# 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. """Fast Image processor class for GLM-4.1V.""" from typing import Optional, Union from ...image_processing_utils import ( BatchFeature, ) from ...image_processing_utils_fast import ( BaseImageProcessorFast, DefaultFastImageProcessorKwargs, ) from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ImageInput, PILImageResampling, SizeDict, ) from ...processing_utils import Unpack from ...utils import ( TensorType, auto_docstring, is_torch_available, is_torchvision_available, is_torchvision_v2_available, logging, ) from .image_processing_glm4v import smart_resize if is_torch_available(): import torch 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 logger = logging.get_logger(__name__) class Glm4vFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): """ patch_size (`int`, *optional*, defaults to 14): The spatial 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. """ patch_size: Optional[int] temporal_patch_size: Optional[int] merge_size: Optional[int] @auto_docstring class Glm4vImageProcessorFast(BaseImageProcessorFast): do_resize = True resample = PILImageResampling.BICUBIC size = {"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 15000} do_rescale = True do_normalize = True image_mean = OPENAI_CLIP_MEAN image_std = OPENAI_CLIP_STD do_convert_rgb = True patch_size = 14 temporal_patch_size = 2 merge_size = 2 valid_kwargs = Glm4vFastImageProcessorKwargs model_input_names = ["pixel_values", "image_grid_thw"] def __init__(self, **kwargs: Unpack[Glm4vFastImageProcessorKwargs]): 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 _preprocess( self, images: list["torch.Tensor"], 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]]], patch_size: int, temporal_patch_size: int, merge_size: int, disable_grouping: Optional[bool], return_tensors: Optional[Union[str, TensorType]], **kwargs, ) -> BatchFeature: """ Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`. """ processed_images = [] processed_grids = [] all_target_sizes = [] for image in images: height, width = image.shape[-2:] resized_height, resized_width = smart_resize( num_frames=temporal_patch_size, height=height, width=width, temporal_factor=temporal_patch_size, factor=patch_size * merge_size, min_pixels=size.shortest_edge, max_pixels=size.longest_edge, ) all_target_sizes.append((resized_height, resized_width)) target_height = max([s[0] for s in all_target_sizes]) target_width = max([s[1] for s in all_target_sizes]) for image in images: if do_resize: image = self.resize( image, size=SizeDict(height=target_height, width=target_width), interpolation=interpolation, ) image = self.rescale_and_normalize( image.unsqueeze(0), do_rescale, rescale_factor, do_normalize, image_mean, image_std ).squeeze(0) patches = image.unsqueeze(0) if patches.shape[0] % temporal_patch_size != 0: repeats = patches[-1:].repeat(temporal_patch_size - (patches.shape[0] % temporal_patch_size), 1, 1, 1) patches = torch.cat([patches, repeats], dim=0) channel = patches.shape[1] grid_t = patches.shape[0] // temporal_patch_size grid_h, grid_w = target_height // patch_size, target_width // patch_size patches = patches.view( 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, 3, 6, 4, 7, 2, 1, 5, 8) flatten_patches = patches.reshape( grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size, ) processed_images.append(flatten_patches) processed_grids.append([grid_t, grid_h, grid_w]) pixel_values = torch.stack(processed_images, dim=0) image_grid_thw = torch.tensor(processed_grids) return BatchFeature( data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors ) @auto_docstring def preprocess( self, images: ImageInput, **kwargs: Unpack[Glm4vFastImageProcessorKwargs], ) -> BatchFeature: return super().preprocess(images, **kwargs) __all__ = ["Glm4vImageProcessorFast"]