Semantic version checking in some modules/tests do not account for epoch, pre/post-release, or development release segments

[lockwoodar]

Describe the bug

Example code in art/estimators/object_detection/pytorch_object_detector.py

Parsed versions make an assumption that there will be no epoch, pre-release, post-release, or development release segments. (e.g. torchvision==0.18.1a0+405940f would assert with an error of ValueError: invalid literal for int() with base 10: '1a0')

This failure mode was detected when testing within a conda environment with installed downstream heart-library (local test build for publication to conda-forge) and the latest version of torchvision via conda-forge.

To Reproduce
Steps to reproduce the behavior:

1. Build a conda venv for adversarial-robustness-toolbox
2. Install pytorch and torchvision via conda-forge
   a. conda install conda-forge::pytorch
   b. conda install conda-forge::torchvision
3. Confirm installed torchvision version

python -c "import torchvision; from importlib.metadata import version; print(version('torchvision'))"
# Currently, this should output:  0.18.1a0+405940f

5. Test semantic version parse line from pytorch_object_detector.py

python -c 'import torchvision; print(list(map(int, torchvision.__version__.lower().split("+", maxsplit=1)[0].split("."))))'

# This will fail with:
# Traceback (most recent call last):
#   File "<string>", line 1, in <module>
# ValueError: invalid literal for int() with base 10: '1a0'

All code that leverages modules that utilize this form of semantic version testing will either fail on assert statements that expect int, or are checking for a specific value (potentially directing code execution into an else pathway unintentionally).

Expected behavior
These semantic version checks should consistently return a valid version identifier and not fail asserts when other identifying segments are present based on PyPA specs

Potential solution:

• leverage parse from packaging.version to isolate release version identifier

Python 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
>>> from packaging.version import parse
>>> from importlib.metadata import version
>>>
>>> print(torchvision.__version__.lower())
0.18.1a0+405940f
>>>
>>> torchvision_version = list(parse(version("torchvision")).release)
>>> print(torchvision_version)
[0, 18, 1]
>>>
>>> # or without leveraging importlib.metadata
>>> torchvision_version = list(parse(torchvision.__version__.lower()).release)
>>> print(torchvision_version)
[0, 18, 1]

Screenshots
n/a

System information (please complete the following information):

• OS = Ubuntu 22.04.4
• Python version = 3.10
• ART version or commit number = 1.18.1
• TensorFlow / Keras / PyTorch / MXNet version:

pytorch                     2.3.1           cpu_generic_py310ha4c588e_0    conda-forge
torchvision               0.18.1          cpu_py310h2ee8361_1    conda-forge

[beat-buesser]

Hi @lockwoodar Thank you very much! We will include this issue in the next patch release ART 1.18.2