init

transformers/tests/models/tvp/test_image_processing_tvp.py

@@ -0,0 +1,384 @@
+# Copyright 2023 The Intel Team Authors, 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.
+
+
+import unittest
+from typing import Optional, Union
+
+import numpy as np
+
+from transformers.image_transforms import PaddingMode
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_video_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import TvpImageProcessor, TvpImageProcessorFast
+
+
+class TvpImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        do_resize: bool = True,
+        size: dict[str, int] = {"longest_edge": 40},
+        do_center_crop: bool = False,
+        crop_size: Optional[dict[str, int]] = None,
+        do_rescale: bool = False,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_pad: bool = True,
+        pad_size: dict[str, int] = {"height": 80, "width": 80},
+        fill: Optional[int] = None,
+        pad_mode: PaddingMode = None,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, list[float]]] = [0.48145466, 0.4578275, 0.40821073],
+        image_std: Optional[Union[float, list[float]]] = [0.26862954, 0.26130258, 0.27577711],
+        batch_size=2,
+        min_resolution=40,
+        max_resolution=80,
+        num_channels=3,
+        num_frames=2,
+    ):
+        self.do_resize = do_resize
+        self.size = size
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_pad = do_pad
+        self.pad_size = pad_size
+        self.fill = fill
+        self.pad_mode = pad_mode
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.num_frames = num_frames
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_rescale": self.do_rescale,
+            "do_center_crop": self.do_center_crop,
+            "do_pad": self.do_pad,
+            "pad_size": self.pad_size,
+        }
+
+    def get_expected_values(self, image_inputs, batched=False):
+        """
+        This function computes the expected height and width when providing images to TvpImageProcessor,
+        assuming do_resize is set to True with a scalar size.
+        """
+        if not batched:
+            return (int(self.pad_size["height"]), int(self.pad_size["width"]))
+
+        else:
+            expected_values = []
+            for image in image_inputs:
+                expected_height, expected_width = self.get_expected_values([image])
+                expected_values.append((expected_height, expected_width))
+            expected_height = max(expected_values, key=lambda item: item[0])[0]
+            expected_width = max(expected_values, key=lambda item: item[1])[1]
+
+        return expected_height, expected_width
+
+    def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_video_inputs(
+            batch_size=self.batch_size,
+            num_frames=self.num_frames,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class TvpImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = TvpImageProcessor if is_vision_available() else None
+    fast_image_processing_class = (
+        TvpImageProcessorFast if is_vision_available() and is_torchvision_available() else None
+    )
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = TvpImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "do_pad"))
+            self.assertTrue(hasattr(image_processing, "pad_size"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"longest_edge": 40})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size={"longest_edge": 12})
+            self.assertEqual(image_processor.size, {"longest_edge": 12})
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL videos
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], Image.Image)
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    def test_call_numpy(self):
+        # Test numpy with both processors
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], np.ndarray)
+
+            # For fast processor, convert numpy to tensor
+            if image_processing_class == self.fast_image_processing_class:
+                # Convert numpy arrays to tensors for fast processor
+                tensor_video_inputs = []
+                for video in video_inputs:
+                    tensor_video = [torch.from_numpy(frame) for frame in video]
+                    tensor_video_inputs.append(tensor_video)
+                test_inputs = tensor_video_inputs
+            else:
+                test_inputs = video_inputs
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(test_inputs[0], return_tensors="pt").pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(test_inputs, return_tensors="pt").pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+
+    def test_call_numpy_4_channels(self):
+        # Test numpy with both processors
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], np.ndarray)
+
+            # For fast processor, convert numpy to tensor
+            if image_processing_class == self.fast_image_processing_class:
+                # Convert numpy arrays to tensors for fast processor
+                tensor_video_inputs = []
+                for video in video_inputs:
+                    tensor_video = [torch.from_numpy(frame) for frame in video]
+                    tensor_video_inputs.append(tensor_video)
+                test_inputs = tensor_video_inputs
+            else:
+                test_inputs = video_inputs
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(
+                test_inputs[0], return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
+            ).pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(
+                test_inputs, return_tensors="pt", image_mean=0, image_std=1, input_data_format="channels_first"
+            ).pixel_values
+            self.assertListEqual(
+                list(encoded_videos.shape),
+                [
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ],
+            )
+        self.image_processor_tester.num_channels = 3
+
+    def test_call_pytorch(self):
+        # Test PyTorch tensors with both processors
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, list)
+                self.assertIsInstance(video[0], torch.Tensor)
+
+            # Test not batched input
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(video_inputs)
+            encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    1,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test batched
+            expected_height, expected_width = self.image_processor_tester.get_expected_values(
+                video_inputs, batched=True
+            )
+            encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(
+                encoded_videos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_frames,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    @require_vision
+    @require_torch
+    @unittest.skip(
+        reason="FIXME: @yoni probably because of an extra 'time' dimension and since image processors don't handle it well?"
