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

Before Change

        onehot = np.ndim(target) != 1
        assert output.shape[0] == target.shape[0], \
                "number of targets and outputs do not match"
        assert output.shape[1] == self.conf.shape[0], \
                "number of outputs does not match size of confusion matrix"
        assert not onehot or target.shape[1] == output.shape[1], \
                "target should be 1D Tensor or have size of output (one-hot)"
        if onehot:
            assert (target >= 0).all() and (target <= 1).all(), \

After Change

        // hack for bincounting 2 arrays together
        x = predicted + self.k * target
        bincount_2d = np.bincount(x.astype(np.int32),
                                  minlength=self.k ** 2)
        assert bincount_2d.size == self.k ** 2
        conf = bincount_2d.reshape((self.k, self.k))

        self.conf += conf

Instances