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

10
tests/models/vit_mae/test_modeling_tf_vit_mae.py
View File

@@ -46,7 +46,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import ViTFeatureExtractor
from transformers import ViTImageProcessor
class TFViTMAEModelTester:
@@ -424,8 +424,8 @@ def prepare_img():
@require_vision
class TFViTMAEModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None
def default_image_processor(self):
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None
@slow
def test_inference_for_pretraining(self):
@@ -434,9 +434,9 @@ class TFViTMAEModelIntegrationTest(unittest.TestCase):
model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")
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")
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)

10
tests/models/vit_mae/test_modeling_vit_mae.py
View File

@@ -42,7 +42,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import ViTFeatureExtractor
from transformers import ViTImageProcessor
class ViTMAEModelTester:
@@ -296,8 +296,8 @@ def prepare_img():
@require_vision
class ViTMAEModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return ViTFeatureExtractor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None
def default_image_processor(self):
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None
@slow
def test_inference_for_pretraining(self):
@@ -306,9 +306,9 @@ class ViTMAEModelIntegrationTest(unittest.TestCase):
model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").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)
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)