CVPR-NAS 2022 Starting Kit

Hi, thanks for participating in the 2nd CVPR-NAS Unseen Dataset Competition!

Contents

The starting kit contains the following:

• datasets/: This contains the first set of datasets in the competition, broken into train, valid, and test splits. You are free to open these up and play with them as you wish as you design your algorithm. (Note, on the competition servers you will be unable to access any of test_y.npy files as they will not exist until after your submission runs, for the purposes of competition integrity)
• evaluation/: These are copies of scripts that will be used to evaluate your submission on our servers.
  • main.py: The main competition pipeline. This will load each dataset, pass it through your pipeline, and then produce test predictions
  • score.py: The scoring script, which will compare the test predictions from main.py and compare it against the true labels.
• submission_template/: This contains everything you need to implement to create a valid submission. See the included README within for more details
• submission_example/: Here's an example submission we made, for reference
• Makefile: Some scripts that will let you build and test your submission in a copy of our server evaluation pipeline, more details on this in the "Testing your Submission" section

Writing Your Submission

In this competition, you will be asked to produce three components:

1. A DataProcessor, that takes in raw numpy arrays comprising the train/valid/splits of the dataset and creates train/valid/test PyTorch dataloaders. These can perform whatever preprocessing or augmentation that you might want/
2. A NAS algorithm, that takes in the dataloaders and produces some optimal PyTorch model
3. A Trainer, that trains that optimal model over the train dataloader

In general, the following pipeline occurs for each dataset:

1. Raw Dataset -> DataProcessor -> Train, Valid, and Test dataloaders
2. Train Dataloader + Valid Datalodaers -> NAS -> Model
3. Model + Train Dataloader + Valid Dataloaders -> TRAINER.train -> Fully-trained model
4. Fully-trained model + Test Dataloader -> Trainer.predict= -> Predictions

See submission_template/README.md for specifics about how to write these, and `submission_example' for an example valid submission

Testing Your Submission

The included Makefile will let you test your submission via the same testing scripts as our servers use. If the Makefile works, then you can be fairly confident your submission will work on our machines. However, you should still be careful about things like package imports, because trying to import something that doesn't exist in our environment will break your submission.

To test your submission from start-to-finish, run:

make submission=$SUBMISSION_DIRECTORY all

For example, to run the example submission:

make submission=submission_example all

Submitting

To bundle your submission, run:

make submission=$SUBMISSION_DIRECTORY zip

Then submit the zip file on the [submission page link here]