diff --git a/tests/models/align/test_processor_align.py b/tests/models/align/test_processor_align.py index 3c904e59a8831a..cc5fdecb8df999 100644 --- a/tests/models/align/test_processor_align.py +++ b/tests/models/align/test_processor_align.py @@ -66,8 +66,6 @@ def setUp(self): image_processor_map = { "do_resize": True, "size": 20, - "do_center_crop": True, - "crop_size": 18, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], diff --git a/tests/models/grounding_dino/test_processor_grounding_dino.py b/tests/models/grounding_dino/test_processor_grounding_dino.py index c0bb186b392eb0..0c90938280916e 100644 --- a/tests/models/grounding_dino/test_processor_grounding_dino.py +++ b/tests/models/grounding_dino/test_processor_grounding_dino.py @@ -263,177 +263,3 @@ def test_model_input_names(self): inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names) - - @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", size={"height": 234, "width": 234}) - tokenizer = self.get_component("tokenizer", max_length=117) - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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) - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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", padding="max_length", max_length=112 - ) - self.assertEqual(len(inputs["input_ids"][0]), 112) - - @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) - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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", padding="max_length") - self.assertEqual(len(inputs["input_ids"][0]), 117) - - @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", size=(234, 234)) - tokenizer = self.get_component("tokenizer", max_length=117) - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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, size=[224, 224]) - self.assertEqual(len(inputs["pixel_values"][0][0]), 224) - - @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") - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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": {"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") - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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": {"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 - @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") - - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - 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", - 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") - if not tokenizer.pad_token: - tokenizer.pad_token = "[TEST_PAD]" - processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) - self.skip_processor_without_typed_kwargs(processor) - - input_str = ["lower newer", "upper older longer string"] - image_input = self.prepare_image_inputs() * 2 - inputs = processor( - text=input_str, - images=image_input, - return_tensors="pt", - crop_size={"height": 214, "width": 214}, - 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) diff --git a/tests/test_processing_common.py b/tests/test_processing_common.py index 074aa2f1d62545..a30c6363b9d7ff 100644 --- a/tests/test_processing_common.py +++ b/tests/test_processing_common.py @@ -61,6 +61,8 @@ def get_component(self, attribute, **kwargs): component_class = processor_class_from_name(component_class_name) component = component_class.from_pretrained(self.tmpdirname, **kwargs) # noqa + if attribute == "tokenizer" and not component.pad_token: + component.pad_token = "[TEST_PAD]" return component @@ -126,7 +128,7 @@ 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) + tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) @@ -141,8 +143,8 @@ def test_tokenizer_defaults_preserved_by_kwargs(self): 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) + image_processor = self.get_component("image_processor", size=(234, 234)) + tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) @@ -159,14 +161,16 @@ 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) + tokenizer = self.get_component("tokenizer", padding="longest") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) 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) + inputs = processor( + text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length" + ) self.assertEqual(len(inputs["input_ids"][0]), 112) @require_torch @@ -174,8 +178,8 @@ def test_kwargs_overrides_default_tokenizer_kwargs(self): 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) + image_processor = self.get_component("image_processor", size=(234, 234)) + tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) @@ -183,7 +187,7 @@ def test_kwargs_overrides_default_image_processor_kwargs(self): input_str = "lower newer" image_input = self.prepare_image_inputs() - inputs = processor(text=input_str, images=image_input, crop_size=[224, 224]) + inputs = processor(text=input_str, images=image_input, size=[224, 224]) self.assertEqual(len(inputs["pixel_values"][0][0]), 224) @require_torch @@ -203,7 +207,7 @@ def test_unstructured_kwargs(self): text=input_str, images=image_input, return_tensors="pt", - crop_size={"height": 214, "width": 214}, + size={"height": 214, "width": 214}, padding="max_length", max_length=76, ) @@ -228,7 +232,7 @@ def test_unstructured_kwargs_batched(self): text=input_str, images=image_input, return_tensors="pt", - crop_size={"height": 214, "width": 214}, + size={"height": 214, "width": 214}, padding="longest", max_length=76, ) @@ -254,8 +258,8 @@ def test_doubly_passed_kwargs(self): _ = processor( text=input_str, images=image_input, - images_kwargs={"crop_size": {"height": 222, "width": 222}}, - crop_size={"height": 214, "width": 214}, + images_kwargs={"size": {"height": 222, "width": 222}}, + size={"height": 214, "width": 214}, ) @require_torch @@ -275,7 +279,7 @@ def test_structured_kwargs_nested(self): # Define the kwargs for each modality all_kwargs = { "common_kwargs": {"return_tensors": "pt"}, - "images_kwargs": {"crop_size": {"height": 214, "width": 214}}, + "images_kwargs": {"size": {"height": 214, "width": 214}}, "text_kwargs": {"padding": "max_length", "max_length": 76}, } @@ -303,7 +307,7 @@ def test_structured_kwargs_nested_from_dict(self): # Define the kwargs for each modality all_kwargs = { "common_kwargs": {"return_tensors": "pt"}, - "images_kwargs": {"crop_size": {"height": 214, "width": 214}}, + "images_kwargs": {"size": {"height": 214, "width": 214}}, "text_kwargs": {"padding": "max_length", "max_length": 76}, }