netC.train()
netC_T.train()
for _ in range(int(FLAGS.n_labels / FLAGS.batch_size)):
data_u, _ = itr.__next__()
_ = netC_T(data_u.to(device))
netC.eval()
netC_T.eval()
with torch.no_grad():
sample_z = torch.randn(100, FLAGS.g_z_dim).to(device)
# tlabel = label[: FLAGS.bs_g // 10]
tlabel = torch.from_numpy(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8,
9])).to(device)
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=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),
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)
