Upload video_processing_interns1.py

video_processing_interns1.py

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+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Video processor class for InternS1."""
+
+from typing import Optional, Union
+
+from transformers.image_processing_utils import BatchFeature
+from transformers.image_utils import (
+    OPENAI_CLIP_MEAN,
+    OPENAI_CLIP_STD,
+    SizeDict,
+)
+from transformers.processing_utils import Unpack, VideosKwargs
+from transformers.utils import (
+    TensorType,
+    is_torch_available,
+    is_torchvision_available,
+    is_torchvision_v2_available,
+    is_vision_available,
+)
+from transformers.utils.import_utils import requires
+from transformers.video_processing_utils import BaseVideoProcessor
+from transformers.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 transformers.image_utils import PILImageResampling
+
+
+class InternS1VideoProcessorInitKwargs(VideosKwargs):
+    initial_shift: Union[bool, float, int]
+
+
+@requires(backends=("torchvision",))
+class InternS1VideoProcessor(BaseVideoProcessor):
+    resample = PILImageResampling.BICUBIC
+    image_mean = OPENAI_CLIP_MEAN
+    image_std = OPENAI_CLIP_STD
+    size = {"height": 384, "width": 384}
+    do_resize = True
+    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 = InternS1VideoProcessorInitKwargs
+    model_input_names = ["pixel_values_videos"]
+
+    def __init__(self, **kwargs: Unpack[InternS1VideoProcessorInitKwargs]):
+        super().__init__(**kwargs)
+
+    def sample_frames(
+        self,
+        video: "torch.Tensor",
+        metadata: Optional[Union[VideoMetadata, dict]] = None,
+        num_frames: Optional[int] = None,
+        fps: Optional[Union[int, float]] = 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` or `float`, *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[Union[int, float]] = None,
+        num_frames: Optional[int] = None,
+        initial_shift: Optional[Union[bool, float, int]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        device: Optional["torch.Tensor"] = None,
+        **kwargs
+    ) -> 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)
+            ]
+
+        # We need to sample frames first before moving to device, if `do_sample_frames=True`. Otherwise
+        # moving the whole video incurs high GPU mem usage for long videos
+        if device is not None:
+            videos = [video.to(device) for video in videos]
+
+        # 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__ = ["InternS1VideoProcessor"]