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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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6
tests/models/maskformer/test_image_processing_maskformer.py
View File

@@ -574,9 +574,9 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
self.assertEqual(segmentation[0].shape, target_sizes[0])
def test_post_process_instance_segmentation(self):
feature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
image_processor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_maskformer_outputs()
segmentation = feature_extractor.post_process_instance_segmentation(outputs, threshold=0)
segmentation = image_processor.post_process_instance_segmentation(outputs, threshold=0)
self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
for el in segmentation:
@@ -587,7 +587,7 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
)
segmentation = feature_extractor.post_process_instance_segmentation(
segmentation = image_processor.post_process_instance_segmentation(
outputs, threshold=0, return_binary_maps=True
)

22
tests/models/maskformer/test_modeling_maskformer.py
View File

@@ -35,7 +35,7 @@ if is_torch_available():
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerFeatureExtractor
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
@@ -326,18 +326,18 @@ def prepare_img():
@slow
class MaskFormerModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-small-coco")
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco")
if is_vision_available()
else None
)
def test_inference_no_head(self):
model = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(image, return_tensors="pt").to(torch_device)
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
@@ -380,9 +380,9 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
.to(torch_device)
.eval()
)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(image, return_tensors="pt").to(torch_device)
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
@@ -424,9 +424,9 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
.to(torch_device)
.eval()
)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
inputs = feature_extractor(image, return_tensors="pt").to(torch_device)
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
@@ -460,9 +460,9 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
.to(torch_device)
.eval()
)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
inputs = feature_extractor(
inputs = image_processor(
[np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))],
segmentation_maps=[np.zeros((384, 384)).astype(np.float32), np.zeros((384, 384)).astype(np.float32)],
return_tensors="pt",