Hi Simon, I think I understood the issue.
*Problem:* The CPUBufferPointer of the CudaImage gets altered after calling .Update() on the backprojection/forwardprojection filter. Because of that, the existing numpy array (I created the CPU and GPU images from) does not get changed from the filter. *Solution:* Obtain the CudaDataManager of the CudaImage *before *applying the back-/forwardprojection filter, apply the filter and .Update(), set cpu pointer and update buffer via manager.SetCPUBufferPointer(cpu_img.GetBufferPointer()) manager.UpdateCPUBuffer() The data from the cuda_img is correctly written in-place into the numpy array I constructed the cpu_img from. I don't know the underlying problem and cause why the CPUBuffer gets mangled, but this method achieves what I need. Thank you very much for your advice! Best Clemens Am Mo., 8. Juli 2019 um 18:07 Uhr schrieb C S <[email protected]>: > Hi Simon, > > I'm not sure I understand but have you tried grafting the Image or >> CudaImage to an existing itk::Image (the Graft function)? >> > I tried that but when I call itk.GetArrayFromImage(cuda_img) on the > grafted image (cpu_img.Graft(cuda_img)) I get the error ```ValueError: > PyMemoryView_FromBuffer(): info->buf must not be NULL``` from within ITK > (or its Python bindings). > > >> Again, I'm not sure I understand but you should be able to graft a >> CudaImage to another CudaImage. >> > If anything I'd like to graft an Image into a CudaImage. When I try > something like `cuda_img.Graft(cpu_img)` I get a TypeError. If this and > the Graft'ing above would work (including the array view), that would be > exactly my initial wish. > > >> You can always ask explicit transfers by calling the functions of the >> data manager (accessible via CudaImage::GetCudaDataManager()) >> > I assume you mean manager.UpdateCPUBuffer()? When I run that, the CPU > image I used to create the GPU image (by this > <https://github.com/SimonRit/RTK/blob/master/examples/FirstReconstruction/FirstCudaReconstruction.py#L64-L70>) > is not updated. > > My scenario is this: I give a numpy array as a volume to be forward > projected. I get a ImageView from that array, set origin and spacing of > that image and transfer to GPU via your method > <https://github.com/SimonRit/RTK/blob/master/examples/FirstReconstruction/FirstCudaReconstruction.py#L64-L70>. > For the output projections, I use an ImageView from a numpy.zeros array > with according shape, spacing and origin and transfer that to GPU the same > way. I then use the CudaForwardProjection filter. Now I'd like to have the > projection data on CPU. Unfortunately, none of the suggested methods worked > for me other than using an itk.ImageDuplicator on the CudaImage :( > > Sorry for the lenghty mail. > > Best > Clemens > > >> > >>> >>> Best >>> Clemens >>> >>> Am Mo., 8. Juli 2019 um 16:20 Uhr schrieb Simon Rit < >>> [email protected]>: >>> >>>> 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 <[email protected]> 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 >>>>> >>>>> >>>>> _______________________________________________ >>>>> Rtk-users mailing list >>>>> [email protected] >>>>> https://public.kitware.com/mailman/listinfo/rtk-users >>>>> >>>>
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