steps += 1
if (opt.throughput_estimation_interval is not None and
steps % opt.throughput_estimation_interval == 0):
print('')
sys.stdout.flush()
print('[THROUGHPUT_ESTIMATION]\t%s\t%d' % (time.time(), steps))
if done or (opt.enable_gavel_iterator and dataloader.done):
break
# Update learning rates
lr_scheduler_G.step()
lr_scheduler_D_A.step()
lr_scheduler_D_B.step()
if opt.enable_gavel_iterator:
dataloader.complete()
state = {
'G_AB': G_AB.state_dict(),
'G_BA': G_BA.state_dict(),
'D_A': D_A.state_dict(),
'D_B': D_B.state_dict(),
'epoch': epoch
}
print('')
if opt.enable_gavel_iterator:
dataloader.save_checkpoint(state, checkpoint_path)
else:
save_checkpoint(state, checkpoint_path)
