[ITK] [ITK-users] GPUDiscreteGaussian not working
Denis Shamonin
dshamoni at gmail.com
Thu Apr 24 03:27:21 EDT 2014
Hi Jim, Won-Ki,
Sorry I did not notice that InputImageType is GPU image.
I’ve tried the itkGPUDiscreteGaussianImageFilterTest with floats as input
and same settings as in Jose example.
It works for my on NVidia, ATI machine on Windows, but as soon as you
change it to short images it does not work anymore. I’ve latest beta driver
for ATI and latest on NVIDIA driver. I think something wrong, if I try to
save CPU image, it looks valid to me.
I general, what I wanted to point is that following call
GPUFilter->GetOutput()->UpdateBuffers(); // synchronization point (GPU->CPU
memcpy) should not happen and have to be removed from the user code. And
that is similar to GPUExplicitSync method that I've to use sometimes. You
may have problem for synchronization will explain bellow.
Following code GPUFilter->GetOutput()->UpdateBuffers() is only possible to
use if input or output image was explicitly created as itk::GPUImage. And
this is not available once
itk::ObjectFactoryBase::RegisterFactory( itk::GPUImageFactory::New() ) is
used.
According to me there are few ways you should be able to enable GPU support
in you code:
1. Using Registry Factory
2. Using explicit definition on the itk::GPUImage and itk::GPU* filters
3. Combination of 1 and 2
1. Using Registry Factory
1.1 I like this approach, but you should not change you original code at
all, no extra code should be added except RegisterFactory. Therefore
UpdateBuffers() or similar sync points should be out from the user code.
Example where sync UpdateBuffers() goes wrong would be something like
implementing filter which use GPU and CPU calculations in GenerateData().
For example itkGPUSmoothingRecursiveGaussianImageFilter.
You could enable GPUImageFactory and GPURecursiveGaussianImageFilterFactory
to convert it to GPU, but it also has a caster which may work on CPU by
design. At this moment you would not be able to change original
itkSmoothingRecursiveGaussianImageFilter, to inject extra sync steps like
UpdateBuffers().
I think that all sync steps should be managed by the GPU pipeline, not by
the user.
1.2 RegisterFactory approach have some problems as well. What if you
original code was based on doubles, where you would like to enable only
float support on GPU. Then RegisterFactory would not be a solution,
sometimes you need extra copy steps. The RegisterFactory also increase
compilation and linking and you need to know where to place it.
You need to solve other things like: Where factories should be created on
the library side or on the application side and how many of them? etc
2. Using explicit definition
The current GPU pipeline does not manage correctly CPU->GPU->CPU pipeline
connections or similar connections.
You need to add extra steps to enable it.
3. Combination of 1 and 2
Should be always available, sometimes that is only correct way to make GPU
filter.
I could think about many examples where synchronization in current
implementation may not work:
1. Most of the problems I’ve experienced is when Grafting() is used for
the output.
The GraftOutput() does not trigger any updates on CPU side as far as I’ve
tried.
Therefore you always have to manage this step yourself.
2. Classes called itkImageAlgorithm, itkImageAdaptor and other similar
classes or any direct access to the raw pointers.
This is much bigger problem. If this classes or direct access to the raw
pointers are used, the data are not synchronized anymore. This is more
tricky to find, you may have GPU image perfectly allocated and somewhere in
ITK pipeline it was used. Not even you have to synchronize, but you have to
do it in the right time.
3. Limited GPU memory. It is possible to run GPU pipeline with limited
memory if you perform more smart
synchronization for the input and output images. Current ITK pipelines
could use Gb’s of data pushed through it. On GPU you don’t have this
possibility. You can’t just keep allocating 512x512x256 images for every
input and output on GPU.
Therefore you would have to create a special way to execute GPU pipeline.
4. Very long GPU pipelines in combination with other ITK filters.
There you will have very complex output allocation, in place filtering,
release outputs, setting output regions, crafting etc.
In this pipelines you don’t have to always copy data back to CPU if next
filter is GPU or even avoid running code on current GPU etc.
