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train.py
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train.py
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import time
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
def get_config(config):
import yaml
with open(config, 'r') as stream:
return yaml.load(stream)
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)
visualizer = Visualizer(opt)
total_steps = 0
for epoch in range(1, 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.display_freq == 0:
visualizer.display_current_results(model.get_current_visuals(), epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors(epoch)
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
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 opt.new_lr:
if epoch == opt.niter:
model.update_learning_rate()
elif epoch == (opt.niter + 20):
model.update_learning_rate()
elif epoch == (opt.niter + 70):
model.update_learning_rate()
elif epoch == (opt.niter + 90):
model.update_learning_rate()
model.update_learning_rate()
model.update_learning_rate()
model.update_learning_rate()
else:
if epoch > opt.niter:
model.update_learning_rate()