train.py

import time
from options.train_options import TrainOptions
from data.dataloader import CreateDataLoader
from util.visualizer import Visualizer
from models.single_gan import SingleGAN


def main():
    opt = TrainOptions().parse()
    data_loader = CreateDataLoader(opt)
    dataset_size = len(data_loader) * opt.batchSize
    visualizer = Visualizer(opt)


    model = SingleGAN()
    model.initialize(opt)


    total_steps = 0
    lr = opt.lr
    for epoch in range(1, opt.niter + opt.niter_decay + 1):
        epoch_start_time = time.time()
        save_result = True
        for i, data in enumerate(data_loader):
            iter_start_time = time.time()
            total_steps += opt.batchSize
            epoch_iter = total_steps - dataset_size * (epoch - 1)
            model.update_model(data)
            
            if save_result or total_steps % opt.display_freq == 0:
                save_result = save_result or total_steps % opt.update_html_freq == 0
                print('mode:{} dataset:{}'.format(opt.mode,opt.name))
                visualizer.display_current_results(model.get_current_visuals(), epoch, ncols=1, save_result=save_result)
                save_result = False
            
            if total_steps % opt.print_freq == 0:
                errors = model.get_current_errors()
                t = (time.time() - iter_start_time) / opt.batchSize
                visualizer.print_current_errors(epoch, epoch_iter, errors, t)
                if opt.display_id > 0:
                    visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
                    
            if total_steps % opt.save_latest_freq == 0:
                print('saving the latest model (epoch %d, total_steps %d)' %(epoch, total_steps))
                model.save('latest')
                
        if epoch % opt.save_epoch_freq == 0:
            print('saving the model at the end of epoch %d, iters %d' %(epoch, total_steps))
            model.save('latest')
            model.save(epoch)
            
        if epoch > opt.niter:
            lr -= opt.lr / opt.niter_decay
            model.update_lr(lr)
        
if __name__ == '__main__':
    main()