train.py

import time
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util import util
import numpy as np
import torch

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)
    model.setup(opt)   
 
    total_steps = 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(dataset):
            iter_start_time = time.time()
            if total_steps % opt.print_freq == 0:
                t_data = iter_start_time - iter_data_time

            total_steps += opt.batch_size
            epoch_iter += opt.batch_size
        
            model.set_input(data)
            model.optimize_parameters()
         
            if total_steps % opt.print_freq == 0:
                losses = model.get_current_losses()
                t = (time.time() - iter_start_time) / opt.batch_size
                util.print_current_losses(epoch, epoch_iter, losses, t, t_data)
               
            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()

        # done from epoch
        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('epoch_%d'%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()