Image-to-Image Translation Using Conditional DCGANs

evaluate_generator.py

G=Generator()
filepath='gdrive/My Drive/pix2pixmodel/G_L1.pth'
G.load_state_dict(torch.load(filepath,map_location=torch.device('cpu')))
G.eval()

for x,y in dataloader:
  z=torch.randn((batch_size,1,128,128)).to(device)
  generated_imgs=G(x[:5],z[:5])
  real_imgs=x[:5]
  imgs=torch.cat([generated_imgs,real_imgs,y[:5]],0).data.cpu()
  grid=make_grid(imgs,nrow=5).permute(1,2,0).numpy()
  plt.imshow(grid)
  plt.show()