def test(encoder_model, epoch, x64_test, x16_test):
encoder.eval()
decoder.eval()
z = torch.randn(9, 1, 16, 16).to(device)
_, z16, _ = X_l_encoder(encoder_model,
(torch.FloatTensor(x16_test[111:112])).to(device))
z16 = (torch.ones([9, 1, 4, 4])).to(device) * z16
z16 = upsample(z16, 16)
z_h = torch.cat((z16, z), 1)
gen_imgs = decoder(z_h)
samples = np.squeeze(gen_imgs.data.cpu().numpy())
plot_generation(samples, epoch, output_dir)
real_imgs = x64_test[[10, 30, 50, 100]]
real_x16 = x16_test[[10, 30, 50, 100]]
_, z16, _ = X_l_encoder(encoder_model,
(torch.FloatTensor(real_x16)).to(device))
z16 = upsample(z16, 16)
real_imgs = (torch.FloatTensor(real_imgs)).to(device)
encoded_imgs, _, _ = encoder(real_imgs)
z_h = torch.cat((z16, encoded_imgs), 1)
decoded_imgs = decoder(z_h)
samples_gen = np.squeeze(decoded_imgs.data.cpu().numpy())
samples_real = np.squeeze(real_imgs.data.cpu().numpy())
samples = np.vstack((samples_real, samples_gen))
plot_reconstruction(samples, epoch, output_dir)
def test(epoch,x_test):
encoder.eval()
decoder.eval()
z = torch.randn(9, 1, 4 ,4).to(device)
imgs = decoder(z)
samples = np.squeeze(imgs.data.cpu().numpy())
plot_generation(samples,epoch,output_dir,1)
real_imgs = x_test[[10,30,50,100]]
real_imgs = (torch.FloatTensor(real_imgs)).to(device)
encoded_imgs,_,_ = encoder(real_imgs)
decoded_imgs = decoder(encoded_imgs)
samples_gen = np.squeeze(decoded_imgs.data.cpu().numpy())
samples_real = np.squeeze(real_imgs.data.cpu().numpy())
samples = np.vstack((samples_real,samples_gen))
plot_reconstruction(samples,epoch,output_dir)
# Saving reconstruction losses
with open('reconst_losses', 'wb') as f:
pickle.dump(rec_losses, f)
# Plot reconstruction losses
plot.plot_reconst_losses(rec_losses, 'reconst_losses.png')
# Saving train total losses
with open('train_total_losses', 'wb') as f:
pickle.dump(train_total_losses, f)
# Saving valid total losses
with open('valid_total_losses', 'wb') as f:
pickle.dump(valid_total_losses, f)
# Plot train and valid total losses
plot.plot_tv_losses(train_total_losses, valid_total_losses,
'tv_total_losses.png')
plot.plot_reconstruction('rec1.png', model, test_loader)
plot.plot_reconstruction('rec2.png', model, test_loader)
plot.plot_reconstruction('rec3.png', model, test_loader)
plot.plot_reconstruction('rec4.png', model, test_loader)
plot.plot_reconstruction('rec5.png', model, test_loader)
plot.plot_generation('gen1.png', model)
plot.plot_generation('gen2.png', model)
plot.plot_generation('gen3.png', model)
plot.plot_generation('gen4.png', model)
plot.plot_generation('gen5.png', model)