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import os |
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import sys |
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import json |
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import re |
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from collections import Counter |
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from .common import BaseMetric |
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def token_normalize(token_text, is_lower=False, is_alphanum_only=False): |
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""" |
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""" |
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if is_lower: |
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token_text = token_text.lower() |
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if is_alphanum_only: |
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token_text = re.sub('[^A-Za-z0-9]+', '', token_text) |
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return token_text |
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def text_normalize_and_tokenize(text, is_keep_blank=True, is_lower=True, is_alphanum_only=False): |
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text = text.replace("\t", " ").replace("\n", " ").replace("###", "").replace("***", "") |
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text = re.sub(r'\s+', ' ', text) |
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if not is_keep_blank: |
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text = text.replace(" ", "") |
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text_tokens = text.split(" ") if is_keep_blank else list(text) |
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text_token_normalized = [token_normalize(t, is_lower, is_alphanum_only) for t in text_tokens] |
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text_token_normalized = [x for x in text_token_normalized if len(x) > 0] |
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return text_token_normalized |
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def evaluate_single_sample(gts, preds): |
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right_num = 0 |
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gt_counter_info = dict(Counter(gts)) |
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pdt_counter_info = dict(Counter(preds)) |
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for gt_token, gt_count in gt_counter_info.items(): |
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pred_count = pdt_counter_info.get(gt_token, 0) |
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right_num += min(gt_count, pred_count) |
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return right_num |
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def calculate_metrics(response_info, gt_info, is_verbose=False): |
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""" |
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""" |
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macro_recall_list, macro_precision_list, macro_f1_list = [], [], [] |
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total_gt_num, total_pred_num, total_right_num = 0, 0, 0 |
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for file_name, fullbox_gts in gt_info.items(): |
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fullbox_preds = response_info.get(file_name, []) |
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right_num = evaluate_single_sample(fullbox_gts, fullbox_preds) |
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total_right_num += right_num |
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total_gt_num += len(fullbox_gts) |
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total_pred_num += len(fullbox_preds) |
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macro_recall = right_num / (len(fullbox_gts) + 1e-9) |
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macro_precision = right_num / (len(fullbox_preds) + 1e-9) |
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macro_f1 = 2 * macro_recall * macro_precision / (macro_recall + macro_precision + 1e-9) |
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macro_recall_list.append(macro_recall) |
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macro_precision_list.append(macro_precision) |
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macro_f1_list.append(macro_f1) |
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final_macro_recall = sum(macro_recall_list) / (len(macro_recall_list) + 1e-9) |
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final_macro_precision = sum(macro_precision_list) / (len(macro_precision_list) + 1e-9) |
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final_macro_f1 = sum(macro_f1_list) / (len(macro_f1_list) + 1e-9) |
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recall_acc = total_right_num / (total_gt_num + 1e-9) |
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preci_acc = total_right_num / (total_pred_num + 1e-9) |
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hmean = 2 * recall_acc * preci_acc / (recall_acc + preci_acc + 1e-9) |
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vbs_eval_result = { |
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'macro_recall': final_macro_recall, 'macro_precision': final_macro_precision, 'macro_f1_score': final_macro_f1, |
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'micro_recall': recall_acc, 'micro_precision': preci_acc, 'mirco_f1_score': hmean |
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} |
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eval_result = vbs_eval_result if is_verbose else {'macro_f1_score': final_macro_f1, 'mirco_f1_score': hmean} |
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return eval_result |
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class OcrEvaluator(BaseMetric): |
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def response_post_func(self, response_text, **kwargs): |
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return response_text |
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def evaluate(self, response_info, gt_info, **kwargs): |
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dataset_name = kwargs['dataset'] |
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is_word_level, is_lower, is_alphanum_only = True, True, False |
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if dataset_name in ["Arabic", "Japanese", "Korean"] or "zh" in dataset_name: |
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is_word_level = False |
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if "multi_scene_ocr" in self.group_name and is_word_level: |
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is_alphanum_only = True |
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eval_config = {"word_level": is_word_level, "alphanum_only": is_alphanum_only, "lowercase": is_lower} |
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image_pdt_info, image_gt_info = {}, {} |
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for file_name, gt_src in gt_info.items(): |
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pred_src = response_info.get(file_name, "") |
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pdt_token_list = text_normalize_and_tokenize( |
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str(pred_src).strip(), is_word_level, is_lower, is_alphanum_only) |
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gt_token_list = text_normalize_and_tokenize( |
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str(gt_src).strip(), is_word_level, is_lower, is_alphanum_only) |
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image_pdt_info[file_name] = pdt_token_list |
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image_gt_info[file_name] = gt_token_list |
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eval_result = calculate_metrics(image_pdt_info, image_gt_info, is_verbose=False) |
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return {"summary": eval_result, "metric_config": eval_config} |
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if __name__ == '__main__': |
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pass |
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