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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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12
tests/models/data2vec/test_modeling_data2vec_vision.py
View File

@@ -44,7 +44,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import BeitFeatureExtractor
from transformers import BeitImageProcessor
class Data2VecVisionModelTester:
@@ -327,11 +327,9 @@ def prepare_img():
@require_vision
class Data2VecVisionModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k")
if is_vision_available()
else None
BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None
)
@slow
@@ -340,9 +338,9 @@ class Data2VecVisionModelIntegrationTest(unittest.TestCase):
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():

12
tests/models/data2vec/test_modeling_tf_data2vec_vision.py
View File

@@ -46,7 +46,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import BeitFeatureExtractor
from transformers import BeitImageProcessor
class TFData2VecVisionModelTester:
@@ -469,20 +469,18 @@ def prepare_img():
@require_vision
class TFData2VecVisionModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k")
if is_vision_available()
else None
BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None
)
@slow
def test_inference_image_classification_head_imagenet_1k(self):
model = TFData2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k")
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="tf")
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)