ans.append(path)
f_w = open(target_path, 'w')
f_w.writelines(ans)
opt = TrainOptions().parse()
opt.phase = 'val'
write_temp(opt, "temp")
opt.phase = "temp"
opt.serial_batches = True
data_loader = CreatePoseConDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
visualizer = Visualizer(opt)
total_steps = 0 # (start_epoch-1) * dataset_size + epoch_iter
display_delta = total_steps % opt.display_freq
print_delta = total_steps % opt.print_freq
save_delta = total_steps % opt.save_latest_freq
for i, data in enumerate(dataset):
if (i % 100 == 0):
print((i, dataset_size))
visuals = OrderedDict([('input_label',
util.tensor2label(data['A'][0][3:6, :, :], 0)),
('real_image', util.tensor2im(data['A2'][0]))])
visualizer.display_current_results2(visuals, 0, i)
train_writer.add_image('val/real_image', util.tensor2im2(data_test['A2'][0]), total_steps)
train_writer.add_image('val/synthesized_image', util.tensor2im2(generated_test.data[0]), total_steps)
train_writer.add_image('val/B', util.tensor2im2(data_test['B'][0], normalize=False), total_steps)
train_writer.add_image('val/B2', util.tensor2im2(data_test['B2'][0]), total_steps)
'''
### display output images
if save_fake:
visuals = OrderedDict([
('input_label', util.tensor2im(data['A'][0])),
('real_image', util.tensor2im(data['A2'][0])),
('synthesized_image', util.tensor2im(generated.data[0])),
('B', util.tensor2im(data['B'][0])),
('B2', util.tensor2im(data['B2'][0]))
])
visualizer.display_current_results2(visuals, epoch, total_steps)
train_writer.add_image(
'train/input_label',
util.tensor2im2(data['A'][0], normalize=False), total_steps)
train_writer.add_image('train/real_image',
util.tensor2im2(data['A2'][0]), total_steps)
train_writer.add_image('train/synthesized_image',
util.tensor2im2(generated.data[0]),
total_steps)
train_writer.add_image(
'train/B', util.tensor2im2(data['B'][0], normalize=False),
total_steps)
train_writer.add_image('train/B2', util.tensor2im2(data['B2'][0]),
total_steps)
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_iters += opt.batch_size
epoch_iter += opt.batch_size
#model.initialize(opt) # initialize model
model.set_input(
data) # unpack data from dataset and apply preprocessing
model.optimize_parameters(
) # calculate loss functions, get gradients, update network weights
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.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(),
epoch, save_result)
visualizer.display_current_results2(
model.get_current_visuals2(), 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, epoch_iter, losses,
t_comp, t_data)
if 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))
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
