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Fix torch device issues (#20304)

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* fix device issue

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2022-11-21 10:12:25 +01:00
committed by GitHub
parent d316037ad7
commit 8503cc7550
6 changed files with 8 additions and 8 deletions
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2
src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py
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@@ -881,7 +881,7 @@ class ConditionalDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtrac
img_w = torch.Tensor([i[1] for i in target_sizes]) img_w = torch.Tensor([i[1] for i in target_sizes])
else: else:
img_h, img_w = target_sizes.unbind(1) img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :] boxes = boxes * scale_fct[:, None, :]
results = [] results = []

2
src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py
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@@ -729,7 +729,7 @@ class DeformableDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtract
img_w = torch.Tensor([i[1] for i in target_sizes]) img_w = torch.Tensor([i[1] for i in target_sizes])
else: else:
img_h, img_w = target_sizes.unbind(1) img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :] boxes = boxes * scale_fct[:, None, :]
results = [] results = []

2
src/transformers/models/detr/feature_extraction_detr.py
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@@ -1103,7 +1103,7 @@ class DetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
else: else:
img_h, img_w = target_sizes.unbind(1) img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :] boxes = boxes * scale_fct[:, None, :]
results = [] results = []

2
src/transformers/models/yolos/feature_extraction_yolos.py
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@@ -694,7 +694,7 @@ class YolosFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin)
else: else:
img_h, img_w = target_sizes.unbind(1) img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
boxes = boxes * scale_fct[:, None, :] boxes = boxes * scale_fct[:, None, :]
results = [] results = []

4
tests/models/conditional_detr/test_modeling_conditional_detr.py
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@@ -511,9 +511,9 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
results = feature_extractor.post_process_object_detection( results = feature_extractor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]] outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0] )[0]
expected_scores = torch.tensor([0.8330, 0.8313, 0.8039, 0.6829, 0.5355]) expected_scores = torch.tensor([0.8330, 0.8313, 0.8039, 0.6829, 0.5355]).to(torch_device)
expected_labels = [75, 17, 17, 75, 63] expected_labels = [75, 17, 17, 75, 63]
expected_slice_boxes = torch.tensor([38.3089, 72.1022, 177.6293, 118.4512]) expected_slice_boxes = torch.tensor([38.3089, 72.1022, 177.6293, 118.4512]).to(torch_device)
self.assertEqual(len(results["scores"]), 5) self.assertEqual(len(results["scores"]), 5)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4)) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))

4
tests/models/deformable_detr/test_modeling_deformable_detr.py
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@@ -569,9 +569,9 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
results = feature_extractor.post_process_object_detection( results = feature_extractor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]] outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0] )[0]
expected_scores = torch.tensor([0.7999, 0.7894, 0.6331, 0.4720, 0.4382]) expected_scores = torch.tensor([0.7999, 0.7894, 0.6331, 0.4720, 0.4382]).to(torch_device)
expected_labels = [17, 17, 75, 75, 63] expected_labels = [17, 17, 75, 75, 63]
expected_slice_boxes = torch.tensor([16.5028, 52.8390, 318.2544, 470.7841]) expected_slice_boxes = torch.tensor([16.5028, 52.8390, 318.2544, 470.7841]).to(torch_device)
self.assertEqual(len(results["scores"]), 5) self.assertEqual(len(results["scores"]), 5)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4)) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))