train_gan.py

import sys
import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

def mian():
    opt = TrainOptions().parse()
    train_dataset = create_dataset(opt, 'train', opt.batch_size)
    dataset_size = len(train_dataset)
    print('The number of training images = %d' % dataset_size)

    model = create_model(opt)
    model.setup(opt)
    visualizer = Visualizer(opt)
    total_iters = 0

    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
        epoch_start_time = time.time()
        iter_data_time = time.time()
        epoch_iter = 0

        for i, data in enumerate(train_dataset):
            sys.stdout.flush()
            iter_start_time = time.time()
            if total_iters % opt.print_freq == 0:
                t_data = iter_start_time - iter_data_time
            visualizer.reset()
            total_iters += opt.batch_size
            epoch_iter += opt.batch_size
            model.set_input(data)
            if opt.model == 'mdgan':
                model.optimize_parameters(shuffle=True, this_iter_val=total_iters // opt.batch_size)
            else:
                model.optimize_parameters()

            if total_iters % opt.display_freq == 0:
                save_result = total_iters % opt.update_html_freq == 0
                model.compute_visuals()
                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)

            if total_iters % opt.print_freq == 0:
                losses = model.get_current_losses()
                t_comp = (time.time() - iter_start_time) / opt.batch_size
                visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
                if opt.display_id > 0:
                    visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)

            if total_iters % opt.save_latest_freq == 0:
                print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
                save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
                model.save_networks(save_suffix)

            iter_data_time = time.time()

        if opt.model == 'fedgan' and epoch % opt.num_epochs == 0:
            model.aggregate(epoch)

        if epoch % opt.save_epoch_freq == 0:
            print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
            model.save_networks('latest')
            model.save_networks(epoch)

        print('End of epoch %d / %d \t Time Taken: %d sec' % (
        epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
        model.update_learning_rate()

if __name__ == '__main__':
    main()