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# -*- coding: utf-8 -*-
# Author: Gaojian Wang@ZJUICSR
# --------------------------------------------------------
# This source code is licensed under the Attribution-NonCommercial 4.0 International License.
# You can find the license in the LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import auc, accuracy_score, balanced_accuracy_score
from scipy.optimize import brentq
from scipy.interpolate import interp1d
def frame_level_acc(labels, y_preds):
return accuracy_score(labels, y_preds) * 100.
def frame_level_balanced_acc(labels, y_preds):
return balanced_accuracy_score(labels, y_preds) * 100.
def frame_level_auc(labels, preds):
return roc_auc_score(labels, preds) * 100.
def frame_level_eer(labels, preds):
# 推荐；更正确的，MaskRelation（TIFS23也是）
fpr, tpr, thresholds = roc_curve(labels, preds, pos_label=1) # 如果标签不是二进制的，则应显式地给出pos_标签
eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
# eer_thresh = interp1d(fpr, thresholds)(eer)
return eer
# def frame_level_eer(labels, preds):
# fpr, tpr, thresholds = roc_curve(labels, preds, pos_label=1)
# eer_threshold = thresholds[(fpr + (1 - tpr)).argmin()]
# fpr_eer = fpr[thresholds == eer_threshold][0]
# fnr_eer = 1 - tpr[thresholds == eer_threshold][0]
# eer = (fpr_eer + fnr_eer) / 2
# metric_logger.meters['eer'].update(eer)
# return eer, eer_thresh
def get_video_level_label_pred(f_label_list, v_name_list, f_pred_list):
"""
References:
CADDM: https://github.com/megvii-research/CADDM
"""
video_res_dict = dict()
video_pred_list = list()
video_y_pred_list = list()
video_label_list = list()
# summarize all the results for each video
for label, video, score in zip(f_label_list, v_name_list, f_pred_list):
if video not in video_res_dict.keys():
video_res_dict[video] = {"scores": [score], "label": label}
else:
video_res_dict[video]["scores"].append(score)
# get the score and label for each video
for video, res in video_res_dict.items():
score = sum(res['scores']) / len(res['scores'])
label = res['label']
video_pred_list.append(score)
video_label_list.append(label)
video_y_pred_list.append(score >= 0.5)
return video_label_list, video_pred_list, video_y_pred_list
def video_level_acc(video_label_list, video_y_pred_list):
return accuracy_score(video_label_list, video_y_pred_list) * 100.
def video_level_balanced_acc(video_label_list, video_y_pred_list):
return balanced_accuracy_score(video_label_list, video_y_pred_list) * 100.
def video_level_auc(video_label_list, video_pred_list):
return roc_auc_score(video_label_list, video_pred_list) * 100.
def video_level_eer(video_label_list, video_pred_list):
# 推荐；更正确的，MaskRelation（TIFS23也是）
fpr, tpr, thresholds = roc_curve(video_label_list, video_pred_list, pos_label=1) # 如果标签不是二进制的，则应显式地给出pos_标签
v_eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
# eer_thresh = interp1d(fpr, thresholds)(eer)
return v_eer
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