init

transformers/tests/models/d_fine/test_modeling_d_fine.py

@@ -0,0 +1,815 @@
+# coding = utf-8
+# 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 D-FINE model."""
+
+import inspect
+import math
+import tempfile
+import unittest
+from functools import cached_property
+
+from parameterized import parameterized
+
+from transformers import (
+    DFineConfig,
+    HGNetV2Config,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import DFineForObjectDetection, DFineModel
+
+if is_vision_available():
+    from PIL import Image
+
+from transformers import RTDetrImageProcessor
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+CHECKPOINT = "ustc-community/dfine-small-coco"
+
+
+class DFineModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        is_training=True,
+        use_labels=True,
+        n_targets=3,
+        num_labels=10,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        batch_norm_eps=1e-5,
+        # backbone
+        backbone_config=None,
+        # encoder HybridEncoder
+        encoder_hidden_dim=32,
+        encoder_in_channels=[128, 256, 512],
+        feat_strides=[8, 16, 32],
+        encoder_layers=1,
+        encoder_ffn_dim=64,
+        encoder_attention_heads=2,
+        dropout=0.0,
+        activation_dropout=0.0,
+        encode_proj_layers=[2],
+        positional_encoding_temperature=10000,
+        encoder_activation_function="gelu",
+        activation_function="silu",
+        eval_size=None,
+        normalize_before=False,
+        # decoder DFineTransformer
+        d_model=32,
+        num_queries=30,
+        decoder_in_channels=[32, 32, 32],
+        decoder_ffn_dim=64,
+        num_feature_levels=3,
+        decoder_n_points=[3, 6, 3],
+        decoder_n_levels=3,
+        decoder_layers=2,
+        decoder_attention_heads=2,
+        decoder_activation_function="relu",
+        attention_dropout=0.0,
+        num_denoising=0,
+        label_noise_ratio=0.5,
+        box_noise_scale=1.0,
+        learn_initial_query=False,
+        anchor_image_size=None,
+        image_size=64,
+        disable_custom_kernels=True,
+        with_box_refine=True,
+        decoder_offset_scale=0.5,
+        eval_idx=-1,
+        layer_scale=1,
+        reg_max=32,
+        reg_scale=4.0,
+        depth_mult=0.34,
+        hidden_expansion=0.5,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = 3
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.n_targets = n_targets
+        self.num_labels = num_labels
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.batch_norm_eps = batch_norm_eps
+        self.backbone_config = backbone_config
+        self.encoder_hidden_dim = encoder_hidden_dim
+        self.encoder_in_channels = encoder_in_channels
+        self.feat_strides = feat_strides
+        self.encoder_layers = encoder_layers
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_attention_heads = encoder_attention_heads
+        self.dropout = dropout
+        self.activation_dropout = activation_dropout
+        self.encode_proj_layers = encode_proj_layers
+        self.positional_encoding_temperature = positional_encoding_temperature
+        self.encoder_activation_function = encoder_activation_function
+        self.activation_function = activation_function
+        self.eval_size = eval_size
+        self.normalize_before = normalize_before
+        self.d_model = d_model
+        self.num_queries = num_queries
+        self.decoder_in_channels = decoder_in_channels
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.num_feature_levels = num_feature_levels
+        self.decoder_n_points = decoder_n_points
+        self.decoder_n_levels = decoder_n_levels
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.decoder_activation_function = decoder_activation_function
+        self.attention_dropout = attention_dropout
+        self.decoder_offset_scale = decoder_offset_scale
+        self.eval_idx = eval_idx
+        self.layer_scale = layer_scale
+        self.reg_max = reg_max
+        self.reg_scale = reg_scale
+        self.depth_mult = depth_mult
+        self.num_denoising = num_denoising
+        self.label_noise_ratio = label_noise_ratio
+        self.box_noise_scale = box_noise_scale
+        self.learn_initial_query = learn_initial_query
+        self.anchor_image_size = anchor_image_size
+        self.image_size = image_size
+        self.disable_custom_kernels = disable_custom_kernels
+        self.with_box_refine = with_box_refine
+        self.hidden_expansion = hidden_expansion
+
+        self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)
+
+    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)
+
+        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"] = torch.rand(self.n_targets, 4, device=torch_device)
+                labels.append(target)
+
+        config = self.get_config()
+        config.num_labels = self.num_labels
+        return config, pixel_values, pixel_mask, labels
+
+    def get_config(self):
+        hidden_sizes = [64, 128, 256, 512]
+        backbone_config = HGNetV2Config(
+            stage_in_channels=[16, 64, 128, 256],
+            stage_mid_channels=[16, 32, 64, 128],
+            stage_out_channels=[64, 128, 256, 512],
+            stage_num_blocks=[1, 1, 2, 1],
+            stage_downsample=[False, True, True, True],
+            stage_light_block=[False, False, True, True],
+            stage_kernel_size=[3, 3, 5, 5],
+            stage_numb_of_layers=[3, 3, 3, 3],
+            embeddings_size=10,
+            hidden_sizes=hidden_sizes,
+            depths=[1, 1, 2, 1],
+            out_features=["stage2", "stage3", "stage4"],
+            out_indices=[2, 3, 4],
+            stem_channels=[3, 16, 16],
+            use_lab=True,
+        )
+        return DFineConfig.from_backbone_configs(
+            backbone_config=backbone_config,
