Merge pull request #2455 from salomonhotegni/my-feature-branch

Update projected_gradient_descent_pytorch.py [Solve non-writable NumPy array and device mismatch issues]

Author: beat-buesser

art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py

@@ -497,11 +497,13 @@ def _projection(
         if (suboptimal or norm == 2) and norm != np.inf:  # Simple rescaling
             values_norm = torch.linalg.norm(values_tmp, ord=norm, dim=1, keepdim=True)  # (n_samples, 1)
             values_tmp = values_tmp * values_norm.where(
-                values_norm == 0, torch.minimum(torch.ones(1), torch.Tensor(eps) / values_norm)
+                values_norm == 0, torch.minimum(torch.ones(1), torch.tensor(eps).to(values_tmp.device) / values_norm)
             )
         else:  # Optimal
             if norm == np.inf:  # Easy exact case
-                values_tmp = values_tmp.sign() * torch.minimum(values_tmp.abs(), torch.Tensor(eps))
+                values_tmp = values_tmp.sign() * torch.minimum(
+                    values_tmp.abs(), torch.tensor(eps).to(values_tmp.device)
+                )
             elif norm >= 1:  # Convex optim
                 raise NotImplementedError(
                     "Finite values of `norm_p >= 1` are currently not supported with `suboptimal=False`."