Regards,
Denis Shamonin
On Wed, Apr 23, 2014 at 8:28 PM, Kristen Zygmunt <krismz at sci.utah.edu>wrote:
> I also get an output image filled with 0 when I run the
> GPUDiscreteGaussianImageFilterTest with the ITK test image
> (Examples/Data/BrainProtonDensitySlice.png) and pixel types changed to
> short. However, I do not see the NaNs that José saw when using float.
> José, can you try running your code again with pixel types as float using
> either the ITK test image or using another image that starts out as float
> (perhaps there is trouble reading in your vtk ushort image) to see if you
> get NaNs with these images as well? I do think there is a bug with the way
> shorts are handled in this filter, but I'm trying to determine whether your
> float NaNs are a separate bug or a related issue.
>
> I used the following command to run the test code with various test images
> :
> >> /path/to/build/ITK/bin/ITKGPUSmoothingTestDriver
> itkGPUDiscreteGaussianImageFilterTest
> /path/to/source/ITK/Examples/Data/BrainProtonDensitySlice.png
> /path/to/output/gpuGaussianImageFilterTest3DOutput.mha 3
>
> -Kris
>
>
> I cannot follow this either. ITK v4 GPU framework do not require users to
> manually synchronize because ITK will handle dirty flags / data
> synchronization automatically.
>
> Won-Ki
>
>
> 2014-04-22 19:43 GMT+09:00 Jim Miller <millerjv at gmail.com>:
>
>> Denis,
>>
>> I am not following your recommendations for Jose.
>>
>> Are you stating that sometimes ITK does not copy the result of the GPU
>> filter back into the CPU memory?
>>
>> A user should not have to use e methods you are directing Jose towards.
>>
>> Jim
>>
>> On Apr 22, 2014, at 5:28 AM, Denis Shamonin <dshamoni at gmail.com> wrote:
>>
>> Hi Jose,
>>
>>
>> The synchronization from CPU to GPU image and back, may or may not be
>> triggered by the default in the current ITK implementation.
>>
>> You have to make an extra effort to control it. Basically, you have to
>> make sure that GPU input image is allocated and copied from CPU to GPU,
>>
>> execute the filter which only use input and output images, copy GPU
>> output image back to CPU.
>>
>>
>> Ideally, you should not control it and that has to be managed by the ITK
>> GPU pipeline itself (hided from the user), but this is not a case right now.
>>
>>
>> There are few problems. First, what you get after calling
>> reader->GetOutput() is normal ITK image that you passing to the GPU filter,
>>
>> while expecting GPU image at this moment. What you want is that GPU image
>> is ready when you call reader->GetOutput() (created, allocated and copied
>> to GPU).
>>
>> The second problem may happen right after calling the GPU filter, the
>> memory for output are not copied back to CPU output image.
>>
>>
>> What you should do is following:
>>
>> 1 Create GPU input image.
>>
>>
>> 1.1 Register the GPUImageFactory before calling GPUReader =
>> ReaderType::New();
>>
>> itk::ObjectFactoryBase::RegisterFactory( itk::GPUImageFactory::New() );
>>
>>
>> At this moment ALL ITK images which are created will be
>> itk::GPUImage's with memory allocated on GPU.
>>
>> Use it with care, you may end up with not intended copying to GPU when
>> you modify this images.
>>
>> The reader once created will also have GPU image inside and that what
>> you need.
>>
>> You may unregister factory right after you have used it.
>>
>>
>> 1.2 Alternative way if registering factory is not possible for you
>> application.
>>
>> But you still want to use GPU filter in the middle of your application
>> you may consider following:
>>
>> gpuInputImage = GPUInputImageType::New();
>>
>> gpuInputImage->GraftITKImage( itkimage ); // normal itk image here
>>
>> gpuInputImage->AllocateGPU(); // allocate only on GPU
>>
>> gpuInputImage->GetGPUDataManager()->SetCPUBufferLock( true ); //
>> we don't want to change it CPU input
>>
>> gpuInputImage->GetGPUDataManager()->SetGPUDirtyFlag( true ); //
>> set gpu dirty flag
>>
>> gpuInputImage->GetGPUDataManager()->UpdateGPUBuffer(); // copy
>> cpu -> gpu
>>
>>
>> 2. Construct you filter, set input from the reader (or gpuInput image),
>> call your filter.
