Grounding DINO Processor standardization (#34853)

* Add input ids to model output

* Add text preprocessing for processor

* Fix snippet

* Add test for equivalence

* Add type checking guard

* Fixing typehint

* Fix test for added `input_ids` in output

* Add deprecations and "text_labels" to output

* Adjust tests

* Fix test

* Update code examples

* Minor docs and code improvement

* Remove one-liner functions and rename class to CamelCase

* Update docstring

* Fixup

docs/source/en/model_doc/grounding-dino.md

@@ -56,25 +56,26 @@ Here's how to use the model for zero-shot object detection:
 >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 >>> image = Image.open(requests.get(image_url, stream=True).raw)
 >>> # Check for cats and remote controls
->>> text = "a cat. a remote control."
+>>> text_labels = [["a cat", "a remote control"]]
 
->>> inputs = processor(images=image, text=text, return_tensors="pt").to(device)
+>>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
 >>> with torch.no_grad():
 ...     outputs = model(**inputs)
 
 >>> results = processor.post_process_grounded_object_detection(
 ...     outputs,
-...     inputs.input_ids,
-...     box_threshold=0.4,
+...     threshold=0.4,
 ...     text_threshold=0.3,
-...     target_sizes=[image.size[::-1]]
+...     target_sizes=[(image.height, image.width)]
 ... )
->>> print(results)
-[{'boxes': tensor([[344.6959,  23.1090, 637.1833, 374.2751],
-        [ 12.2666,  51.9145, 316.8582, 472.4392],
-        [ 38.5742,  70.0015, 176.7838, 118.1806]], device='cuda:0'),
-  'labels': ['a cat', 'a cat', 'a remote control'],
-  'scores': tensor([0.4785, 0.4381, 0.4776], device='cuda:0')}]
+>>> # Retrieve the first image result
+>>> result = results[0]
+>>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]):
+...     box = [round(x, 2) for x in box.tolist()]
+...     print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
+Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28]
+Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44]
+Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18]
 ```
 
 ## Grounded SAM

src/transformers/models/grounding_dino/modeling_grounding_dino.py

@@ -286,7 +286,7 @@ class GroundingDinoObjectDetectionOutput(ModelOutput):
         pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
             Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
             values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
-            possible padding). You can use [`~GroundingDinoProcessor.post_process_object_detection`] to retrieve the
+            possible padding). You can use [`~GroundingDinoProcessor.post_process_grounded_object_detection`] to retrieve the
             unnormalized bounding boxes.
         auxiliary_outputs (`List[Dict]`, *optional*):
             Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
@@ -331,6 +331,8 @@ class GroundingDinoObjectDetectionOutput(ModelOutput):
             background).
         enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
             Logits of predicted bounding boxes coordinates in the first stage.
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Encoded candidate labels sequence. Used in processor to post process object detection result.
     """
 
     loss: Optional[torch.FloatTensor] = None
@@ -351,6 +353,7 @@ class GroundingDinoObjectDetectionOutput(ModelOutput):
     encoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
     enc_outputs_class: Optional[torch.FloatTensor] = None
     enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
+    input_ids: Optional[torch.LongTensor] = None
 
 
 # Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->GroundingDino
@@ -2546,30 +2549,41 @@ class GroundingDinoForObjectDetection(GroundingDinoPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import AutoProcessor, GroundingDinoForObjectDetection
-        >>> from PIL import Image
         >>> import requests
 
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-        >>> text = "a cat."
+        >>> import torch
+        >>> from PIL import Image
+        >>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
 
-        >>> processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny")
-        >>> model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny")
+        >>> model_id = "IDEA-Research/grounding-dino-tiny"
+        >>> device = "cuda"
 
-        >>> inputs = processor(images=image, text=text, return_tensors="pt")
-        >>> outputs = model(**inputs)
+        >>> processor = AutoProcessor.from_pretrained(model_id)
+        >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
 
-        >>> # convert outputs (bounding boxes and class logits) to COCO API
-        >>> target_sizes = torch.tensor([image.size[::-1]])
-        >>> results = processor.image_processor.post_process_object_detection(
-        ...     outputs, threshold=0.35, target_sizes=target_sizes
-        ... )[0]
