Blame for VideoX-Fun/videox_fun/reward/video_processor.py

# coding=utf-8
# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""video processor class for Qwen2-VL."""

import math
from typing import Optional, Union

import torch

from ...image_processing_utils import BatchFeature
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    PILImageResampling,
    SizeDict,
    get_image_size,
)
from ...processing_utils import Unpack, VideosKwargs
from ...utils import TensorType, add_start_docstrings, is_torchvision_v2_available
from ...video_processing_utils import BASE_VIDEO_PROCESSOR_DOCSTRING, BaseVideoProcessor
from ...video_utils import VideoMetadata, group_videos_by_shape, reorder_videos
from .image_processing_qwen2_vl import smart_resize


if is_torchvision_v2_available():
    from torchvision.transforms.v2 import functional as F
else:
    from torchvision.transforms import functional as F


class Qwen2VLVideoProcessorInitKwargs(VideosKwargs):
    min_pixels: Optional[int]
    max_pixels: Optional[int]
    patch_size: Optional[int]
    temporal_patch_size: Optional[int]
    merge_size: Optional[int]
    min_frames: Optional[int]
    max_frames: Optional[int]


@add_start_docstrings(
    "Constructs a fast Qwen2-VL image processor that dynamically resizes videos based on the original videos.",
    BASE_VIDEO_PROCESSOR_DOCSTRING,
    """
        min_pixels (`int`, *optional*, defaults to `56 * 56`):
            The min pixels of the image to resize the image.
        max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
            The max pixels of the image to resize the image.
        patch_size (`int`, *optional*, defaults to 14):
            The spacial patch size of the vision encoder.
        temporal_patch_size (`int`, *optional*, defaults to 2):
            The temporal patch size of the vision encoder.
        merge_size (`int`, *optional*, defaults to 2):
            The merge size of the vision encoder to llm encoder.
        min_frames (`int`, *optional*, defaults to 4):
            The minimum number of frames that can be sampled.
        max_frames (`int`, *optional*, defaults to 768):
            The maximum number of frames that can be sampled.
    """,
)


def group_videos_by_shape(
    videos: list["torch.Tensor"],
) -> tuple[dict[tuple[int, int], list["torch.Tensor"]], dict[int, tuple[tuple[int, int], int]]]:
    """
    Groups videos by shape.
    Returns a dictionary with the shape as key and a list of videos with that shape as value,
    and a dictionary with the index of the video in the original list as key and the shape and index in the grouped list as value.
    """
    grouped_videos = {}
    grouped_videos_index = {}
    for i, video in enumerate(videos):
        shape = video.shape[-2::]
        num_frames = video.shape[-4]  # video format BTCHW
        shape = (num_frames, *shape)
        if shape not in grouped_videos:
            grouped_videos[shape] = []
        grouped_videos[shape].append(video)
        grouped_videos_index[i] = (shape, len(grouped_videos[shape]) - 1)
    # stack videos with the same size and number of frames
    grouped_videos = {shape: torch.stack(videos, dim=0) for shape, videos in grouped_videos.items()}
    return grouped_videos, grouped_videos_index


class Qwen2VLVideoProcessor(BaseVideoProcessor):
    resample = PILImageResampling.BICUBIC
    size = {"shortest_edge": 128 * 28 * 28, "longest_edge": 28 * 28 * 768}
    image_mean = OPENAI_CLIP_MEAN
    image_std = OPENAI_CLIP_STD
    do_resize = True
    do_rescale = True
    do_normalize = True
    do_convert_rgb = True
    min_pixels = 128 * 28 * 28
    max_pixels = 28 * 28 * 768
    patch_size = 14
    temporal_patch_size = 2
    merge_size = 2
    min_frames = 4
    max_frames = 768
    do_sample_frames = False  # Set to False for BC, recommended to set `True` in new models
    valid_kwargs = Qwen2VLVideoProcessorInitKwargs
    model_input_names = ["pixel_values_videos", "video_grid_thw"]

