Need Clarification for Round 2

[leocd]

Hi AIcrowd Team, just want to clarify something :

1. In the post-purchase training phase,

# Create a runtime instance of the purchased dataset with the right labels
purchased_dataset = instantiate_purchased_dataset(unlabelled_dataset, purchased_labels)
aggregated_dataset = torch.utils.data.ConcatDataset(
    [training_dataset, purchased_dataset]
)
print("Training Dataset Size : ", len(training_dataset))
print("Purchased Dataset Size : ", len(purchased_dataset))
print("Aggregataed Dataset Size : ", len(aggregated_dataset))

DEBUG_MODE = os.getenv("AICROWD_DEBUG_MODE", False)
if DEBUG_MODE:
    TRAINER_CLASS = ZEWDPCDebugTrainer
else:
    TRAINER_CLASS = ZEWDPCTrainer

trainer = ZEWDPCTrainer(num_classes=6, use_pretrained=True)
trainer.train(
    training_dataset, num_epochs=10, validation_percentage=0.1, batch_size=5
)

y_pred = trainer.predict(val_dataset)
y_true = val_dataset_gt._get_all_labels()

shouldn’t it be something like this?

trainer.train(
    aggregated_dataset , num_epochs=10, validation_percentage=0.1, batch_size=5
)

2. Because the combined and different time budget, shouldn’t it be something like this?
   
   

instead of the original diagram?

or did I assume it wrong?

Thanks.

[gaurav_singhal]

tfreidel raised the same bug here (:aicrowd: [Update] Round 2 of Data Purchasing Challenge is now live! - #11 by tfriedel). I also think it should be aggregated_dataset instead of training_dataset . Although it is in local_evaluation.py which will not be part of any sort of evaluation.