Uniformize kwargs for processors - GroundingDINO

src/transformers/models/grounding_dino/modeling_grounding_dino.py

@@ -1580,7 +1580,7 @@ def _set_gradient_checkpointing(self, module, value=False):
             Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
             it.
 
-            Indices can be obtained using [`AutoTokenizer`]. See [`GroundingDinoTokenizer.__call__`] for details.
+            Indices can be obtained using [`AutoTokenizer`]. See [`BertTokenizer.__call__`] for details.
 
         token_type_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
             Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,

src/transformers/models/grounding_dino/processing_grounding_dino.py

@@ -16,20 +16,37 @@
 Processor class for Grounding DINO.
 """
 
-from typing import List, Optional, Tuple, Union
+import pathlib
+import sys
+from typing import Dict, List, Optional, Tuple, Union
 
 from ...image_processing_utils import BatchFeature
 from ...image_transforms import center_to_corners_format
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessorMixin
-from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
-from ...utils import TensorType, is_torch_available
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin
+
+
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
+
+from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
+from ...utils import ExplicitEnum, TensorType, is_torch_available
 
 
 if is_torch_available():
     import torch
 
 
+AnnotationType = Dict[str, Union[int, str, List[Dict]]]
+
+
+class AnnotationFormat(ExplicitEnum):
+    COCO_DETECTION = "coco_detection"
+    COCO_PANOPTIC = "coco_panoptic"
+
+
 def get_phrases_from_posmap(posmaps, input_ids):
     """Get token ids of phrases from posmaps and input_ids.
 
@@ -56,6 +73,31 @@ def get_phrases_from_posmap(posmaps, input_ids):
     return token_ids
 
 
+class GroundingDinoImagesKwargs(ImagesKwargs, total=False):
+    annotations: Optional[Union[AnnotationType, List[AnnotationType]]]
+    return_segmentation_masks: Optional[bool]
+    masks_path: Optional[Union[str, pathlib.Path]]
+    do_convert_annotations: Optional[bool]
+    format: Optional[Union[str, AnnotationFormat]]
+
+
+class GroundingDinoProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: GroundingDinoImagesKwargs
+    _defaults = {
+        "text_kwargs": {
+            "add_special_tokens": True,
+            "padding": False,
+            "stride": 0,
+            "return_overflowing_tokens": False,
+            "return_special_tokens_mask": False,
+            "return_offsets_mapping": False,
+            "return_token_type_ids": True,
+            "return_length": False,
+            "verbose": True,
+        }
+    }
+
+
 class GroundingDinoProcessor(ProcessorMixin):
     r"""
     Constructs a Grounding DINO processor which wraps a Deformable DETR image processor and a BERT tokenizer into a
@@ -83,21 +125,9 @@ def __call__(
         self,
         images: ImageInput = None,
         text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_token_type_ids: bool = True,
-        return_length: bool = False,
-        verbose: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        **kwargs,
+        audio=None,
+        videos=None,
+        **kwargs: Unpack[GroundingDinoProcessorKwargs],
     ) -> BatchEncoding:
         """
         This method uses [`GroundingDinoImageProcessor.__call__`] method to prepare image(s) for the model, and
@@ -106,32 +136,24 @@ def __call__(
         Please refer to the docstring of the above two methods for more information.
         """
         if images is None and text is None:
-            raise ValueError("You have to specify either images or text.")
+            raise ValueError("You must specify either text or images.")
+
+        output_kwargs = self._merge_kwargs(
+            GroundingDinoProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
 
         # Get only text
         if images is not None:
-            encoding_image_processor = self.image_processor(images, return_tensors=return_tensors)
+            encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
         else:
             encoding_image_processor = BatchFeature()
 
         if text is not None:
             text_encoding = self.tokenizer(
                 text=text,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_token_type_ids=return_token_type_ids,
-                return_length=return_length,
-                verbose=verbose,
-                return_tensors=return_tensors,
-                **kwargs,
+                **output_kwargs["text_kwargs"],
             )
         else:
             text_encoding = BatchEncoding()

src/transformers/processing_utils.py

@@ -736,12 +736,12 @@ def _merge_kwargs(
         The order of operations is as follows:
             1) kwargs passed as before have highest priority to preserve BC.
                 ```python
-                high_priority_kwargs = {"crop_size" = (224, 224), "padding" = "max_length"}
+                high_priority_kwargs = {"crop_size" = {"height": 222, "width": 222}, "padding" = "max_length"}
                 processor(..., **high_priority_kwargs)
                 ```
             2) kwargs passed as modality-specific kwargs have second priority. This is the recommended API.
                 ```python
-                processor(..., text_kwargs={"padding": "max_length"}, images_kwargs={"crop_size": (224, 224)}})
+                processor(..., text_kwargs={"padding": "max_length"}, images_kwargs={"crop_size": {"height": 222, "width": 222}}})
                 ```
             3) kwargs passed during instantiation of a modality processor have fourth priority.
                 ```python

tests/models/grounding_dino/test_processor_grounding_dino.py

@@ -26,6 +26,8 @@
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
 
+from ...test_processing_common import ProcessorTesterMixin
+
 
 if is_torch_available():
     import torch
@@ -40,7 +42,10 @@
 
 @require_torch
 @require_vision
-class GroundingDinoProcessorTest(unittest.TestCase):
+class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    from_pretrained_id = "IDEA-Research/grounding-dino-base"
+    processor_class = GroundingDinoProcessor
+
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
 
@@ -63,6 +68,13 @@ def setUp(self):
         with open(self.image_processor_file, "w", encoding="utf-8") as fp:
             json.dump(image_processor_map, fp)
 
+        image_processor = GroundingDinoImageProcessor()
+        tokenizer = BertTokenizer.from_pretrained(self.from_pretrained_id)
+
+        processor = GroundingDinoProcessor(image_processor, tokenizer)
+
+        processor.save_pretrained(self.tmpdirname)
+
         self.batch_size = 7
         self.num_queries = 5
         self.embed_dim = 5
@@ -251,3 +263,177 @@ 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)