-        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-        ...     box = [round(i, 1) for i in box.tolist()]
-        ...     print(f"Detected {label.item()} with confidence " f"{round(score.item(), 2)} at location {box}")
-        Detected 1 with confidence 0.45 at location [344.8, 23.2, 637.4, 373.8]
-        Detected 1 with confidence 0.41 at location [11.9, 51.6, 316.6, 472.9]
+        >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(image_url, stream=True).raw)
+        >>> # Check for cats and remote controls
+        >>> text_labels = [["a cat", "a remote control"]]
+
+        >>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> results = processor.post_process_grounded_object_detection(
+        ...     outputs,
+        ...     threshold=0.4,
+        ...     text_threshold=0.3,
+        ...     target_sizes=[(image.height, image.width)]
+        ... )
+        >>> # Retrieve the first image result
+        >>> result = results[0]
+        >>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]):
+        ...     box = [round(x, 2) for x in box.tolist()]
+        ...     print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
+        Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28]
+        Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44]
+        Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18]
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -2639,13 +2653,10 @@ class GroundingDinoForObjectDetection(GroundingDinoPreTrainedModel):
             )
 
         if not return_dict:
-            if auxiliary_outputs is not None:
-                output = (logits, pred_boxes) + auxiliary_outputs + outputs
-            else:
-                output = (logits, pred_boxes) + outputs
-            tuple_outputs = ((loss, loss_dict) + output) if loss is not None else output
-
-            return tuple_outputs
+            auxiliary_outputs = auxiliary_outputs if auxiliary_outputs is not None else []
+            output = [loss, loss_dict, logits, pred_boxes, *auxiliary_outputs, *outputs, input_ids]
+            output = tuple(out for out in output if out is not None)
+            return output
 
         dict_outputs = GroundingDinoObjectDetectionOutput(
             loss=loss,
@@ -2666,6 +2677,7 @@ class GroundingDinoForObjectDetection(GroundingDinoPreTrainedModel):
             init_reference_points=outputs.init_reference_points,
             enc_outputs_class=outputs.enc_outputs_class,
             enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
+            input_ids=input_ids,
         )
 
         return dict_outputs

src/transformers/models/grounding_dino/processing_grounding_dino.py

@@ -17,7 +17,8 @@ Processor class for Grounding DINO.
 """
 
 import pathlib
-from typing import Dict, List, Optional, Tuple, Union
+import warnings
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 from ...image_processing_utils import BatchFeature
 from ...image_transforms import center_to_corners_format
@@ -25,11 +26,15 @@ from ...image_utils import AnnotationFormat, ImageInput
 from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
 from ...utils import TensorType, is_torch_available
+from ...utils.deprecation import deprecate_kwarg
 
 
 if is_torch_available():
     import torch
 
+if TYPE_CHECKING:
+    from .modeling_grounding_dino import GroundingDinoObjectDetectionOutput
+
 
 AnnotationType = Dict[str, Union[int, str, List[Dict]]]
 
@@ -60,6 +65,42 @@ def get_phrases_from_posmap(posmaps, input_ids):
     return token_ids
 
 
+def _is_list_of_candidate_labels(text) -> bool:
+    """Check that text is list/tuple of strings and each string is a candidate label and not merged candidate labels text.
+    Merged candidate labels text is a string with candidate labels separated by a dot.
+    """
+    if isinstance(text, (list, tuple)):
+        return all(isinstance(t, str) and "." not in t for t in text)
+    return False
+
+
+def _merge_candidate_labels_text(text: List[str]) -> str:
+    """
+    Merge candidate labels text into a single string. Ensure all labels are lowercase.
+    For example, ["A cat", "a dog"] -> "a cat. a dog."
+    """
+    labels = [t.strip().lower() for t in text]  # ensure lowercase
+    merged_labels_str = ". ".join(labels) + "."  # join with dot and add a dot at the end
+    return merged_labels_str
+
+
+class DictWithDeprecationWarning(dict):
+    message = (
+        "The key `labels` is will return integer ids in `GroundingDinoProcessor.post_process_grounded_object_detection` "
+        "output since v4.51.0. Use `text_labels` instead to retrieve string object names."
+    )
+
+    def __getitem__(self, key):
+        if key == "labels":
+            warnings.warn(self.message, FutureWarning)
+        return super().__getitem__(key)
+
+    def get(self, key, *args, **kwargs):
+        if key == "labels":
+            warnings.warn(self.message, FutureWarning)
+        return super().get(key, *args, **kwargs)
+
+
 class GroundingDinoImagesKwargs(ImagesKwargs, total=False):
