updater.py 文件源码

def calc_loss(self):
        batchsize = self.ground_truth.shape[0]
        self.loss = F.softmax_cross_entropy(self.pred_label_map, self.ground_truth, normalize=False)

        # log report
        label_true = chainer.cuda.to_cpu(self.ground_truth.data)
        label_pred = chainer.cuda.to_cpu(self.pred_label_map.data).argmax(axis=1)
        logs = []
        for i in six.moves.range(batchsize):
            acc, acc_cls, iu, fwavacc = utils.label_accuracy_score(
                label_true[i], label_pred[i], self.n_class)
            logs.append((acc, acc_cls, iu, fwavacc))
        log = np.array(logs).mean(axis=0)
        values = {
            'loss': self.loss,
            'accuracy': log[0],
            'accuracy_cls': log[1],
            'iu': log[2],
            'fwavacc': log[3],
        }
        chainer.report(values, self.model)