import json from argparse import ArgumentParser import torch import os import json from tqdm import tqdm from PIL import Image import math import multiprocessing from multiprocessing import Pool, Queue, Manager from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from llava.conversation import conv_templates, SeparatorStyle from llava.model.builder import load_pretrained_model from llava.utils import disable_torch_init from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path # https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/model_vqa_loader.py def split_list(lst, n): length = len(lst) avg = length // n # 每份的大小 result = [] # 存储分割后的子列表 for i in range(n - 1): result.append(lst[i*avg:(i+1)*avg]) result.append(lst[(n-1)*avg:]) return result def save_json(json_list,save_path): with open(save_path, 'w') as file: json.dump(json_list, file,indent=4) def _get_args(): parser = ArgumentParser() parser.add_argument("--image_folder", type=str, default="./OCRBench_Images") parser.add_argument("--output_folder", type=str, default="./results") parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json") parser.add_argument("--model_path", type=str, default="liuhaotian/llava-v1.5-7b") parser.add_argument("--model_base", type=str, default=None) parser.add_argument("--save_name", type=str, default="llava1_5_7b") parser.add_argument("--conv_mode", type=str, default="vicuna_v1") parser.add_argument("--num_workers", type=int, default=8) parser.add_argument("--temperature", type=float, default=0.0) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) args = parser.parse_args() return args OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0, "Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,"Doc-oriented VQA":0, "Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0} AllDataset_score = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0, "STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0} num_all = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0, "STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0} def eval_worker(args, data, eval_id, output_queue): print(f"Process {eval_id} start.") device = f"cuda:{eval_id}" disable_torch_init() model_path = os.path.expanduser(args.model_path) model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model( model_path = model_path, model_base = args.model_base, model_name = model_name,device = device) if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode: args.conv_mode = args.conv_mode + '_mmtag' print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.') for i in tqdm(range(len(data))): img_path = os.path.join(args.image_folder, data[i]['image_path']) qs = data[i]['question'] qs = qs+"\nAnswer the question using a single word or phrase." if model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN + '\n' + qs conv = conv_templates[args.conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() image = Image.open(img_path).convert('RGB') image_tensor = process_images([image], image_processor, model.config) input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0) if data[i].get("predict", 0)!=0: print(f"{img_path} predict exist, continue.") continue stop_str = conv_templates[args.conv_mode].sep if conv_templates[args.conv_mode].sep_style != SeparatorStyle.TWO else conv_templates[args.conv_mode].sep2 input_ids = input_ids.to(device=device, non_blocking=True) with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensor.to(dtype=torch.float16, device=device, non_blocking=True), do_sample=True if args.temperature > 0 else False, temperature=args.temperature, top_p=args.top_p, num_beams=args.num_beams, max_new_tokens=128, use_cache=True) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() data[i]['predict'] = outputs output_queue.put({eval_id: data}) print(f"Process {eval_id} has completed.") if __name__=="__main__": multiprocessing.set_start_method('spawn') args = _get_args() if os.path.exists(os.path.join(args.output_folder,f"{args.save_name}.json")): data_path = os.path.join(args.output_folder,f"{args.save_name}.json") print(f"output_path:{data_path} exist! Only generate the results that were not generated in {data_path}.") else: data_path = args.OCRBench_file with open(data_path, "r") as f: data = json.load(f) data_list = split_list(data, args.num_workers) output_queue = Manager().Queue() pool = Pool(processes=args.num_workers) for i in range(len(data_list)): pool.apply_async(eval_worker, args=(args, data_list[i], i, output_queue)) pool.close() pool.join() results = {} while not output_queue.empty(): result = output_queue.get() results.update(result) data = [] for i in range(len(data_list)): data.extend(results[i]) for i in range(len(data)): data_type = data[i]["type"] dataset_name = data[i]["dataset_name"] answers = data[i]["answers"] if data[i].get('predict',0)==0: continue predict = data[i]['predict'] data[i]['result'] = 0 if dataset_name == "HME100k": if type(answers)==list: for j in range(len(answers)): answer = answers[j].strip().replace("\n"," ").replace(" ","") predict = predict.strip().replace("\n"," ").replace(" ","") if answer in predict: data[i]['result'] = 1 else: answers = answers.strip().replace("\n"," ").replace(" ","") predict = predict.strip().replace("\n"," ").replace(" ","") if answers in predict: data[i]['result'] = 1 else: if type(answers)==list: for j in range(len(answers)): answer = answers[j].lower().strip().replace("\n"," ") predict = predict.lower().strip().replace("\n"," ") if answer in predict: data[i]['result'] = 1 else: answers = answers.lower().strip().replace("\n"," ") predict = predict.lower().strip().replace("\n"," ") if answers in predict: data[i]['result'] = 1 save_json(data, os.path.join(args.output_folder,f"{args.save_name}.json")) if len(data)==1000: for i in range(len(data)): if data[i].get("result",100)==100: continue OCRBench_score[data[i]['type']] += data[i]['result'] recognition_score = OCRBench_score['Regular Text Recognition']+OCRBench_score['Irregular Text Recognition']+OCRBench_score['Artistic Text Recognition']+OCRBench_score['Handwriting Recognition']+OCRBench_score['Digit String Recognition']+OCRBench_score['Non-Semantic Text Recognition'] Final_score = recognition_score+OCRBench_score['Scene Text-centric VQA']+OCRBench_score['Doc-oriented VQA']+OCRBench_score['Key Information Extraction']+OCRBench_score['Handwritten Mathematical Expression Recognition'] print("###########################OCRBench##############################") print(f"Text Recognition(Total 300):{recognition_score}") print("------------------Details of Recognition Score-------------------") print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}") print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}") print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}") print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}") print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}") print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}") print("----------------------------------------------------------------") print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}") print("----------------------------------------------------------------") print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}") print("----------------------------------------------------------------") print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}") print("----------------------------------------------------------------") print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}") print("----------------------Final Score-------------------------------") print(f"Final Score(Total 1000): {Final_score}") else: for i in range(len(data)): num_all[data[i]['dataset_name']] += 1 if data[i].get("result",100)==100: continue AllDataset_score[data[i]['dataset_name']] += data[i]['result'] for key in AllDataset_score.keys(): print(f"{key}: {AllDataset_score[key]/float(num_all[key])}")