File size: 4,366 Bytes
b5beb60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | import os
import sys
import json
import re
from collections import Counter
# local import
from .common import BaseMetric
def token_normalize(token_text, is_lower=False, is_alphanum_only=False):
"""
"""
if is_lower:
token_text = token_text.lower()
if is_alphanum_only:
token_text = re.sub('[^A-Za-z0-9]+', '', token_text)
return token_text
def text_normalize_and_tokenize(text, is_keep_blank=True, is_lower=True, is_alphanum_only=False):
text = text.replace("\t", " ").replace("\n", " ").replace("###", "").replace("***", "")
text = re.sub(r'\s+', ' ', text)
if not is_keep_blank:
text = text.replace(" ", "")
text_tokens = text.split(" ") if is_keep_blank else list(text)
text_token_normalized = [token_normalize(t, is_lower, is_alphanum_only) for t in text_tokens]
text_token_normalized = [x for x in text_token_normalized if len(x) > 0]
return text_token_normalized
def evaluate_single_sample(gts, preds):
right_num = 0
gt_counter_info = dict(Counter(gts))
pdt_counter_info = dict(Counter(preds))
for gt_token, gt_count in gt_counter_info.items():
pred_count = pdt_counter_info.get(gt_token, 0)
right_num += min(gt_count, pred_count)
return right_num
def calculate_metrics(response_info, gt_info, is_verbose=False):
"""
"""
macro_recall_list, macro_precision_list, macro_f1_list = [], [], []
total_gt_num, total_pred_num, total_right_num = 0, 0, 0
for file_name, fullbox_gts in gt_info.items():
fullbox_preds = response_info.get(file_name, [])
right_num = evaluate_single_sample(fullbox_gts, fullbox_preds)
total_right_num += right_num
total_gt_num += len(fullbox_gts)
total_pred_num += len(fullbox_preds)
macro_recall = right_num / (len(fullbox_gts) + 1e-9)
macro_precision = right_num / (len(fullbox_preds) + 1e-9)
macro_f1 = 2 * macro_recall * macro_precision / (macro_recall + macro_precision + 1e-9)
macro_recall_list.append(macro_recall)
macro_precision_list.append(macro_precision)
macro_f1_list.append(macro_f1)
# marco
final_macro_recall = sum(macro_recall_list) / (len(macro_recall_list) + 1e-9)
final_macro_precision = sum(macro_precision_list) / (len(macro_precision_list) + 1e-9)
final_macro_f1 = sum(macro_f1_list) / (len(macro_f1_list) + 1e-9)
# micro
recall_acc = total_right_num / (total_gt_num + 1e-9)
preci_acc = total_right_num / (total_pred_num + 1e-9)
hmean = 2 * recall_acc * preci_acc / (recall_acc + preci_acc + 1e-9)
vbs_eval_result = {
'macro_recall': final_macro_recall, 'macro_precision': final_macro_precision, 'macro_f1_score': final_macro_f1,
'micro_recall': recall_acc, 'micro_precision': preci_acc, 'mirco_f1_score': hmean
}
eval_result = vbs_eval_result if is_verbose else {'macro_f1_score': final_macro_f1, 'mirco_f1_score': hmean}
return eval_result
class OcrEvaluator(BaseMetric):
def response_post_func(self, response_text, **kwargs):
return response_text
def evaluate(self, response_info, gt_info, **kwargs):
# hard code here
dataset_name = kwargs['dataset']
is_word_level, is_lower, is_alphanum_only = True, True, False
if dataset_name in ["Arabic", "Japanese", "Korean"] or "zh" in dataset_name:
is_word_level = False
if "multi_scene_ocr" in self.group_name and is_word_level:
is_alphanum_only = True
eval_config = {"word_level": is_word_level, "alphanum_only": is_alphanum_only, "lowercase": is_lower}
image_pdt_info, image_gt_info = {}, {}
for file_name, gt_src in gt_info.items():
pred_src = response_info.get(file_name, "")
pdt_token_list = text_normalize_and_tokenize(
str(pred_src).strip(), is_word_level, is_lower, is_alphanum_only)
gt_token_list = text_normalize_and_tokenize(
str(gt_src).strip(), is_word_level, is_lower, is_alphanum_only)
image_pdt_info[file_name] = pdt_token_list
image_gt_info[file_name] = gt_token_list
eval_result = calculate_metrics(image_pdt_info, image_gt_info, is_verbose=False)
return {"summary": eval_result, "metric_config": eval_config}
if __name__ == '__main__':
pass
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