def main():
args = get_args()
wandb.init()
wandb.config.update(args)
seed = 42
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.deterministic = True
torch.backends.cudnn.benchmark = False
loaded_model = False
[train_loader, valid_loader, model,
optimizer] = initialize(args, loaded_model)
scaler = torch.cuda.amp.GradScaler()
wandb.watch(model)
best_acc = 0
run_avg = RunningAverage()
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
# scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.001, max_lr=0.1, cycle_momentum=False)
for epoch in range(1, args.epochs_number + 1):
run_avg.reset_train()
run_avg.reset_val()
train(args, model, train_loader, epoch, optimizer, scaler, run_avg)
val_acc = evaluation(args, model, valid_loader, epoch, run_avg)
# scheduler.step()
if best_acc < val_acc:
best_acc = val_acc
save_checkpoint(model, optimizer, args, epoch)
def test_running_average():
train_losses = [1, 0.5, 0.3]
train_accuracies = [0.3, 0.5, 0.1]
running_average = RunningAverage()
for i in range(len(train_losses)):
running_average.update_train_loss_avg(train_losses[i], 1)
running_average.update_train_acc_avg(train_accuracies[i], 1)
assert running_average.train_loss_run_avg == 0.6
assert running_average.train_acc_run_avg == 0.3
running_average.update_train_loss_avg(0.2, 1)
running_average.update_train_acc_avg(0.1, 1)
assert running_average.train_loss_run_avg == 0.5
assert running_average.train_acc_run_avg == 0.25
val_losses = [1, 0.7, 1.3]
val_accuracies = [0.3, 0.4, 8.3]
for i in range(len(val_losses)):
running_average.update_val_loss_avg(val_losses[i], 1)
running_average.update_val_acc_avg(val_accuracies[i], 1)
assert running_average.val_loss_run_avg == 1
assert running_average.val_acc_run_avg == 3
running_average.update_val_loss_avg(3, 1)
running_average.update_val_acc_avg(7, 1)
assert running_average.val_loss_run_avg == 1.5
assert running_average.val_acc_run_avg == 4
running_average.reset_train()
running_average.reset_val()
assert running_average.sum_train_loss == 0
assert running_average.sum_train_acc == 0
assert running_average.train_loss_counter == 0
assert running_average.train_acc_counter == 0
assert running_average.train_loss_run_avg == 0
assert running_average.train_acc_run_avg == 0
assert running_average.sum_val_loss == 0
assert running_average.sum_val_acc == 0
assert running_average.val_loss_counter == 0
assert running_average.val_acc_counter == 0
assert running_average.val_loss_run_avg == 0
assert running_average.val_acc_run_avg == 0