    def __init__(self, **kwargs: Unpack[Qwen2VLVideoProcessorInitKwargs]):
        size = kwargs.pop("size", None)
        min_pixels = kwargs.pop("min_pixels", None)
        max_pixels = kwargs.pop("max_pixels", None)
        # backward compatibility: override size with min_pixels and max_pixels if they are provided
        size = self.size if size is None else size
        if min_pixels is not None:
            size["shortest_edge"] = min_pixels
            size.pop("min_pixels", None)
        if max_pixels is not None:
            size["longest_edge"] = max_pixels
            size.pop("max_pixels", None)
        if "shortest_edge" not in size or "longest_edge" not in size:
            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")

        super().__init__(size=size, min_pixels=min_pixels, max_pixels=max_pixels, **kwargs)

    def sample_frames(
        self,
        metadata: VideoMetadata,
        temporal_patch_size: Optional[int] = None,
        min_frames: Optional[int] = None,
        max_frames: Optional[int] = None,
        num_frames: Optional[int] = None,
        fps: Optional[Union[int, float]] = None,
        **kwargs,
    ):
        """
        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:
            metadata (`VideoMetadata`):
                Metadata of the video containing information about total duration, fps and total number of frames.
            temporal_patch_size (`int`, *optional*):
                The temporal patch size of the vision encoder. Number of sampled frames will be rounded to be divisible by frame factor.
            min_frames (`int`, *optional*):
                The minimum number of frames that can be sampled.
            max_frames (`int`, *optional*):
                The maximum number of frames that can be sampled.
            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`.

        Returns:
            np.ndarray:
                Indices to sample video frames.
        """
        if fps is not None and num_frames is not None:
            raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!")

        num_frames = num_frames if num_frames is not None else self.num_frames
        fps = fps if fps is not None else self.fps
        temporal_patch_size = temporal_patch_size if temporal_patch_size is not None else self.temporal_patch_size
        min_frames = min_frames if min_frames is not None else self.min_frames
        max_frames = max_frames if max_frames is not None else self.max_frames
        total_num_frames = metadata.total_num_frames

        # If num_frames is not given but fps is, calculate num_frames from fps
        if num_frames is not None:
            num_frames = round(num_frames / temporal_patch_size) * temporal_patch_size
        elif fps is not None:
            if metadata is None or metadata.fps 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"
                )
            max_frames = math.floor(min(max_frames, total_num_frames) / temporal_patch_size) * temporal_patch_size
            num_frames = total_num_frames / metadata.fps * fps
            num_frames = min(min(max(num_frames, min_frames), max_frames), total_num_frames)
            num_frames = math.floor(num_frames / temporal_patch_size) * temporal_patch_size

        if num_frames > total_num_frames:
            raise ValueError(
                f"Video can't be sampled. The inferred `num_frames={num_frames}` exceeds `total_num_frames={total_num_frames}`. "
                "Decrease `num_frames` or `fps` for sampling."
            )

        if num_frames is not None:
            indices = torch.arange(0, total_num_frames, total_num_frames / num_frames).int()
        else:
            indices = torch.arange(0, total_num_frames).int()

        return indices

    def _preprocess(
        self,
        videos: list["torch.Tensor"],
        do_convert_rgb: bool,
        do_resize: bool,
        size: SizeDict,
        interpolation: Optional["F.InterpolationMode"],
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: Optional[Union[float, list[float]]],
        image_std: Optional[Union[float, list[float]]],
        min_pixels: Optional[int] = None,
        max_pixels: Optional[int] = None,
        patch_size: Optional[int] = None,
        temporal_patch_size: Optional[int] = None,
        merge_size: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        device: Optional["torch.Tensor"] = None,
        **kwargs,
    ):
        # Ensure gradients are enabled for the input videos
        # You can set requires_grad here.
        videos = [v.requires_grad_() for v in videos]

        # The rest of the original code follows...
        # 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():
            # The operations inside this loop (smart_resize, resize, rescale_and_normalize, etc.)
            # must also be performed outside a no_grad() context.
            # This is typically handled by the caller, so the modification is mostly at the top.
            
