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import json
import time
import sys
from abc import abstractmethod
from tabulate import tabulate
def pick_response_text(json_path):
"""
"""
try:
with open(json_path, "r") as f:
json_data = json.load(f)
except Exception as e:
print("--> file error: msg: {}, path: {}".format(e, json_path))
return None
for required_key in ["model_name", "response"]:
if required_key not in json_data:
print("--> required key not exists, name: {}, path: {}".format(required_key, json_path))
return None
model_name = json_data["model_name"]
model_response = json_data["response"]
response_text = None
if model_name.startswith("gpt") or model_name.startswith("o1"):
response_text = model_response.get("data", {}).get("response", {}).get("choices", [{}])[0].get("message", {}).get("content", None) # noqa: E501
elif model_name.startswith("local_"):
response_text = model_response
else:
if model_name.startswith("claude"):
content_list = model_response.get("content", None)
elif model_name.startswith("gemini"):
content_list = model_response.get("candidates", [{}])[0].get("content", {}).get("parts", None)
elif model_name.startswith("qwen"):
content_list = model_response.get("output", {}).get("choices", [{}])[0].get("message", {}).get("content", None) # noqa: E501
else:
raise NotImplementedError("The pick_response_text NOT implemented for model: {}".format(model_name))
if isinstance(content_list, list) and len(content_list) > 0:
response_text = content_list[0].get("text", None)
if response_text is None:
print("--> [error][{}] text pick error, path: {}".format(model_name, json_path))
return response_text
def load_response_from_dir(res_dir):
"""
"""
response_info = {}
for file_name in os.listdir(res_dir):
file_path = os.path.abspath(os.path.join(res_dir, file_name))
if not file_name.endswith(".json"):
print("--> skip: result file should be a json: but got: {}".format(file_path))
continue
response_text = pick_response_text(file_path)
if response_text is None:
continue
file_name_wo_ext, ext = os.path.splitext(file_name)
response_info[file_name_wo_ext] = response_text
return response_info
class BaseMetric(object):
""" BaseMetric """
""" OCRMetric """
def __init__(self, group_name, **kwargs):
self.group_name = group_name
self.kwargs = kwargs
def response_post_func(self, response_text, **kwargs):
return response_text
@abstractmethod
# Given the prediction and gt, return the evaluation results in the format of a dictionary
# results should contain a 'summary' key, for example:
# {
# "summary": {
# "f1-score": 99.99,
# "metric_name": "metric_value" # used for summary，only metric info could be placed in this dict.
# },
# "your other info": "xxx"
# }
def evaluate(self, response_info, gt_info, normalize_func=None, **kwargs):
pass
def __call__(self, pdt_res_dir, gt_info, with_response_ratio=True, **kwargs):
if isinstance(pdt_res_dir, dict):
raw_response_info = pdt_res_dir
elif os.path.exists(pdt_res_dir) and os.path.isdir(pdt_res_dir):
raw_response_info = load_response_from_dir(pdt_res_dir)
else:
return ValueError("invalid input: response dict or folder are required, but got {}".format(pdt_res_dir))
post_error_list, response_info = [], {}
response_error_list = list(gt_info.keys() - raw_response_info.keys())
for file_name, single_pdt_str in raw_response_info.items():
single_pdt_str = self.response_post_func(single_pdt_str, **kwargs)
if single_pdt_str is None:
post_error_list.append(file_name)
continue
response_info[file_name] = single_pdt_str
meta_info = {
"gt_total_num": len(gt_info), "pdt_total_num": len(response_info),
"post_error_list": post_error_list, "response_error_list": response_error_list,
}
eval_info = self.evaluate(response_info, gt_info, **kwargs)
# add response_success_ratio
if "summary" in eval_info and with_response_ratio:
success_ratio = (len(response_info) + len(post_error_list)) / (len(gt_info) + 1e-9)
