Make mart importable with the core dependency group alone

[mzweilin]

What does this PR do?

This PR allows import mart with the "core" dependency group alone. In this way, a non-image domain user does not need to install torchvision etc.

$ pip install -e .[core,developer]

$ python
Python 3.9.19 (main, May  9 2024, 16:03:05) 
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import mart
fiftyone is not installed, so some features in MART are unavailable.
torchvision is not installed, so some features in MART are unavailable.
timm is not installed, so some features in MART are unavailable.
pycocotools is not installed, so some features in MART are unavailable.
>>> 

pytest automatically skips related tests if dependency is not available locally.

$ pytest
...
tests/test_utils.py::test_flatten_dict_key_collision PASSED                 [100%]

================================ slowest durations ================================
0.23s call     tests/test_adversary.py::test_forward_with_model
0.03s call     tests/test_adversary_connector.py::test_adversary_connector_callback
0.03s setup    tests/test_adversary.py::test_with_model
0.01s call     tests/test_adversary.py::test_with_model

(351 durations < 0.005s hidden.  Use -vv to show these durations.)
========================= 113 passed, 10 skipped in 0.71s =========================

Type of change

Please check all relevant options.

• [x] 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

Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.

• [x] pytest
• [ ] CUDA_VISIBLE_DEVICES=0 python -m mart experiment=CIFAR10_CNN_Adv trainer=gpu trainer.precision=16 reports 70% (21 sec/epoch).
• [ ] CUDA_VISIBLE_DEVICES=0,1 python -m mart experiment=CIFAR10_CNN_Adv trainer=ddp trainer.precision=16 trainer.devices=2 model.optimizer.lr=0.2 trainer.max_steps=2925 datamodule.ims_per_batch=256 datamodule.world_size=2 reports 70% (14 sec/epoch).

Before submitting

• [x] The title is self-explanatory and the description concisely explains the PR
• [x] My PR does only one thing, instead of bundling different changes together
• [ ] I list all the breaking changes introduced by this pull request
• [x] I have commented my code
• [ ] I have added tests that prove my fix is effective or that my feature works
• [x] New and existing unit tests pass locally with my changes
• [x] I have run pre-commit hooks with pre-commit run -a command without errors

Did you have fun?

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