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import time
from options.train_options import TrainOptions
from dataloader.data_loader import dataloader
from model import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = dataloader(opt) # create a dataset
dataset_size = len(dataset) * opt.batch_size
print('training images = %d' % dataset_size)
model = create_model(opt) # create a model given opt.model and other options
visualizer = Visualizer(opt) # create a visualizer
total_iters = opt.iter_count # the total number of training iterations
epoch = 0
max_iteration = opt.n_iter + opt.n_iter_decay
while (total_iters < max_iteration):
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch += 1 # the number of training iterations in current epoch, reset to 0 every epoch
epoch_iter = 0
visualizer.reset() # reset the visualizer
for i, data in enumerate(dataset):
iter_start_time = time.time()
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
if total_iters == 0:
model.setup(opt)
model.parallelize()
total_iters += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters()
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.log_imgs()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, total_iters, losses, t_comp, t_data)
if opt.display_id is None or opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
print(opt.name) # it's useful to occasionally show the experiment name on console
model.save_networks('latest')
if total_iters % opt.save_iters_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of iters %d' % (total_iters))
model.save_networks('latest')
model.save_networks(total_iters)
print('End of iters %d / %d \t Time Taken: %d sec' % (total_iters, max_iteration, time.time() - epoch_start_time))
model.update_learning_rate() |