riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 23 Packages Projects Releases Wiki Activity

Clean up semantic segmentation tests (#16801)

Browse Source 
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge 2022-04-19 09:02:19 +02:00 committed by GitHub
parent 989a15d173
commit 494c2a8c4d
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 2 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
tests/beit/test_modeling_beit.py
View File

@ -244,13 +244,7 @@ class BeitModelTest(ModelTesterMixin, unittest.TestCase):
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(MODEL_MAPPING), BeitForMaskedImageModeling]:
continue
# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
# this can then be incorporated into _prepare_for_class in test_modeling_common.py
elif model_class.__name__ == "BeitForSemanticSegmentation":
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device
).long()
model = model_class(config)
model.to(torch_device)
model.train()

8
tests/segformer/test_modeling_segformer.py
View File

@ -316,13 +316,7 @@ class SegformerModelTest(ModelTesterMixin, unittest.TestCase):
for model_class in self.all_model_classes:
if model_class in get_values(MODEL_MAPPING):
continue
# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
# this can then be incorporated into _prepare_for_class in test_modeling_common.py
if model_class.__name__ == "SegformerForSemanticSegmentation":
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device
).long()
model = model_class(config)
model.to(torch_device)
model.train()