+            encoder_hidden_dim=self.encoder_hidden_dim,
+            encoder_in_channels=self.encoder_in_channels,
+            feat_strides=self.feat_strides,
+            encoder_layers=self.encoder_layers,
+            encoder_ffn_dim=self.encoder_ffn_dim,
+            encoder_attention_heads=self.encoder_attention_heads,
+            dropout=self.dropout,
+            activation_dropout=self.activation_dropout,
+            encode_proj_layers=self.encode_proj_layers,
+            positional_encoding_temperature=self.positional_encoding_temperature,
+            encoder_activation_function=self.encoder_activation_function,
+            activation_function=self.activation_function,
+            eval_size=self.eval_size,
+            normalize_before=self.normalize_before,
+            d_model=self.d_model,
+            num_queries=self.num_queries,
+            decoder_in_channels=self.decoder_in_channels,
+            decoder_ffn_dim=self.decoder_ffn_dim,
+            num_feature_levels=self.num_feature_levels,
+            decoder_n_points=self.decoder_n_points,
+            decoder_n_levels=self.decoder_n_levels,
+            decoder_layers=self.decoder_layers,
+            decoder_attention_heads=self.decoder_attention_heads,
+            decoder_activation_function=self.decoder_activation_function,
+            decoder_offset_scale=self.decoder_offset_scale,
+            eval_idx=self.eval_idx,
+            layer_scale=self.layer_scale,
+            reg_max=self.reg_max,
+            reg_scale=self.reg_scale,
+            depth_mult=self.depth_mult,
+            attention_dropout=self.attention_dropout,
+            num_denoising=self.num_denoising,
+            label_noise_ratio=self.label_noise_ratio,
+            box_noise_scale=self.box_noise_scale,
+            learn_initial_query=self.learn_initial_query,
+            anchor_image_size=self.anchor_image_size,
+            image_size=self.image_size,
+            disable_custom_kernels=self.disable_custom_kernels,
+            with_box_refine=self.with_box_refine,
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+    def create_and_check_d_fine_model(self, config, pixel_values, pixel_mask, labels):
+        model = DFineModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(pixel_values)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))
+
+    def create_and_check_d_fine_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
+        model = DFineForObjectDetection(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(pixel_values)
+
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
+        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
+
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
+        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
+
+
+@require_torch
+class DFineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (DFineModel, DFineForObjectDetection) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"image-feature-extraction": DFineModel, "object-detection": DFineForObjectDetection}
+        if is_torch_available()
+        else {}
+    )
+    is_encoder_decoder = True
+    test_torchscript = False
+    test_pruning = False
+    test_head_masking = False
+    test_missing_keys = False
+    test_torch_exportable = True
+
+    # 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__ == "DFineForObjectDetection":
+                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
+                    )
+                    labels.append(target)
+                inputs_dict["labels"] = labels
+
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = DFineModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=DFineConfig,
+            has_text_modality=False,
+            common_properties=["hidden_size", "num_attention_heads"],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_d_fine_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_d_fine_model(*config_and_inputs)
+
+    def test_d_fine_object_detection_head_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_d_fine_object_detection_head_model(*config_and_inputs)
+
+    @unittest.skip(reason="DFine doesn't work well with `nn.DataParallel")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @unittest.skip(reason="DFine does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="DFine does not use test_inputs_embeds_matches_input_ids")
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+    @unittest.skip(reason="DFine does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="DFine does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="DFine 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
+            self.assertEqual(len(attentions), self.model_tester.encoder_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
+            self.assertEqual(len(attentions), self.model_tester.encoder_layers)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [
+                    self.model_tester.encoder_attention_heads,
+                    self.model_tester.encoder_seq_length,
+                    self.model_tester.encoder_seq_length,
+                ],
+            )
+            out_len = len(outputs)
+
+            correct_outlen = 15
+
+            # 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
+            if model_class.__name__ == "DFineForObjectDetection":
+                correct_outlen += 2
+
+            self.assertEqual(out_len, correct_outlen)
+
+            # decoder attentions
+            decoder_attentions = outputs.decoder_attentions
+            self.assertIsInstance(decoder_attentions, (list, tuple))
+            self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
+            self.assertListEqual(
+                list(decoder_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.decoder_attention_heads,
