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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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10
tests/models/swin/test_modeling_swin.py
View File

@@ -39,7 +39,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
from transformers import AutoImageProcessor
class SwinModelTester:
@@ -482,9 +482,9 @@ class SwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
@require_torch
class SwinModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
AutoFeatureExtractor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
if is_vision_available()
else None
)
@@ -492,10 +492,10 @@ class SwinModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification_head(self):
model = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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():

10
tests/models/swin/test_modeling_tf_swin.py
View File

@@ -45,7 +45,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
from transformers import AutoImageProcessor
class TFSwinModelTester:
@@ -382,9 +382,9 @@ class TFSwinModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase
@require_tf
class TFSwinModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
AutoFeatureExtractor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
if is_vision_available()
else None
)
@@ -392,10 +392,10 @@ class TFSwinModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification_head(self):
model = TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = feature_extractor(images=image, return_tensors="tf")
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(inputs)