def load_model(filename):
checkpoint = torch.load(filename, map_location=device)
global generator
global discriminator
generator = Generator(len(data['mapping']), data['max_length'],
latent_size)
generator.load_state_dict(checkpoint['generator_model_state_dict'])
generator.eval()
discriminator = Discriminator(len(data['mapping']))
discriminator.load_state_dict(checkpoint['discriminator_model_state_dict'])
discriminator.eval()
print('Loaded model')
g_optimizer.step()
g_train_loss = g_loss.item()
### -------------- END GENERATOR STEP ------------------------
losses_train.append((d_train_wloss, d_train_gploss, g_train_loss))
if i_batch % print_every == 0:
print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.
format(i_epoch + 1, num_epochs, d_loss.item(),
g_loss.item()))
# ----- End training epoch ------
# ----------- Validation Loop --------------
G.eval()
D.eval()
for i_batch in range(N_val_btot):
(data_b, i_vc) = sdat_val.get_rand_batch()
real_wfs = torch.from_numpy(data_b).float()
i_vc = [torch.from_numpy(i_v).float() for i_v in i_vc]
Nsamp = real_wfs.size(0)
if cuda:
real_wfs = real_wfs.cuda()
i_vc = [i_v.cuda() for i_v in i_vc]
z = noise(Nbatch, z_size)
z = torch.from_numpy(z).float()
if cuda:
z = z.cuda()
fake_wfs = G(z, *i_vc)
netG.zero_grad()
out_fake = torch.sigmoid(netD(x_fake))
lossG = criterion(out_fake, valid)
lossG.backward()
optimizerG.step()
# Print training stats
if i % n_print == 0:
print(
"[Epoch {:5}/{:5}] [Batch {:3}/{:3}] [D loss: {:2.6f}] [G loss: {:2.6f}]"
.format(epoch, n_epochs, i, len(loader['train']),
fake_lossD.item() + real_lossD.item(), lossG.item()))
# Validation
netD.eval()
netG.eval()
valLossD, val_size = 0, 0
for data in loader['val']:
x_real = data['moves'].type(Tensor)
valid = Tensor(x_real.size(0), 1).fill_(1)
fake = Tensor(x_real.size(0), 1).fill_(0)
z = torch.randn(x_real.size(0), latent_size, 1, 1, device=device)
x_fake = netG(z)
out_real = torch.sigmoid(netD(x_real))
out_fake = torch.sigmoid(netD(x_fake))
valReal_lossD = criterion(out_real, valid)