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Image-to-Image Translation Using Conditional DCGANs
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| 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() |
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