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# Adapted from score written by wkentaro
# https://github.com/wkentaro/pytorch-fcn/blob/master/torchfcn/utils.py
import numpy as np
class runningScore(object):
def __init__(self, n_classes):
self.n_classes = n_classes
self.confusion_matrix = np.zeros((n_classes, n_classes))
def _fast_hist(self, label_true, label_pred, n_class):
mask = (label_true >= 0) & (label_true < n_class)
if np.sum((label_pred[mask] < 0)) > 0:
print(label_pred[label_pred < 0])
hist = np.bincount(n_class * label_true[mask].astype(int) +
label_pred[mask], minlength=n_class ** 2).reshape(n_class, n_class)
return hist
def update(self, label_trues, label_preds):
# print label_trues.dtype, label_preds.dtype
for lt, lp in zip(label_trues, label_preds):
try:
self.confusion_matrix += self._fast_hist(lt.flatten(), lp.flatten(), self.n_classes)
except:
pass
def get_scores(self):
"""Returns accuracy score evaluation result.
- overall accuracy
- mean accuracy
- mean IU
- fwavacc
"""
hist = self.confusion_matrix
acc = np.diag(hist).sum() / (hist.sum() + 0.0001)
acc_cls = np.diag(hist) / (hist.sum(axis=1) + 0.0001)
acc_cls = np.nanmean(acc_cls)
iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist) + 0.0001)
mean_iu = np.nanmean(iu)
freq = hist.sum(axis=1) / (hist.sum() + 0.0001)
fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
cls_iu = dict(zip(range(self.n_classes), iu))
return {'Overall Acc': acc,
'Mean Acc': acc_cls,
'FreqW Acc': fwavacc,
'Mean IoU': mean_iu, }, cls_iu
def reset(self):
self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))
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