+    )
+    def test_slow_fast_equivalence(self):
+        super().test_slow_fast_equivalence()
+
+    @require_vision
+    @require_torch
+    @unittest.skip(
+        reason="FIXME: @yoni probably because of an extra 'time' dimension and since image processors don't handle it well?"
+    )
+    def test_slow_fast_equivalence_batched(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
+            self.skipTest(
+                reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
+            )
+
+        dummy_images = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
+        encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
+        # Higher max atol for video processing, mean_atol still 5e-3 -> 1e-2
+        self._assert_slow_fast_tensors_equivalence(
+            encoding_slow.pixel_values, encoding_fast.pixel_values, atol=10.0, mean_atol=1e-2
+        )

transformers/tests/models/tvp/test_modeling_tvp.py

@@ -0,0 +1,337 @@
+# Copyright 2023 The Intel Team Authors, 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 TVP model."""
+
+import unittest
+from functools import cached_property
+
+from transformers import ResNetConfig, TimmBackboneConfig, TvpConfig
+from transformers.testing_utils import require_timm, require_torch, require_vision, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    _config_zero_init,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import TvpForVideoGrounding, TvpModel
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import TvpImageProcessor
+
+
+# Copied from test.models.videomae.test_modeling_videomae.VideoMAEModelTester with VideoMAE->TVP
+class TVPModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=1,
+        seq_length=2,
+        alpha=1.0,
+        beta=0.1,
+        visual_prompter_type="framepad",
+        visual_prompter_apply="replace",
+        num_frames=2,
+        max_img_size=448,
+        visual_prompt_size=96,
+        vocab_size=100,
+        hidden_size=32,
+        intermediate_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        max_position_embeddings=30,
+        max_grid_col_position_embeddings=30,
+        max_grid_row_position_embeddings=30,
+        hidden_dropout_prob=0.1,
+        hidden_act="gelu",
+        layer_norm_eps=1e-12,
+        initializer_range=0.02,
+        pad_token_id=0,
+        type_vocab_size=2,
+        attention_probs_dropout_prob=0.1,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.input_id_length = seq_length
+        self.seq_length = seq_length + 10 + 784  # include text prompt length and visual input length
+        self.alpha = alpha
+        self.beta = beta
+        self.visual_prompter_type = visual_prompter_type
+        self.visual_prompter_apply = visual_prompter_apply
+        self.num_frames = num_frames
+        self.max_img_size = max_img_size
+        self.visual_prompt_size = visual_prompt_size
+        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.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.max_grid_col_position_embeddings = max_grid_col_position_embeddings
+        self.max_grid_row_position_embeddings = max_grid_row_position_embeddings
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        self.pad_token_id = pad_token_id
+        self.type_vocab_size = type_vocab_size
+        self.is_training = False
+        self.num_channels = 3
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.input_id_length], self.vocab_size)
+        attention_mask = random_attention_mask([self.batch_size, self.input_id_length])
+        pixel_values = floats_tensor(
+            [self.batch_size, self.num_frames, self.num_channels, self.max_img_size, self.max_img_size]
+        )
+
+        config = self.get_config()
+
+        return (config, input_ids, pixel_values, attention_mask)
+
+    def get_config(self):
+        resnet_config = ResNetConfig(
+            num_channels=3,
+            embeddings_size=64,
+            hidden_sizes=[64, 128],
+            depths=[2, 2],
+            hidden_act="relu",
+            out_features=["stage2"],
+            out_indices=[2],
+        )
+        return TvpConfig(
+            backbone_config=resnet_config,
+            backbone=None,
+            alpha=self.alpha,
+            beta=self.beta,
+            visual_prompter_type=self.visual_prompter_type,
+            visual_prompter_apply=self.visual_prompter_apply,
+            num_frames=self.num_frames,
+            max_img_size=self.max_img_size,
+            visual_prompt_size=self.visual_prompt_size,
+            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,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            max_grid_col_position_embeddings=self.max_grid_col_position_embeddings,
+            max_grid_row_position_embeddings=self.max_grid_row_position_embeddings,
+            layer_norm_eps=self.layer_norm_eps,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+            type_vocab_size=self.type_vocab_size,
+        )
+
+    def create_and_check_model(self, config, input_ids, pixel_values, attention_mask):
+        model = TvpModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, pixel_values, attention_mask)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, pixel_values, attention_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "pixel_values": pixel_values, "attention_mask": attention_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class TVPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as TVP does not use, inputs_embeds.
+    The seq_length in TVP contain textual and visual inputs, and prompt.