+                    self.model_tester.num_queries,
+                    self.model_tester.num_queries,
+                ],
+            )
+
+            # cross attentions
+            cross_attentions = outputs.cross_attentions
+            self.assertIsInstance(cross_attentions, (list, tuple))
+            self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
+            self.assertListEqual(
+                list(cross_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.num_queries,
+                    self.model_tester.decoder_attention_heads,
+                    self.model_tester.decoder_n_levels * self.model_tester.decoder_n_points
+                    if isinstance(self.model_tester.decoder_n_points, int)
+                    else sum(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))
+
+            if hasattr(self.model_tester, "num_hidden_states_types"):
+                added_hidden_states = self.model_tester.num_hidden_states_types
+            else:
+                # DFine should maintin encoder_hidden_states output
+                added_hidden_states = 2
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.encoder_attentions
+
+            self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.encoder_attention_heads,
+                    self.model_tester.encoder_seq_length,
+                    self.model_tester.encoder_seq_length,
+                ],
+            )
+
+    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_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            self.assertListEqual(
+                list(hidden_states[1].shape[-2:]),
+                [
+                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
+                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
+                ],
+            )
+
+            if config.is_encoder_decoder:
+                hidden_states = outputs.decoder_hidden_states
+
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
+                )
+
+                self.assertIsInstance(hidden_states, (list, tuple))
+                self.assertEqual(len(hidden_states), expected_num_layers)
+
+                self.assertListEqual(
+                    list(hidden_states[0].shape[-2:]),
+                    [self.model_tester.num_queries, self.model_tester.d_model],
+                )
+
+        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)
+
+    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
+
+        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)
+
+        # we take the first output since last_hidden_state is the first item
+        output = outputs[0]
+
+        encoder_hidden_states = outputs.encoder_hidden_states[0]
+        encoder_attentions = outputs.encoder_attentions[0]
+        encoder_hidden_states.retain_grad()
+        encoder_attentions.retain_grad()
+
+        decoder_attentions = outputs.decoder_attentions[0]
+        decoder_attentions.retain_grad()
+
+        cross_attentions = outputs.cross_attentions[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(decoder_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)
+            arg_names = [*signature.parameters.keys()]
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], 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.encoder_in_channels = [24, 40, 432]
+        config.backbone = "tf_mobilenetv3_small_075"
+        config.backbone_config = None
+        config.use_timm_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__ == "DFineForObjectDetection":
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.num_queries,
+                    self.model_tester.num_labels,
+                )
+                self.assertEqual(outputs.logits.shape, expected_shape)
+                # Confirm out_indices was propagated to backbone
+                self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
+            else:
+                # Confirm out_indices was propagated to backbone
+                self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
+
+            self.assertTrue(outputs)
+
+    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__ == "DFineForObjectDetection":
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.num_queries,
+                    self.model_tester.num_labels,
+                )
+                self.assertEqual(outputs.logits.shape, expected_shape)
+                # Confirm out_indices was propagated to backbone
+                self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
+            else:
+                # Confirm out_indices was propagated to backbone
+                self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
+
+            self.assertTrue(outputs)
+
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        configs_no_init.initializer_bias_prior_prob = 0.2
+        bias_value = -1.3863  # log_e ((1 - 0.2) / 0.2)
+
+        failed_cases = []
+
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            # Skip the check for the backbone
+            for name, module in model.named_modules():
+                if module.__class__.__name__ == "DFineConvEncoder":
+                    backbone_params = [f"{name}.{key}" for key in module.state_dict()]
+                    break
+
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    if ("class_embed" in name and "bias" in name) or "enc_score_head.bias" in name:
+                        bias_tensor = torch.full_like(param.data, bias_value)
+                        try:
+                            torch.testing.assert_close(param.data, bias_tensor, atol=1e-4, rtol=1e-4)
+                        except AssertionError:
+                            failed_cases.append(
+                                f"Parameter {name} of model {model_class} seems not properly initialized. "
