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9e14838 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | from sklearn import metrics
import numpy as np
def parse_metric_for_print(metric_dict):
if metric_dict is None:
return "\n"
str = "\n"
str += "================================ Each dataset best metric ================================ \n"
for key, value in metric_dict.items():
if key != 'avg':
str= str+ f"| {key}: "
for k,v in value.items():
str = str + f" {k}={v} "
str= str+ "| \n"
else:
str += "============================================================================================= \n"
str += "================================== Average best metric ====================================== \n"
avg_dict = value
for avg_key, avg_value in avg_dict.items():
if avg_key == 'dataset_dict':
for key,value in avg_value.items():
str = str + f"| {key}: {value} | \n"
else:
str = str + f"| avg {avg_key}: {avg_value} | \n"
str += "============================================================================================="
return str
# def get_test_metrics(y_pred, y_true, img_names):
# def get_video_metrics(image, pred, label):
# result_dict = {}
# new_label = []
# new_pred = []
# # print(image[0])
# # print(pred.shape)
# # print(label.shape)
# for item in np.transpose(np.stack((image, pred, label)), (1, 0)):
# # 分割字符串，获取'a'和'b'的值
# s = item[0]
# if '\\' in s:
# parts = s.split('\\')
# else:
# parts = s.split('/')
# a = parts[-2]
# b = parts[-1]
# # 如果'a'的值还没有在字典中，添加一个新的键值对
# if a not in result_dict:
# result_dict[a] = []
# # 将'b'的值添加到'a'的列表中
# result_dict[a].append(item)
# image_arr = list(result_dict.values())
# # 将字典的值转换为一个列表，得到二维数组
# for video in image_arr:
# pred_sum = 0
# label_sum = 0
# leng = 0
# for frame in video:
# pred_sum += float(frame[1])
# label_sum += int(frame[2])
# leng += 1
# new_pred.append(pred_sum / leng)
# new_label.append(int(label_sum / leng))
# fpr, tpr, thresholds = metrics.roc_curve(new_label, new_pred)
# v_auc = metrics.auc(fpr, tpr)
# fnr = 1 - tpr
# v_eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
# return v_auc, v_eer
# y_pred = y_pred.squeeze()
# # auc
# fpr, tpr, thresholds = metrics.roc_curve(y_true, y_pred, pos_label=1)
# auc = metrics.auc(fpr, tpr)
# # eer
# fnr = 1 - tpr
# eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
# # ap
# ap = metrics.average_precision_score(y_true, y_pred)
# # acc
# prediction_class = (y_pred > 0.5).astype(int)
# correct = (prediction_class == np.clip(y_true, a_min=0, a_max=1)).sum().item()
# acc = correct / len(prediction_class)
# if type(img_names[0]) is not list:
# # calculate video-level auc for the frame-level methods.
# try:
# v_auc, _ = get_video_metrics(img_names, y_pred, y_true)
# except Exception as e:
# print(e)
# v_auc=auc
# else:
# # video-level methods
# v_auc=auc
# return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'video_auc': v_auc, 'label': y_true}
def get_test_metrics(y_pred, y_true, img_names):
def get_video_metrics(image, pred, label):
result_dict = {}
new_label = []
new_pred = []
for item in np.transpose(np.stack((image, pred, label)), (1, 0)):
s = item[0]
if '\\' in s:
parts = s.split('\\')
else:
parts = s.split('/')
a = parts[-2]
b = parts[-1]
if a not in result_dict:
result_dict[a] = []
result_dict[a].append(item)
image_arr = list(result_dict.values())
for video in image_arr:
pred_sum = 0
label_sum = 0
leng = 0
for frame in video:
pred_sum += float(frame[1])
label_sum += int(frame[2])
leng += 1
new_pred.append(pred_sum / leng)
new_label.append(int(label_sum / leng))
fpr, tpr, thresholds = metrics.roc_curve(new_label, new_pred)
v_auc = metrics.auc(fpr, tpr)
fnr = 1 - tpr
v_eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
# Calculate video-level acc
prediction_class = (np.array(new_pred) > 0.5).astype(int)
correct = (prediction_class == np.array(new_label)).sum().item()
v_acc = correct / len(prediction_class)
return v_auc, v_eer, v_acc
y_pred = y_pred.squeeze()
# auc
fpr, tpr, thresholds = metrics.roc_curve(y_true, y_pred, pos_label=1)
auc = metrics.auc(fpr, tpr)
# eer
fnr = 1 - tpr
eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
# ap
ap = metrics.average_precision_score(y_true, y_pred)
# acc
prediction_class = (y_pred > 0.5).astype(int)
correct = (prediction_class == np.clip(y_true, a_min=0, a_max=1)).sum().item()
acc = correct / len(prediction_class)
if type(img_names[0]) is not list:
# calculate video-level auc for the frame-level methods.
try:
v_auc, v_eer, v_acc = get_video_metrics(img_names, y_pred, y_true)
return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'video_auc': v_auc, 'video_eer': v_eer, 'video_acc': v_acc, 'label': y_true}
except Exception as e:
print(e)
v_auc=auc
return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'label': y_true}
else:
# video-level methods
v_auc=auc
v_eer=eer
v_acc=acc
return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'video_auc': v_auc, 'video_eer': v_eer, 'video_acc': v_acc, 'label': y_true}
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