import sys
from options.train_options import TrainOptions
import data
from util.iter_counter import IterationCounter
from util.visualizer import Visualizer
from trainers.trainer import Trainer

# parse options
opt = TrainOptions().parse()

# print options to help debugging
print(' '.join(sys.argv))

# load the dataset
dataloader = data.create_dataloader(opt)

# create trainer for our model
trainer = Trainer(opt)


# create tool for counting iterations
iter_counter = IterationCounter(opt, len(dataloader))

# create tool for visualization
visualizer = Visualizer(opt)

for epoch in iter_counter.training_epochs():
    iter_counter.record_epoch_start(epoch)
    for i, data in enumerate(dataloader, start=iter_counter.epoch_iter):

        iter_counter.record_one_iter()
        trainer.g_losses, trainer.d_losses = {}, {}
        CT, MR = data['CT'].squeeze(1), data['MR'].squeeze(1)
        
        if opt.D_steps_per_G:
            trainer.run_generator_one_step(CT, MR)
        trainer.run_discriminator_one_step(CT, MR)
        
        data =  {**data, **trainer.get_latest_generated()}
        
        # Visualizations
        if iter_counter.needs_printing():
            losses = trainer.get_latest_losses()
            visualizer.print_current_errors(epoch, iter_counter.epoch_iter,
                                            losses, iter_counter.time_per_iter)
            visualizer.plot_current_errors(
                losses, iter_counter.total_steps_so_far)

        if iter_counter.needs_displaying():
            visuals = OrderedDict([**data])
            visualizer.display_current_results(
                visuals, epoch, iter_counter.total_steps_so_far)

        if iter_counter.needs_saving():
            print('saving the latest model (epoch %d, total_steps %d)' %
                  (epoch, iter_counter.total_steps_so_far))
            trainer.save('latest')
            iter_counter.record_current_iter()

    trainer.update_learning_rate(epoch)
    iter_counter.record_epoch_end()

    # save the model
    if epoch % opt.save_epoch_freq == 0 or \
       epoch == iter_counter.total_epochs:
        print('saving the model at the end of epoch %d, iters %d' %
              (epoch, iter_counter.total_steps_so_far))
        trainer.save('latest')
        trainer.save(epoch)

print('Training was successfully finished.')
