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

14
tests/models/deit/test_modeling_deit.py
View File

@@ -55,7 +55,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import DeiTFeatureExtractor
from transformers import DeiTImageProcessor
class DeiTModelTester:
@@ -381,9 +381,9 @@ def prepare_img():
@require_vision
class DeiTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
if is_vision_available()
else None
)
@@ -394,9 +394,9 @@ class DeiTModelIntegrationTest(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():
@@ -420,10 +420,10 @@ class DeiTModelIntegrationTest(unittest.TestCase):
model = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224", torch_dtype=torch.float16, device_map="auto"
)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="pt")
inputs = image_processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# forward pass to make sure inference works in fp16

10
tests/models/deit/test_modeling_tf_deit.py
View File

@@ -46,7 +46,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import DeiTFeatureExtractor
from transformers import DeiTImageProcessor
class TFDeiTModelTester:
@@ -266,9 +266,9 @@ def prepare_img():
@require_vision
class DeiTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
if is_vision_available()
else None
)
@@ -277,9 +277,9 @@ class DeiTModelIntegrationTest(unittest.TestCase):
def test_inference_image_classification_head(self):
model = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224")
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)