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tests/models/owlvit/test_modeling_owlvit.py

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+# Copyright 2022 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 OwlViT model."""
+
+import inspect
+import tempfile
+import unittest
+
+import numpy as np
+import requests
+from parameterized import parameterized
+
+from transformers import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_accelerator,
+    require_torch_fp16,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import OwlViTForObjectDetection, OwlViTModel, OwlViTTextModel, OwlViTVisionModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import OwlViTProcessor
+
+
+class OwlViTVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=32,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        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.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return OwlViTVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = OwlViTVisionModel(config=config).to(torch_device)
+        model.eval()
+
+        pixel_values = pixel_values.to(torch.float32)
+
+        with torch.no_grad():
+            result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (self.image_size // self.patch_size) ** 2
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class OwlViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as OWLVIT does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (OwlViTVisionModel,) if is_torch_available() else ()
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = OwlViTVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=OwlViTVisionConfig, has_text_modality=False, hidden_size=32
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="OWLVIT does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    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"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTVisionModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+class OwlViTTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        num_queries=4,
+        seq_length=16,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=64,
+        num_hidden_layers=12,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=16,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_queries = num_queries
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        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.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size)
+        input_mask = None
+
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length])
+
+        if input_mask is not None:
+            num_text, seq_length = input_mask.shape
+
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,))
+            for idx, start_index in enumerate(rnd_start_indices):
+                input_mask[idx, :start_index] = 1
+                input_mask[idx, start_index:] = 0
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return OwlViTTextConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = OwlViTTextModel(config=config).to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_ids=input_ids, attention_mask=input_mask)
+
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size * self.num_queries, self.seq_length, self.hidden_size)
+        )
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_queries, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class OwlViTTextModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (OwlViTTextModel,) if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = OwlViTTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=OwlViTTextConfig, hidden_size=32)
+
+    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)
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    @unittest.skip(reason="OWLVIT does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTTextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+class OwlViTModelTester:
+    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
+        if text_kwargs is None:
+            text_kwargs = {}
+        if vision_kwargs is None:
+            vision_kwargs = {}
+
+        self.parent = parent
+        self.text_model_tester = OwlViTTextModelTester(parent, **text_kwargs)
+        self.vision_model_tester = OwlViTVisionModelTester(parent, **vision_kwargs)
+        self.is_training = is_training
+        self.text_config = self.text_model_tester.get_config().to_dict()
+        self.vision_config = self.vision_model_tester.get_config().to_dict()
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+        return config, input_ids, attention_mask, pixel_values
+
+    def get_config(self):
+        return OwlViTConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            projection_dim=64,
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
+        model = OwlViTModel(config).to(torch_device).eval()
+
+        with torch.no_grad():
+            result = model(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                attention_mask=attention_mask,
+            )
+
+        image_logits_size = (
+            self.vision_model_tester.batch_size,
+            self.text_model_tester.batch_size * self.text_model_tester.num_queries,
+        )
+        text_logits_size = (
+            self.text_model_tester.batch_size * self.text_model_tester.num_queries,
+            self.vision_model_tester.batch_size,
+        )
+        self.parent.assertEqual(result.logits_per_image.shape, image_logits_size)
+        self.parent.assertEqual(result.logits_per_text.shape, text_logits_size)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values = config_and_inputs
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "return_loss": False,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (OwlViTModel,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": OwlViTModel,
+            "zero-shot-object-detection": OwlViTForObjectDetection,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_resize_embeddings = False
+    test_attention_outputs = False
+    additional_model_inputs = ["pixel_values"]
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = OwlViTModelTester(self)
+        common_properties = ["projection_dim", "logit_scale_init_value"]
+        self.config_tester = ConfigTester(
+            self, config_class=OwlViTConfig, has_text_modality=False, common_properties=common_properties
+        )
+
+    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)