+    """
+
+    all_model_classes = (TvpModel, TvpForVideoGrounding) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": TvpModel, "temporal-video-grounding": TvpForVideoGrounding}
+        if is_torch_available()
+        else {}
+    )
+
+    # TODO: Enable this once this model gets more usage
+    test_torchscript = False
+
+    def setUp(self):
+        self.model_tester = TVPModelTester(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="TVP does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="TVPModel does not have input/output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    # override as the `logit_scale` parameter initialization is different for TVP
+    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:
+                    # params are randomly initialized.
+                    self.assertAlmostEqual(
+                        param.data.mean().item(),
+                        0.0,
+                        delta=1.0,
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+    @require_timm
+    def test_backbone_selection(self):
+        def _validate_backbone_init():
+            for model_class in self.all_model_classes:
+                model = model_class(config)
+                model.to(torch_device)
+                model.eval()
+
+                # Confirm out_indices propagated to backbone
+                if model.__class__.__name__ == "TvpModel":
+                    self.assertEqual(len(model.vision_model.backbone.out_indices), 2)
+                elif model.__class__.__name__ == "TvpForVideoGrounding":
+                    self.assertEqual(len(model.model.vision_model.backbone.out_indices), 2)
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        # Force load_backbone path
+        config.is_hybrid = False
+
+        # We load through configs, as the modeling file assumes config.backbone_config is always set
+        config.use_pretrained_backbone = False
+        config.backbone_kwargs = None
+
+        # Load a timm backbone
+        # We hack adding hidden_sizes to the config to test the backbone loading
+        backbone_config = TimmBackboneConfig("resnet18", out_indices=[-2, -1], hidden_sizes=[64, 128])
+        config.backbone_config = backbone_config
+        _validate_backbone_init()
+
+        # Load a HF backbone
+        backbone_config = ResNetConfig.from_pretrained("facebook/dinov2-small", out_indices=[-2, -1])
+        config.backbone_config = backbone_config
+        _validate_backbone_init()
+
+
+# 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_vision
+@require_torch
+class TvpModelIntegrationTests(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return TvpImageProcessor.from_pretrained("Jiqing/tiny-random-tvp")
+
+    def test_inference_no_head(self):
+        model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        encoding = image_processor(images=image, return_tensors="pt")
+        input_ids = torch.tensor([[1, 2]])
+        attention_mask = torch.tensor([[1, 1]])
+        encoding.update({"input_ids": input_ids, "attention_mask": attention_mask})
+        encoding.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**encoding)
+
+        expected_shape = torch.Size((1, 796, 128))
+        assert outputs.last_hidden_state.shape == expected_shape
+        expected_slice = torch.tensor(
+            [[-0.4902, -0.4121, -1.7872], [-0.2184, 2.1211, -0.9371], [0.1180, 0.5003, -0.1727]]
+        ).to(torch_device)
+        torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+    def test_inference_with_head(self):
+        model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        encoding = image_processor(images=image, return_tensors="pt")
+        input_ids = torch.tensor([[1, 2]])
+        attention_mask = torch.tensor([[1, 1]])
+        encoding.update({"input_ids": input_ids, "attention_mask": attention_mask})
+        encoding.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**encoding)
+
+        expected_shape = torch.Size((1, 2))
+        assert outputs.logits.shape == expected_shape
+        expected_slice = torch.tensor([[0.5061, 0.4988]]).to(torch_device)
+        torch.testing.assert_close(outputs.logits, expected_slice, rtol=1e-4, atol=1e-4)
+
+    def test_interpolate_inference_no_head(self):
+        model = TvpModel.from_pretrained("Jiqing/tiny-random-tvp").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()  # 480X640
+        encoding = image_processor(
+            images=image, return_tensors="pt", do_resize=False, do_pad=False, do_center_crop=False
+        )
+        input_ids = torch.tensor([[1, 2]])
+        attention_mask = torch.tensor([[1, 1]])
+        encoding.update({"input_ids": input_ids, "attention_mask": attention_mask})
+        encoding.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**encoding, interpolate_pos_encoding=True)
+
+        expected_shape = torch.Size((1, 1212, 128))
+        assert outputs.last_hidden_state.shape == expected_shape
+
+    def test_interpolate_inference_with_head(self):
+        model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()  # 480X640
+        encoding = image_processor(
+            images=image, return_tensors="pt", do_resize=False, do_pad=False, do_center_crop=False
+        )
+        input_ids = torch.tensor([[1, 2]])
+        attention_mask = torch.tensor([[1, 1]])
+        encoding.update({"input_ids": input_ids, "attention_mask": attention_mask})
+        encoding.to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**encoding, interpolate_pos_encoding=True, output_hidden_states=True)
+
+        expected_shape = torch.Size((1, 1212, 128))
+        assert outputs.hidden_states[-1].shape == expected_shape