# coding=utf-8 # Copyright 2025 The ZhipuAI Inc. team and 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. """video processor class for GLM-4.1V.""" import math from typing import Optional, Union import numpy as np 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_vision_available, ) from .image_processing_glm4v import smart_resize if is_torch_available(): import torch 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 class Glm4vVideoProcessorInitKwargs(VideosKwargs): max_image_size: dict[str, int] = None patch_size: Optional[int] = None temporal_patch_size: Optional[int] = None merge_size: Optional[int] = None image_mean: Optional[list[float]] = None image_std: Optional[list[float]] = None @add_start_docstrings( "Constructs a fast GLM-4V image processor that dynamically resizes videos based on the original videos.", BASE_VIDEO_PROCESSOR_DOCSTRING, """ 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 Glm4vVideoProcessor(BaseVideoProcessor): resample = PILImageResampling.BICUBIC size = {"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 2 * 30000} max_image_size = {"longest_edge": 28 * 28 * 2 * 30000} image_mean = OPENAI_CLIP_MEAN image_std = OPENAI_CLIP_STD do_resize = True do_rescale = True do_normalize = True do_convert_rgb = True do_sample_frames = True patch_size = 14 temporal_patch_size = 2 max_duration = 300 merge_size = 2 valid_kwargs = Glm4vVideoProcessorInitKwargs num_frames = 16 fps = 2 model_input_names = ["pixel_values_videos", "video_grid_thw"] def __init__(self, **kwargs: Unpack[Glm4vVideoProcessorInitKwargs]): super().__init__(**kwargs) if self.size is not None and ( self.size.get("shortest_edge", None) is None or self.size.get("longest_edge", None) is None ): raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") def _further_process_kwargs( self, size: Optional[SizeDict] = None, **kwargs, ) -> dict: """ Update kwargs that need further processing before being validated Can be overridden by subclasses to customize the processing of kwargs. """ if size is not None and ("shortest_edge" not in size or "longest_edge" not in size): raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") return super()._further_process_kwargs(size=size, **kwargs) def sample_frames( self, metadata: VideoMetadata, fps: Optional[Union[int, float]] = None, **kwargs, ): """ Args: metadata (`VideoMetadata`): Metadata of the video containing information about total duration, fps and total number of 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 metadata is None or getattr(metadata, "fps", None) is None: raise ValueError( "Asked to sample frames per second but no video metadata was provided which is required when sampling in GLM4V. " "Please pass in `VideoMetadata` object or set `do_sample_frames=False`" ) total_frames = metadata.total_num_frames requested_fps = fps if fps is not None else self.fps max_frame_idx = total_frames - 1 duration = metadata.duration or round(max_frame_idx / metadata.fps) + 1 if duration <= self.max_duration: n = int(math.floor(duration * requested_fps)) frame_indices = [min(max_frame_idx, int(math.ceil(i * metadata.fps / requested_fps))) for i in range(n)] else: num_samples = int(self.max_duration * requested_fps) if num_samples >= total_frames: frame_indices = list(range(total_frames)) else: target_seconds = np.linspace(0, duration, num_samples, endpoint=True) frame_indices = [min(max_frame_idx, int(math.ceil(t * metadata.fps))) for t in target_seconds] seen, uniq = set(), [] for idx in frame_indices: if idx not in seen: seen.add(idx) uniq.append(idx) if len(uniq) & 1: uniq.append(uniq[-1]) return np.array(uniq) def _preprocess( self, videos: list[torch.Tensor], do_convert_rgb: bool = True, do_resize: bool = True, size: Optional[SizeDict] = None, interpolation: PILImageResampling = PILImageResampling.BICUBIC, do_rescale: bool = True, rescale_factor: float = 1 / 255.0, do_normalize: bool = True, image_mean: Optional[Union[float, list[float]]] = None, image_std: Optional[Union[float, list[float]]] = 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, ): grouped_videos, grouped_videos_index = group_videos_by_shape(videos) resized_videos_grouped = {} for shape, stacked_videos in grouped_videos.items(): B, T, C, H, W = stacked_videos.shape num_frames, height, width = T, H, W if do_resize: resized_height, resized_width = smart_resize( num_frames=num_frames, height=height, width=width, temporal_factor=temporal_patch_size, factor=patch_size * merge_size, min_pixels=size.shortest_edge, max_pixels=size.longest_edge, ) stacked_videos = stacked_videos.view(B * T, C, H, W) stacked_videos = self.resize( stacked_videos, size=SizeDict(height=resized_height, width=resized_width), interpolation=interpolation, ) stacked_videos = stacked_videos.view(B, T, C, resized_height, resized_width) 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, 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) data = { "pixel_values_videos": pixel_values_videos, "video_grid_thw": video_grid_thw, } return BatchFeature(data=data, tensor_type=return_tensors) __all__ = ["Glm4vVideoProcessor"]