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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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18
tests/models/dpt/test_modeling_dpt.py
View File

@@ -39,7 +39,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import DPTFeatureExtractor
from transformers import DPTImageProcessor
class DPTModelTester:
@@ -293,11 +293,11 @@ def prepare_img():
@slow
class DPTModelIntegrationTest(unittest.TestCase):
def test_inference_depth_estimation(self):
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large").to(torch_device)
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():
@@ -315,11 +315,11 @@ class DPTModelIntegrationTest(unittest.TestCase):
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
def test_inference_semantic_segmentation(self):
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device)
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():
@@ -336,11 +336,11 @@ class DPTModelIntegrationTest(unittest.TestCase):
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))
def test_post_processing_semantic_segmentation(self):
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device)
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():
@@ -348,10 +348,10 @@ class DPTModelIntegrationTest(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((480, 480))
self.assertEqual(segmentation[0].shape, expected_shape)

6
tests/models/dpt/test_modeling_dpt_hybrid.py
View File

@@ -39,7 +39,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import DPTFeatureExtractor
from transformers import DPTImageProcessor
class DPTModelTester:
@@ -314,11 +314,11 @@ def prepare_img():
@slow
class DPTModelIntegrationTest(unittest.TestCase):
def test_inference_depth_estimation(self):
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(torch_device)
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():