Add YOLO object detection model

[senarvi]

What does this PR do?

This PR adds the YOLO object detection model. The implementation is based on the YOLOv3 and YOLOv4 Darknet implementations, although it doesn't include all the features of YOLOv4. Detection seems to work with weights that have been trained using the Darknet implementation, so the network architecture should be more or less identical. The network architecture is read from a configuration file in the same format as in the Darknet implementation. It supports loading weights from a Darknet model file too, if you don't want to start training from a randomly initialized model.

Fixes #22

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• Did you write any new necessary tests? [not needed for typos/docs]
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[pep8speaks]

Hello @senarvi! Thanks for updating this PR.

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2021-08-11 15:29:11 UTC

[codecov]

Codecov Report

Merging #552 (66c8111) into master (47eb2aa) will decrease coverage by 48.52%.
The diff coverage is 14.21%.

@@             Coverage Diff             @@
##           master     #552       +/-   ##
===========================================
- Coverage   72.17%   23.64%   -48.53%     
===========================================
  Files         121      124        +3     
  Lines        7550     8098      +548     
===========================================
- Hits         5449     1915     -3534     
- Misses       2101     6183     +4082     

Flag	Coverage Δ
cpu	23.64% <14.21%> (-48.53%)	⬇️
pytest	23.64% <14.21%> (-48.53%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
pl_bolts/models/detection/yolo/yolo_module.py	12.80% <12.80%> (ø)
pl_bolts/models/detection/yolo/yolo_config.py	14.40% <14.40%> (ø)
pl_bolts/models/detection/yolo/yolo_layers.py	14.47% <14.47%> (ø)
pl_bolts/models/detection/__init__.py	100.00% <100.00%> (ø)
pl_bolts/models/rl/__init__.py	0.00% <0.00%> (-100.00%)	⬇️
pl_bolts/models/rl/common/agents.py	0.00% <0.00%> (-100.00%)	⬇️
pl_bolts/models/rl/dueling_dqn_model.py	0.00% <0.00%> (-100.00%)	⬇️
pl_bolts/models/rl/advantage_actor_critic_model.py	0.00% <0.00%> (-97.71%)	⬇️
pl_bolts/models/rl/double_dqn_model.py	0.00% <0.00%> (-95.84%)	⬇️
...l_bolts/models/rl/vanilla_policy_gradient_model.py	0.00% <0.00%> (-93.45%)	⬇️
... and 73 more

---

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[akihironitta]

We recently fixed some of the tests, so would you mind merging master branch? Also, it seems the change here is causing errors in CI, so would you mind resolving that, too?

[williamFalcon]

this looks really good @senarvi!
@Borda @kaushikb11 let's get this merged?

[senarvi]

@Borda I noticed also the formatting issues and fixed them by running the new formatting tools.

[Borda]

@Borda I noticed also the formatting issues and fixed them by running the new formatting tools.

cool, just restarting CI

[senarvi]

VOCDetectionDataModule was updated to better conform to the new data module interface. The constructor takes batch size and the transforms take image and target. We can now pass the data module to Trainer, instead of the dataloaders. train_dataloader() and val_dataloader() still support image-only transforms, but I don't see any need for using them.

[senarvi]

The test suite found still one problem with integer division with an older PyTorch version, but it should be fixed now. @Borda could you restart the CI? I don't see the error when running the tests with my PyTorch installation.

[akihironitta]

@senarvi Hi, thanks again for all your work and patience! Just restarted CI again. I'll try to be active on this PR for the next few days until we get this PR merged :]

[senarvi]

@akihironitta correct me if I'm wrong, but the remaining error was something about CIFAR dataset and not related to this pull request.

[akihironitta]

@akihironitta correct me if I'm wrong, but the remaining error was something about CIFAR dataset and not related to this pull request.

@senarvi You're right that all of the related tests here have passed. Once the following comments gets resolved, I believe we can land this PR finally!

[senarvi]

@akihironitta ok, done.