eval_info["summary"].update({"response_success_ratio": success_ratio})
return meta_info, eval_info
def summary(index_path, exp_dir_base, is_weighted_sum=False):
"""
"""
with open(index_path, "r") as f:
data_list = json.load(f)
all_data_info = {}
for data_info_item in data_list:
data_name = data_info_item["dataset"]
if not data_info_item.get("release", True):
continue
all_data_info[data_name] = data_info_item
dataset_list = list(all_data_info.keys())
summary_path = summary_multi_exp(exp_dir_base, dataset_list, is_weighted_sum=is_weighted_sum)
return summary_path
def summary_multi_exp(exp_dir_base, dataset_list=None, is_weighted_sum=False):
"""
"""
if dataset_list is None:
all_dataset_name = []
for exp_name in os.listdir(exp_dir_base):
dir_status_path = os.path.join(exp_dir_base, exp_name, "status.json")
if not os.path.exists(dir_status_path):
continue
with open(dir_status_path, "r") as f:
data_status_info = json.load(f)
all_dataset_name.extend(data_status_info.keys())
dataset_list = sorted(set(all_dataset_name))
# summary main code
all_evaluate_info, _ = {}, 0
for exp_name in os.listdir(exp_dir_base):
dir_status_path = os.path.join(exp_dir_base, exp_name, "status.json")
if not os.path.exists(dir_status_path):
print("--> skip: status.json not exist: {}".format(dir_status_path))
continue
with open(dir_status_path, "r") as f:
all_status_info = json.load(f)
for data_name in dataset_list:
total_num = all_status_info.get(data_name, {}).get("config", {}).get("num", "-1")
summary_info = all_status_info.get(data_name, {}).get("evaluation", {}).get("summary", {})
for metric_name, metric_value in summary_info.items():
if metric_name not in all_evaluate_info:
all_evaluate_info[metric_name] = {}
if exp_name not in all_evaluate_info[metric_name]:
all_evaluate_info[metric_name][exp_name] = {}
all_evaluate_info[metric_name][exp_name][data_name] = (metric_value, total_num)
all_table_md = []
for metric_name, metric_info in all_evaluate_info.items():
formatted_time = time.strftime("%Y-%m-%d %H:%M", time.localtime(time.time()))
summary_line_list = []
summary_key_name = "summary(weighted)" if is_weighted_sum else "summary"
summary_head = [f"exp_name({metric_name}_{formatted_time})"] + dataset_list + [summary_key_name]
for exp_name, data_eval_info in metric_info.items():
summary_line = [exp_name, ]
all_metric_value = 0
is_summary_valid, all_total_num, all_weighted_metric = True, 0, 0
for data_name in dataset_list:
metric_value, total_num = data_eval_info.get(data_name, ("-1", "-1"))
summary_line.append("{:.2f}".format(float(metric_value) * 100))
if str(metric_value) == "-1" or str(metric_value) == "-1":
is_summary_valid = False
continue
all_total_num += float(total_num)
all_weighted_metric += float(total_num) * float(metric_value)
all_metric_value += float(metric_value)
summary_value_valid = ((all_weighted_metric / (all_total_num + 1e-9)) * 100) if is_weighted_sum \
else (all_metric_value / (len(dataset_list) + 1e-9) * 100)
summary_value = "-" if not is_summary_valid else "{:.2f}".format(summary_value_valid)
summary_line.append(summary_value)
summary_line_list.append(summary_line)
md_table_info = tabulate(summary_line_list, headers=summary_head, tablefmt='pipe')
all_table_md.append(md_table_info)
print("\n\n".join(all_table_md))
summary_path = os.path.abspath(os.path.join(exp_dir_base, "summary.md"))
with open(summary_path, "w") as f:
f.write("\n\n".join(all_table_md))
return summary_path
if __name__ == '__main__':
if len(sys.argv) != 2:
print("Usage: python {} exp_base_dir".format(__file__))
exit(-1)
else:
print('--> info: {}'.format(sys.argv))
exp_base_dir = sys.argv[1]
summary_path = summary_multi_exp(exp_base_dir, dataset_list=None, is_weighted_sum=False)
print("--> info: summary saved at : {}".format(summary_path))
print("happy coding.")
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