192 lines
10 KiB
Python
192 lines
10 KiB
Python
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 TextStreamer
|
|
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
|
|
from mplug_owl2.conversation import conv_templates, SeparatorStyle
|
|
from mplug_owl2.model.builder import load_pretrained_model
|
|
from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
|
|
|
|
# https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2
|
|
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="./model_weights/mplug-owl2")
|
|
parser.add_argument("--save_name", type=str, default="mplug-owl2")
|
|
parser.add_argument("--conv_mode", type=str, default="mplug_owl2")
|
|
parser.add_argument("--num_workers", type=int, default=8)
|
|
parser.add_argument("--temperature", type=float, default=0.0)
|
|
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.")
|
|
model_name = get_model_name_from_path(args.model_path)
|
|
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, None, model_name, load_8bit=False, load_4bit=False, device=f"cuda:{eval_id}")
|
|
for i in tqdm(range(len(data))):
|
|
img_path = os.path.join(args.image_folder, data[i]['image_path'])
|
|
qs = data[i]['question']
|
|
if data[i].get("predict", 0)!=0:
|
|
print(f"{img_path} predict exist, continue.")
|
|
continue
|
|
conv = conv_templates[args.conv_mode].copy()
|
|
roles = conv.roles
|
|
image = Image.open(img_path).convert('RGB')
|
|
max_edge = max(image.size) # We recommand you to resize to squared image for BEST performance.
|
|
image = image.resize((max_edge, max_edge))
|
|
image_tensor = process_images([image], image_processor)
|
|
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
|
|
|
|
inp = DEFAULT_IMAGE_TOKEN + qs
|
|
conv.append_message(conv.roles[0], inp)
|
|
conv.append_message(conv.roles[1], None)
|
|
prompt = conv.get_prompt()
|
|
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)
|
|
stop_str = conv.sep2
|
|
keywords = [stop_str]
|
|
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
|
|
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
|
|
with torch.inference_mode():
|
|
output_ids = model.generate(
|
|
input_ids,
|
|
images=image_tensor,
|
|
do_sample=False,
|
|
temperature=args.temperature,
|
|
max_new_tokens=100,
|
|
streamer=streamer,
|
|
use_cache=True,
|
|
stopping_criteria=[stopping_criteria])
|
|
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).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])}")
|