train.py

import time
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
import math

if __name__ == '__main__':
    opt = TrainOptions().parse()
    data_loader = CreateDataLoader(opt)
    dataset = data_loader.load_data()
    dataset_size = len(data_loader)
    print('#training images = %d' % dataset_size)

    model = create_model(opt)
    visualizer = Visualizer(opt)
    total_steps = 0
    total_steps = (opt.epoch_count-1)*dataset_size

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

        for i, data in enumerate(dataset):
            iter_start_time = time.time()
            t_data = iter_start_time - iter_data_time
            if total_steps % opt.print_freq == 0:
                t_data = iter_start_time - iter_data_time
            visualizer.reset()
            # if total_steps % 5 == 0:
            #    sign = '0'
            #elif total_steps % 5 == 1:
            #    sign = '1'
            #elif total_steps % 5 == 2:
            #    sign = '2'
            #elif total_steps % 5 == 3:
            #    sign = '3'
            #elif total_steps % 5 == 4:
            #    sign = '4'
            #else:
            #    print("Error occur when getting the 0, 1, 0to1")
            # interval = int((opt.progressive_epoch-1)/11);
            # print("interval:", interval)
            if total_steps % 5 == 0:
                sign = '1'
            elif total_steps % 5 == 1:
                sign = '1'
            elif total_steps % 5 == 2:
                sign = '1'
            elif total_steps % 5 == 3:
                sign = '1'
            elif total_steps % 5 == 4:
                sign = '2' 
            beta = 1
            # alpha = math.exp((epoch-1.0/2.0*opt.progressive_epoch)/(1.0/4.0*opt.progressive_epoch))
            alpha = math.exp((total_steps-1.0/2.0*(opt.progressive_epoch-1)*dataset_size)/(1.0/4.0*(opt.progressive_epoch-1)*dataset_size))
            total_steps += opt.batchSize
            epoch_iter += opt.batchSize
            model.set_input(data, beta, alpha, sign)
            model.optimize_parameters()

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

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

            if total_steps % opt.save_latest_freq == 0:
                print('saving the latest model (epoch %d, total_steps %d)' %
                      (epoch, total_steps))
                model.save_networks('latest')

            iter_data_time = time.time()
        if epoch % opt.save_epoch_freq == 0:
            print('saving the model at the end of epoch %d, iters %d' %
                  (epoch, total_steps))
            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()