# 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 from ...image_processing_utils import ( BatchFeature, ) from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, SizeDict, get_image_size, ) from ...processing_utils import Unpack, VideosKwargs from ...utils import ( TensorType, add_start_docstrings, is_torch_available, is_torchvision_available, is_torchvision_v2_available, is_vision_available, ) from ...utils.import_utils import requires from ...video_processing_utils import ( BASE_VIDEO_PROCESSOR_DOCSTRING, BaseVideoProcessor, ) from ...video_utils import VideoMetadata, group_videos_by_shape, reorder_videos if is_vision_available(): from ...image_utils import PILImageResampling from .image_processing_qwen2_vl import smart_resize 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 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. """, ) @requires(backends=("torchvision",)) 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, video: "torch.Tensor", frame_factor: int, min_frames: int, max_frames: int, metadata: Optional[Union[VideoMetadata, dict]] = None, num_frames: Optional[int] = None, fps: Optional[Union[int, float]] = 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. frame_factor (`int`): The temporal patch size of the vision encoder. Number of sampled frames will be rounded to be divisible by frame factor. min_frames (`int`): The minimum number of frames that can be sampled. max_frames (`int`): The maximum number of frames that can 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`. Returns: torch.Tensor: Sampled 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 total_num_frames = video.shape[0] # 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 / frame_factor) * frame_factor elif 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" ) max_frames = math.floor(min(max_frames, total_num_frames) / frame_factor) * frame_factor 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 / frame_factor) * frame_factor 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() 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, interpolation: Optional["F.InterpolationMode"], do_rescale: 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]]], 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, fps: Optional[Union[int, float]] = None, num_frames: Optional[int] = None, min_frames: Optional[int] = None, max_frames: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, device: Optional["torch.Tensor"] = None, **kwargs, ): if do_sample_frames: # Sample video frames videos = [ self.sample_frames( video, frame_factor=temporal_patch_size, min_frames=min_frames, max_frames=max_frames, metadata=metadata, num_frames=num_frames, fps=fps, ) 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(): height, width = get_image_size(stacked_videos[0], channel_dim=ChannelDimension.FIRST) resized_height, resized_width = height, width if do_resize: 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) # 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 = {} processed_grids = {} for shape, stacked_videos in grouped_videos.items(): resized_height, resized_width = get_image_size(stacked_videos[0], channel_dim=ChannelDimension.FIRST) # Fused rescale and normalize stacked_videos = self.rescale_and_normalize( stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std ) patches = stacked_videos # Check that videos have `num_frames` divisible by `temporal_patch_size` if patches.shape[1] % temporal_patch_size != 0: repeats = patches[:, -1:].repeat(1, self.temporal_patch_size - 1, 1, 1, 1) patches = torch.cat([patches, repeats], dim=1) 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 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"]