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train.py
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train.py
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import time
import os
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from tensorboardX import SummaryWriter
from util import util
opt = TrainOptions().parse()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
model = create_model(opt)
summary_dir = os.path.join(opt.checkpoints_dir, 'logs')
# util.remove_file_end_with(summary_dir, '*.cglabmark3')
util.mkdirs([summary_dir])
summary = SummaryWriter(summary_dir)
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()
# visualizer.reset()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
# save_result = total_steps % opt.update_html_freq == 0
# visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
for key, val in model.get_current_visuals().items():
summary.add_image('image/{}'.format(key), val, total_steps)
if total_steps % opt.print_freq == 0:
for key, val in model.get_current_errors().items():
summary.add_scalar('loss/{}'.format(key), val, total_steps)
# 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')
print('[', epoch, ']', i, ' ', opt.name)
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)
# if epoch % opt.delete_log_epoch_freq == 0:
# util.remove_file_end_with(summary_dir, '*.cglabmark3')
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()