Comments (3)
That is unfortunately due to the expensive CPU-to-GPU memory copies employed by the code in the master
branch.
If you'd like a bit more speed (on par with the Torch implementations out there), try this version of Caffe: https://github.com/fzliu/caffe/tree/gram-layer, along with the gram-layer
branch of this repository. That Caffe contains dpaiton's code for Gramian computation, which should speed things up significantly and better utilize the GPU.
from style-transfer.
Hi, ok I see. I'm wondering if everything shouldn't be done in CUDA or C++ AMP ? Python & lua are good for prototyping but if someone wants the algorithm to run really fast one has to port every piece of code to the GPU, no ?.
I'll try to compile the gram-layer Caffe and keep you up to date. Thank you for the code :) !
from style-transfer.
@fzliu I have tried what you suggest about gram layer, very huge speedup. Thank you!
But I find that when using python profiler to dig into it, Scipy optimizer costs quite a lot of times even though numpy now is compiled with multi-threads support.
Any suggestion about this performance issue?
Thank you.
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Related Issues (20)
- Some about implementation HOT 2
- Issue about Loss function HOT 1
- Why do backpropagation for original VGG net? HOT 1
- i try to use it, but not working
- The program stops at "successfully loaded images" HOT 7
- training script
- where the parameter ‘n_iter’ affects in the minimize() function
- style doesn't tranformed successfully but only content HOT 1
- Add description for OpenCL, works well as replacement for CUDA HOT 2
- Check failed ! HOT 1
- why net to flatten img0? HOT 1
- CPU only? HOT 4
- Support for multiple style images HOT 1
- Model.ckpt
- On Windows, minimisation fails
- style.py: AttributeError: 'module' object has no attribute 'set_device' HOT 3
- Some way to set the standard output to lossless .png? HOT 1
- No module named 'caffe' HOT 2
- Segmentation fault
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