From f878996feac659a8e93701b4592e2329d54613f9 Mon Sep 17 00:00:00 2001 From: RUFFY-369 Date: Wed, 9 Oct 2024 16:46:57 +0530 Subject: [PATCH] test:remove unwanted tests as they are already available with ProcessorTesterMixin --- .../imagebind/test_processor_imagebind.py | 219 ------------------ 1 file changed, 219 deletions(-) diff --git a/tests/models/imagebind/test_processor_imagebind.py b/tests/models/imagebind/test_processor_imagebind.py index 70e59c60e08624..918432eb252a92 100644 --- a/tests/models/imagebind/test_processor_imagebind.py +++ b/tests/models/imagebind/test_processor_imagebind.py @@ -250,225 +250,6 @@ def test_model_input_names(self): self.assertListEqual(list(inputs.keys()), processor.model_input_names) - @require_vision - @require_torch - def test_tokenizer_defaults_preserved_by_kwargs(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer", max_length=117) - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - input_str = "lower newer" - image_input = self.prepare_image_inputs() - - inputs = processor(text=input_str, images=image_input, return_tensors="pt") - self.assertEqual(len(inputs["input_ids"][0]), 4) - - @require_torch - @require_vision - def test_image_processor_defaults_preserved_by_image_kwargs(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor", crop_size=(234, 234)) - tokenizer = self.get_component("tokenizer", max_length=117) - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - - input_str = "lower newer" - image_input = self.prepare_image_inputs() - - inputs = processor(text=input_str, images=image_input) - self.assertEqual(len(inputs["pixel_values"][0][0]), 234) - - @require_vision - @require_torch - def test_kwargs_overrides_default_tokenizer_kwargs(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer", max_length=117) - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - input_str = "lower newer" - image_input = self.prepare_image_inputs() - - inputs = processor(text=input_str, images=image_input, return_tensors="pt", max_length=112) - self.assertEqual(len(inputs["input_ids"][0]), 4) - - @require_torch - @require_vision - def test_kwargs_overrides_default_image_processor_kwargs(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor", crop_size=(234, 234)) - tokenizer = self.get_component("tokenizer", max_length=117) - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - - input_str = "lower newer" - image_input = self.prepare_image_inputs() - - inputs = processor(text=input_str, images=image_input, crop_size=[224, 224]) - self.assertEqual(len(inputs["pixel_values"][0][0]), 224) - - @require_torch - @require_vision - def test_unstructured_kwargs(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer") - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - - input_str = "lower newer" - image_input = self.prepare_image_inputs() - inputs = processor( - text=input_str, - images=image_input, - return_tensors="pt", - crop_size={"height": 214, "width": 214}, - padding="max_length", - max_length=76, - ) - - self.assertEqual(inputs["pixel_values"].shape[2], 214) - self.assertEqual(len(inputs["input_ids"][0]), 76) - - @require_torch - @require_vision - def test_unstructured_kwargs_batched(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer") - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - - input_str = ["lower newer", "upper older longer string"] - image_input = self.prepare_image_inputs(batch_size=2) - inputs = processor( - text=input_str, - images=image_input, - return_tensors="pt", - crop_size={"height": 214, "width": 214}, - padding="longest", - max_length=76, - ) - - self.assertEqual(inputs["pixel_values"].shape[2], 214) - - self.assertEqual(len(inputs["input_ids"][0]), 6) - - @require_torch - @require_vision - def test_doubly_passed_kwargs(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer") - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - - input_str = ["lower newer"] - image_input = self.prepare_image_inputs() - with self.assertRaises(ValueError): - _ = processor( - text=input_str, - images=image_input, - images_kwargs={"crop_size": {"height": 222, "width": 222}}, - crop_size={"height": 214, "width": 214}, - ) - - @require_torch - @require_vision - def test_structured_kwargs_nested(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer") - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - - input_str = "lower newer" - image_input = self.prepare_image_inputs() - - # Define the kwargs for each modality - all_kwargs = { - "common_kwargs": {"return_tensors": "pt"}, - "images_kwargs": {"crop_size": {"height": 214, "width": 214}}, - "text_kwargs": {"padding": "max_length", "max_length": 76}, - } - - inputs = processor(text=input_str, images=image_input, **all_kwargs) - self.skip_processor_without_typed_kwargs(processor) - - self.assertEqual(inputs["pixel_values"].shape[2], 214) - - self.assertEqual(len(inputs["input_ids"][0]), 76) - - @require_torch - @require_vision - def test_structured_kwargs_nested_from_dict(self): - if "image_processor" not in self.processor_class.attributes: - self.skipTest(f"image_processor attribute not present in {self.processor_class}") - - image_processor = self.get_component("image_processor") - tokenizer = self.get_component("tokenizer") - feature_extractor = self.get_component("feature_extractor") - - processor = self.processor_class( - tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor - ) - self.skip_processor_without_typed_kwargs(processor) - input_str = "lower newer" - image_input = self.prepare_image_inputs() - - # Define the kwargs for each modality - all_kwargs = { - "common_kwargs": {"return_tensors": "pt"}, - "images_kwargs": {"crop_size": {"height": 214, "width": 214}}, - "text_kwargs": {"padding": "max_length", "max_length": 76}, - } - - inputs = processor(text=input_str, images=image_input, **all_kwargs) - self.assertEqual(inputs["pixel_values"].shape[2], 214) - - self.assertEqual(len(inputs["input_ids"][0]), 76) - @require_torch def test_doubly_passed_kwargs_audio(self): if "feature_extractor" not in self.processor_class.attributes: