train.py

import time
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model


def get_config(config):
    import yaml
    with open(config, 'r') as stream:
        return yaml.safe_load(stream)


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

    model = create_model(opt)

    total_steps = 0
    if(opt.continue_train == 0):
        init = 1
        init = opt.continue_epoch+1
    for epoch in range(init, opt.niter + opt.niter_decay + 1):
        epoch_start_time = time.time()
        for i, data in enumerate(dataset):
            iter_start_time = time.time()
            total_steps += opt.batchSize
            epoch_iter = total_steps - dataset_size * (epoch - 1)
            model.set_input(data)
            model.optimize_parameters(epoch)

            if total_steps % opt.print_freq == 0:
                errors = model.get_current_errors(epoch)
                t = (time.time() - iter_start_time) / opt.batchSize

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

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

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