init

transformers/tests/models/mm_grounding_dino/test_modeling_mm_grounding_dino.py

@@ -0,0 +1,871 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch MM Grounding DINO model."""
+
+import collections
+import inspect
+import math
+import re
+import unittest
+from functools import cached_property
+
+from datasets import load_dataset
+
+from transformers import (
+    MMGroundingDinoConfig,
+    SwinConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    is_flaky,
+    require_timm,
+    require_torch,
+    require_torch_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import MMGroundingDinoConfig, MMGroundingDinoForObjectDetection, MMGroundingDinoModel
+    from transformers.pytorch_utils import id_tensor_storage
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoProcessor
+
+
+# Copied from tests.models.grounding_dino.test_modeling_grounding_dino.generate_fake_bounding_boxes
+def generate_fake_bounding_boxes(n_boxes):
+    """Generate bounding boxes in the format (center_x, center_y, width, height)"""
+    # Validate the input
+    if not isinstance(n_boxes, int):
+        raise TypeError("n_boxes must be an integer")
+    if n_boxes <= 0:
+        raise ValueError("n_boxes must be a positive integer")
+
+    # Generate random bounding boxes in the format (center_x, center_y, width, height)
+    bounding_boxes = torch.rand((n_boxes, 4))
+
+    # Extract the components
+    center_x = bounding_boxes[:, 0]
+    center_y = bounding_boxes[:, 1]
+    width = bounding_boxes[:, 2]
+    height = bounding_boxes[:, 3]
+
+    # Ensure width and height do not exceed bounds
+    width = torch.min(width, torch.tensor(1.0))
+    height = torch.min(height, torch.tensor(1.0))
+
+    # Ensure the bounding box stays within the normalized space
+    center_x = torch.where(center_x - width / 2 < 0, width / 2, center_x)
+    center_x = torch.where(center_x + width / 2 > 1, 1 - width / 2, center_x)
+    center_y = torch.where(center_y - height / 2 < 0, height / 2, center_y)
+    center_y = torch.where(center_y + height / 2 > 1, 1 - height / 2, center_y)
+
+    # Combine back into bounding boxes
+    bounding_boxes = torch.stack([center_x, center_y, width, height], dim=1)
+
+    return bounding_boxes
+
+
+# Copied from tests.models.grounding_dino.test_modeling_grounding_dino.GroundingDinoModelTester with GroundingDino->MMGroundingDino
+class MMGroundingDinoModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=4,
+        is_training=True,
+        use_labels=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=4,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        num_queries=2,
+        num_channels=3,
+        image_size=98,
+        n_targets=8,
+        num_labels=2,
+        num_feature_levels=4,
+        encoder_n_points=2,
+        decoder_n_points=6,
+        max_text_len=7,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.num_queries = num_queries
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.n_targets = n_targets
+        self.num_labels = num_labels
+        self.num_feature_levels = num_feature_levels
+        self.encoder_n_points = encoder_n_points
+        self.decoder_n_points = decoder_n_points
+        self.max_text_len = max_text_len
+
+        # we also set the expected seq length for both encoder and decoder
+        self.encoder_seq_length_vision = (
+            math.ceil(self.image_size / 8) ** 2
+            + math.ceil(self.image_size / 16) ** 2
+            + math.ceil(self.image_size / 32) ** 2
+            + math.ceil(self.image_size / 64) ** 2
+        )
+
+        self.encoder_seq_length_text = self.max_text_len
+
+        self.decoder_seq_length = self.num_queries
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
+
+        # When using `MMGroundingDino` the text input template is '{label1}. {label2}. {label3. ... {labelN}.'
+        # Therefore to avoid errors when running tests with `labels` `input_ids` have to follow this structure.
+        # Otherwise when running `build_label_maps` it will throw an error when trying to split the input_ids into segments.
