/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/VGG19.py:137: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number
  return loss.data[0], grad
Traceback (most recent call last):
  File "main.py", line 49, in <module>
    img_AP, img_B = analogy(img_A, img_BP, config)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/DeepAnalogy.py", line 85, in analogy
    iters=400, display=False)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/VGG19.py", line 142, in get_deconvoluted_feat
    noise, stat = lbfgs(f, noise, maxIter=iters, gEps=1e-4, histSize=4, lr=lr, display=display)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/lbfgs.py", line 60, in lbfgs
    step, stat_ls, args = linesearch(xk.clone(), z, f, fk, gk.clone(), fkm1,gkm1.clone(), 10000, lr)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/lbfgs.py", line 131, in linesearch
    strong_wolfe = (np.abs(phi_prime_alpha) <= -c2 * phi_prime_0)
RuntimeError: Expected object of type torch.DoubleTensor but found type torch.cuda.DoubleTensor for argument #2 'other'

I cannot run it at all! I tried different versions of py-torch and cuda, but I keep getting ELF header error. My OS is latest Ubuntu (just in case).

ImportError: /data/miniconda3/envs/analogy4/lib/python3.6/site-packages/torch/lib/../../../../libmkl_core.so: invalid ELF header

Unable to access the model url in VGG19.py. "https://s3-us-west-2.amazonaws.com/jcjohns-models/vgg19-d01eb7cb.pth" seems to be forbidden can you please check or suggest alternate.

I use numba instead of parallel computing. The PatchMatch process runtime can be reduced. Python is not good for 'for' loops and numba can solve this. The main time cost is in the LBFGS process.

sudo python3 main.py
libpng warning: iCCP: known incorrect sRGB profile
patch_size:3; iters:10; rand_d:32
Done All Iterations
patch_size:3; iters:10; rand_d:32
Done All Iterations
12 20 29
Traceback (most recent call last):
  File "main.py", line 49, in <module>
    img_AP, img_B = analogy(img_A, img_BP, config)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/DeepAnalogy.py", line 85, in analogy
    iters=400, display=False)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/VGG19.py", line 142, in get_deconvoluted_feat
    noise, stat = lbfgs(f, noise, maxIter=iters, gEps=1e-4, histSize=4, lr=lr, display=display)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/lbfgs.py", line 60, in lbfgs
    step, stat_ls, args = linesearch(xk.clone(), z, f, fk, gk.clone(), fkm1,gkm1.clone(), 10000, lr)
  File "/home/kadia/Documents/Deep-Image-Analogy-PyTorch-master/lbfgs.py", line 131, in linesearch
    strong_wolfe = torch.DoubleTensor(np.abs(phi_prime_alpha) <= -c2 * phi_prime_0)
RuntimeError: Expected object of type torch.DoubleTensor but found type torch.cuda.DoubleTensor for argument #2 'other'

@Ben-Louis Can you help me in this?

Hi,

I maintain my own version https://github.com/Ben-Louis/Deep-Image-Analogy-PyTorch of this paper, but I was unable to get results close to the paper.

However, your results match almost perfectly. Could you tell me if there is anything different that I am doing, or I should try out ?

Hi, thanks for this great pytorch implementation. I run the code for the demo image pair on GPU and it takes about 40min. The result is good. Just double check, is my run-time close to yours?

Hey, just got this running. My test case is:

to:

Using weight 2. Testing with weight 3 now. A couple questions for you as I'm relatively new to ML.

I notice that Harvey Slash made some modifications, moving to adam optimizer for example. Is there a reason you stuck with lbfgs? Would it be worth attempting a few of his changes?

This is very fun, thanks for releasing it. Any tips are much appreciated.