Cyclegan Training Script (with copy to google drive functionality)

cyclegan_training.py

# adapted from: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/test.py

for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1):    # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
    epoch_start_time = time.time()  # timer for entire epoch
    iter_data_time = time.time()    # timer for data loading per iteration
    epoch_iter = 0                  # the number of training iterations in current epoch, reset to 0 every epoch
    visualizer.reset()              # reset the visualizer: make sure it saves the results to HTML at least once every epoch
    model.update_learning_rate()    # update learning rates in the beginning of every epoch.
    
    for i, data in enumerate(dataset):  # inner loop within one epoch
        iter_start_time = time.time()  # timer for computation per iteration

        if total_iters % opt.print_freq == 0:
            t_data = iter_start_time - iter_data_time

        total_iters += opt.batch_size
        epoch_iter += opt.batch_size
        model.set_input(data)         # unpack data from dataset and apply preprocessing
        model.optimize_parameters()   # calculate loss functions, get gradients, update network weights

        if total_iters % opt.display_freq == 0:   # display images on visdom and save images to a HTML file
            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:    # print training losses and save logging information to the disk
            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:   # cache our latest model every <save_latest_freq> iterations
            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 epoch % opt.save_epoch_freq == 0:              # cache our model every <save_epoch_freq> epochs
        print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
        model.save_networks('latest')
        model.save_networks(epoch)
        copy_to_drive()
        test_model(1, 'AtoB')
        test_model(1, 'BtoA')

    print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))