From b90446573fe3cd3a6e3a4d8f838a9785cfc48779 Mon Sep 17 00:00:00 2001 From: echo840 <87795401+echo840@users.noreply.github.com> Date: Tue, 12 Mar 2024 11:40:24 +0800 Subject: [PATCH] Create qwenvl.py --- scripts/qwenvl.py | 181 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 scripts/qwenvl.py diff --git a/scripts/qwenvl.py b/scripts/qwenvl.py new file mode 100644 index 0000000..e9e6443 --- /dev/null +++ b/scripts/qwenvl.py @@ -0,0 +1,181 @@ +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 transformers import AutoModelForCausalLM, AutoTokenizer + +# https://github.com/QwenLM/Qwen-VL/blob/master/eval_mm/evaluate_vqa.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="./data") + 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="./model_weights/qwenvl") + parser.add_argument("--save_name", type=str, default="qwenvl") + parser.add_argument("--num_workers", 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, +"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.") + checkpoint = args.model_path + model = AutoModelForCausalLM.from_pretrained( + checkpoint, device_map=f'cuda:{eval_id}', trust_remote_code=True).eval() + + tokenizer = AutoTokenizer.from_pretrained(checkpoint, + trust_remote_code=True) + tokenizer.padding_side = 'left' + tokenizer.pad_token_id = tokenizer.eod_id + + for i in tqdm(range(len(data))): + img_path = os.path.join(args.image_folder, data[i]['image_path']) + qs = data[i]['question'] + # query = f'{img_path} {qs} Answer: ' + query = f'{img_path}{qs} Answer:' + input_ids = tokenizer(query, return_tensors='pt', padding='longest') + attention_mask = input_ids.attention_mask + input_ids = input_ids.input_ids + + pred = model.generate( + input_ids=input_ids.to(f'cuda:{eval_id}'), + attention_mask=attention_mask.to(f'cuda:{eval_id}'), + do_sample=False, + num_beams=1, + max_new_tokens=100, + min_new_tokens=1, + length_penalty=1, + num_return_sequences=1, + output_hidden_states=True, + use_cache=True, + pad_token_id=tokenizer.eod_id, + eos_token_id=tokenizer.eod_id, + ) + response = tokenizer.decode(pred[0][input_ids.size(1):].cpu(), skip_special_tokens=True).strip() + data[i]['predict'] = response + 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])}")