+                                f"Biases should be initialized to {bias_value}, got {param.data}"
+                            )
+                    elif (
+                        "level_embed" in name
+                        or "sampling_offsets.bias" in name
+                        or "value_proj" in name
+                        or "output_proj" in name
+                        or "reference_points" in name
+                        or "enc_score_head.weight" in name
+                        or ("class_embed" in name and "weight" in name)
+                        or name in backbone_params
+                    ):
+                        continue
+                    else:
+                        mean = param.data.mean()
+                        round_mean = (mean * 1e9).round() / 1e9
+                        round_mean = round_mean.item()
+                        if round_mean not in [0.0, 1.0]:
+                            failed_cases.append(
+                                f"Parameter {name} of model {model_class} seems not properly initialized. "
+                                f"Mean is {round_mean}, but should be in [0, 1]"
+                            )
+
+        message = "\n" + "\n".join(failed_cases)
+        self.assertTrue(not failed_cases, message)
+
+    @parameterized.expand(["float32", "float16", "bfloat16"])
+    @require_torch_accelerator
+    @slow
+    def test_inference_with_different_dtypes(self, dtype_str):
+        dtype = {
+            "float32": torch.float32,
+            "float16": torch.float16,
+            "bfloat16": torch.bfloat16,
+        }[dtype_str]
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device).to(dtype)
+            model.eval()
+            for key, tensor in inputs_dict.items():
+                if tensor.dtype == torch.float32:
+                    inputs_dict[key] = tensor.to(dtype)
+            with torch.no_grad():
+                _ = model(**self._prepare_for_class(inputs_dict, model_class))
+
+    @parameterized.expand(["float32", "float16", "bfloat16"])
+    @require_torch_accelerator
+    @slow
+    def test_inference_equivalence_for_static_and_dynamic_anchors(self, dtype_str):
+        dtype = {
+            "float32": torch.float32,
+            "float16": torch.float16,
+            "bfloat16": torch.bfloat16,
+        }[dtype_str]
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        h, w = inputs_dict["pixel_values"].shape[-2:]
+
+        # convert inputs to the desired dtype
+        for key, tensor in inputs_dict.items():
+            if tensor.dtype == torch.float32:
+                inputs_dict[key] = tensor.to(dtype)
+
+        for model_class in self.all_model_classes:
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model_class(config).save_pretrained(tmpdirname)
+                model_static = model_class.from_pretrained(
+                    tmpdirname, anchor_image_size=[h, w], device_map=torch_device, dtype=dtype
+                ).eval()
+                model_dynamic = model_class.from_pretrained(
+                    tmpdirname, anchor_image_size=None, device_map=torch_device, dtype=dtype
+                ).eval()
+
+            self.assertIsNotNone(model_static.config.anchor_image_size)
+            self.assertIsNone(model_dynamic.config.anchor_image_size)
+
+            with torch.no_grad():
+                outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
+                outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))
+
+            torch.testing.assert_close(
+                outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4
+            )
+
+
+TOLERANCE = 1e-4
+
+
+# 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
+
+
+@require_torch
+@require_vision
+@slow
+class DFineModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return RTDetrImageProcessor.from_pretrained(CHECKPOINT) if is_vision_available() else None
+
+    def test_inference_object_detection_head(self):
+        model = DFineForObjectDetection.from_pretrained(CHECKPOINT).to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
+        self.assertEqual(outputs.logits.shape, expected_shape_logits)
+
+        expected_logits = torch.tensor(
+            [
+                [-3.8098, -4.7725, -5.9945],
+                [-5.2975, -9.4991, -6.1654],
+                [-5.3502, -3.9532, -6.3631],
+            ]
+        ).to(torch_device)
+        expected_boxes = torch.tensor(
+            [
+                [0.7678, 0.4148, 0.4644],
+                [0.1691, 0.1987, 0.2124],
+                [0.2582, 0.5482, 0.4751],
+            ]
+        ).to(torch_device)
+
+        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=2e-4, rtol=2e-4)
+
+        expected_shape_boxes = torch.Size((1, 300, 4))
+        self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
+        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=2e-4, rtol=2e-4)
+
+        # verify postprocessing
+        results = image_processor.post_process_object_detection(
+            outputs, threshold=0.0, target_sizes=[image.size[::-1]]
+        )[0]
+
+        expected_scores = torch.tensor([0.9642, 0.9542, 0.9536, 0.8548], device=torch_device)
+        expected_labels = [15, 65, 15, 57]
+        expected_slice_boxes = torch.tensor(
+            [
+                [1.3186e01, 5.4130e01, 3.1727e02, 4.7212e02],
+                [4.0275e01, 7.2975e01, 1.7620e02, 1.1777e02],
+                [3.4276e02, 2.3428e01, 6.3998e02, 3.7477e02],
+                [5.8418e-01, 1.1794e00, 6.3933e02, 4.7486e02],
+            ],
+            device=torch_device,
+        )
+        torch.testing.assert_close(results["scores"][:4], expected_scores, atol=1e-3, rtol=1e-4)
+        self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
+        torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes[:4], atol=1e-3, rtol=1e-4)