+        input_ids = torch.tensor([101, 3869, 1012, 11420, 3869, 1012, 102], device=torch_device)
+        input_ids = input_ids.unsqueeze(0).expand(self.batch_size, -1)
+
+        labels = None
+        if self.use_labels:
+            # labels is a list of Dict (each Dict being the labels for a given example in the batch)
+            labels = []
+            for i in range(self.batch_size):
+                target = {}
+                target["class_labels"] = torch.randint(
+                    high=self.num_labels, size=(self.n_targets,), device=torch_device
+                )
+                target["boxes"] = generate_fake_bounding_boxes(self.n_targets).to(torch_device)
+                target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device)
+                labels.append(target)
+
+        config = self.get_config()
+        return config, pixel_values, pixel_mask, input_ids, labels
+
+    def get_config(self):
+        swin_config = SwinConfig(
+            window_size=7,
+            embed_dim=8,
+            depths=[1, 1, 1, 1],
+            num_heads=[1, 1, 1, 1],
+            image_size=self.image_size,
+            out_features=["stage2", "stage3", "stage4"],
+            out_indices=[2, 3, 4],
+        )
+        text_backbone = {
+            "hidden_size": 8,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 2,
+            "intermediate_size": 8,
+            "max_position_embeddings": 8,
+            "model_type": "bert",
+        }
+        return MMGroundingDinoConfig(
+            d_model=self.hidden_size,
+            encoder_layers=self.num_hidden_layers,
+            decoder_layers=self.num_hidden_layers,
+            encoder_attention_heads=self.num_attention_heads,
+            decoder_attention_heads=self.num_attention_heads,
+            encoder_ffn_dim=self.intermediate_size,
+            decoder_ffn_dim=self.intermediate_size,
+            dropout=self.hidden_dropout_prob,
+            attention_dropout=self.attention_probs_dropout_prob,
+            num_queries=self.num_queries,
+            num_labels=self.num_labels,
+            num_feature_levels=self.num_feature_levels,
+            encoder_n_points=self.encoder_n_points,
+            decoder_n_points=self.decoder_n_points,
+            use_timm_backbone=False,
+            backbone_config=swin_config,
+            max_text_len=self.max_text_len,
+            text_config=text_backbone,
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, pixel_mask, input_ids, labels = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask, "input_ids": input_ids}
+        return config, inputs_dict
+
+    def create_and_check_model(self, config, pixel_values, pixel_mask, input_ids, labels):
+        model = MMGroundingDinoModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size))
+
+    def create_and_check_object_detection_head_model(self, config, pixel_values, pixel_mask, input_ids, labels):
+        model = MMGroundingDinoForObjectDetection(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids)
+
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len))
+        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids, labels=labels)
+
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len))
+        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
+
+
+@require_torch
+# Copied from tests.models.grounding_dino.test_modeling_grounding_dino.GroundingDinoModelTest with Grounding->MMGrounding
+class MMGroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (MMGroundingDinoModel, MMGroundingDinoForObjectDetection) if is_torch_available() else ()
+    is_encoder_decoder = True
+    test_torchscript = False
+    test_pruning = False
+    test_head_masking = False
+    test_missing_keys = False
+    pipeline_model_mapping = (
+        {
+            "image-feature-extraction": MMGroundingDinoModel,
+            "zero-shot-object-detection": MMGroundingDinoForObjectDetection,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    # special case for head models
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class.__name__ == "MMGroundingDinoForObjectDetection":
+                labels = []
+                for i in range(self.model_tester.batch_size):
+                    target = {}
+                    target["class_labels"] = torch.ones(
+                        size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
+                    )
+                    target["boxes"] = torch.ones(
+                        self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
+                    )
+                    target["masks"] = torch.ones(
+                        self.model_tester.n_targets,
+                        self.model_tester.image_size,
+                        self.model_tester.image_size,
+                        device=torch_device,
+                        dtype=torch.float,
+                    )