>>
>> At the moment of construction GPU filter will create GPU output image
>> for you.
>>
>>
>> 3. Call extra synchronization step after GPUFilter->Update(); (listed
>> below)
>>
>> itk::GPUExplicitSync< FilterType, OutputImageType >( GPUFilter, false );
>>
>>
>> 4. Write results
>>
>>
>> I hope that helps a bit. Making or using GPU filters with ITK is a bit of
>> the challenge right now.
>>
>> Specially to get it working across all GPU cards available.
>>
>>
>> You could check the correct execution for itkGPUShrinkImageFilter as
>> example in The Insight Journal paper:
>>
>> http://www.insight-journal.org/browse/publication/884
>>
>>
>> Regards,
>>
>> -Denis Shamonin
>>
>> Division of Image Processing (LKEB)
>>
>> Department of Radiology
>>
>> Leiden University Medical Center
>>
>> PO Box 9600, 2300 RC Leiden, The Netherlands
>>
>>
>>
>> //------------------------------------------------------------------------------
>> // GPU explicit synchronization helper function
>> template< class ImageToImageFilterType, class OutputImageType >
>> void
>> GPUExplicitSync( typename ImageToImageFilterType::Pointer & filter,
>> const bool filterUpdate = true,
>> const bool releaseGPUMemory = false )
>> {
>> if( filter.IsNotNull() )
>> {
>> if( filterUpdate )
>> {
>> filter->Update();
>> }
>>
>> typedef typename
>> OutputImageType::PixelType
>> OutputImagePixelType;
>> typedef GPUImage< OutputImagePixelType,
>> OutputImageType::ImageDimension > GPUOutputImageType;
>> GPUOutputImageType * GPUOutput = dynamic_cast< GPUOutputImageType *
>> >( filter->GetOutput() );
>> if( GPUOutput )
>> {
>> GPUOutput->UpdateBuffers();
>> }
>>
>> if( releaseGPUMemory )
>> {
>> GPUOutput->GetGPUDataManager()->Initialize();
>> }
>> }
>> else
>> {
>> itkGenericExceptionMacro( << "The filter pointer is null." );
>> }
>> }
>>
>>
>>
>> On Wed, Apr 16, 2014 at 3:24 PM, Jose Ignacio Prieto <
>> joseignacio.prieto at gmail.com> wrote:
>>
>>> Hi Jim,
>>>
>>> It had a different problem when using float. It would show a NAN on the
>>> results. That's why I changed to short.
>>> The card has 4GB ram.
>>>
>>>
>>> On Tue, Apr 15, 2014 at 7:40 PM, Jim Miller <millerjv at gmail.com> wrote:
>>>
>>>> Does the test for GPUDiscreteGaussian run on your platform?
>>>>
>>>> The test uses a pixel type of float. Your code does not. You might try
>>>> float.
>>>>
>>>> The Gaussian filter will require much more GPU memory than the mean
>>>> filter. How much memory does your GPU have?
>>>>
>>>> Jim
>>>>
>>>> On Apr 15, 2014, at 11:18 AM, Jose Ignacio Prieto <
>>>> joseignacio.prieto at gmail.com> wrote:
>>>>
>>>> Hi all, I am having trouble using GPUdiscretegaussian. It works for me
>>>> on CPU but GPU version gives output 0. I tried running the test code but no
>>>> help. I do run GPUMean filter. My card is AMDw7000 and using opencl 1.2,
>>>> itk 4.6
>>>>
>>>> Here is the code and the output. The images are vtk files of
>>>> 320x320x231, ushort.
>>>>
>>>> /*=========================================================================
>>>>
>>>> *
>>>>
>>>> * Copyright Insight Software Consortium
>>>>
>>>> *
>>>>
>>>> * Licensed under the Apache License, Version 2.0 (the "License");
>>>>
>>>> * you may not use this file except in compliance with the License.
