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d43892c | 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 | import numpy as np
################### metrics ###################
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.initialized = False
self.val = None
self.avg = None
self.sum = None
self.count = None
def initialize(self, val, weight):
self.val = val
self.avg = val
self.sum = val * weight
self.count = weight
self.initialized = True
def update(self, val, weight=1):
if not self.initialized:
self.initialize(val, weight)
else:
self.add(val, weight)
def add(self, val, weight):
self.val = val
self.sum += val * weight
self.count += weight
self.avg = self.sum / self.count
def value(self):
return self.val
def average(self):
return self.avg
def get_scores(self):
scores_dict = cm2score(self.sum)
return scores_dict
def clear(self):
self.initialized = False
################### cm metrics ###################
class ConfuseMatrixMeter(AverageMeter):
"""Computes and stores the average and current value"""
def __init__(self, n_class):
super(ConfuseMatrixMeter, self).__init__()
self.n_class = n_class
def update_cm(self, pr, gt, weight=1):
"""获得当前混淆矩阵，并计算当前F1得分，并更新混淆矩阵"""
val = get_confuse_matrix(num_classes=self.n_class, label_gts=gt, label_preds=pr)
self.update(val, weight)
current_score = cm2F1(val)
return current_score
def get_scores(self):
scores_dict = cm2score(self.sum)
return scores_dict
def harmonic_mean(xs):
harmonic_mean = len(xs) / sum((x+1e-6)**-1 for x in xs)
return harmonic_mean
def cm2F1(confusion_matrix):
hist = confusion_matrix
n_class = hist.shape[0]
tp = np.diag(hist)
sum_a1 = hist.sum(axis=1)
sum_a0 = hist.sum(axis=0)
# ---------------------------------------------------------------------- #
# 1. Accuracy & Class Accuracy
# ---------------------------------------------------------------------- #
acc = tp.sum() / (hist.sum() + np.finfo(np.float32).eps)
# recall
recall = tp / (sum_a1 + np.finfo(np.float32).eps)
# acc_cls = np.nanmean(recall)
# precision
precision = tp / (sum_a0 + np.finfo(np.float32).eps)
# F1 score
F1 = 2 * recall * precision / (recall + precision + np.finfo(np.float32).eps)
mean_F1 = np.nanmean(F1)
return mean_F1
def cm2score(confusion_matrix):
hist = confusion_matrix
n_class = hist.shape[0]
tp = np.diag(hist)
sum_a1 = hist.sum(axis=1)
sum_a0 = hist.sum(axis=0)
# ---------------------------------------------------------------------- #
# 1. Accuracy & Class Accuracy
# ---------------------------------------------------------------------- #
acc = tp.sum() / (hist.sum() + np.finfo(np.float32).eps)
# recall
recall = tp / (sum_a1 + np.finfo(np.float32).eps)
# acc_cls = np.nanmean(recall)
# precision
precision = tp / (sum_a0 + np.finfo(np.float32).eps)
# F1 score
F1 = 2*recall * precision / (recall + precision + np.finfo(np.float32).eps)
mean_F1 = np.nanmean(F1)
# ---------------------------------------------------------------------- #
# 2. Frequency weighted Accuracy & Mean IoU
# ---------------------------------------------------------------------- #
iu = tp / (sum_a1 + hist.sum(axis=0) - tp + np.finfo(np.float32).eps)
mean_iu = np.nanmean(iu)
freq = sum_a1 / (hist.sum() + np.finfo(np.float32).eps)
fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
#
cls_iou = dict(zip(['iou_'+str(i) for i in range(n_class)], iu))
cls_precision = dict(zip(['precision_'+str(i) for i in range(n_class)], precision))
cls_recall = dict(zip(['recall_'+str(i) for i in range(n_class)], recall))
cls_F1 = dict(zip(['F1_'+str(i) for i in range(n_class)], F1))
score_dict = {'acc': acc, 'miou': mean_iu, 'mf1':mean_F1}
score_dict.update(cls_iou)
score_dict.update(cls_F1)
score_dict.update(cls_precision)
score_dict.update(cls_recall)
return score_dict
def get_confuse_matrix(num_classes, label_gts, label_preds):
"""计算一组预测的混淆矩阵"""
def __fast_hist(label_gt, label_pred):
"""
Collect values for Confusion Matrix
For reference, please see: https://en.wikipedia.org/wiki/Confusion_matrix
:param label_gt: <np.array> ground-truth
:param label_pred: <np.array> prediction
:return: <np.ndarray> values for confusion matrix
"""
mask = (label_gt >= 0) & (label_gt < num_classes)
hist = np.bincount(num_classes * label_gt[mask].astype(int) + label_pred[mask],
minlength=num_classes**2).reshape(num_classes, num_classes)
return hist
confusion_matrix = np.zeros((num_classes, num_classes))
for lt, lp in zip(label_gts, label_preds):
confusion_matrix += __fast_hist(lt.flatten(), lp.flatten())
return confusion_matrix
def get_mIoU(num_classes, label_gts, label_preds):
confusion_matrix = get_confuse_matrix(num_classes, label_gts, label_preds)
score_dict = cm2score(confusion_matrix)
return score_dict['miou']
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