Video frames sampled in preprocess?

src/transformers/models/instructblipvideo/video_processing_instructblipvideo.py

@@ -35,7 +35,7 @@
 )
 from ...utils.import_utils import requires
 from ...video_processing_utils import BaseVideoProcessor
-from ...video_utils import group_videos_by_shape, reorder_videos
+from ...video_utils import VideoMetadata, group_videos_by_shape, reorder_videos
 
 
 if is_vision_available():
@@ -66,6 +66,7 @@ class InstructBlipVideoVideoProcessor(BaseVideoProcessor):
     do_rescale = True
     do_normalize = True
     do_convert_rgb = True
+    do_sample_frames = False  # Set to False for BC, recommended to set `True` in new models
     valid_kwargs = InstructBlipVideoVideoProcessorInitKwargs
     model_input_names = ["pixel_values"]
 
@@ -75,6 +76,7 @@ def __init__(self, **kwargs: Unpack[InstructBlipVideoVideoProcessorInitKwargs]):
     def _preprocess(
         self,
         videos: List["torch.Tensor"],
+        video_metadata: Union[List[VideoMetadata], List[dict]],
         do_convert_rgb: bool,
         do_resize: bool,
         size: SizeDict,
@@ -86,10 +88,18 @@ def _preprocess(
         do_pad: bool,
         rescale_factor: float,
         do_normalize: bool,
+        do_sample_frames: bool,
         image_mean: Optional[Union[float, List[float]]],
         image_std: Optional[Union[float, List[float]]],
-        return_tensors: Optional[Union[str, TensorType]],
+        fps: Optional[int] = None,
+        num_frames: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
     ) -> BatchFeature:
+        if do_sample_frames:
+            videos = [
+                self.sample_frames(video, metadata, num_frames, fps) for video, metadata in zip(videos, video_metadata)
+            ]
+
         # Group videos by size for batched resizing
         grouped_videos, grouped_videos_index = group_videos_by_shape(videos)
         resized_videos_grouped = {}

src/transformers/models/internvl/processing_internvl.py

@@ -21,7 +21,7 @@
 from ...image_utils import ImageInput, concatenate_list, make_flat_list_of_images
 from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...video_utils import VideoInput, VideoMetadata, load_video, make_batched_videos
+from ...video_utils import VideoInput, make_batched_videos
 
 
 class InternVLImagesKwargs(ImagesKwargs, total=False):
@@ -290,32 +290,6 @@ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
 
         return MultiModalData(**vision_data)
 
-    def sample_indices_fn(
-        self, metadata: VideoMetadata, num_frames: Optional[int] = None, initial_shift: Union[bool, float, int] = True
-    ):
-        """
-        The function to generate indices of frames to sample from a video.
-
-        Args:
-            metadata (`VideoMetadata`):
-                `VideoMetadata` object containing metadata about the video, such as "total_num_frames" or "fps".
-            num_frames (`int`, *optional*):
-                Number of frames to sample uniformly. If None, all frames are sampled.
-            initial_shift (`bool`, `float` or `int`, defaults to `0`):
-                The initial shift to apply when sampling frames. If `True`, the shift is set so that frames are sampled from the middle of the video.
-
-        Returns:
-            `np.ndarray`: Array of frame indices to sample.
-        """
-        num_frames = num_frames if num_frames is not None else metadata.total_num_frames
-
-        if initial_shift is True:
-            initial_shift = metadata.total_num_frames / num_frames / 2
-        indices = np.arange(initial_shift, metadata.total_num_frames, metadata.total_num_frames / num_frames).astype(
-            int
-        )
-        return indices
-
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
@@ -336,39 +310,5 @@ def model_input_names(self):
         image_processor_input_names = self.image_processor.model_input_names
         return list(tokenizer_input_names) + list(image_processor_input_names)
 
-    # TODO: raushan, has to be public method under `VideoProcessorBase` when API is added
-    def _load_video_for_model(
-        self,
-        video: Union[str, "VideoInput"],
-        num_frames: Optional[int],
-        backend: str = "pyav",
-        initial_shift: bool = True,
-        **kwargs,
-    ) -> np.array:
-        """
-        Loads `video` to a numpy array.
-
-        Args:
-            video (`str` or `VideoInput`):
-                The video to convert to the numpy array format. Can be a link to video or local path.
-            num_frames (`int`, *optional*):
-                Number of frames to sample uniformly. If not passed, the whole video is loaded.
-            backend (`str`, *optional*, defaults to `"pyav"`):
-                The backend to use when loading the video. Can be any of ["decord", "pyav", "opencv", "torchvision"]. Defaults to "pyav".
-            initial_shift (`bool`, *optional*, defaults to `True`):
-                The initial shift to apply when sampling frames. If `True`, the shift is set so that frames are sampled from the middle of the video.
-
-        Returns:
-            Tuple[`np.array`, Dict]: A tuple containing:
-                - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
-                - Metadata dictionary.
-        """
-
-        def sample_indices_fn_func(metadata, **fn_kwargs):
-            return self.sample_indices_fn(metadata, num_frames=num_frames, initial_shift=initial_shift, **fn_kwargs)
-
-        video, metadata = load_video(video, backend=backend, sample_indices_fn=sample_indices_fn_func)
-        return video, metadata
-
 
 __all__ = ["InternVLProcessor"]

src/transformers/models/internvl/video_processing_internvl.py

@@ -14,25 +14,43 @@
 # limitations under the License.
 """Fast Video processor class for InternVL."""
 
