ba256835a4f33d9139a70b6440c3223123132bc8,torchnet/meter/confusionmeter.py,ConfusionMeter,add,#ConfusionMeter#Any#Any#,42

Before Change

 
        pred = output.argmax(1)
        for i,n in enumerate(pred):
            pos = onehot and target[i].argmax(0) or int(target[i])
            self.conf[pos][n] += 1

    def value(self):
        if self.normalized:

After Change

        if np.ndim(predicted) != 1:
            assert predicted.shape[1] == self.k, \
                "number of predictions does not match size of confusion matrix"
            predicted = np.argmax(predicted, 1)
        else:
            assert (predicted.max() < self.k) and (predicted.min() >= 0), \
                "predicted values are not between 1 and k"

Instances