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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)) | |