# 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.""" from typing import List, 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 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] @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. """, ) @requires(backends=("torchvision",)) class Qwen2VLVideoProcessor(BaseVideoProcessor): resample = PILImageResampling.BICUBIC size = {"shortest_edge": 56 * 56, "longest_edge": 28 * 28 * 1280} 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 = 56 * 56 max_pixels = 28 * 28 * 1280 patch_size = 14 temporal_patch_size = 2 merge_size = 2 valid_kwargs = Qwen2VLVideoProcessorInitKwargs model_input_names = ["pixel_values_videos", "video_grid_thw"] def __init__(self, **kwargs: Unpack[Qwen2VLVideoProcessorInitKwargs]): super().__init__(**kwargs) self.size = {"shortest_edge": self.min_pixels, "longest_edge": self.max_pixels} 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, **kwargs, ): # 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 = F.resize( stacked_videos, size=(resized_height, 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, ) __all__ = ["Qwen2VLVideoProcessor"]