+from typing import List, Optional, Union
+
+from ...image_processing_utils import BatchFeature
 from ...image_utils import (
     OPENAI_CLIP_MEAN,
     OPENAI_CLIP_STD,
+    SizeDict,
 )
 from ...processing_utils import Unpack, VideosKwargs
 from ...utils import (
+    TensorType,
+    is_torch_available,
+    is_torchvision_available,
+    is_torchvision_v2_available,
     is_vision_available,
 )
 from ...utils.import_utils import requires
-from ...video_processing_utils import (
-    BaseVideoProcessor,
-)
+from ...video_processing_utils import BaseVideoProcessor
+from ...video_utils import VideoMetadata, group_videos_by_shape, reorder_videos
+
+
+if is_torchvision_available():
+    if is_torchvision_v2_available():
+        from torchvision.transforms.v2 import functional as F
+    else:
+        from torchvision.transforms import functional as F
+
 
+if is_torch_available():
+    import torch
 
 if is_vision_available():
     from ...image_utils import PILImageResampling
 
 
-class InternVLVideoProcessorInitKwargs(VideosKwargs): ...
+class InternVLVideoProcessorInitKwargs(VideosKwargs):
+    initial_shift: Union[bool, float, int]
 
 
 @requires(backends=("torchvision",))
@@ -45,11 +63,128 @@ class InternVLVideoProcessor(BaseVideoProcessor):
     do_rescale = True
     do_normalize = True
     do_convert_rgb = True
+    initial_shift = True
+    do_sample_frames = False  # Set to False for BC, recommended to set `True` in new models
     valid_kwargs = InternVLVideoProcessorInitKwargs
     model_input_names = ["pixel_values_videos"]
 
     def __init__(self, **kwargs: Unpack[InternVLVideoProcessorInitKwargs]):
         super().__init__(**kwargs)
 
+    def sample_frames(
+        self,
+        video: "torch.Tensor",
+        metadata: Optional[Union[VideoMetadata, dict]] = None,
+        num_frames: Optional[int] = None,
+        fps: Optional[int] = None,
+        initial_shift: Optional[Union[bool, float, int]] = None,
+    ):
+        """
+        Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames.
+        If `fps` is passed along with metadata, `fps` frames per second are sampled uniformty. Arguments `num_frames`
+        and `fps` are mutually exclusive.
+
+        Args:
+            video (`torch.Tensor`):
+                Video that need to be sampled.
+            metadata (`VideoMetadata`, *optional*):
+                Metadata of the video containing information about total duration, fps and total number of frames.
+            num_frames (`int`, *optional*):
+                Maximum number of frames to sample. Defaults to `self.num_frames`.
+            fps (`int`, *optional*):
+                Target frames to sample per second. Defaults to `self.fps`.
+            initial_shift (`bool`, `float` or `int`, defaults to `self.initial_shift`):
+                The initial shift to apply when sampling frames. If `True`, the shift is set so that frames are sampled from the middle of the video.
+
+        Returns:
+            torch.Tensor:
+                Sampled video frames.
+        """
+        num_frames = num_frames if num_frames is not None else self.num_frames
+        initial_shift = initial_shift if initial_shift is not None else self.initial_shift
+        total_num_frames = video.shape[0]
+
+        # If num_frames is not given but fps is, calculate num_frames from fps
+        if num_frames is None and fps is not None:
+            if metadata is None:
+                raise ValueError(
+                    "Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. "
+                    "Please pass in `VideoMetadata` object or use a fixed `num_frames` per input video"
+                )
+            num_frames = int(total_num_frames / metadata["fps"] * fps)
+
+        if initial_shift is True:
+            initial_shift = total_num_frames / num_frames / 2
+
+        if num_frames > total_num_frames:
+            raise ValueError(
+                f"Video can't be sampled. The `num_frames={num_frames}` exceeds `total_num_frames={total_num_frames}`. "
+            )
+
+        indices = torch.arange(initial_shift, total_num_frames, total_num_frames / num_frames).int()
+        video = video[indices].contiguous()
+        return video
+
+    def _preprocess(
+        self,
+        videos: List["torch.Tensor"],
+        video_metadata: Union[List[VideoMetadata], List[dict]],
+        do_convert_rgb: bool,
+        do_resize: bool,
+        size: SizeDict,
+        size_divisor: Optional[int],
+        interpolation: Optional["F.InterpolationMode"],
+        do_center_crop: bool,
+        crop_size: SizeDict,
+        do_rescale: bool,
+        do_pad: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: Optional[Union[float, List[float]]],
+        image_std: Optional[Union[float, List[float]]],
+        do_sample_frames: Optional[bool] = None,
+        fps: Optional[int] = None,
+        num_frames: Optional[int] = None,
+        initial_shift: Optional[Union[bool, float, int]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ) -> BatchFeature:
+        if do_sample_frames:
+            # Sample video frames
+            videos = [
+                self.sample_frames(video, metadata, fps=fps, num_frames=num_frames, initial_shift=initial_shift)
+                for video, metadata in zip(videos, video_metadata)
+            ]
+
+        # Group videos by size for batched resizing
+        grouped_videos, grouped_videos_index = group_videos_by_shape(videos)
+        resized_videos_grouped = {}
+        for shape, stacked_videos in grouped_videos.items():
+            if do_convert_rgb:
+                stacked_videos = self.convert_to_rgb(stacked_videos)
+            if do_resize:
+                stacked_videos = self.resize(
+                    stacked_videos, size=size, size_divisor=size_divisor, interpolation=interpolation
+                )
+            resized_videos_grouped[shape] = stacked_videos
+        resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index)
+
+        # Group videos by size for further processing
+        # Needed in case do_resize is False, or resize returns videos with different sizes
+        grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos)
+        processed_videos_grouped = {}
+        for shape, stacked_videos in grouped_videos.items():
+            if do_center_crop:
+                stacked_videos = self.center_crop(stacked_videos, crop_size)
+            # Fused rescale and normalize
+            stacked_videos = self.rescale_and_normalize(
+                stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            processed_videos_grouped[shape] = stacked_videos
+
+        processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
+        processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
+
+        return BatchFeature(data={"pixel_values_videos": processed_videos}, tensor_type=return_tensors)
+
 