+                    labels.append(target)
+                inputs_dict["labels"] = labels
+
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = MMGroundingDinoModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=MMGroundingDinoConfig,
+            has_text_modality=False,
+            common_properties=["d_model", "encoder_attention_heads", "decoder_attention_heads"],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_object_detection_head_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_object_detection_head_model(*config_and_inputs)
+
+    @unittest.skip(reason="MMGrounding DINO does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="MMGrounding DINO does not have a get_input_embeddings method")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="MMGrounding DINO does not use token embeddings")
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.encoder_attentions[-1]
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.encoder_attentions[-1]
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [
+                    self.model_tester.num_attention_heads,
+                    self.model_tester.num_feature_levels,
+                    self.model_tester.encoder_n_points,
+                ],
+            )
+            out_len = len(outputs)
+
+            correct_outlen = 12
+
+            # 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 and input_ids
+            if model_class.__name__ == "MMGroundingDinoForObjectDetection":
+                correct_outlen += 3
+
+            self.assertEqual(out_len, correct_outlen)
+
+            # decoder attentions
+            decoder_attentions = outputs.decoder_attentions[0]
+            self.assertIsInstance(decoder_attentions, (list, tuple))
+            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
+            self.assertListEqual(
+                list(decoder_attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries],
+            )
+
+            # cross attentions
+            cross_attentions = outputs.decoder_attentions[-1]
+            self.assertIsInstance(cross_attentions, (list, tuple))
+            self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
+            self.assertListEqual(
+                list(cross_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.num_attention_heads,
+                    self.model_tester.num_feature_levels,
+                    self.model_tester.decoder_n_points,
+                ],
+            )
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertEqual(out_len + 3, len(outputs))
+
+            self_attentions = outputs.encoder_attentions[-1]
+
+            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.num_attention_heads,
+                    self.model_tester.num_feature_levels,
+                    self.model_tester.encoder_n_points,
+                ],
+            )
+
+    # overwrite since hidden_states are called encoder_text_hidden_states
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.encoder_vision_hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_len = self.model_tester.encoder_seq_length_vision
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_len, self.model_tester.hidden_size],
+            )
+
+            hidden_states = outputs.encoder_text_hidden_states
+
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_len = self.model_tester.encoder_seq_length_text
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_len, self.model_tester.hidden_size],
+            )
+
+            hidden_states = outputs.decoder_hidden_states
+
+            self.assertIsInstance(hidden_states, (list, tuple))
+            self.assertEqual(len(hidden_states), expected_num_layers)
+            seq_len = getattr(self.model_tester, "seq_length", None)
+            decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [decoder_seq_length, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
+    def test_retain_grad_hidden_states_attentions(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        # no need to test all models as different heads yield the same functionality
+        model_class = self.all_model_classes[0]
+        model = model_class(config)
+        model.to(torch_device)
+
+        inputs = self._prepare_for_class(inputs_dict, model_class)
+
+        outputs = model(**inputs)
+
+        output = outputs[0]
+
+        encoder_hidden_states = outputs.encoder_vision_hidden_states[0]
+        encoder_attentions = outputs.encoder_attentions[0][0]
+        encoder_hidden_states.retain_grad()
+        encoder_attentions.retain_grad()
+
+        cross_attentions = outputs.decoder_attentions[-1][0]
+        cross_attentions.retain_grad()
+
+        output.flatten()[0].backward(retain_graph=True)
+
+        self.assertIsNotNone(encoder_hidden_states.grad)
+        self.assertIsNotNone(encoder_attentions.grad)
+        self.assertIsNotNone(cross_attentions.grad)
