import torch from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor import warnings from PIL import Image from .base import BaseModel from ..smp import * from ..dataset import DATASET_TYPE import pandas as pd import string import torchvision.transforms as T import transformers from torchvision.transforms.functional import InterpolationMode import random IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_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 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=5, 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_preprocess2(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 def load_image(image_file, input_size=448, min_num=1, max_num=6): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values, target_aspect_ratio def load_image2(image_file, input_size=448, target_aspect_ratio=(1,1), min_num=1, max_num=6): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images = dynamic_preprocess2(image, image_size=input_size, prior_aspect_ratio=target_aspect_ratio, use_thumbnail=True, min_num=min_num, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values class InternVLChat(BaseModel): INSTALL_REQ = False INTERLEAVE = False def __init__(self, model_path='OpenGVLab/InternVL-Chat-V1-5', load_in_8bit=False, **kwargs): assert model_path is not None assert version_cmp(transformers.__version__, '4.36.2', 'ge') self.model_path = model_path self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False) device = torch.cuda.current_device() self.device = device self.model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True, load_in_8bit=load_in_8bit).eval() if not load_in_8bit: self.model = self.model.to(device) self.image_size = self.model.config.vision_config.image_size if 'V1-1' in model_path: kwargs_default = dict(do_sample=False, max_new_tokens=1024, top_p=None, num_beams=5) else: kwargs_default = dict(do_sample=False, max_new_tokens=1024, top_p=None, num_beams=1) kwargs_default.update(kwargs) self.kwargs = kwargs_default warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ') def use_custom_prompt(self, dataset): return True def build_multi_choice_prompt(self, line, dataset=None): question = line['question'] hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None if hint is not None: question = hint + '\n' + question options = { cand: line[cand] for cand in string.ascii_uppercase if cand in line and not pd.isna(line[cand]) } for key, item in options.items(): question += f'\n{key}. {item}' prompt = question if len(options): prompt += '\n请直接回答选项字母。' if cn_string( prompt) else "\nAnswer with the option's letter from the given choices directly." else: prompt += '\n请直接回答问题。' if cn_string(prompt) else '\nAnswer the question directly.' return prompt def build_prompt(self, line, dataset=None): assert self.use_custom_prompt(dataset) assert dataset is None or isinstance(dataset, str) tgt_path = self.dump_image(line, dataset) if 'V1-1' in self.model_path: kwargs_default = dict(do_sample=False, max_new_tokens=1024, top_p=None, num_beams=5) else: kwargs_default = dict(do_sample=False, max_new_tokens=1024, top_p=None, num_beams=1) self.kwargs = kwargs_default if dataset is not None and listinstr(['MME'], dataset): question = line['question'] prompt = question + ' Answer the question using a single word or phrase.' if 'V1-2' not in self.model_path: self.kwargs = dict(do_sample=True, max_new_tokens=5, top_k=50, num_beams=5, top_p=0.9) elif dataset is not None and listinstr(['HallusionBench'], dataset): question = line['question'] prompt = question + ' Please answer yes or no. Answer the question using a single word or phrase.' elif dataset is not None and DATASET_TYPE(dataset) == 'multi-choice': prompt = self.build_multi_choice_prompt(line, dataset) elif dataset is not None and DATASET_TYPE(dataset) == 'VQA': if 'MathVista' in dataset: prompt = line['question'] elif listinstr(['LLaVABench'], dataset): question = line['question'] prompt = question + '\nAnswer this question in detail.' elif listinstr(['MMVet'], dataset): prompt = line['question'] else: question = line['question'] prompt = question + '\nAnswer the question using a single word or phrase.' else: prompt = line['question'] message = [dict(type='text', value=prompt)] message.extend([dict(type='image', value=s) for s in tgt_path]) return message def generate(self, message, dataset=None): prompt, image_path = self.message_to_promptimg(message) if dataset is not None and listinstr(['ChartQA_TEST'], dataset): self.max_num = 12 self.max_num2 = 3 elif dataset is not None and listinstr(['DocVQA_VAL', 'DocVQA_TEST', 'TextVQA_VAL'], dataset): self.max_num = 23 self.max_num2 = 15 self.min_num = 14 self.min_num2 = 5 elif dataset is not None and listinstr(['InfoVQA_VAL', 'InfoVQA_TEST'], dataset): self.max_num = 23 self.max_num2 = 5 self.min_num = 15 self.min_num2 = 3 elif dataset is not None and listinstr(['OCRBench'], dataset): self.max_num = 24 self.max_num2 = 8 self.min_num = 9 self.min_num2 = 5 else: self.max_num = 8 self.max_num2 = 4 self.min_num = 3 self.min_num2 = 1 pixel_values, target_aspect_ratio = load_image(image_path, min_num=self.min_num, max_num=self.max_num) pixel_values = pixel_values.cuda().to(torch.bfloat16) pixel_values2 = load_image2(image_path, target_aspect_ratio=target_aspect_ratio, min_num=self.min_num2, max_num=self.max_num2) pixel_values2 = pixel_values2.cuda().to(torch.bfloat16) pixel_values = torch.cat((pixel_values[:-1], pixel_values2[:-1], pixel_values[-1:]), 0) with torch.no_grad(): response = self.model.chat(self.tokenizer, pixel_values=pixel_values, target_aspect_ratio=target_aspect_ratio, question=prompt, generation_config=self.kwargs) response = response.split('[UNUSED_TOKEN_145]')[0] return response def generate_inner(self, message, dataset=None): return self.generate(message, dataset)