 __all__ = ["InternVLVideoProcessor"]

src/transformers/models/llava_next_video/video_processing_llava_next_video.py

@@ -46,6 +46,7 @@ class LlavaNextVideoVideoProcessor(BaseVideoProcessor):
     do_rescale = True
     do_normalize = True
     do_convert_rgb = True
+    do_sample_frames = False  # Set to False for BC, recommended to set `True` in new models
     valid_kwargs = LlavaNextVideoFastVideoProcessorInitKwargs
     model_input_names = ["pixel_values_videos"]
 

src/transformers/models/llava_onevision/video_processing_llava_onevision.py

@@ -47,6 +47,7 @@ class LlavaOnevisionVideoProcessor(BaseVideoProcessor):
     do_rescale = True
     do_normalize = True
     do_convert_rgb = True
+    do_sample_frames = False  # Set to False for BC, recommended to set `True` in new models
     valid_kwargs = LlavaOnevisionFastVideoProcessorInitKwargs
     model_input_names = ["pixel_values_videos"]
 

src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py

@@ -154,7 +154,7 @@ def __call__(
         seconds_per_chunk = output_kwargs["videos_kwargs"].pop("seconds_per_chunk")
         position_id_per_seconds = output_kwargs["videos_kwargs"].pop("position_id_per_seconds")
         use_audio_in_video = output_kwargs["videos_kwargs"].pop("use_audio_in_video")
-        fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
+        fps = output_kwargs["videos_kwargs"].get("fps", 2.0)
 
         if audio is not None:
             output_kwargs["audio_kwargs"]["padding"] = "max_length"  # Support "max_length" padding only here

src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py

@@ -928,7 +928,6 @@ class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
             "padding": False,
             "return_mm_token_type_ids": False,
         },
-        "videos_kwargs": {"fps": 2.0},
     }
 
 
@@ -1013,9 +1012,7 @@ def __call__(
             image_grid_thw = image_inputs["image_grid_thw"]
 
         if videos is not None:
-            # pop fps in advance for passing kwargs validation
-            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
-
+            fps = output_kwargs["videos_kwargs"].get("fps", 2.0)
             videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
             video_grid_thw = videos_inputs["video_grid_thw"]
 

src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py

@@ -54,7 +54,6 @@ class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
             "padding": False,
             "return_mm_token_type_ids": False,
         },
-        "videos_kwargs": {"fps": 2.0},
     }
 
 
@@ -151,9 +150,7 @@ def __call__(
             image_grid_thw = image_inputs["image_grid_thw"]
 
         if videos is not None:
-            # pop fps in advance for passing kwargs validation
-            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
-
+            fps = output_kwargs["videos_kwargs"].get("fps", 2.0)
             videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
             video_grid_thw = videos_inputs["video_grid_thw"]