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Update projected_gradient_descent_pytorch.py [Solve non-writable NumPy array and device mismatch issues] #2455
Update projected_gradient_descent_pytorch.py [Solve non-writable NumPy array and device mismatch issues] #2455
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files
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Hi @salomonhotegni Could you please fix the DCO check? |
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Hi @salomonhotegni Could you please also make the same change in line 500?
Please run Python formatter |
Sure! |
Hi @salomonhotegni Thank you very much. To pass the DCO check, could you please add your signed-off-by line to every commit in this branch using the following commands:
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@salomonhotegni Could you please also take a look at updating the code to pass the style checks with the instructions above? |
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Solve non-writable NumPy array and device mismatch issues. Signed-off-by: salomonhotegni <salomon.hotegni@aims.ac.rw>
…y array and device mismatch issues] Signed-off-by: salomonhotegni <salomon.hotegni@aims.ac.rw>
Signed-off-by: salomonhotegni <salomon.hotegni@aims.ac.rw>
…y array and device mismatch issues] Signed-off-by: salomonhotegni <salomon.hotegni@aims.ac.rw>
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Hi @salomonhotegni Could you please run command |
Thank you for fixing DCO. |
Signed-off-by: salomonhotegni <salomon.hotegni@aims.ac.rw>
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Hi @salomonhotegni Thank you very much for your first contribution to ART!
Description
This pull request addresses two issues in the
ProjectedGradientDescent
attack of the Adversarial Robustness Toolbox (ART):_projection
function.The solution involves copying the non-writable NumPy array to ensure it is writable and modifying the tensor conversion in the
_projection
function to ensure all tensors are on the same device.Fixes # (issue)
Type of change
Please check all relevant options.
Testing
I have performed the following tests to verify the changes:
Test Configuration:
Checklist