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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/regnet/test_modeling_flax_regnet.py
View File

@@ -33,7 +33,7 @@ if is_flax_available():
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
from transformers import AutoImageProcessor
class FlaxRegNetModelTester(unittest.TestCase):
@@ -215,16 +215,16 @@ def prepare_img():
@require_flax
class FlaxRegNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="np")
inputs = image_processor(images=image, return_tensors="np")
outputs = model(**inputs)

10
tests/models/regnet/test_modeling_regnet.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 RegNetModelTester:
@@ -248,9 +248,9 @@ def prepare_img():
@require_vision
class RegNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
AutoFeatureExtractor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@@ -259,9 +259,9 @@ class RegNetModelIntegrationTest(unittest.TestCase):
def test_inference_image_classification_head(self):
model = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).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():

10
tests/models/regnet/test_modeling_tf_regnet.py
View File

@@ -38,7 +38,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
from transformers import AutoImageProcessor
class TFRegNetModelTester:
@@ -267,9 +267,9 @@ def prepare_img():
@require_vision
class RegNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
AutoFeatureExtractor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@@ -278,9 +278,9 @@ class RegNetModelIntegrationTest(unittest.TestCase):
def test_inference_image_classification_head(self):
model = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
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, training=False)