"""Image processor class for KimiVL.""" import math import numpy as np from PIL import Image from typing import Optional, Union import torch import torchvision.transforms as T from torchvision.transforms.functional import InterpolationMode from transformers.image_utils import ImageInput, make_list_of_images, valid_images from transformers.image_processing_utils import BaseImageProcessor, BatchFeature from transformers.utils import TensorType IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float('inf') best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def dynamic_preprocess_msac1(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images, target_aspect_ratio def dynamic_preprocess_msac2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) new_target_ratios = [] if prior_aspect_ratio is not None: for i in target_ratios: if prior_aspect_ratio[0]%i[0] != 0 or prior_aspect_ratio[1]%i[1] != 0: new_target_ratios.append(i) else: continue # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, new_target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images class SAILVLImageProcessor(BaseImageProcessor): model_type = "sailvl" def __init__( self, patch_size: int = 14, image_mean: tuple[float, float, float] = IMAGENET_MEAN, image_std: tuple[float, float, float] = IMAGENET_STD, max_dynamic_patch: int = 10, image_size: int = 448, use_msac: bool = False, **kwargs, ): super().__init__(**kwargs) self.patch_size = patch_size self.image_mean = image_mean self.image_std = image_std self.max_dynamic_patch = max_dynamic_patch self.image_size = image_size self.use_msac = use_msac def build_transform(self, input_size): MEAN, STD = self.image_mean, self.image_std transform = T.Compose([ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD) ]) return transform def load_image(self, image, input_size=448, max_num=6, upscale=False): # image = Image.open(image_file).convert('RGB') if upscale: image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR) transform = self.build_transform(input_size=input_size) images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values def load_image_msac(self, image, input_size=448, max_num=6, upscale=False): # image = Image.open(image_file).convert('RGB') if upscale: image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR) transform = self.build_transform(input_size=input_size) images,target_aspect_ratio = dynamic_preprocess_msac1(image, image_size=input_size, use_thumbnail=True, max_num=max_num) images = images[:-1] + dynamic_preprocess_msac2(image,max_num=max_num,image_size=input_size,use_thumbnail=False,prior_aspect_ratio=target_aspect_ratio) + images[-1:] pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values def preprocess( self, images: ImageInput, return_tensors: Optional[Union[str, TensorType]] = None, ) -> BatchFeature: images = make_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) # print('图片个数：',len(images)) image_num = len(images) if image_num > 1: # image_path = [x['value'] for x in message if x['type'] == 'image'] num_patches_list = [] pixel_values_list = [] for image_idx, image_pil in enumerate(images): upscale_flag = False curr_pixel_values = self.load_image( image_pil, max_num=self.max_dynamic_patch, upscale=upscale_flag, input_size=self.image_size).cuda().to(torch.bfloat16) num_patches_list.append(curr_pixel_values.size(0)) pixel_values_list.append(curr_pixel_values) pixel_values = torch.cat(pixel_values_list, dim=0) elif image_num == 1: # image_path = [x['value'] for x in message if x['type'] == 'image'][0] image_pil = images[0] upscale_flag = False if self.use_msac: pixel_values = self.load_image_msac( image_pil, max_num=self.max_dynamic_patch, upscale=upscale_flag, input_size=self.image_size).cuda().to(torch.bfloat16) else: pixel_values = self.load_image( image_pil, max_num=self.max_dynamic_patch, upscale=upscale_flag, input_size=self.image_size).cuda().to(torch.bfloat16) num_patches_list = [pixel_values.size(0)] else: pixel_values = None num_patches_list = None # pixel_values, image_grid_hws = [], [] # for image in images: # patches, image_grid_hw = self._preprocess(image) # pixel_values.append(patches) # image_grid_hws.append(image_grid_hw) # pixel_values = torch.concat(pixel_values, dim=0) # image_grid_hws = np.array(image_grid_hws) data = {"pixel_values": pixel_values, "num_patches_list": num_patches_list} return BatchFeature(data=data, tensor_type=return_tensors)