8b084618a0ba7d0f79275c3e5578c85b4d6d8279,chainercv/links/loss/semantic_segmentation_loss.py,PixelwiseSoftmaxClassifier,__call__,#PixelwiseSoftmaxClassifier#Any#Any#,35

Before Change

    def __call__(self, x, t):
        self.y = self.predictor(x)

        if self.class_weight is not None:
            if hasattr(x.data, "device") \
                    and not hasattr(self.class_weight, "device"):
                self.class_weight = cuda.to_gpu(
                    self.class_weight, x.data.device)
            self.loss = F.softmax_cross_entropy(
                self.y, t, class_weight=self.class_weight,
                ignore_label=self.ignore_label)
        else:
            self.loss = F.softmax_cross_entropy(
                self.y, t, ignore_label=self.ignore_label)
        
        reporter.report({"loss": self.loss}, self)
        if self.compute_accuracy:
            pa, mpa, miou, fwiou = _segmentation_accuracies(
                self.y, t, self.n_class)

After Change

            label = self.xp.argmax(self.y.data, axis=1)
            pas, mpas, mious, fwious =\
                eval_semantic_segmentation(label, t.data, self.n_class)
            reporter.report({
                "pixel_accuracy": self.xp.mean(pas),
                "mean_pixel_accuracy": self.xp.mean(mpas),
                "mean_iou": self.xp.mean(mious),
                "frequency_weighted_iou": self.xp.mean(fwious)
            }, self)
        return self.loss

Instances