     annotations: Optional[Union[AnnotationType, List[AnnotationType]]]
     return_segmentation_masks: Optional[bool]
@@ -120,7 +161,15 @@ class GroundingDinoProcessor(ProcessorMixin):
         This method uses [`GroundingDinoImageProcessor.__call__`] method to prepare image(s) for the model, and
         [`BertTokenizerFast.__call__`] to prepare text for the model.
 
-        Please refer to the docstring of the above two methods for more information.
+        Args:
+            images (`ImageInput`, `List[ImageInput]`, *optional*):
+                The image or batch of images to be processed. The image might be either PIL image, numpy array or a torch tensor.
+            text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`, *optional*):
+                Candidate labels to be detected on the image. The text might be one of the following:
+                - A list of candidate labels (strings) to be detected on the image (e.g. ["a cat", "a dog"]).
+                - A batch of candidate labels to be detected on the batch of images (e.g. [["a cat", "a dog"], ["a car", "a person"]]).
+                - A merged candidate labels string to be detected on the image, separated by "." (e.g. "a cat. a dog.").
+                - A batch of merged candidate labels text to be detected on the batch of images (e.g. ["a cat. a dog.", "a car. a person."]).
         """
         if images is None and text is None:
             raise ValueError("You must specify either text or images.")
@@ -138,6 +187,7 @@ class GroundingDinoProcessor(ProcessorMixin):
             encoding_image_processor = BatchFeature()
 
         if text is not None:
+            text = self._preprocess_input_text(text)
             text_encoding = self.tokenizer(
                 text=text,
                 **output_kwargs["text_kwargs"],
@@ -149,6 +199,23 @@ class GroundingDinoProcessor(ProcessorMixin):
 
         return text_encoding
 
+    def _preprocess_input_text(self, text):
+        """
+        Preprocess input text to ensure that labels are in the correct format for the model.
+        If the text is a list of candidate labels, merge the candidate labels into a single string,
+        for example, ["a cat", "a dog"] -> "a cat. a dog.". In case candidate labels are already in a form of
+        "a cat. a dog.", the text is returned as is.
+        """
+
+        if _is_list_of_candidate_labels(text):
+            text = _merge_candidate_labels_text(text)
+
+        # for batched input
+        elif isinstance(text, (list, tuple)) and all(_is_list_of_candidate_labels(t) for t in text):
+            text = [_merge_candidate_labels_text(sample) for sample in text]
+
+        return text
+
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
     def batch_decode(self, *args, **kwargs):
         """
@@ -172,13 +239,15 @@ class GroundingDinoProcessor(ProcessorMixin):
         image_processor_input_names = self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
 
+    @deprecate_kwarg("box_threshold", new_name="threshold", version="4.51.0")
     def post_process_grounded_object_detection(
         self,
-        outputs,
-        input_ids,
-        box_threshold: float = 0.25,
+        outputs: "GroundingDinoObjectDetectionOutput",
+        input_ids: Optional[TensorType] = None,
+        threshold: float = 0.25,
         text_threshold: float = 0.25,
-        target_sizes: Union[TensorType, List[Tuple]] = None,
+        target_sizes: Optional[Union[TensorType, List[Tuple]]] = None,
+        text_labels: Optional[List[List[str]]] = None,
     ):
         """
         Converts the raw output of [`GroundingDinoForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
@@ -187,32 +256,38 @@ class GroundingDinoProcessor(ProcessorMixin):
         Args:
             outputs ([`GroundingDinoObjectDetectionOutput`]):
                 Raw outputs of the model.
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                The token ids of the input text.
-            box_threshold (`float`, *optional*, defaults to 0.25):
-                Score threshold to keep object detection predictions.
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                The token ids of the input text. If not provided will be taken from the model output.
+            threshold (`float`, *optional*, defaults to 0.25):
+                Threshold to keep object detection predictions based on confidence score.
             text_threshold (`float`, *optional*, defaults to 0.25):
                 Score threshold to keep text detection predictions.
             target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
                 Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
                 `(height, width)` of each image in the batch. If unset, predictions will not be resized.
+            text_labels (`List[List[str]]`, *optional*):
+                List of candidate labels to be detected on each image. At the moment it's *NOT used*, but required
