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

18
tests/models/vit/test_modeling_vit.py
View File

@@ -45,7 +45,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import ViTFeatureExtractor
from transformers import ViTImageProcessor
class ViTModelTester:
@@ -264,16 +264,16 @@ def prepare_img():
@require_vision
class ViTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None
def default_image_processor(self):
return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224").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():
@@ -295,9 +295,9 @@ class ViTModelIntegrationTest(unittest.TestCase):
# to visualize self-attention on higher resolution images.
model = ViTModel.from_pretrained("facebook/dino-vits8").to(torch_device)
feature_extractor = ViTFeatureExtractor.from_pretrained("facebook/dino-vits8", size=480)
image_processor = ViTImageProcessor.from_pretrained("facebook/dino-vits8", size=480)
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
@@ -322,10 +322,10 @@ class ViTModelIntegrationTest(unittest.TestCase):
A small test to make sure that inference work in half precision without any problem.
"""
model = ViTModel.from_pretrained("facebook/dino-vits8", 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