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Update old existing feature extractor references (#24552)

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* 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
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16
tests/models/conditional_detr/test_modeling_conditional_detr.py
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@@ -43,7 +43,7 @@ if is_timm_available():
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrFeatureExtractor
from transformers import ConditionalDetrImageProcessor
class ConditionalDetrModelTester:
@@ -493,9 +493,9 @@ def prepare_img():
@slow
class ConditionalDetrModelIntegrationTests(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
ConditionalDetrFeatureExtractor.from_pretrained("microsoft/conditional-detr-resnet-50")
ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
if is_vision_available()
else None
)
@@ -503,9 +503,9 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
def test_inference_no_head(self):
model = ConditionalDetrModel.from_pretrained("microsoft/conditional-detr-resnet-50").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)
with torch.no_grad():
outputs = model(**encoding)
@@ -522,9 +522,9 @@ class ConditionalDetrModelIntegrationTests(unittest.TestCase):
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)
@@ -547,7 +547,7 @@ class ConditionalDetrModelIntegrationTests(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([0.8330, 0.8313, 0.8039, 0.6829, 0.5355]).to(torch_device)