# 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 import torch import torchvision.transforms.v2.functional as tvF from ...image_processing_utils import BatchFeature from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling, SizeDict, ) from ...processing_utils import Unpack, VideosKwargs from ...utils import TensorType, auto_docstring, logging from ...video_processing_utils import BaseVideoProcessor from ...video_utils import VideoMetadata, group_videos_by_shape, reorder_videos logger = logging.get_logger(__name__) # Copied from transformers.models.qwen2_vl.image_processing_qwen2_vl.smart_resize def smart_resize( height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280 ): """Rescales the image so that the following conditions are met: 1. Both dimensions (height and width) are divisible by 'factor'. 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. 3. The aspect ratio of the image is maintained as closely as possible. """ if max(height, width) / min(height, width) > 200: raise ValueError( f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" ) h_bar = round(height / factor) * factor w_bar = round(width / factor) * factor if h_bar * w_bar > max_pixels: beta = math.sqrt((height * width) / max_pixels) h_bar = max(factor, math.floor(height / beta / factor) * factor) w_bar = max(factor, math.floor(width / beta / factor) * factor) elif h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (height * width)) h_bar = math.ceil(height * beta / factor) * factor w_bar = math.ceil(width * beta / factor) * factor return h_bar, w_bar class Qwen2VLVideoProcessorInitKwargs(VideosKwargs, total=False): r""" 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 spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to 1): 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. use_token_compression (`bool`, *optional*, defaults to `True`): Whether to compress videos when processing or not. cap_pixels_per_frame (`bool`, *optional*): Whether to bound a video's total pixel cost the way the reference implementation (qwen-vl-utils) does: on top of the per-frame `size["longest_edge"]` cap, each frame is limited to an even share of the total-video pixel budget (`max_video_tokens` tokens' worth of pixels), floored at `1.05 * size["shortest_edge"]`, so densely sampled videos cannot grow without bound. If unset, the current behavior (no total bound) is kept and a warning is emitted: the default will change to `True` in v5.22, after which the argument will be removed. max_video_tokens (`int`, *optional*, defaults to 128000): The model context length assumed when deriving the total-video pixel budget used by `cap_pixels_per_frame` (the budget is 90% of this many tokens. """ min_pixels: int max_pixels: int patch_size: int temporal_patch_size: int merge_size: int min_frames: int max_frames: int cap_pixels_per_frame: bool max_video_tokens: int @auto_docstring 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 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 cap_pixels_per_frame = None max_video_tokens = 128000 valid_kwargs = Qwen2VLVideoProcessorInitKwargs model_input_names = ["pixel_values_videos", "video_grid_thw"] def __init__(self, **kwargs: Unpack[Qwen2VLVideoProcessorInitKwargs]): # backward compatibility: override size with min_pixels and max_pixels if they are provided size = kwargs.pop("size", None) size = self.size if size is None else size if (min_pixels := kwargs.pop("min_pixels", None)) is not None: size["shortest_edge"] = min_pixels size.pop("min_pixels", None) if (max_pixels := kwargs.pop("max_pixels", None)) is not None: size["longest_edge"] = max_pixels size.pop("max_pixels", None) super().__init__(size=size, **kwargs) def _standardize_kwargs( self, size: SizeDict | None = None, min_pixels: int | None = None, max_pixels: int | None = None, **kwargs, ) -> dict: if min_pixels is not None and max_pixels is not None: size = SizeDict(shortest_edge=min_pixels, longest_edge=max_pixels) return super()._standardize_kwargs(size=size, **kwargs) def sample_frames( self, metadata: VideoMetadata, temporal_patch_size: int | None = None, min_frames: int | None = None, max_frames: int | None = None, num_frames: int | None = None, fps: int | float | None = 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 uniformly. 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(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 resize( self, videos: "torch.Tensor", size: SizeDict, resample: "PILImageResampling | tvF.InterpolationMode | int | None", factor: int, temporal_factor: int = 2, cap_pixels_per_frame: bool | None = None, **kwargs, ) -> "torch.Tensor": """Resize dynamically based on input video aspect ratio.""" if not size.shortest_edge or not size.longest_edge: raise ValueError(f"`size` dict must contain 'shortest_edge' and 'longest_edge' keys but got {size}.") max_pixels = size.longest_edge if cap_pixels_per_frame: # the per-frame cap (`size.longest_edge`) is bounded by an even share of the `max_video_tokens` num_frames = videos.shape[1] total_pixels = int(self.max_video_tokens * factor * factor * 0.9) max_pixels = max( min(max_pixels, total_pixels * temporal_factor // num_frames), int(size.shortest_edge * 1.05) ) height, width = videos.shape[-2:] resized_height, resized_width = smart_resize( height, width, factor=factor, min_pixels=size.shortest_edge, max_pixels=max_pixels, ) return super().resize( image=videos, size=SizeDict(height=resized_height, width=resized_width), resample=resample, ) def patchify( self, videos: "torch.Tensor", patch_size: int, merge_size: int, temporal_patch_size: int, ) -> tuple["torch.Tensor", int, int]: "Patchifies each video into flat layout of shape (`seq_len`, `patch_dim`) so we can concat dynamically shaped pixels." batch_size, num_frames, channel, resized_height, resized_width = videos.shape # Check that videos have `num_frames` divisible by `temporal_patch_size` if pad := -num_frames % temporal_patch_size: repeats = videos[:, -1:].expand(-1, pad, -1, -1, -1) videos = torch.cat((videos, repeats), dim=1) num_frames += pad grid_t = num_frames // temporal_patch_size grid_h, grid_w = resized_height // patch_size, resized_width // patch_size patches = videos.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, ) return flatten_patches, grid_t, grid_h, grid_w def _preprocess( self, videos: list["torch.Tensor"], do_convert_rgb: bool, do_resize: bool, size: SizeDict, resample: "PILImageResampling | tvF.InterpolationMode | int | None", do_rescale: bool, rescale_factor: float, do_normalize: bool, image_mean: float | list[float] | None, image_std: float | list[float] | None, patch_size: int | None = None, temporal_patch_size: int | None = None, merge_size: int | None = None, cap_pixels_per_frame: bool | None = None, return_tensors: str | TensorType | None = None, **kwargs, ): if cap_pixels_per_frame is None: logger.warning_once( "Qwen2VL video processing does not apply the per-frame pixel cap the reference " "implementation (qwen-vl-utils) applies, so some videos cost far more tokens than they " "would there. In v5.22 the capped behavior will become the default and " "`cap_pixels_per_frame` will be removed. Pass `cap_pixels_per_frame=True` to adopt the " "reference behavior now, or `False` to keep the current behavior and silence this " "warning." ) cap_pixels_per_frame = False # 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( videos=stacked_videos, size=size, resample=resample, factor=patch_size * merge_size, temporal_factor=temporal_patch_size, cap_pixels_per_frame=cap_pixels_per_frame, ) 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(): # Fused rescale and normalize stacked_videos = self.rescale_and_normalize( stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std ) patches, grid_t, grid_h, grid_w = self.patchify( stacked_videos, patch_size=patch_size, merge_size=merge_size, temporal_patch_size=temporal_patch_size, ) processed_videos_grouped[shape] = patches processed_grids[shape] = [[grid_t, grid_h, grid_w]] * len(stacked_videos) 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"]