train.py

import time
from options.train_options import TrainOptions
from data import DataLoader
from models import create_model
from util.writer import Writer
from test import run_test

if __name__ == '__main__':
    opt = TrainOptions().parse()
    dataset = DataLoader(opt)
    dataset_size = len(dataset)
    print('#training meshes = %d' % dataset_size)
    model = create_model(opt)
    writer = Writer(opt)
    total_steps = 0
    model.load_network('latest')
    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
        try:
            epoch_start_time = time.time()
            iter_data_time = time.time()
            epoch_iter = 0
            print('start of epoch %d' % (epoch))
            for i, data in enumerate(dataset):
                #print('done')
                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)

                """
                for malla in data['mesh']:
                    print(malla.filename)
                """

                model.optimize_parameters()
                print('epoch {} : {}'.format(epoch, epoch_iter/dataset_size))
                if total_steps % opt.print_freq == 0:
                    loss = model.loss
                    t = (time.time() - iter_start_time) / opt.batch_size
                    writer.print_current_losses(epoch, epoch_iter, loss, t, t_data)
                    writer.plot_loss(loss, epoch, epoch_iter, dataset_size)

                if i % opt.save_latest_freq == 0:
                    print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps))
                    model.save_network('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_network('latest')
                model.save_network(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 opt.verbose_plot:
                writer.plot_model_wts(model, epoch)

            """
            if epoch % opt.run_test_freq == 0:
                acc = run_test(epoch)
                writer.plot_acc(acc, epoch)
            """
        except:
            print('epoca fallida')
    writer.close()