metrics.py

# 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)
        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):
        for lt, lp in zip(label_trues, label_preds):
            self.confusion_matrix += self._fast_hist(lt.flatten(), lp.flatten(), self.n_classes)
    def get_scores(self):
        hist = self.confusion_matrix
        acc = np.diag(hist).sum() / hist.sum()
        acc_cls = np.diag(hist) / hist.sum(axis=1)
        acc_cls = np.nanmean(acc_cls)
        iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
        dice=np.divide(np.multiply(iu,2),np.add(iu,1))
        mean_iu = np.nanmean(iu[1:9])
        mean_dice=(mean_iu*2)/(mean_iu+1)
        freq = hist.sum(axis=1) / hist.sum()
        fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
        cls_iu = dict(zip(range(self.n_classes), iu))
        
        return {#'Overall Acc: \t': acc,
                #'Mean Acc : \t': acc_cls,
                #'FreqW Acc : \t': fwavacc,
                'Dice : \t': dice,
                'Mean Dice : \t': mean_dice,}, cls_iu
    def reset(self):
        self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))