            # ... (rest of the original code)
        
            # Ensure that intermediate operations don't break the gradient flow.
            # The key is that the initial tensors need requires_grad=True.
            height, width = get_image_size(stacked_videos[0], channel_dim=ChannelDimension.FIRST)
            resized_height, resized_width = height, width
            
            if do_resize:
                # The smart_resize function likely doesn't have a gradient path, but the resize op does.
                resized_height, resized_width = smart_resize(
                    height,
                    width,
                    factor=patch_size * merge_size,
                    min_pixels=min_pixels,
                    max_pixels=max_pixels,
                )
                stacked_videos = self.resize(
                    image=stacked_videos,
                    size=SizeDict(height=resized_height, width=resized_width),
                    interpolation=interpolation,
                )
            resized_videos_grouped[shape] = stacked_videos
        resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index)

        grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos)
        processed_videos_grouped = {}
        processed_grids = {}
        
        for shape, stacked_videos in grouped_videos.items():
            resized_height, resized_width = get_image_size(stacked_videos[0], channel_dim=ChannelDimension.FIRST)

            # Rescale and normalize
            # This operation must be differentiable
            stacked_videos = self.rescale_and_normalize(
                stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std
            )
            patches = stacked_videos

            # The reshaping and permutation operations are also differentiable
            # ... (rest of the original code)
            
            batch_size, grid_t, channel = patches.shape[:3]
            grid_t = grid_t // temporal_patch_size
            grid_h, grid_w = resized_height // patch_size, resized_width // patch_size

            # This part is key for the gradient to flow through the patch creation
            patches = patches.view(
                batch_size,
                grid_t,
                temporal_patch_size,
                channel,
                grid_h // merge_size,
                merge_size,
                patch_size,
                grid_w // merge_size,
                merge_size,
                patch_size,
            )
            patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
            flatten_patches = patches.reshape(
                batch_size,
                grid_t * grid_h * grid_w,
                channel * temporal_patch_size * patch_size * patch_size,
            )

            processed_videos_grouped[shape] = flatten_patches
            processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size

        processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
        processed_grids = reorder_videos(processed_grids, grouped_videos_index)
        pixel_values_videos = torch.cat(processed_videos, dim=0)
        video_grid_thw = torch.tensor(processed_grids)

        return BatchFeature(
            data={"pixel_values_videos": pixel_values_videos, "video_grid_thw": video_grid_thw},
            tensor_type=return_tensors,
        )
        
    def get_num_of_video_patches(self, num_frames: int, height: int, width: int, videos_kwargs=None):
        """
        A utility that returns number of video patches a given video size.

        Args:
            num_frames (`int`):
                Number of frames in the input video.
            height (`int`):
                Height of the input video.
            width (`int`):
                Width of the input video.
            videos_kwargs (`dict`, *optional*)
                Any kwargs to override defaults of the video processor.
        Returns:
            `Tuple(int, int)`: Number of placeholder tokens required and number of patches per image.
        """
        min_pixels = videos_kwargs.get("min_pixels", None) or self.size["shortest_edge"]
        max_pixels = videos_kwargs.get("max_pixels", None) or self.size["longest_edge"]
        patch_size = videos_kwargs.get("patch_size", None) or self.patch_size
        merge_size = videos_kwargs.get("merge_size", None) or self.merge_size
        temporal_patch_size = videos_kwargs.get("temporal_patch_size", None) or self.temporal_patch_size

        factor = patch_size * merge_size
        resized_height, resized_width = smart_resize(
            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
        )
        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
        grid_t = num_frames // temporal_patch_size
        return grid_t * grid_h * grid_w


__all__ = ["Qwen2VLVideoProcessor"]