def main():
opt = TrainOptions().parse(args)
dataset = DataLoader(opt)
print('# training images = %d' % len(dataset))
model = ComboGANModel(opt)
visualizer = Visualizer(opt)
total_steps = 0
# Update initially if continuing
if opt.which_epoch > 0:
model.update_hyperparams(opt.which_epoch)
prefix = os.path.join('.', 'checkpoints', opt.name, 'web')
for epoch in range(opt.which_epoch + 1, 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.display_freq == 0:
visualizer.display_current_results(model.get_current_visuals(), epoch, prefix)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
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 epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))
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))
model.update_hyperparams(epoch)
def main():
opt = TestOptions().parse(args)
dataset = DataLoader(opt)
model = ComboGANModel(opt)
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name,
'%s_%d' % (opt.phase, opt.which_epoch))
webpage = html.HTML(
web_dir, 'Experiment = %s, Phase = %s, Epoch = %d' %
(opt.name, opt.phase, opt.which_epoch))
# store images for matrix visualization
vis_buffer = []
prefix = os.path.join('.', 'results', opt.name, f'test_{opt.which_epoch}')
# test
for i, data in enumerate(dataset):
# if not opt.serial_test and i >= opt.how_many:
# break
model.set_input(data)
model.test()
visuals = model.get_current_visuals(testing=True)
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path, prefix)
if opt.show_matrix:
vis_buffer.append(visuals)
if (i + 1) % opt.n_domains == 0:
save_path = os.path.join(web_dir,
'mat_%d.png' % (i // opt.n_domains))
visualizer.save_image_matrix(vis_buffer, save_path)
vis_buffer.clear()
webpage.save()
opt.batchSize = 1 # test code only supports batchSize = 1
dataset = DataLoader(opt)
model = ComboGANModel(opt)
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%d' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %d' % (opt.name, opt.phase, opt.which_epoch))
# store images for matrix visualization
vis_buffer = []
# test
for i, data in enumerate(dataset):
if i >= opt.how_many:
break
model.set_input(data)
model.test()
visuals = model.get_current_visuals(testing=True)
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path)
if opt.show_matrix:
vis_buffer.append(visuals)
if (i+1) % opt.n_domains == 0:
save_path = os.path.join(web_dir, 'mat_%d.png' % (i//opt.n_domains))
visualizer.save_image_matrix(vis_buffer, save_path)
vis_buffer.clear()
webpage.save()
opt = TrainOptions().parse()
dataset = DataLoader(opt)
print('# training images = %d' % len(dataset))
model = ComboGANModel(opt)
visualizer = Visualizer(opt)
total_steps = 0
for epoch in range(opt.which_epoch + 1, 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.display_freq == 0:
visualizer.display_current_results(model.get_current_visuals(), epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
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 epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))
model.save(epoch)