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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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14
tests/models/videomae/test_modeling_videomae.py
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@@ -46,7 +46,7 @@ if is_torch_available():
if is_vision_available():
from transformers import VideoMAEFeatureExtractor
from transformers import VideoMAEImageProcessor
class VideoMAEModelTester:
@@ -359,10 +359,10 @@ def prepare_video():
@require_vision
class VideoMAEModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
def default_image_processor(self):
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEFeatureExtractor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
if is_vision_available()
else None
)
@@ -373,9 +373,9 @@ class VideoMAEModelIntegrationTest(unittest.TestCase):
torch_device
)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
video = prepare_video()
inputs = feature_extractor(video, return_tensors="pt").to(torch_device)
inputs = image_processor(video, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
@@ -393,9 +393,9 @@ class VideoMAEModelIntegrationTest(unittest.TestCase):
def test_inference_for_pretraining(self):
model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(torch_device)
feature_extractor = self.default_feature_extractor
image_processor = self.default_image_processor
video = prepare_video()
inputs = feature_extractor(video, return_tensors="pt").to(torch_device)
inputs = image_processor(video, return_tensors="pt").to(torch_device)
# add boolean mask, indicating which patches to mask
local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt")