Is there the Linux version of the code?

[TruePhone]

The project is very awesome and it would be more convenient for testing if there would be the Linux version of the code. I know the Colorization Subnet is based on Pytorch, which is easily used on Linux, so I hope I can get the Linux version of the Similarity SubNet if possible. Thank you very much.

[hmmlillian]

@TruePhone The Similarity SubNet (and Deep Image Analogy if you use it to build correspondence) is implemented in C++. It should be easy to transplant to Linux. Deep Image Analogy already has a Linux version (https://github.com/msracver/Deep-Image-Analogy/tree/linux) and our Similarity SubNet is actually built based on it. You may copy the source code from Deep-Exemplar-based-Colorization/similarity_subnet/windows/similarity_combo/ to msracver/Deep-Image-Analogy/tree/linux and slightly modify it on Linux.

[TruePhone]

@hmmlillian Thanks for your prompts very much. I will have a try.

[JixiangGao]

@TruePhone Hello, I am also planning to tranfer it to Linux version recently. Now I can compile the Deep-Image-Analogy successfully, but can't compile similarity_combo. So, could you provide the executable file of similarity_combo if you compile it successfully? Thanks very much!

[xuzhongyou]

@TruePhone The Similarity SubNet (and Deep Image Analogy if you use it to build correspondence) is implemented in C++. It should be easy to transplant to Linux. Deep Image Analogy already has a Linux version (https://github.com/msracver/Deep-Image-Analogy/tree/linux) and our Similarity SubNet is actually built based on it. You may copy the source code from Deep-Exemplar-based-Colorization/similarity_subnet/windows/similarity_combo/ to msracver/Deep-Image-Analogy/tree/linux and slightly modify it on Linux.

Thank you for your job, but i think most of people work on linux. Please give a intergrated version about linux or tell us what we should do in detail.

[jqueguiner]

#13

[mattclifford1]

Did anyone manage to get this transfer successfully to Linux?

[ncianeo]

#21
I got this transfer successfully to linux!

[Travsmakoy]

@ncianeo How ? Can you share it with me?

[ncianeo]

@Travsmakoy Use this repository: https://github.com/ncianeo/Deep-Exemplar-based-Colorization/tree/linux-docker-cv-caffe-build