705d7ff759a0b76ddf1b30ab8ef135c0b67efa8b,art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py,ProjectedGradientDescentPytorch,generate,#ProjectedGradientDescentPytorch#Any#Any#,123

Before Change

            targets = y

        inputs = torch.from_numpy(x).to(self.classifier._device)
        targets = torch.from_numpy(targets.astype(float)).to(self.classifier._device)

        // TODO
        // if self.random_eps:
        //     ratio = self.eps_step / self.eps

After Change

        if mask is not None:
            // Ensure the mask is broadcastable
            if len(mask.shape) > len(x.shape) or mask.shape != x.shape[-len(mask.shape):]:
                raise ValueError("Mask shape must be broadcastable to input shape.")

        adv_x_best = None
        rate_best = None

Instances