+                to be in signature for the zero-shot object detection pipeline. Text labels are instead extracted
+                from the `input_ids` tensor provided in `outputs`.
+
         Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
-            in the batch as predicted by the model.
+            `List[Dict]`: A list of dictionaries, each dictionary containing the
+                - **scores**: tensor of confidence scores for detected objects
+                - **boxes**: tensor of bounding boxes in [x0, y0, x1, y1] format
+                - **labels**: list of text labels for each detected object (will be replaced with integer ids in v4.51.0)
+                - **text_labels**: list of text labels for detected objects
         """
-        logits, boxes = outputs.logits, outputs.pred_boxes
+        batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes
+        input_ids = input_ids if input_ids is not None else outputs.input_ids
 
-        if target_sizes is not None:
-            if len(logits) != len(target_sizes):
-                raise ValueError(
-                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
-                )
+        if target_sizes is not None and len(target_sizes) != len(batch_logits):
+            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
 
-        probs = torch.sigmoid(logits)  # (batch_size, num_queries, 256)
-        scores = torch.max(probs, dim=-1)[0]  # (batch_size, num_queries)
+        batch_probs = torch.sigmoid(batch_logits)  # (batch_size, num_queries, 256)
+        batch_scores = torch.max(batch_probs, dim=-1)[0]  # (batch_size, num_queries)
 
         # Convert to [x0, y0, x1, y1] format
-        boxes = center_to_corners_format(boxes)
+        batch_boxes = center_to_corners_format(batch_boxes)
 
         # Convert from relative [0, 1] to absolute [0, height] coordinates
         if target_sizes is not None:
@@ -222,17 +297,30 @@ class GroundingDinoProcessor(ProcessorMixin):
             else:
                 img_h, img_w = target_sizes.unbind(1)
 
-            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
-            boxes = boxes * scale_fct[:, None, :]
+            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(batch_boxes.device)
+            batch_boxes = batch_boxes * scale_fct[:, None, :]
 
         results = []
-        for idx, (s, b, p) in enumerate(zip(scores, boxes, probs)):
-            score = s[s > box_threshold]
-            box = b[s > box_threshold]
-            prob = p[s > box_threshold]
+        for idx, (scores, boxes, probs) in enumerate(zip(batch_scores, batch_boxes, batch_probs)):
+            keep = scores > threshold
+            scores = scores[keep]
+            boxes = boxes[keep]
+
+            # extract text labels
+            prob = probs[keep]
             label_ids = get_phrases_from_posmap(prob > text_threshold, input_ids[idx])
-            label = self.batch_decode(label_ids)
-            results.append({"scores": score, "labels": label, "boxes": box})
+            objects_text_labels = self.batch_decode(label_ids)
+
+            result = DictWithDeprecationWarning(
+                {
+                    "scores": scores,
+                    "boxes": boxes,
+                    "text_labels": objects_text_labels,
+                    # TODO: @pavel, set labels to None since v4.51.0 or find a way to extract ids
+                    "labels": objects_text_labels,
+                }
+            )
+            results.append(result)
 
         return results
 

tests/models/grounding_dino/test_modeling_grounding_dino.py

@@ -322,9 +322,9 @@ class GroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Tes
             # loss is at first position
             if "labels" in inputs_dict:
                 correct_outlen += 1  # loss is added to beginning
-            # Object Detection model returns pred_logits and pred_boxes
+            # Object Detection model returns pred_logits and pred_boxes and input_ids
             if model_class.__name__ == "GroundingDinoForObjectDetection":
-                correct_outlen += 2
+                correct_outlen += 3
 
             self.assertEqual(out_len, correct_outlen)
 
@@ -653,7 +653,7 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
 
         # verify postprocessing
         results = processor.image_processor.post_process_object_detection(
-            outputs, threshold=0.35, target_sizes=[image.size[::-1]]
+            outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
         )[0]
         expected_scores = torch.tensor([0.4526, 0.4082]).to(torch_device)
         expected_slice_boxes = torch.tensor([344.8143, 23.1796, 637.4004, 373.8295]).to(torch_device)
@@ -667,14 +667,14 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
         results = processor.post_process_grounded_object_detection(
             outputs=outputs,
             input_ids=encoding.input_ids,
-            box_threshold=0.35,