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values", "input_ids"]
+            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
+
+    def test_different_timm_backbone(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # let's pick a random timm backbone
+        config.backbone = "tf_mobilenetv3_small_075"
+        config.use_timm_backbone = True
+        config.backbone_config = None
+        config.backbone_kwargs = {"in_chans": 3, "out_indices": (2, 3, 4)}
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            if model_class.__name__ == "MMGroundingDinoForObjectDetection":
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.num_queries,
+                    config.max_text_len,
+                )
+                self.assertEqual(outputs.logits.shape, expected_shape)
+
+            self.assertTrue(outputs)
+
+    @require_timm
+    def test_hf_backbone(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Load a pretrained HF checkpoint as backbone
+        config.backbone = "microsoft/resnet-18"
+        config.backbone_config = None
+        config.use_timm_backbone = False
+        config.use_pretrained_backbone = True
+        config.backbone_kwargs = {"out_indices": [2, 3, 4]}
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            if model_class.__name__ == "MMGroundingDinoForObjectDetection":
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.num_queries,
+                    config.max_text_len,
+                )
+                self.assertEqual(outputs.logits.shape, expected_shape)
+
+            self.assertTrue(outputs)
+
+    # Ignore copy
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    if (
+                        "level_embed" in name
+                        or "sampling_offsets.bias" in name
+                        or "text_param" in name
+                        or "vision_param" in name
+                        or "value_proj" in name
+                        or "output_proj" in name
+                        or "reference_points" in name
+                        or "vision_proj" in name
+                        or "text_proj" in name
+                        or ("class_embed" in name and "bias" in name)
+                    ):
+                        continue
+                    self.assertIn(
+                        ((param.data.mean() * 1e9).round() / 1e9).item(),
+                        [0.0, 1.0],
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+    # Copied from tests.models.deformable_detr.test_modeling_deformable_detr.DeformableDetrModelTest.test_two_stage_training with DeformableDetr->MMGroundingDino
+    def test_two_stage_training(self):
+        model_class = MMGroundingDinoForObjectDetection
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        config.two_stage = True
+        config.auxiliary_loss = True
+        config.with_box_refine = True
+
+        model = model_class(config)
+        model.to(torch_device)
+        model.train()
+        inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+        loss = model(**inputs).loss
+        loss.backward()
+
+    def test_tied_weights_keys(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        config.tie_word_embeddings = True
+        for model_class in self.all_model_classes:
+            model_tied = model_class(config)
+
+            ptrs = collections.defaultdict(list)
+            for name, tensor in model_tied.state_dict().items():
+                ptrs[id_tensor_storage(tensor)].append(name)
+
+            # These are all the pointers of shared tensors.
+            tied_params = [names for _, names in ptrs.items() if len(names) > 1]
+
+            tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
+            # Detect we get a hit for each key
+            for key in tied_weight_keys:
+                if not any(re.search(key, p) for group in tied_params for p in group):
+                    raise ValueError(f"{key} is not a tied weight key for {model_class}.")
+
+            # Removed tied weights found from tied params -> there should only be one left after
+            for key in tied_weight_keys:
+                for i in range(len(tied_params)):
+                    tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
+
+            # MMGroundingDino when sharing weights also uses the shared ones in MMGroundingDinoDecoder
+            # Therefore, differently from DeformableDetr, we expect the group lens to be 2
+            # one for self.bbox_embed in MMGroundingDinoForObjectDetection and another one
+            # in the decoder
+            tied_params = [group for group in tied_params if len(group) > 2]
+            self.assertListEqual(
+                tied_params,
+                [],
+                f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
+            )
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+def prepare_text():
+    text = "a cat."
