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interlm

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echo840
2024-02-06 14:25:25 +08:00
parent e29f4de057
commit 52c557c8a3
14 changed files with 512 additions and 11 deletions
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2
scripts/GPT4V.py
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@@ -23,7 +23,7 @@ def save_json(json_list,save_path):
json.dump(json_list, file,indent=4)
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
parser.add_argument("--image_folder", type=str, default="./OCRBench_Images")
parser.add_argument("--output_path", type=str, default="./results")
parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
parser.add_argument("--OPENAI_API_KEY", type=str, default="")

2
scripts/Genimi.py
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@@ -21,7 +21,7 @@ def save_json(json_list,save_path):
json.dump(json_list, file,indent=4)
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
parser.add_argument("--image_folder", type=str, default="./OCRBench_Images")
parser.add_argument("--output_path", type=str, default="./results")
parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
parser.add_argument("--GOOGLE_API_KEY", type=str, default="")

2
scripts/LLaVA1_5.py
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@@ -32,7 +32,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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")

2
scripts/blip2.py
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@@ -28,7 +28,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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/blip2-opt-6.7b")

2
scripts/blip2_vicuna_instruct.py
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@@ -27,7 +27,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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/instructblip-vicuna-7b")

2
scripts/bliva.py
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@@ -36,7 +36,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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="bliva_vicuna")

161
scripts/interlm.py Normal file
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@@ -0,0 +1,161 @@
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 AutoModel, AutoTokenizer
# https://github.com/InternLM/InternLM-XComposer/tree/main/InternLM-XComposer-1.0
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='internlm/internlm-xcomposer-7b')#TODO Set the address of your model's weights
parser.add_argument("--save_name", type=str, default="internlm-xcomposer-7b") #TODO Set the name of the JSON file you save in the output_folder.
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,"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
torch.set_grad_enabled(False)
# init model and tokenizer
model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
model.tokenizer = tokenizer
for i in tqdm(range(len(data))):
img_path = os.path.join(args.image_folder, data[i]['image_path'])
qs = data[i]['question']
response = model.generate(qs, img_path)
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])}")

162
scripts/interlm2.py Normal file
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@@ -0,0 +1,162 @@
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 AutoModel, AutoTokenizer
#https://github.com/InternLM/InternLM-XComposer/tree/main
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='internlm/internlm-xcomposer2-vl-7b')#TODO Set the address of your model's weights
parser.add_argument("--save_name", type=str, default="internlm-xcomposer2-vl-7b") #TODO Set the name of the JSON file you save in the output_folder.
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,"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
torch.set_grad_enabled(False)
# init model and tokenizer
model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
for i in tqdm(range(len(data))):
img_path = os.path.join(args.image_folder, data[i]['image_path'])
qs = data[i]['question']
text = f'<ImageHere>{qs}'
with torch.cuda.amp.autocast():
response, _ = model.chat(tokenizer, query=text, image=img_path, history=[], do_sample=False)
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])}")

178
scripts/intervl.py Normal file
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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 PIL import Image
from transformers import AutoModel, CLIPImageProcessor
from transformers import AutoTokenizer
#https://github.com/OpenGVLab/InternVL
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='OpenGVLab/InternVL-Chat-Chinese-V1-1')#TODO Set the address of your model's weights
parser.add_argument("--save_name", type=str, default="InternVL-Chat-Chinese-V1-1") #TODO Set the name of the JSON file you save in the output_folder.
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,"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 = AutoModel.from_pretrained(
checkpoint,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map='cuda').eval()
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
for i in tqdm(range(len(data))):
img_path = os.path.join(args.image_folder, data[i]['image_path'])
qs = data[i]['question']
image = Image.open(img_path).convert('RGB')
image = image.resize((448, 448))
image_processor = CLIPImageProcessor.from_pretrained(checkpoint)
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
generation_config = dict(
num_beams=1,
max_new_tokens=512,
do_sample=False,
)
response = model.chat(tokenizer, pixel_values, qs, generation_config)
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])}")

2
scripts/llavar.py
View File

@@ -71,7 +71,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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/LLaVar")

2
scripts/mPLUG-owl.py
View File

@@ -31,7 +31,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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-owl")

2
scripts/mPLUG-owl2.py
View File

@@ -31,7 +31,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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")

2
scripts/minigpt4v2.py
View File

@@ -33,7 +33,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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("--cfg-path", default='./scripts/MiniGPT-4/eval_configs/minigptv2_eval.yaml')

2
scripts/monkey.py
View File

@@ -28,7 +28,7 @@ def save_json(json_list,save_path):
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./data")
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="echo840/Monkey")