Correct f-strings in parallel and non-parallel metadata

[lockwoodar]

Description

Full Issue Details: #2548

• Fixes string literals in returned AutoAttack metadata (both parallel and non-parallel) to correctly use f-strings

Type of change

Please check all relevant options.

• Improvement (non-breaking)
• Bug fix (non-breaking)
• New feature (non-breaking)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Testing

• I utilized the same test that is outlined in Erroneous literal string in AutoAttack metadata #2548
• I corrected the f-strings locally and built a local wheel for ART-Core, which was then installed
• I repeated the testing to assert that the metadata returned correctly uses the f-string substitution

>>> attack_noparallel = AutoAttack(estimator=ptc, attacks=attacks, targeted=True, parallel_pool_size = 0)
>>> attack_parallel = AutoAttack(estimator=ptc, attacks=attacks, targeted=True, parallel_pool_size = pool)
>>>
>>>
>>> no_parallel_adv = attack_noparallel.generate(x=x_train, y=y_train)
>>> parallel_adv = attack_parallel.generate(x=x_train, y=y_train)
>>> print(repr(attack_noparallel))
AutoAttack(targeted=True, parallel_pool_size=0, num_attacks=1)
BestAttacks:
image 1: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 2: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 3: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 4: n/a
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image 8: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 9: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 10: n/a
>>>
>>>
>>> print(repr(attack_parallel))
AutoAttack(targeted=True, parallel_pool_size=15, num_attacks=10)
BestAttacks:
image 1: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 2: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 3: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
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image 8: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 9: ProjectedGradientDescentPyTorch(norm=inf, eps=0.1, eps_step=0.1, targeted=True, num_random_init=0, batch_size=32, minimal=False, summary_writer=None, decay=None, max_iter=10, random_eps=False, verbose=False, )
image 10: n/a
>>>

Test Configuration:
OS = Ubuntu 22.04.4 LTS
Python version = 3.11
ART version or commit number = 1.19.0
PyTorch version = 2.3.1

Checklist

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• My changes have been tested using both CPU and GPU devices

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 85.40%. Comparing base (27e4c81) to head (6d0fa9c).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2549      +/-   ##
==========================================
- Coverage   85.41%   85.40%   -0.02%     
==========================================
  Files         334      334              
  Lines       31001    31001              
  Branches     5300     5300              
==========================================
- Hits        26481    26476       -5     
- Misses       3053     3056       +3     
- Partials     1467     1469       +2     

Files with missing lines	Coverage Δ
art/attacks/evasion/auto_attack.py	86.15% <ø> (ø)

... and 3 files with indirect coverage changes

[lockwoodar]

In the process of trying to correct the missing --signoff, I apparently put this pull request into a bad state. Closing and starting from a clean fork to avoid having a complicated commit history.