186 lines
9.4 KiB
Python
186 lines
9.4 KiB
Python
import json
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from argparse import ArgumentParser
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import torch
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import os
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import json
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from tqdm import tqdm
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from PIL import Image
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import math
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import multiprocessing
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from multiprocessing import Pool, Queue, Manager
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import sys
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sys.path.append("./scripts/mPLUG-Owl/mPLUG-Owl/")
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from mplug_owl.modeling_mplug_owl import MplugOwlForConditionalGeneration
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from mplug_owl.tokenization_mplug_owl import MplugOwlTokenizer
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from mplug_owl.processing_mplug_owl import MplugOwlImageProcessor, MplugOwlProcessor
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# https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl
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def split_list(lst, n):
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length = len(lst)
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avg = length // n # 每份的大小
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result = [] # 存储分割后的子列表
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for i in range(n - 1):
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result.append(lst[i*avg:(i+1)*avg])
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result.append(lst[(n-1)*avg:])
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return result
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def save_json(json_list,save_path):
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with open(save_path, 'w') as file:
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json.dump(json_list, file,indent=4)
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def _get_args():
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parser = ArgumentParser()
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parser.add_argument("--image_folder", type=str, default="./OCRBench_Images")
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parser.add_argument("--output_folder", type=str, default="./results")
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parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
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parser.add_argument("--model_path", type=str, default="./model_weights/mplug-owl")
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parser.add_argument("--save_name", type=str, default="mplug-owl")
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parser.add_argument("--num_workers", type=int, default=8)
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args = parser.parse_args()
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return args
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OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0,
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"Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,"Doc-oriented VQA":0,
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"Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0}
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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,
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"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}
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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,
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"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}
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def eval_worker(args, data, eval_id, output_queue):
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print(f"Process {eval_id} start.")
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pretrained_ckpt = args.model_path
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model = MplugOwlForConditionalGeneration.from_pretrained(
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pretrained_ckpt,
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torch_dtype=torch.bfloat16,
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)
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model.to(f"cuda:{eval_id}")
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image_processor = MplugOwlImageProcessor.from_pretrained(pretrained_ckpt)
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tokenizer = MplugOwlTokenizer.from_pretrained(pretrained_ckpt)
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processor = MplugOwlProcessor(image_processor, tokenizer)
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for i in tqdm(range(len(data))):
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img_path = os.path.join(args.image_folder, data[i]['image_path'])
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qs = data[i]['question']
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prompts = [
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f'''The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
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Human: <image>
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Human: {qs}
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AI: ''']
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if data[i].get("predict", 0)!=0:
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print(f"{img_path} predict exist, continue.")
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continue
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generate_kwargs = {
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'do_sample': False,
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'top_k': 1,
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'max_length': 100
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}
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images = [Image.open(img_path)]
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inputs = processor(text=prompts, images=images, return_tensors='pt')
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inputs = {k: v.bfloat16() if v.dtype == torch.float else v for k, v in inputs.items()}
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inputs = {k: v.to(model.device) for k, v in inputs.items()}
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with torch.no_grad():
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res = model.generate(**inputs, **generate_kwargs)
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sentence = tokenizer.decode(res.tolist()[0], skip_special_tokens=True)
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data[i]['predict'] = sentence
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output_queue.put({eval_id: data})
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print(f"Process {eval_id} has completed.")
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if __name__=="__main__":
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multiprocessing.set_start_method('spawn')
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args = _get_args()
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if os.path.exists(os.path.join(args.output_folder,f"{args.save_name}.json")):
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data_path = os.path.join(args.output_folder,f"{args.save_name}.json")
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print(f"output_path:{data_path} exist! Only generate the results that were not generated in {data_path}.")
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else:
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data_path = args.OCRBench_file
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with open(data_path, "r") as f:
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data = json.load(f)
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data_list = split_list(data, args.num_workers)
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output_queue = Manager().Queue()
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pool = Pool(processes=args.num_workers)
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for i in range(len(data_list)):
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pool.apply_async(eval_worker, args=(args, data_list[i], i, output_queue))
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pool.close()
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pool.join()
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results = {}
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while not output_queue.empty():
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result = output_queue.get()
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results.update(result)
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data = []
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for i in range(len(data_list)):
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data.extend(results[i])
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for i in range(len(data)):
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data_type = data[i]["type"]
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dataset_name = data[i]["dataset_name"]
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answers = data[i]["answers"]
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if data[i].get('predict',0)==0:
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continue
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predict = data[i]['predict']
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data[i]['result'] = 0
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if dataset_name == "HME100k":
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if type(answers)==list:
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for j in range(len(answers)):
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answer = answers[j].strip().replace("\n"," ").replace(" ","")
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predict = predict.strip().replace("\n"," ").replace(" ","")
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if answer in predict:
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data[i]['result'] = 1
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else:
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answers = answers.strip().replace("\n"," ").replace(" ","")
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predict = predict.strip().replace("\n"," ").replace(" ","")
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if answers in predict:
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data[i]['result'] = 1
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else:
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if type(answers)==list:
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for j in range(len(answers)):
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answer = answers[j].lower().strip().replace("\n"," ")
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predict = predict.lower().strip().replace("\n"," ")
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if answer in predict:
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data[i]['result'] = 1
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else:
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answers = answers.lower().strip().replace("\n"," ")
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predict = predict.lower().strip().replace("\n"," ")
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if answers in predict:
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data[i]['result'] = 1
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save_json(data, os.path.join(args.output_folder,f"{args.save_name}.json"))
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if len(data)==1000:
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for i in range(len(data)):
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if data[i].get("result",100)==100:
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continue
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OCRBench_score[data[i]['type']] += data[i]['result']
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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']
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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']
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print("###########################OCRBench##############################")
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print(f"Text Recognition(Total 300):{recognition_score}")
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print("------------------Details of Recognition Score-------------------")
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print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}")
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print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}")
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print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}")
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print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}")
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print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}")
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print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}")
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print("----------------------------------------------------------------")
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print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}")
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print("----------------------------------------------------------------")
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print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}")
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print("----------------------------------------------------------------")
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print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}")
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print("----------------------------------------------------------------")
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print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}")
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print("----------------------Final Score-------------------------------")
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print(f"Final Score(Total 1000): {Final_score}")
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else:
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for i in range(len(data)):
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num_all[data[i]['dataset_name']] += 1
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if data[i].get("result",100)==100:
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continue
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AllDataset_score[data[i]['dataset_name']] += data[i]['result']
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for key in AllDataset_score.keys():
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print(f"{key}: {AllDataset_score[key]/float(num_all[key])}")
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