Create qwenvl_api.py
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138
scripts/qwenvl_api.py
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138
scripts/qwenvl_api.py
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import pathlib
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from argparse import ArgumentParser
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import json
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from tqdm import tqdm
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import os
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import sys
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from http import HTTPStatus
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from dashscope import MultiModalConversation
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import time
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# You should follow the instructions here befor strat: https://help.aliyun.com/zh/dashscope/developer-reference/vl-plus-quick-start
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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,
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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 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 call_with_local_file(img_path, question, model_name):
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"""Sample of use local file.
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linux&mac file schema: file:///home/images/test.png
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windows file schema: file://D:/images/abc.png
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"""
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local_file_path1 = f'file://{img_path}'
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messages = [{
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'role': 'system',
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'content': [{
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'text': 'You are a helpful assistant.'
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}]
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}, {
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'role':
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'user',
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'content': [
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{
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'image': local_file_path1
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},
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{
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'text': question
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},
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]
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}]
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response = MultiModalConversation.call(model=model_name, messages=messages)
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# time.sleep(2) #For qwenvl-max you may need to add this line to avoid the limits.
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print(response)
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return response['output']['choices'][0]["message"]['content'][0]['text']
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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="./data")
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parser.add_argument("--output_path", 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", type=str, default="qwen-vl-max")
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = _get_args()
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if os.path.exists(os.path.join(args.output_path,f"{args.model}.json")):
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data_path = os.path.join(args.output_path,f"{args.model}.json")
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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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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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question = data[i]['question']
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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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try:
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response = call_with_local_file(img_path, question, args.model)
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data[i]['predict'] = response
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except:
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print("QwenVL api failed")
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save_json(data, os.path.join(args.output_path,f"{args.model}.json"))
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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_path,f"{args.model}.json"))
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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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