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train_multi_view.py
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train_multi_view.py
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from logger import Logger
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
import os
import shutil
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)
visualizer = Visualizer(opt)
total_steps = 0
if not opt.continue_train and os.path.exists(opt.logroot):
shutil.rmtree(opt.logroot)
os.mkdir(opt.logroot)
if not os.path.exists(opt.logroot):
os.mkdir(opt.logroot)
logger = Logger(opt.logroot)
freq = 50
opt.display_freq = freq
opt.print_freq = freq
opt.save_sample_freq = freq * 4
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)
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)
log = "Elapsed [{}], Epoch [{}], Iter [{}]".format(
t, epoch + 1, dataset_size * dataset_size + i + 1)
for tag, value in errors.items():
logger.scalar_summary(tag, value, epoch * dataset_size + i + 1)
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 total_steps % opt.save_sample_freq == 0:
visualizer.save_current_imgs(model.get_current_visuals(), epoch, i, save_result)
# if total_steps % opt.save_sample_freq == 0:
#
# image_list = []
# patch_list = []
# for k in range(show_col):
# fixed_x = fixed_x_set[k]
# image_filtered = model.netG_A(fixed_x)
# sample_path = os.path.join(opt.checkpoints_dir, opt.name, 'samples')
# util.mkdir(sample_path)
#
# image_list_new = torch.cat([fixed_x, image_filtered], dim = 3)
# image_list.append(image_list_new)
#
# crop_img = get_crop_img(fixed_x, fixed_x_boxes[k])
# crop_img_filtered = get_crop_img(image_filtered, fixed_x_boxes[k])
# crop_list_new = torch.cat([crop_img, crop_img_filtered], dim=3)
# patch_list.append(crop_list_new)
#
# image_list = torch.cat((image_list[0], image_list[1]), dim=3)
# patch_list = torch.cat((patch_list[0], patch_list[1]), dim=3)
#
# save_image(denorm(image_list.data),
# os.path.join(sample_path, '{}_{}_fake.png'.format(epoch, total_steps)), nrow=1, padding=0)
#
# save_image(denorm(patch_list.data),
# os.path.join(sample_path, 'patch_{}_{}_fake.png'.format(epoch, total_steps)), nrow=1, padding=0)
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))
model.update_learning_rate()