What does this PR do?

Add two different FiftyOne callbacks:

FiftyOnePredictionAdder: Callback to store predictions to the corresponding FiftyOne dataset.
FiftyOneEvaluateDetections: Callback used to perform an evaluation of detection in some FiftyOne dataset.

The predictions and evaluation metrics can be automatically visualized into the FiftyOne app:

This PR is dependant of PR #209

Type of change

Please check all relevant options.

[ ] Improvement (non-breaking)
[ ] Bug fix (non-breaking)
[x] 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

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).

[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
[x] I list all the breaking changes introduced by this pull request
[x] I have commented my code
[x] 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
[ ] I have run pre-commit hooks with pre-commit run -a command without errors

When using the GPU device, the trainer.predict throws the following error:

RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same

The input data is on GPU, but the model's weights is on CPU.