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[fix] Add DeformableDetrFeatureExtractor (#19140)

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* Add DeformableDetrFeatureExtractor

* Fix post_process

* Fix name

* Add tests for feature extractor

* Fix doc tests

* Fix name

* Address comments

* Apply same fix to DETR and YOLOS as well

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge
2022-09-22 09:45:24 +02:00
committed by GitHub
parent 126a739058
commit 9393f966bc
13 changed files with 1380 additions and 53 deletions
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43
tests/models/detr/test_feature_extraction_detr.py
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@@ -240,8 +240,12 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")
assert torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
assert torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
)
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
)
@slow
def test_call_pytorch_with_coco_detection_annotations(self):
@@ -261,31 +265,31 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
assert torch.allclose(encoding["labels"][0]["area"], expected_area)
self.assertTrue(torch.allclose(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])
assert torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
# verify image_id
expected_image_id = torch.tensor([39769])
assert torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
assert torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
assert torch.allclose(encoding["labels"][0]["size"], expected_size)
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
@slow
def test_call_pytorch_with_coco_panoptic_annotations(self):
@@ -299,8 +303,7 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
# encode them
# TODO replace by .from_pretrained facebook/detr-resnet-50-panoptic
feature_extractor = DetrFeatureExtractor(format="coco_panoptic")
feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
encoding = feature_extractor(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
# verify pixel values
@@ -308,31 +311,31 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
assert torch.allclose(encoding["labels"][0]["area"], expected_area)
self.assertTrue(torch.allclose(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])
assert torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
# verify image_id
expected_image_id = torch.tensor([39769])
assert torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
assert torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
# verify class_labels
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify masks
expected_masks_sum = 822338
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
assert torch.allclose(encoding["labels"][0]["size"], expected_size)
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))