+
+    @unittest.skip(reason="Hidden_states is tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="OwlViTModel does not have input/output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    def test_load_vision_text_config(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Save OwlViTConfig and check if we can load OwlViTVisionConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            vision_config = OwlViTVisionConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
+
+        # Save OwlViTConfig and check if we can load OwlViTTextConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            text_config = OwlViTTextConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+class OwlViTForObjectDetectionTester:
+    def __init__(self, parent, is_training=True):
+        self.parent = parent
+        self.text_model_tester = OwlViTTextModelTester(parent)
+        self.vision_model_tester = OwlViTVisionModelTester(parent)
+        self.is_training = is_training
+        self.text_config = self.text_model_tester.get_config().to_dict()
+        self.vision_config = self.vision_model_tester.get_config().to_dict()
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+        return config, pixel_values, input_ids, attention_mask
+
+    def get_config(self):
+        return OwlViTConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            projection_dim=64,
+        )
+
+    def create_and_check_model(self, config, pixel_values, input_ids, attention_mask):
+        model = OwlViTForObjectDetection(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(
+                pixel_values=pixel_values,
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                return_dict=True,
+            )
+
+        pred_boxes_size = (
+            self.vision_model_tester.batch_size,
+            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
+            4,
+        )
+        pred_logits_size = (
+            self.vision_model_tester.batch_size,
+            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
+            4,
+        )
+        pred_class_embeds_size = (
+            self.vision_model_tester.batch_size,
+            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
+            self.text_model_tester.hidden_size,
+        )
+        self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size)
+        self.parent.assertEqual(result.logits.shape, pred_logits_size)
+        self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, input_ids, attention_mask = config_and_inputs
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class OwlViTForObjectDetectionTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (OwlViTForObjectDetection,) if is_torch_available() else ()
+
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    additional_model_inputs = ["pixel_values", "attention_mask"]
+
+    def setUp(self):
+        self.model_tester = OwlViTForObjectDetectionTester(self)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="Hidden_states is tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    def test_eager_matches_sdpa_inference(self, *args):
+        self.skipTest("OwlViTObjectDetectionOutput has no top-level hidden_states; SDPA tested in sub-models")
+
+    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="OwlViTModel does not have input/output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Test_forward_signature is tested in individual model tests")
+    def test_forward_signature(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTForObjectDetection.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@require_vision
+@require_torch
+class OwlViTModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTModel.from_pretrained(model_name).to(torch_device)
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(
+            text=[["a photo of a cat", "a photo of a dog"]],
+            images=image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        self.assertEqual(
+            outputs.logits_per_image.shape,
+            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+        expected_logits = torch.tensor([[3.4612, 0.9404]], device=torch_device)
+        torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTModel.from_pretrained(model_name).to(torch_device)
+        processor = OwlViTProcessor.from_pretrained(model_name)
+        processor.image_processor.size = {"height": 800, "width": 800}
+
+        image = prepare_img()
+        inputs = processor(
+            text=[["a photo of a cat", "a photo of a dog"]],
+            images=image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        # verify the logits
+        self.assertEqual(
+            outputs.logits_per_image.shape,
+            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+        expected_logits = torch.tensor([[3.6282, 0.8859]], device=torch_device)
+        torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
+
+        expected_shape = torch.Size((1, 626, 768))
+        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)
+
+        # OwlViTForObjectDetection part.
+        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        num_queries = int((inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size) ** 2)
+        self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
+
+        expected_slice_boxes = torch.tensor(
+            [[0.0679, 0.0422, 0.1347], [0.2073, 0.0450, 0.4151], [0.2002, 0.0418, 0.3483]]
+        ).to(torch_device)
+        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
+
+        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
+        query_image = prepare_img()
+        inputs = processor(
+            images=image,
+            query_images=query_image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model.image_guided_detection(**inputs, interpolate_pos_encoding=True)
+
+        # No need to check the logits, we just check inference runs fine.
+        num_queries = int((inputs.pixel_values.shape[-1] / model.config.vision_config.patch_size) ** 2)
+        self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
+
+        # Deactivate interpolate_pos_encoding on same model, and use default image size.
+        # Verify the dynamic change caused by the activation/deactivation of interpolate_pos_encoding of variables: (self.sqrt_num_patch_h, self.sqrt_num_patch_w), self.box_bias from (OwlViTForObjectDetection).
