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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 ConfusionMatrix(object):
def __init__(self, n_classes):
self.n_classes = n_classes
# axis = 0: prediction
# axis = 1: target
self.confusion_matrix = np.zeros((n_classes, n_classes))
def _fast_hist(self, label_true, label_pred, n_class):
hist = np.zeros((n_class, n_class))
hist[label_pred, label_true] += 1
return hist
def update(self, label_trues, label_preds):
for lt, lp in zip(label_trues, label_preds):
tmp = self._fast_hist(lt.item(), lp.item(), self.n_classes) #lt.item(), lp.item()
self.confusion_matrix += tmp
def get_scores(self):
"""Returns accuracy score evaluation result.
- overall accuracy
- mean accuracy
- mean IU
- fwavacc
"""
hist = self.confusion_matrix
# accuracy is recall/sensitivity for each class, predicted TP / all real positives
# axis in sum: perform summation along
if sum(hist.sum(axis=1)) != 0:
acc = sum(np.diag(hist)) / sum(hist.sum(axis=1))
else:
acc = 0.0
return acc
def plotcm(self):
print(self.confusion_matrix)
def reset(self):
self.confusion_matrix = np.zeros((self.n_classes, self.n_classes)) |