Clean up semantic segmentation tests (huggingface#16801)

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
elusenji · Jun 12, 2022 · 6d9291b · 6d9291b
1 parent 597772a
commit 6d9291b
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Showing 2 changed files with 2 additions and 14 deletions.
diff --git a/tests/beit/test_modeling_beit.py b/tests/beit/test_modeling_beit.py
@@ -244,13 +244,7 @@ def test_training(self):
             # 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()

diff --git a/tests/segformer/test_modeling_segformer.py b/tests/segformer/test_modeling_segformer.py
@@ -316,13 +316,7 @@ def test_training(self):
         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()