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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/layoutlmv3/test_modeling_layoutlmv3.py
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@@ -45,7 +45,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import LayoutLMv3FeatureExtractor
from transformers import LayoutLMv3ImageProcessor
class LayoutLMv3ModelTester:
@@ -382,16 +382,16 @@ def prepare_img():
@require_torch
class LayoutLMv3ModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return LayoutLMv3FeatureExtractor(apply_ocr=False) if is_vision_available() else None
def default_image_processor(self):
return LayoutLMv3ImageProcessor(apply_ocr=False) if is_vision_available() else None
@slow
def test_inference_no_head(self):
model = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base").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)
input_ids = torch.tensor([[1, 2]])
bbox = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

10
tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py
View File

@@ -51,7 +51,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import LayoutLMv3FeatureExtractor
from transformers import LayoutLMv3ImageProcessor
class TFLayoutLMv3ModelTester:
@@ -482,16 +482,16 @@ def prepare_img():
@require_tf
class TFLayoutLMv3ModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return LayoutLMv3FeatureExtractor(apply_ocr=False) if is_vision_available() else None
def default_image_processor(self):
return LayoutLMv3ImageProcessor(apply_ocr=False) if is_vision_available() else None
@slow
def test_inference_no_head(self):
model = TFLayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base")
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
pixel_values = feature_extractor(images=image, return_tensors="tf").pixel_values
pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
input_ids = tf.constant([[1, 2]])
bbox = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]), axis=0)