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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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