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Rename detr targets to labels (#12280)

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* Rename target to labels in DetrFeatureExtractor

* Update DetrFeatureExtractor tests accordingly

* Improve docs of DetrFeatureExtractor

* Improve docs

* Make style
This commit is contained in:
NielsRogge
2021-06-29 09:07:46 +02:00
committed by GitHub
parent 7682e97702
commit 1fc6817a30
4 changed files with 24 additions and 30 deletions
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37
tests/test_feature_extraction_detr.py
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@@ -253,8 +253,7 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
target = {"image_id": 39769, "annotations": target}
# encode them
# TODO replace by facebook/detr-resnet-50
feature_extractor = DetrFeatureExtractor.from_pretrained("nielsr/detr-resnet-50")
feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
encoding = feature_extractor(images=image, annotations=target, return_tensors="pt")
# verify pixel values
@@ -266,27 +265,27 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
assert torch.allclose(encoding["target"][0]["area"], expected_area)
assert torch.allclose(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["target"][0]["boxes"].shape, expected_boxes_shape)
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["target"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
assert 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["target"][0]["image_id"], expected_image_id)
assert 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["target"][0]["iscrowd"], expected_is_crowd)
assert 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["target"][0]["class_labels"], expected_class_labels)
assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
assert torch.allclose(encoding["target"][0]["orig_size"], expected_orig_size)
assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1066])
assert torch.allclose(encoding["target"][0]["size"], expected_size)
assert torch.allclose(encoding["labels"][0]["size"], expected_size)
@slow
def test_call_pytorch_with_coco_panoptic_annotations(self):
@@ -313,27 +312,27 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
assert torch.allclose(encoding["target"][0]["area"], expected_area)
assert torch.allclose(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["target"][0]["boxes"].shape, expected_boxes_shape)
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["target"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
assert 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["target"][0]["image_id"], expected_image_id)
assert 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["target"][0]["iscrowd"], expected_is_crowd)
assert 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["target"][0]["class_labels"], expected_class_labels)
assert torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify masks
expected_masks_sum = 822338
self.assertEqual(encoding["target"][0]["masks"].sum().item(), expected_masks_sum)
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["target"][0]["orig_size"], expected_orig_size)
assert torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1066])
assert torch.allclose(encoding["target"][0]["size"], expected_size)
assert torch.allclose(encoding["labels"][0]["size"], expected_size)