Add Video Llava (#29733)

* add model draft

* update docstring

* add tests

* support image and video as input

* update for better handling of mixed input and clean-up a bit

* bug when mixed inputs & add tests

* Update README.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Merge remote-tracking branch 'upstream/main' into video_llava

* link to abstract of paper in README

* fix test

* fix-copies

* make tests happy

* skip docstest for now

* do not run doctest for now

* Update src/transformers/models/video_llava/processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/image_processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/image_processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/image_processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/image_processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/video_llava/test_modeling_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/image_processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* address review comments

* failing tests

* Fix vocab_size in common tests for VLMs

* codestyle

* Update src/transformers/models/video_llava/configuration_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/configuration_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/modeling_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/modeling_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/video_llava.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/video_llava.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/image_processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/video_llava.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/processing_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/video_llava/test_modeling_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/video_llava/test_modeling_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/video_llava/test_modeling_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* PR suggestions

* fix-copies

* Update src/transformers/models/video_llava/configuration_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/video_llava/configuration_video_llava.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add full example in docs

* clean-up with new model-id

* [run-slow] video_llava

* update docstring

* [run-slow] video_llava

* remove all achive maps

* fix some tests

* test was supposed to be skipped for llava :)

---------
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/utils/dummy_pt_objects.py

@@ -8410,6 +8410,34 @@ class UperNetPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class VideoLlavaForConditionalGeneration(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class VideoLlavaImageProcessor(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class VideoLlavaPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class VideoLlavaProcessor(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class VideoMAEForPreTraining(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/video_llava/test_image_processing_video_llava.py

