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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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32
tests/models/beit/test_modeling_beit.py
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@@ -49,7 +49,7 @@ if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitFeatureExtractor
from transformers import BeitImageProcessor
class BeitModelTester:
@@ -342,18 +342,16 @@ def prepare_img():
@require_vision
class BeitModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return (
BeitFeatureExtractor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
)
def default_image_processor(self):
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
@slow
def test_inference_masked_image_modeling_head(self):
model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(torch_device)
pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device)
# prepare bool_masked_pos
bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device)
@@ -377,9 +375,9 @@ class BeitModelIntegrationTest(unittest.TestCase):
def test_inference_image_classification_head_imagenet_1k(self):
model = BeitForImageClassification.from_pretrained("microsoft/beit-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():
@@ -403,9 +401,9 @@ class BeitModelIntegrationTest(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():
@@ -428,11 +426,11 @@ class BeitModelIntegrationTest(unittest.TestCase):
model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
model = model.to(torch_device)
feature_extractor = BeitFeatureExtractor(do_resize=True, size=640, do_center_crop=False)
image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
image = Image.open(ds[0]["file"])
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():
@@ -471,11 +469,11 @@ class BeitModelIntegrationTest(unittest.TestCase):
model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
model = model.to(torch_device)
feature_extractor = BeitFeatureExtractor(do_resize=True, size=640, do_center_crop=False)
image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
image = Image.open(ds[0]["file"])
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():
@@ -483,10 +481,10 @@ class BeitModelIntegrationTest(unittest.TestCase):
outputs.logits = outputs.logits.detach().cpu()
segmentation = feature_extractor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
expected_shape = torch.Size((500, 300))
self.assertEqual(segmentation[0].shape, expected_shape)
segmentation = feature_extractor.post_process_semantic_segmentation(outputs=outputs)
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
expected_shape = torch.Size((160, 160))
self.assertEqual(segmentation[0].shape, expected_shape)