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train.py
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train.py
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import time
import copy
from data import CreateDataLoader
from models import create_model
from options.train_options import TrainOptions
from util.visualizer import Visualizer, save_images
if __name__ == '__main__':
opt = TrainOptions().parse()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
if opt.validate_freq > 0:
validate_opt = copy.deepcopy(opt)
validate_opt.phase = 'val'
validate_opt.serial_batches = True # no shuffle
val_data_loader = CreateDataLoader(validate_opt)
val_dataset = val_data_loader.load_data()
val_dataset_size = len(val_data_loader)
print('#validation images = %d' % val_dataset_size)
model = create_model(opt)
model.setup(opt)
visualizer = Visualizer(opt)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
model.train()
for i, data in enumerate(dataset):
iter_start_time = time.time()
if total_steps % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_steps += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data)
if not model.is_train():
continue
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, total_steps, save_result)
if total_steps % opt.print_freq == 0:
losses = model.get_current_losses()
t = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, opt, losses)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
print("experiment name:", opt.name)
model.save_networks('latest')
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save_networks('latest')
model.save_networks(epoch)
if opt.validate_freq > 0 and epoch % opt.validate_freq == 0:
model.eval()
validation_loss_B = 0.0
validation_loss_C = 0.0
b = 0
for i, data in enumerate(val_dataset):
model.set_input(data)
real_in, fake_out_B, real_out_B, fake_out, real_out, val_loss_B, val_loss_C = model.validate()
validation_loss_B += val_loss_B
validation_loss_C += val_loss_C
b += 1
ABC_path = data['ABC_path']
# print("ABC_path len", len(ABC_path))
# last batch will be smaller than batch size
for i in range(len(ABC_path)):
ABC_path_i = ABC_path[i]
file_name = ABC_path_i.split('/')[-1].split('.')[0]
real_out_i = real_out[i].unsqueeze(0)
fake_out_i = fake_out[i].unsqueeze(0)
real_out_B_i = real_out_B[i].unsqueeze(0)
fake_out_B_i = fake_out_B[i].unsqueeze(0)
images = [real_out_i, fake_out_i, real_out_B_i, fake_out_B_i]
names = ['real', 'fake', 'real_B', 'fake_B']
img_path = str(epoch) + '_' + file_name
save_images(images, names, img_path, opt=validate_opt, aspect_ratio=1.0,
width=validate_opt.fineSize)
validation_loss_B /= b
validation_loss_C /= b
visualizer.print_val_losses(epoch, {'val_l1_B': validation_loss_B, 'val_l1': validation_loss_C})
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()