main.py

import adHocSL
import datasets.cifar_data as cifar_data

import torch
import numpy as np
import random

def main():
    sys_ = adHocSL.AdHocSL(pointa=3, pointb=5, num_dataowners=2, model_name="ignore for now")

    # Load Data NOTE: adapt accordingly
    SEED = 1234

    random.seed(SEED)
    np.random.seed(SEED)
    torch.manual_seed(SEED)
    torch.backends.cudnn.deterministic = True

    trainset, testset = cifar_data.get_dataset()
    trainloader, validloader, testloader = cifar_data.get_dataloaders(trainset, testset, batch_size=sys_.training_par.batch_size)
    
    train_iterator = iter(trainloader)
    valid_iterator = iter(validloader)
    test_iterator = iter(testloader)
    
    classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

    for epoch in range(sys_.training_par.epoch_num):
        train_iterator = iter(trainloader)
        #print(len(train_iterator))
        epoch_loss = 0
        epoch_acc = 0

        
        
        for batch in train_iterator:
            images, labels = batch
            (loss, acc) = sys_.local_update( 1, images, labels)
            print(f'{loss} {acc}')
            (loss, acc) = sys_.adHoc_update( 1, 2, images, labels)
            print(f'{loss} {acc}')
        
    sys_.aggregate()

if __name__ == '__main__':
    main()