train.py

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

opt = TrainOptions().parse()

dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset)    # get the number of images in the dataset.
print('The number of training images = %d' % dataset_size)

# 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

for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
    epoch_start_time = time.time()
    epoch_iter = 0
    for i, data in enumerate(dataset):
        iter_start_time = time.time()
        total_steps += opt.batchSize
        epoch_iter += opt.batchSize
        model.set_input(data)
        model.optimize_parameters()

        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()