SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Update old existing feature extractor references (#24552)

Browse Source 
* Update old existing feature extractor references

* Typo

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

* Address comments from review - update 'feature extractor'
Co-authored by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
This commit is contained in:
amyeroberts
2023-06-29 10:17:36 +01:00
committed by GitHub
parent 10c2ac7bc6
commit ae454f41d4
138 changed files with 762 additions and 743 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
tests/models/deformable_detr/test_modeling_deformable_detr.py
View File

@@ -39,7 +39,7 @@ if is_timm_available():
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
from transformers import AutoImageProcessor
class DeformableDetrModelTester:
@@ -563,15 +563,15 @@ def prepare_img():
@slow
class DeformableDetrModelIntegrationTests(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return AutoFeatureExtractor.from_pretrained("SenseTime/deformable-detr") if is_vision_available() else None
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") if is_vision_available() else None
def test_inference_object_detection_head(self):
model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
pixel_values = encoding["pixel_values"].to(torch_device)
pixel_mask = encoding["pixel_mask"].to(torch_device)
@@ -595,7 +595,7 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
# verify postprocessing
results = feature_extractor.post_process_object_detection(
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)
@@ -612,9 +612,9 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
"SenseTime/deformable-detr-with-box-refine-two-stage"
).to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
pixel_values = encoding["pixel_values"].to(torch_device)
pixel_mask = encoding["pixel_mask"].to(torch_device)
@@ -639,9 +639,9 @@ class DeformableDetrModelIntegrationTests(unittest.TestCase):
@require_torch_gpu
def test_inference_object_detection_head_equivalence_cpu_gpu(self):
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt")
encoding = image_processor(images=image, return_tensors="pt")
pixel_values = encoding["pixel_values"]
pixel_mask = encoding["pixel_mask"]