# HPs
optimizer_choice = int(sys.argv[batch_size_index + 1])
arg1 = float(sys.argv[batch_size_index + 2]) # lr
arg2 = float(sys.argv[batch_size_index + 3]) # momentum
arg3 = float(sys.argv[batch_size_index + 4]) # weight decay
arg4 = float(sys.argv[batch_size_index + 5]) # dampening
dropout_rate = float(sys.argv[batch_size_index + 6])
activation = int(sys.argv[batch_size_index + 7])
# Load the data
print('> Preparing the data..')
if dataset is not 'CUSTOM':
dataloader = DataHandler(dataset, batch_size)
image_size, number_classes = dataloader.get_info_data
trainloader, validloader, testloader = dataloader.get_loaders()
else:
# Add here the adequate information
image_size = None
number_classes = None
trainloader = None
validloader = None
testloader = None
# Test if the correct information is passed - especially in the case of CUSTOM dataset
assert isinstance(trainloader, torch.utils.data.dataloader.DataLoader
), 'Trainloader given is not of class DataLoader'
assert isinstance(validloader, torch.utils.data.dataloader.DataLoader
), 'Validloader given is not of class DataLoader'
assert isinstance(testloader, torch.utils.data.dataloader.DataLoader
), 'Testloader given is not of class DataLoader'