+    return text
+
+
+@require_timm
+@require_vision
+@slow
+class MMGroundingDinoModelIntegrationTests(unittest.TestCase):
+    @cached_property
+    def default_processor(self):
+        return (
+            AutoProcessor.from_pretrained("openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det")
+            if is_vision_available()
+            else None
+        )
+
+    def test_inference_object_detection_head(self):
+        model = MMGroundingDinoForObjectDetection.from_pretrained(
+            "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
+        ).to(torch_device)
+
+        processor = self.default_processor
+        image = prepare_img()
+        text = prepare_text()
+        encoding = processor(images=image, text=text, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**encoding)
+
+        expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.d_model))
+        self.assertEqual(outputs.logits.shape, expected_shape_logits)
+
+        expected_boxes = torch.tensor(
+            [[0.7666, 0.4142, 0.4590], [0.2557, 0.5480, 0.4812], [0.5049, 0.5133, 0.9767]]
+        ).to(torch_device)
+        expected_logits = torch.tensor(
+            [[-5.1160, -0.2143, -0.2089], [-5.0592, -0.4269, -0.4169], [-4.9087, -1.7608, -1.7372]]
+        ).to(torch_device)
+
+        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
+
+        expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
+        self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
+        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
+
+        # verify postprocessing
+        results = processor.image_processor.post_process_object_detection(
+            outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
+        )[0]
+        expected_scores = torch.tensor([0.4480, 0.3973]).to(torch_device)
+        expected_slice_boxes = torch.tensor([343.7321, 23.8182, 637.5044, 373.8593]).to(torch_device)
+
+        self.assertEqual(len(results["scores"]), 2)
+        torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
+        torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
+
+        # verify grounded postprocessing
+        expected_labels = ["a cat", "a cat"]
+        results = processor.post_process_grounded_object_detection(
+            outputs=outputs,
+            input_ids=encoding.input_ids,
+            threshold=0.35,
+            text_threshold=0.3,
+            target_sizes=[(image.height, image.width)],
+        )[0]
+
+        torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
+        torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
+        self.assertListEqual(results["text_labels"], expected_labels)
+
+    @require_torch_accelerator
+    @is_flaky()
+    def test_inference_object_detection_head_equivalence_cpu_gpu(self):
+        processor = self.default_processor
+        image = prepare_img()
+        text = prepare_text()
+        encoding = processor(images=image, text=text, return_tensors="pt")
+
+        # 1. run model on CPU
+        model = MMGroundingDinoForObjectDetection.from_pretrained(
+            "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
+        )
+        # HACK: the issue happens during top-k (k=900) after the encoder
+        # there are some flips between cpu and gpu query ordering (idxs 195<->196 and 267<->268 on my machine)
+        # which causes different query position embedding assignments
+        # which in turn significantly changes the decoder pass due to self attention
+        model.config.num_queries = 100
+        model.model.query_position_embeddings.weight.data = model.model.query_position_embeddings.weight.data[:100]
+
+        with torch.no_grad():
+            cpu_outputs = model(**encoding)
+
+        # 2. run model on GPU
+        model.to(torch_device)
+        encoding = encoding.to(torch_device)
+        with torch.no_grad():
+            gpu_outputs = model(**encoding)
+
+        # 3. assert equivalence
+        for key in cpu_outputs.keys():
+            torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), rtol=1e-3, atol=1e-3)
+
+        expected_logits = torch.tensor(
+            [[-5.0188, -1.0069, -1.0005], [-5.1177, -1.0537, -1.0444], [-5.3986, -2.4935, -2.4716]]
+        )
+        torch.testing.assert_close(cpu_outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
+
+        # assert postprocessing
+        results_cpu = processor.image_processor.post_process_object_detection(
+            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.height, image.width)]
+        )[0]
+
+        torch.testing.assert_close(results_cpu["scores"], result_gpu["scores"].cpu(), rtol=1e-3, atol=1e-3)
+        torch.testing.assert_close(results_cpu["boxes"], result_gpu["boxes"].cpu(), rtol=1e-3, atol=1e-3)
+
+    @is_flaky()
+    def test_cross_attention_mask(self):
+        model = MMGroundingDinoForObjectDetection.from_pretrained(
+            "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
+        ).to(torch_device)
+        # HACK: the issue happens during top-k (k=900) after the encoder
+        # there are some flips between cpu and gpu query ordering
+        # which causes different query position embedding assignments
+        # which in turn significantly changes the decoder pass due to self attention
+        model.config.num_queries = 100
+        model.model.query_position_embeddings.weight.data = model.model.query_position_embeddings.weight.data[:100]
+
+        processor = self.default_processor
+        image = prepare_img()
+        text1 = "a cat."
