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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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30
tests/models/detr/test_modeling_detr.py
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@@ -38,7 +38,7 @@ if is_timm_available():
if is_vision_available():
from PIL import Image
from transformers import DetrFeatureExtractor
from transformers import DetrImageProcessor
class DetrModelTester:
@@ -512,15 +512,15 @@ def prepare_img():
@slow
class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") if is_vision_available() else None
def default_image_processor(self):
return DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") if is_vision_available() else None
def test_inference_no_head(self):
model = DetrModel.from_pretrained("facebook/detr-resnet-50").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**encoding)
@@ -535,9 +535,9 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
def test_inference_object_detection_head(self):
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
pixel_values = encoding["pixel_values"].to(torch_device)
pixel_mask = encoding["pixel_mask"].to(torch_device)
@@ -560,7 +560,7 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
# verify postprocessing
results = feature_extractor.post_process_object_detection(
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.9982, 0.9960, 0.9955, 0.9988, 0.9987]).to(torch_device)
@@ -575,9 +575,9 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
def test_inference_panoptic_segmentation_head(self):
model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
pixel_values = encoding["pixel_values"].to(torch_device)
pixel_mask = encoding["pixel_mask"].to(torch_device)
@@ -607,7 +607,7 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
self.assertTrue(torch.allclose(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, atol=1e-3))
# verify postprocessing
results = feature_extractor.post_process_panoptic_segmentation(
results = image_processor.post_process_panoptic_segmentation(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
@@ -633,9 +633,9 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
@slow
class DetrModelIntegrationTests(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
return (
DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
if is_vision_available()
else None
)
@@ -643,9 +643,9 @@ class DetrModelIntegrationTests(unittest.TestCase):
def test_inference_no_head(self):
model = DetrModel.from_pretrained("facebook/detr-resnet-50", revision="no_timm").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
image = prepare_img()
encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
encoding = image_processor(images=image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**encoding)