nrow=FLAGS.bs_g // 10)
total_t, correct_t, loss_t = evaluation.test_classifier(netC)
total_tt, correct_tt, loss_tt = evaluation.test_classifier(netC_T)
netC.train()
netC_T.train()
if FLAGS.c_step == "ramp_swa":
netC_swa.train()
for _ in range(300):
data_u, _ = itr.__next__()
_ = netC_swa(data_u.to(device))
netC_swa.eval()
total_s, correct_s, loss_s = evaluation.test_classifier(netC_swa)
logger.add("testing", "loss_s", loss_s.item(), i + 1)
logger.add("testing", "accuracy_s", 100 * (correct_s / total_s),
i + 1)
logger.add("testing", "loss", loss_t.item(), i + 1)
logger.add("testing", "accuracy", 100 * (correct_t / total_t), i + 1)
logger.add("testing", "loss_t", loss_tt.item(), i + 1)
logger.add("testing", "accuracy_t", 100 * (correct_tt / total_tt),
i + 1)
str_meg = logger_prefix.format(i + 1, max_iter,
100 * ((i + 1) / max_iter))
logger.log_info(str_meg, text_logger.info, ["testing"])
if (i + 1) % FLAGS.save_every == 0:
logger.save_stats("Model_stats.pkl")
file_name = "model" + str(i + 1) + ".pt"
checkpoint_io.save(file_name)
str_meg = "Iteration {}/{} ({:.0f}%), grad_norm {:e}, E_real {:e},"
str_meg += " E_noise {:e}, tLoss {:e}, Normalized Loss {:e}, time {:4.3f}"
text_logger.info(
str_meg.format(
i + 1,
max_iter,
100 * ((i + 1) / max_iter),
grad_norm,
E_real.item(),
E_noise.item(),
tloss.item(),
nloss.item(),
time_dur,
)
)
time_dur = 0.0
logger.add("training", "E_real", E_real.item(), i + 1)
logger.add("training", "E_noise", E_noise.item(), i + 1)
logger.add("training", "loss", tloss.item(), i + 1)
del E_real
del E_noise
del nloss
del tloss
if (i + 1) % FLAGS.save_every == 0:
text_logger.info("-" * 50)
logger.save_stats("stats.pkl")
file_name = "model" + str(i + 1) + ".pt"
torch.save(netE.state_dict(), MODELS_FOLDER + "/" + file_name)
if FLAGS.c_step == "ramp_swa":
netC_swa.train()
for _ in range(300):
data_u, _ = itr_u.__next__()
_ = netC_swa(data_u.to(device))
netC_swa.eval()
total_s, correct_s, loss_s = evaluation.test_classifier(netC_swa)
logger.add("testing", "loss_s", loss_s.item(), i + 1)
logger.add("testing", "accuracy_s", 100 * (correct_s / total_s),
i + 1)
total_t, correct_t, loss_t = evaluation.test_classifier(netC_T)
logger.add("testing", "loss_t", loss_t.item(), i + 1)
logger.add("testing", "accuracy_t", 100 * (correct_t / total_t), i + 1)
total_t, correct_t, loss_t = evaluation.test_classifier(netC)
logger.add("testing", "loss", loss_t.item(), i + 1)
logger.add("testing", "accuracy", 100 * (correct_t / total_t), i + 1)
prefix = logger_prefix.format(i + 1, max_iter,
(100 * i + 1) / max_iter)
cats = ["testing"]
logger.log_info(prefix, text_logger.info, cats=cats)
netC.train()
netC_T.train()
if (i + 1) % FLAGS.save_every == 0:
logger.save_stats("{:08d}.pkl".format(i))
file_name = "model" + str(i + 1) + ".pt"
checkpoint_io.save(file_name)
sample_z = torch.randn(batch_size, FLAGS.g_z_dim).to(device)
loss_g, dfake_g = loss_func_g(netD, netG, sample_z, label)
optim_G.zero_grad()
loss_g.backward()
if FLAGS.clip_value > 0:
torch.nn.utils.clip_grad_norm_(netG.parameters(), FLAGS.clip_value)
optim_G.step()
logger.add("training_g", "loss", loss_g.item(), i + 1)
logger.add("training_g", "dfake", dfake_g.item(), i + 1)
# Torture.shortcuts.update_average(netG_T, net_G, 0.999)
if (i + 1) % print_interval == 0:
prefix = logger_prefix.format(i + 1, max_iter,
(100 * i + 1) / max_iter)
cats = ["training_d", "training_g"]
logger.log_info(prefix, text_logger.info, cats=cats)
if (i + 1) % image_interval == 0:
with torch.no_grad():
sample_z = torch.randn(100, FLAGS.g_z_dim).to(device)
tlabel = label[:10]
tlabel = torch.cat([tlabel for _ in range(10)], 0)
x_fake = netG(sample_z, tlabel)
logger.add_imgs(x_fake, "img{:08d}".format(i + 1), nrow=10)
if (i + 1) % FLAGS.save_every == 0:
logger.save_stats("ModelStat.pkl")
file_name = "model" + str(i + 1) + ".pt"
checkpoint_io.save(file_name)
