_sizes=[(image.height, image.width)],
)[0]
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-3))
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes, atol=1e-2))
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
self.assertListEqual(results["text_labels"], expected_labels)
@require_torch_accelerator
@@ -697,12 +697,12 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
# 3. assert equivalence
for key in cpu_outputs.keys():
self.assertTrue(torch.allclose(cpu_outputs[key], gpu_outputs[key].cpu(), atol=1e-3))
torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), rtol=1e-3, atol=1e-3)
expected_logits = torch.tensor(
[[-4.8915, -0.1900, -0.2161], [-4.9658, -0.3716, -0.3948], [-5.9596, -3.3763, -3.3103]]
)
self.assertTrue(torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=1e-3))
torch.testing.assert_close(cpu_outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
# assert postprocessing
results_cpu = processor.image_processor.post_process_object_detection(
@@ -713,8 +713,8 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
gpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
)[0]
self.assertTrue(torch.allclose(results_cpu["scores"], result_gpu["scores"].cpu(), atol=1e-3))
self.assertTrue(torch.allclose(results_cpu["boxes"], result_gpu["boxes"].cpu(), atol=1e-3))
torch.testing.assert_close(results_cpu["scores"], result_gpu["scores"].cpu(), rtol=1e-3, atol=1e-3)
torch.testing.assert_close(results_cpu["boxes"], result_gpu["boxes"].cpu(), rtol=1e-3, atol=1e-3)
def test_cross_attention_mask(self):
model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device)
@@ -738,6 +738,6 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
outputs2 = model(**encoding2)
outputs_batched = model(**encoding_batched)
self.assertTrue(torch.allclose(outputs1.logits, outputs_batched.logits[:1], atol=1e-3))
torch.testing.assert_close(outputs1.logits, outputs_batched.logits[:1], rtol=1e-3, atol=1e-3)
# For some reason 12 elements are > 1e-3, but the rest are fine
self.assertTrue(torch.allclose(outputs2.logits, outputs_batched.logits[1:], atol=1.8e-3))
torch.testing.assert_close(outputs2.logits, outputs_batched.logits[1:], rtol=1.8e-3, atol=1.8e-3)

4
tests/models/grounding_dino/test_processor_grounding_dino.py
View File

@@ -136,10 +136,10 @@ class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
self.assertEqual(post_processed[0]["scores"].shape, (self.num_queries,))
expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220])
self.assertTrue(torch.allclose(post_processed[0]["scores"], expected_scores, atol=1e-4))
torch.testing.assert_close(post_processed[0]["scores"], expected_scores, rtol=1e-4, atol=1e-4)
expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947])
self.assertTrue(torch.allclose(post_processed[0]["boxes"][0], expected_box_slice, atol=1e-4))
torch.testing.assert_close(post_processed[0]["boxes"][0], expected_box_slice, rtol=1e-4, atol=1e-4)
# Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_save_load_pretrained_default with CLIP->GroundingDino,GroundingDinoTokenizer->BertTokenizer
def test_save_load_pretrained_default(self):