Regarding CPU implementation of correlation function.

[Somdyuti2]

Hi, thanks for the implementation. In my use case, I need to perform the inference on CPU. Inspecting your code in the file correlation.py, I kind of get that for that, we need to call the extern C functions ourselves instead of invoking CuPy functions to do it for us. In your code, each C function is called like this: cupy_launch('kernel_Correlation_rearrange', cupy_kernel('kernel_Correlation_rearrange', { 'input': second, 'output': rbot1 }))( grid=tuple([ int((n + 16 - 1) / 16), second.shape[1], second.shape[0] ]), block=tuple([ 16, 1, 1 ]), args=[ n, second.data_ptr(), rbot1.data_ptr() ] )

I am not familiar with CuPy code, so it will be helpful if you could explain these function calls a bit and give any clue about how to do the equivalent stuff on CPU. I understand that the args in each call are the arguments passed to the C function, but I am not sure what grid and blocksignify here. Probably, they may not be needed when CuPy is not used. As I only need to run on CPU at test time, I guess I don't need to care about the updateGrad functions.

I will appreciate your help/suggestion regarding this.

[sniklaus]

To make inference on CPUs work, you will have to convert the following CUDA code to something that runs on CPUs instead.

https://github.com/sniklaus/pytorch-pwc/blob/cf0d2f2cbef4bcb0f6cbf09011960a4899d77dec/correlation/correlation.py#L8-L33

https://github.com/sniklaus/pytorch-pwc/blob/cf0d2f2cbef4bcb0f6cbf09011960a4899d77dec/correlation/correlation.py#L35-L103

There is nothing you need to be familiar with in terms of CuPy really, the grid and block arguments are something from CUDA. I would recommend you look into the fundamentals for CUDA, it shouldn't take much to understand what the code is doing once you know about the basics of CUDA. Good luck!

[duongpaKikai]

i get problem in the code above when trace model from Pytorch to TorchScript

``Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\envs\DIFRINT-v4\lib\site-packages\cupy\cuda\compiler.py", line 449, in compile
nvrtc.compileProgram(self.ptr, options)
File "cupy\cuda\nvrtc.pyx", line 101, in cupy.cuda.nvrtc.compileProgram
File "cupy\cuda\nvrtc.pyx", line 111, in cupy.cuda.nvrtc.compileProgram
File "cupy\cuda\nvrtc.pyx", line 56, in cupy.cuda.nvrtc.check_status
cupy.cuda.nvrtc.NVRTCError: NVRTC_ERROR_COMPILATION (6)

During handling of the above exception, another exception occurred:`

` File "D:\project\test_1.0.0.pytorch\DIFRINT\models\correlation\correlation.py", line 143, in cupy_launch
return cupy.cuda.compile_with_cache(strKernel).get_function(strFunction)
File "C:\ProgramData\Anaconda3\envs\DIFRINT-v4\lib\site-packages\cupy\cuda\compiler.py", line 297, in compile_with_cache
return _compile_with_cache_cuda(source, options, arch, cache_dir,
File "C:\ProgramData\Anaconda3\envs\DIFRINT-v4\lib\site-packages\cupy\cuda\compiler.py", line 350, in _compile_with_cache_cuda
ptx = compile_using_nvrtc(source, options, arch, name + '.cu')
File "C:\ProgramData\Anaconda3\envs\DIFRINT-v4\lib\site-packages\cupy\cuda\compiler.py", line 158, in compile_using_nvrtc
ptx = prog.compile(options)
File "C:\ProgramData\Anaconda3\envs\DIFRINT-v4\lib\site-packages\cupy\cuda\compiler.py", line 453, in compile
raise CompileException(log, self.src, self.name, options, 'nvrtc')
cupy.cuda.compiler.CompileException: C:\Users\Admin\AppData\Local\Temp\tmpg65_9di1\8f8f5ff490d72ae331cc951d3896a60d_2.cubin.cu(16): error: identifier "tensor" is undefined

1 error detected in the compilation of "C:\Users\Admin\AppData\Local\Temp\tmpg65_9di1\8f8f5ff490d72ae331cc951d3896a60d_2.cubin.cu".`

[jiaweiHu-XDU]

I would like to ask, is there a PyTorch implementation of this CUDA_C code, because I really haven't changed the Python version, if you have done this work before, I hope you can help me solve this problem