+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# 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
+
+import numpy as np
+from parameterized import parameterized
+
+from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import VideoLlavaImageProcessor
+
+
+class VideoLlavaImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=5,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=80,
+        do_resize=True,
+        size=None,
+        do_center_crop=True,
+        crop_size=None,
+        do_normalize=True,
+        image_mean=OPENAI_CLIP_MEAN,
+        image_std=OPENAI_CLIP_STD,
+        do_convert_rgb=True,
+    ):
+        size = size if size is not None else {"shortest_edge": 20}
+        crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.crop_size["height"], self.crop_size["width"]
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+    def prepare_video_inputs(self, equal_resolution=False, torchify=False):
+        numpify = not torchify
+        images = prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+        # let's simply copy the frames to fake a long video-clip
+        videos = []
+        for image in images:
+            if numpify:
+                video = image[None, ...].repeat(8, 0)
+            else:
+                video = image[None, ...].repeat(8, 1, 1, 1)
+            videos.append(video)
+
+        return videos
+
+
+@require_torch
+@require_vision
+class VideoLlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = VideoLlavaImageProcessor if is_vision_available() else None
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->VideoLlava
+    def setUp(self):
+        self.image_processor_tester = VideoLlavaImageProcessingTester(self)
+
+    @property
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        self.assertTrue(hasattr(image_processing, "do_resize"))
+        self.assertTrue(hasattr(image_processing, "size"))
+        self.assertTrue(hasattr(image_processing, "do_center_crop"))
+        self.assertTrue(hasattr(image_processing, "center_crop"))
+        self.assertTrue(hasattr(image_processing, "do_normalize"))
+        self.assertTrue(hasattr(image_processing, "image_mean"))
+        self.assertTrue(hasattr(image_processing, "image_std"))
+        self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.test_image_processor_from_dict_with_kwargs
+    def test_image_processor_from_dict_with_kwargs(self):
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        self.assertEqual(image_processor.size, {"shortest_edge": 20})
+        self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
+
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
+        self.assertEqual(image_processor.size, {"shortest_edge": 42})
+        self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+
+    def test_call_pil(self):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random PIL images
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, Image.Image)
+
+        # Test not batched input
+        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values_images
+        expected_output_image_shape = (1, 3, 18, 18)
+        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+        # Test batched
+        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values_images
+        expected_output_image_shape = (5, 3, 18, 18)
+        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_numpy(self):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random numpy tensors
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, np.ndarray)
+
+        # Test not batched input
+        encoded_images = image_processing(images=image_inputs[0], return_tensors="pt").pixel_values_images
+        expected_output_image_shape = (1, 3, 18, 18)
+        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+        # Test batched
+        encoded_images = image_processing(images=image_inputs, return_tensors="pt").pixel_values_images
+        expected_output_image_shape = (5, 3, 18, 18)
+        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_numpy_videos(self):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random numpy tensors
+        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=True)
+        for video in video_inputs:
+            self.assertIsInstance(video, np.ndarray)
+
+        # Test not batched input
+        encoded_videos = image_processing(images=None, videos=video_inputs[0], return_tensors="pt").pixel_values_videos
+        expected_output_video_shape = (1, 8, 3, 18, 18)
+        self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape)
+
+        # Test batched
+        encoded_videos = image_processing(images=None, videos=video_inputs, return_tensors="pt").pixel_values_videos
+        expected_output_video_shape = (5, 8, 3, 18, 18)
+        self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape)
+
+    def test_call_pytorch(self):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random PyTorch tensors
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+        for image in image_inputs:
+            self.assertIsInstance(image, torch.Tensor)
+
+        # Test not batched input
+        encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values_images
+        expected_output_image_shape = (1, 3, 18, 18)
+        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+        # Test batched
+        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values_images
+        expected_output_image_shape = (5, 3, 18, 18)
+        self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_pytorch_videos(self):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random PyTorch tensors
+        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=True, torchify=True)
+        for video in video_inputs:
+            self.assertIsInstance(video, torch.Tensor)
+
+        # Test not batched input
+        encoded_videos = image_processing(images=None, videos=video_inputs[0], return_tensors="pt").pixel_values_videos
+        expected_output_video_shape = (1, 8, 3, 18, 18)
+        self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape)
+
+        # Test batched
+        encoded_videos = image_processing(images=None, videos=video_inputs, return_tensors="pt").pixel_values_videos
+        expected_output_video_shape = (5, 8, 3, 18, 18)
+        self.assertEqual(tuple(encoded_videos.shape), expected_output_video_shape)
+
+    @parameterized.expand([(True, False), (False, True)])
+    def test_call_mixed(self, numpify, torchify):
+        # Initialize image_processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random numpy tensors
+        image_inputs = self.image_processor_tester.prepare_image_inputs(
+            equal_resolution=True, numpify=numpify, torchify=torchify
+        )
+        video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=True, torchify=torchify)
+
+        # Test not batched input
+        encoded = image_processing(images=image_inputs[0], videos=video_inputs[0], return_tensors="pt")
+        expected_output_video_shape = (1, 8, 3, 18, 18)
+        expected_output_image_shape = (1, 3, 18, 18)
+        self.assertEqual(tuple(encoded.pixel_values_videos.shape), expected_output_video_shape)
+        self.assertEqual(tuple(encoded.pixel_values_images.shape), expected_output_image_shape)
+
+        # Test batched
+        encoded = image_processing(images=image_inputs, videos=video_inputs, return_tensors="pt")
+        expected_output_video_shape = (5, 8, 3, 18, 18)
+        expected_output_image_shape = (5, 3, 18, 18)
+        self.assertEqual(tuple(encoded.pixel_values_videos.shape), expected_output_video_shape)
+        self.assertEqual(tuple(encoded.pixel_values_images.shape), expected_output_image_shape)
+
+    def test_call_numpy_4_channels(self):
+        # Test that can process images which have an arbitrary number of channels
+        # Initialize image_processing
+        image_processor = self.image_processing_class(**self.image_processor_dict)
+
+        # create random numpy tensors
+        self.image_processor_tester.num_channels = 4
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+
+        # Test not batched input
+        encoded_images = image_processor(
+            image_inputs[0],
+            return_tensors="pt",
+            input_data_format="channels_first",
+            image_mean=0,
+            image_std=1,
+        ).pixel_values_images
+        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
+        self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+        # Test batched
+        encoded_images = image_processor(
+            image_inputs,
+            return_tensors="pt",
+            input_data_format="channels_first",
+            image_mean=0,
+            image_std=1,
+        ).pixel_values_images
+        expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
+        self.assertEqual(
+            tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+        )

tests/test_modeling_common.py

@@ -3946,7 +3946,7 @@ class ModelTesterMixin:
 
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
             inputs_dict = self._prepare_for_class(inputs_dict, model_class)
-            if config.model_type in ["llava", "llava_next", "vipllava"]:
+            if config.model_type in ["llava", "llava_next", "vipllava", "video_llava"]:
                 self.skipTest("Llava-like models currently (transformers==4.39.1) requires an attention_mask input")
             if config.model_type in ["paligemma"]:
                 self.skipTest(