+        text2 = "a remote control."
+        text_batched = [text1, text2]
+
+        encoding1 = processor(images=image, text=text1, return_tensors="pt").to(torch_device)
+        encoding2 = processor(images=image, text=text2, return_tensors="pt").to(torch_device)
+        # If we batch the text and cross attention masking is working the batched result should be equal to
+        # The singe text result
+        encoding_batched = processor(
+            images=[image] * len(text_batched), text=text_batched, padding="longest", return_tensors="pt"
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs1 = model(**encoding1)
+            outputs2 = model(**encoding2)
+            outputs_batched = model(**encoding_batched)
+
+        torch.testing.assert_close(outputs1.logits, outputs_batched.logits[:1], rtol=1e-3, atol=1e-3)
+        # For some reason 12 elements are > 1e-3, but the rest are fine
+        self.assertTrue(torch.allclose(outputs2.logits, outputs_batched.logits[1:], atol=1.8e-3))
+
+    def test_mm_grounding_dino_loss(self):
+        ds = load_dataset("EduardoPacheco/aquarium-sample", split="train")
+        image_processor = self.default_processor.image_processor
+        tokenizer = self.default_processor.tokenizer
+        id2label = {0: "fish", 1: "jellyfish", 2: "penguins", 3: "sharks", 4: "puffins", 5: "stingrays", 6: "starfish"}
+        prompt = ". ".join(id2label.values()) + "."
+
+        text_inputs = tokenizer([prompt, prompt], return_tensors="pt")
+        image_inputs = image_processor(
+            images=list(ds["image"]), annotations=list(ds["annotations"]), return_tensors="pt"
+        )
+
+        # Passing auxiliary_loss=True to compare with the expected loss
+        model = MMGroundingDinoForObjectDetection.from_pretrained(
+            "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det",
+            auxiliary_loss=True,
+        )
+        # Interested in the loss only
+        model.eval()
+        with torch.no_grad():
+            outputs = model(**text_inputs, **image_inputs)
+
+        # Loss differs by CPU and GPU, also this can be changed in future.
+        expected_loss_dict = {
+            "loss_ce": torch.tensor(1.1799),
+            "loss_bbox": torch.tensor(0.2348),
+            "loss_giou": torch.tensor(0.5834),
+            "loss_ce_0": torch.tensor(1.1199),
+            "loss_bbox_0": torch.tensor(0.3083),
+            "loss_giou_0": torch.tensor(0.6555),
+            "loss_ce_1": torch.tensor(1.2075),
+            "loss_bbox_1": torch.tensor(0.2641),
+            "loss_giou_1": torch.tensor(0.6073),
+            "loss_ce_2": torch.tensor(1.2915),
+            "loss_bbox_2": torch.tensor(0.2616),
+            "loss_giou_2": torch.tensor(0.5730),
+            "loss_ce_3": torch.tensor(1.0243),
+            "loss_bbox_3": torch.tensor(0.2799),
+            "loss_giou_3": torch.tensor(0.6326),
+            "loss_ce_4": torch.tensor(1.2019),
+            "loss_bbox_4": torch.tensor(0.2430),
+            "loss_giou_4": torch.tensor(0.5679),
+            "loss_ce_enc": torch.tensor(10.2381),
+            "loss_bbox_enc": torch.tensor(0.2886),
+            "loss_giou_enc": torch.tensor(0.6335),
+        }
+
+        expected_loss = torch.tensor(52.4340)
+
+        for key in expected_loss_dict:
+            self.assertTrue(torch.allclose(outputs.loss_dict[key], expected_loss_dict[key], atol=1e-3))
+
+        self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-3))