+            threshold=0.35,
             text_threshold=0.3,
-            target_sizes=[image.size[::-1]],
+            target_sizes=[(image.height, image.width)],
         )[0]
 
         self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-3))
         self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes, atol=1e-2))
-        self.assertListEqual(results["labels"], expected_labels)
+        self.assertListEqual(results["text_labels"], expected_labels)
 
     @require_torch_accelerator
     def test_inference_object_detection_head_equivalence_cpu_gpu(self):
@@ -706,11 +706,11 @@ class GroundingDinoModelIntegrationTests(unittest.TestCase):
 
         # assert postprocessing
         results_cpu = processor.image_processor.post_process_object_detection(
-            cpu_outputs, threshold=0.35, target_sizes=[image.size[::-1]]
+            cpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
         )[0]
 
         result_gpu = processor.image_processor.post_process_object_detection(
-            gpu_outputs, threshold=0.35, target_sizes=[image.size[::-1]]
+            gpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
         )[0]
 
         self.assertTrue(torch.allclose(results_cpu["scores"], result_gpu["scores"].cpu(), atol=1e-3))

tests/models/grounding_dino/test_processor_grounding_dino.py

@@ -17,6 +17,7 @@ import os
 import shutil
 import tempfile
 import unittest
+from typing import Optional
 
 import pytest
 
@@ -77,6 +78,20 @@ class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         self.embed_dim = 5
         self.seq_length = 5
 
+    def prepare_text_inputs(self, batch_size: Optional[int] = None):
+        labels = ["a cat", "remote control"]
+        labels_longer = ["a person", "a car", "a dog", "a cat"]
+
+        if batch_size is None:
+            return labels
+
+        if batch_size < 1:
+            raise ValueError("batch_size must be greater than 0")
+
+        if batch_size == 1:
+            return [labels]
+        return [labels, labels_longer] + [labels] * (batch_size - 2)
+
     # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.get_tokenizer with CLIP->Bert
     def get_tokenizer(self, **kwargs):
         return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
@@ -98,6 +113,7 @@ class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         return GroundingDinoObjectDetectionOutput(
             pred_boxes=torch.rand(self.batch_size, self.num_queries, 4),
             logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim),
+            input_ids=self.get_fake_grounding_dino_input_ids(),
         )
 
     def get_fake_grounding_dino_input_ids(self):
@@ -111,14 +127,11 @@ class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor)
 
         grounding_dino_output = self.get_fake_grounding_dino_output()
-        grounding_dino_input_ids = self.get_fake_grounding_dino_input_ids()
 
-        post_processed = processor.post_process_grounded_object_detection(
-            grounding_dino_output, grounding_dino_input_ids
-        )
+        post_processed = processor.post_process_grounded_object_detection(grounding_dino_output)
 
         self.assertEqual(len(post_processed), self.batch_size)
-        self.assertEqual(list(post_processed[0].keys()), ["scores", "labels", "boxes"])
+        self.assertEqual(list(post_processed[0].keys()), ["scores", "boxes", "text_labels", "labels"])
         self.assertEqual(post_processed[0]["boxes"].shape, (self.num_queries, 4))
         self.assertEqual(post_processed[0]["scores"].shape, (self.num_queries,))
 
@@ -248,3 +261,26 @@ class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         inputs = processor(text=input_str, images=image_input)
 
         self.assertListEqual(list(inputs.keys()), processor.model_input_names)
+
+    def test_text_preprocessing_equivalence(self):
+        processor = GroundingDinoProcessor.from_pretrained(self.tmpdirname)
+
+        # check for single input
+        formatted_labels = "a cat. a remote control."
+        labels = ["a cat", "a remote control"]
+        inputs1 = processor(text=formatted_labels, return_tensors="pt")
+        inputs2 = processor(text=labels, return_tensors="pt")
+        self.assertTrue(
+            torch.allclose(inputs1["input_ids"], inputs2["input_ids"]),
+            f"Input ids are not equal for single input: {inputs1['input_ids']} != {inputs2['input_ids']}",
+        )
+
+        # check for batched input
+        formatted_labels = ["a cat. a remote control.", "a car. a person."]
+        labels = [["a cat", "a remote control"], ["a car", "a person"]]
+        inputs1 = processor(text=formatted_labels, return_tensors="pt", padding=True)
+        inputs2 = processor(text=labels, return_tensors="pt", padding=True)
+        self.assertTrue(
+            torch.allclose(inputs1["input_ids"], inputs2["input_ids"]),
+            f"Input ids are not equal for batched input: {inputs1['input_ids']} != {inputs2['input_ids']}",
+        )