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(
+            text=[["a photo of a cat", "a photo of a dog"]],
+            images=image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=False)
+
+        num_queries = int((inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size) ** 2)
+        self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
+
+        expected_default_box_bias = torch.tensor(
+            [
+                [-3.1332, -3.1332, -3.1332, -3.1332],
+                [-2.3968, -3.1332, -3.1332, -3.1332],
+                [-1.9452, -3.1332, -3.1332, -3.1332],
+            ]
+        ).to(torch_device)
+        torch.testing.assert_close(model.box_bias[:3, :4], expected_default_box_bias, rtol=1e-4, atol=1e-4)
+
+        # Interpolate with any resolution size.
+        processor.image_processor.size = {"height": 1264, "width": 1024}
+
+        image = prepare_img()
+        inputs = processor(
+            text=[["a photo of a cat", "a photo of a dog"]],
+            images=image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        num_queries = int(
+            (inputs.pixel_values.shape[-2] // model.config.vision_config.patch_size)
+            * (inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size)
+        )
+        self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
+        expected_slice_boxes = torch.tensor(
+            [[0.0498, 0.0301, 0.0982], [0.2241, 0.0364, 0.4654], [0.1389, 0.0314, 0.1860]]
+        ).to(torch_device)
+        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-2, atol=1e-2)
+
+        query_image = prepare_img()
+        inputs = processor(
+            images=image,
+            query_images=query_image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model.image_guided_detection(**inputs, interpolate_pos_encoding=True)
+
+        # No need to check the logits, we just check inference runs fine.
+        num_queries = int(
+            (inputs.pixel_values.shape[-2] // model.config.vision_config.patch_size)
+            * (inputs.pixel_values.shape[-1] // model.config.vision_config.patch_size)
+        )
+        self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
+
+    @slow
+    def test_inference_object_detection(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
+
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        text_labels = [["a photo of a cat", "a photo of a dog"]]
+        inputs = processor(
+            text=text_labels,
+            images=image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
+        self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
+
+        expected_slice_boxes = torch.tensor(
+            [[0.0691, 0.0445, 0.1374], [0.1592, 0.0456, 0.3191], [0.1636, 0.0423, 0.2489]]
+        ).to(torch_device)
+        torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
+
+        # test post-processing
+        post_processed_output = processor.post_process_grounded_object_detection(outputs)
+        self.assertIsNone(post_processed_output[0]["text_labels"])
+
+        post_processed_output_with_text_labels = processor.post_process_grounded_object_detection(
+            outputs, text_labels=text_labels
+        )
+
+        objects_labels = post_processed_output_with_text_labels[0]["labels"].tolist()
+        self.assertListEqual(objects_labels, [0, 0])
+
+        objects_text_labels = post_processed_output_with_text_labels[0]["text_labels"]
+        self.assertIsNotNone(objects_text_labels)
+        self.assertListEqual(objects_text_labels, ["a photo of a cat", "a photo of a cat"])
+
+    @slow
+    def test_inference_one_shot_object_detection(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
+
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        query_image = prepare_img()
+        inputs = processor(
+            images=image,
+            query_images=query_image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model.image_guided_detection(**inputs)
+
+        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
+        self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
+
+        expected_slice_boxes = torch.tensor(
+            [[0.0691, 0.0445, 0.1374], [0.1592, 0.0456, 0.3191], [0.1636, 0.0423, 0.2489]]
+        ).to(torch_device)
+        torch.testing.assert_close(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4)
+
+    @slow
+    @require_torch_accelerator
+    @require_torch_fp16
+    def test_inference_one_shot_object_detection_fp16(self):
+        model_name = "google/owlvit-base-patch32"
+        model = OwlViTForObjectDetection.from_pretrained(model_name, dtype=torch.float16).to(torch_device)
+
+        processor = OwlViTProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        query_image = prepare_img()
+        inputs = processor(
+            images=image,
+            query_images=query_image,
+            max_length=16,
+            padding="max_length",
+            return_tensors="pt",
+        ).to(torch_device)
+
+        with torch.no_grad():
+            outputs = model.image_guided_detection(**inputs)
+
+        # No need to check the logits, we just check inference runs fine.
+        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
+        self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))