[Rtk-users] Use existing Images when copying from/to GPU

Simon Rit simon.rit at creatis.insa-lyon.fr
Mon Jul 8 22:20:50 CEST 2019


Hi,
Conversion from Image to CudaImage is not optimal. The way I'm doing it now
is shown in an example in these few lines
<https://github.com/SimonRit/RTK/blob/master/examples/FirstReconstruction/FirstCudaReconstruction.py#L64-L70>.
I am aware of the problem and discussed it on the ITK forum
<https://discourse.itk.org/t/shadowed-functions-in-gpuimage-or-cudaimage/1614>
but I don't have a better solution yet.
I'm not sure what you mean by explicitely transferring data from/to GPU but
I guess you can always work with itk::Image and do your own CUDA
computations in the GenerateData of the ImageFilter if you don't like the
CudaImage mechanism.
I hope this helps,
Simon

On Mon, Jul 8, 2019 at 10:06 PM C S <clem.schmid at gmail.com> wrote:

> Dear RTK users,
>
> I'm looking for a way to use exisiting ITK Images (either on GPU or in
> RAM) when transfering data from/to GPU. That is, not only re-using the
> Image object, but writing into the memory where its buffer is.
>
> Why: As I'm using the Python bindings, I guess this ties in with ITK
> wrapping the CudaImage type. In
> https://github.com/SimonRit/RTK/blob/master/utilities/ITKCudaCommon/include/itkCudaImage.h#L32 I
> read that the memory management is done implicitly and the CudaImage can be
> used with CPU filters. However when using the bindings,
> only rtk.BackProjectionImageFilter can be used with CudaImages. The other
> filters complain about not being wrapped for that type.
>
> That is why I want to explicitely transfer the data from/to GPU, but
> preferably using the exisiting Images and buffers. I can't rely on RTK
> managing GPU memory implicitly.
>
>
> Thank you very much for your help!
> Clemens
>
>
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