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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/segformer/test_modeling_segformer.py
View File

@@ -42,7 +42,7 @@ if is_torch_available():
if is_vision_available():
from PIL import Image
from transformers import SegformerFeatureExtractor
from transformers import SegformerImageProcessor
class SegformerConfigTester(ConfigTester):
@@ -365,7 +365,7 @@ class SegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_segmentation_ade(self):
# only resize + normalize
feature_extractor = SegformerFeatureExtractor(
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
@@ -373,7 +373,7 @@ class SegformerModelIntegrationTest(unittest.TestCase):
)
image = prepare_img()
encoded_inputs = feature_extractor(images=image, return_tensors="pt")
encoded_inputs = image_processor(images=image, return_tensors="pt")
pixel_values = encoded_inputs.pixel_values.to(torch_device)
with torch.no_grad():
@@ -394,7 +394,7 @@ class SegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_segmentation_city(self):
# only resize + normalize
feature_extractor = SegformerFeatureExtractor(
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = SegformerForSemanticSegmentation.from_pretrained(
@@ -402,7 +402,7 @@ class SegformerModelIntegrationTest(unittest.TestCase):
).to(torch_device)
image = prepare_img()
encoded_inputs = feature_extractor(images=image, return_tensors="pt")
encoded_inputs = image_processor(images=image, return_tensors="pt")
pixel_values = encoded_inputs.pixel_values.to(torch_device)
with torch.no_grad():
@@ -423,7 +423,7 @@ class SegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_post_processing_semantic_segmentation(self):
# only resize + normalize
feature_extractor = SegformerFeatureExtractor(
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
@@ -431,7 +431,7 @@ class SegformerModelIntegrationTest(unittest.TestCase):
)
image = prepare_img()
encoded_inputs = feature_extractor(images=image, return_tensors="pt")
encoded_inputs = image_processor(images=image, return_tensors="pt")
pixel_values = encoded_inputs.pixel_values.to(torch_device)
with torch.no_grad():
@@ -439,10 +439,10 @@ class SegformerModelIntegrationTest(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((128, 128))
self.assertEqual(segmentation[0].shape, expected_shape)

10
tests/models/segformer/test_modeling_tf_segformer.py
View File

@@ -39,7 +39,7 @@ if is_tf_available():
if is_vision_available():
from PIL import Image
from transformers import SegformerFeatureExtractor
from transformers import SegformerImageProcessor
class TFSegformerConfigTester(ConfigTester):
@@ -454,13 +454,13 @@ class TFSegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_segmentation_ade(self):
# only resize + normalize
feature_extractor = SegformerFeatureExtractor(
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
image = prepare_img()
encoded_inputs = feature_extractor(images=image, return_tensors="tf")
encoded_inputs = image_processor(images=image, return_tensors="tf")
pixel_values = encoded_inputs.pixel_values
outputs = model(pixel_values, training=False)
@@ -480,7 +480,7 @@ class TFSegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_segmentation_city(self):
# only resize + normalize
feature_extractor = SegformerFeatureExtractor(
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
@@ -488,7 +488,7 @@ class TFSegformerModelIntegrationTest(unittest.TestCase):
)
image = prepare_img()
encoded_inputs = feature_extractor(images=image, return_tensors="tf")
encoded_inputs = image_processor(images=image, return_tensors="tf")
pixel_values = encoded_inputs.pixel_values
outputs = model(pixel_values, training=False)