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

12
tests/models/yolos/test_modeling_yolos.py
View File

@@ -38,7 +38,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
from transformers import AutoImageProcessor
class YolosModelTester:
@@ -345,16 +345,16 @@ def prepare_img():
@require_vision
class YolosModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return AutoFeatureExtractor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None
@slow
def test_inference_object_detection_head(self):
model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
@@ -375,7 +375,7 @@ class YolosModelIntegrationTest(unittest.TestCase):
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_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([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(torch_device)