| import os | |
| import numpy as np | |
| import torch | |
| import json | |
| def json_map(cls_id, pred_json, ann_json, types): | |
| assert len(ann_json) == len(pred_json) | |
| num = len(ann_json) | |
| predict = np.zeros((num), dtype=np.float64) | |
| target = np.zeros((num), dtype=np.float64) | |
| for i in range(num): | |
| predict[i] = pred_json[i]["scores"][cls_id] | |
| target[i] = ann_json[i]["target"][cls_id] | |
| if types == 'wider': | |
| tmp = np.where(target != 99)[0] | |
| predict = predict[tmp] | |
| target = target[tmp] | |
| num = len(tmp) | |
| if types == 'voc07': | |
| tmp = np.where(target != 0)[0] | |
| predict = predict[tmp] | |
| target = target[tmp] | |
| neg_id = np.where(target == -1)[0] | |
| target[neg_id] = 0 | |
| num = len(tmp) | |
| tmp = np.argsort(-predict) | |
| target = target[tmp] | |
| predict = predict[tmp] | |
| pre, obj = 0, 0 | |
| for i in range(num): | |
| if target[i] == 1: | |
| obj += 1.0 | |
| pre += obj / (i+1) | |
| pre /= obj | |
| return pre | |