def eval(self,
val_loader,
dataset_name,
savedir=None,
loss_key=None,
**kwargs):
avg_meters = util.AverageMeters()
model = self.model
opt = self.opt
with torch.no_grad():
for i, data in enumerate(val_loader):
index = model.eval(data, savedir=savedir, **kwargs)
avg_meters.update(index)
util.progress_bar(i, len(val_loader), str(avg_meters))
if not opt.no_log:
util.write_loss(self.writer, join('eval', dataset_name),
avg_meters, self.epoch)
if loss_key is not None:
val_loss = avg_meters[loss_key]
if val_loss < self.best_val_loss:
self.best_val_loss = val_loss
print(
'saving the best model at the end of epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save(label='best_{}_{}'.format(loss_key, dataset_name))
return avg_meters
def train(self, train_loader, **kwargs):
print('\nEpoch: %d' % self.epoch)
avg_meters = util.AverageMeters()
opt = self.opt
model = self.model
epoch = self.epoch
epoch_start_time = time.time()
# model.print_optimizer_param()
for i, data in enumerate(train_loader):
iter_start_time = time.time()
iterations = self.iterations
model.set_input(data, mode='train')
model.optimize_parameters(**kwargs)
errors = model.get_current_errors()
avg_meters.update(errors)
util.progress_bar(i, len(train_loader), str(avg_meters))
if not opt.no_log:
util.write_loss(self.writer, 'train', avg_meters, iterations)
if iterations % opt.display_freq == 0 and opt.display_id != 0:
save_result = iterations % opt.update_html_freq == 0
self.visualizer.display_current_results(
model.get_current_visuals(), epoch, save_result)
if iterations % opt.print_freq == 0 and opt.display_id != 0:
t = (time.time() - iter_start_time)
# self.visualizer.print_current_errors(epoch, i, errors, t)
# self.visualizer.plot_current_errors(epoch, i/len(train_loader), opt, errors)
self.iterations += 1
self.epoch += 1
if not self.opt.no_log:
if self.epoch % opt.save_epoch_freq == 0:
print('saving the model at epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save()
print('saving the latest model at the end of epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save(label='latest')
print('Time Taken: %d sec' % (time.time() - epoch_start_time))
model.update_learning_rate()
def train(self, train_loader, **kwargs):
print('\nEpoch: %d' % self.epoch)
avg_meters = util.AverageMeters()
opt = self.opt
model = self.model
epoch = self.epoch
epoch_start_time = time.time()
########### for debug only
# for i, data in enumerate(train_loader):
# iterations = self.iterations
# model.set_input(data, mode='train')
# model.optimize_parameters(**kwargs)
# # print(data.keys()) # ['input', 'target_t', 'fn', 'real', 'target_r', 'unaligned']
# # from PIL import Image
# # from util.util import tensor2im
# # for st in ['input', 'target_t', 'target_r']:
# # A = tensor2im(data[st])
# # im = Image.fromarray(A)
# # im.save(st+'.jpeg')
# # del A
# # del im
# return 0
############# for debug only
for i, data in enumerate(train_loader):
iter_start_time = time.time()
iterations = self.iterations
model.set_input(data, mode='train')
model.optimize_parameters(**kwargs)
errors = model.get_current_errors()
avg_meters.update(errors)
util.progress_bar(i, len(train_loader), str(avg_meters), opt)
if not opt.no_log:
util.write_loss(self.writer, 'train', avg_meters, iterations)
if iterations % opt.display_freq == 0 and opt.display_id != 0:
save_result = iterations % opt.update_html_freq == 0
self.visualizer.display_current_results(
model.get_current_visuals(), epoch, save_result)
if iterations % opt.print_freq == 0 and opt.display_id != 0:
t = (time.time() - iter_start_time)
self.iterations += 1
self.epoch += 1
if not self.opt.no_log:
if self.epoch % opt.save_epoch_freq == 0:
print('saving the model at epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save()
print('saving the latest model at the end of epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save(label='latest')
print('Time Taken: %d sec' % (time.time() - epoch_start_time))
# model.update_learning_rate()
train_loader.reset()