>>>>
>>>> * You may obtain a copy of the License at
>>>>
>>>> *
>>>>
>>>> * http://www.apache.org/licenses/LICENSE-2.0.txt
>>>>
>>>> *
>>>>
>>>> * Unless required by applicable law or agreed to in writing, software
>>>>
>>>> * distributed under the License is distributed on an "AS IS" BASIS,
>>>>
>>>> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
>>>>
>>>> * See the License for the specific language governing permissions and
>>>>
>>>> * limitations under the License.
>>>>
>>>> *
>>>>
>>>> *=========================================================================*/
>>>>
>>>>
>>>> #include "itkImageFileReader.h"
>>>>
>>>> #include "itkImageFileWriter.h"
>>>>
>>>>
>>>> #include "itkGPUImage.h"
>>>>
>>>> #include "itkGPUKernelManager.h"
>>>>
>>>> #include "itkGPUContextManager.h"
>>>>
>>>> #include "itkGPUImageToImageFilter.h"
>>>>
>>>> #include "itkGPUNeighborhoodOperatorImageFilter.h"
>>>>
>>>>
>>>> #include "itkTimeProbe.h"
>>>>
>>>> #include "itkGaussianOperator.h"
>>>>
>>>>
>>>> #include "itkDiscreteGaussianImageFilter.h"
>>>>
>>>> #include "itkGPUDiscreteGaussianImageFilter.h"
>>>>
>>>> #include "itkMeanImageFilter.h"
>>>>
>>>> #include "itkGPUMeanImageFilter.h"
>>>>
>>>>
>>>> // typedef float InputPixelType;
>>>>
>>>> // typedef float OutputPixelType;
>>>>
>>>> typedef short InputPixelType;
>>>>
>>>> typedef short OutputPixelType;
>>>>
>>>>
>>>> typedef itk::GPUImage< InputPixelType, 3 > InputImageType;
>>>>
>>>> typedef itk::GPUImage< OutputPixelType, 3 > OutputImageType;
>>>>
>>>>
>>>>
>>>>
>>>> typedef itk::ImageFileReader< InputImageType > ReaderType;
>>>>
>>>> typedef itk::ImageFileWriter< OutputImageType > WriterType;
>>>>
>>>>
>>>>
>>>>
>>>> int main(int argc, char *argv[])
>>>>
>>>> {
>>>>
>>>> if(!itk::IsGPUAvailable())
>>>>
>>>> {
>>>>
>>>> std::cerr << "OpenCL-enabled GPU is not present." << std::endl;
>>>>
>>>> return EXIT_FAILURE;
>>>>
>>>> }
>>>>
>>>>
>>>> if( argc < 3 )
>>>>
>>>> {
>>>>
>>>> std::cerr << "Error: missing arguments" << std::endl;
>>>>
>>>> std::cerr << "inputfile outputfile [num_dimensions]" << std::endl;
>>>>
>>>> return EXIT_FAILURE;
>>>>
>>>> }
>>>>
>>>>
>>>> std::string inFile( argv[1] );
>>>>
>>>> std::string outFile( argv[2] );
>>>>
>>>>
>>>> unsigned int dim = 3;
>>>>
>>>> ReaderType::Pointer reader;
>>>>
>>>> WriterType::Pointer writer;
>>>>
>>>> reader = ReaderType::New();
>>>>
>>>> writer = WriterType::New();
>>>>
>>>>
>>>> reader->SetFileName( inFile );
>>>>
>>>> writer->SetFileName( outFile );
>>>>
>>>>
>>>> float variance = 4.0;
>>>>
>>>>
>>>> // test 1~8 threads for CPU
>>>>
>>>> int nThreads = 8;
>>>>
>>>>
>>>> typedef itk::DiscreteGaussianImageFilter< InputImageType, OutputImageType> CPUFilterType;
>>>>
>>>> CPUFilterType::Pointer CPUFilter = CPUFilterType::New();
>>>>
>>>> itk::TimeProbe cputimer;
>>>>
>>>> cputimer.Start();
>>>>
>>>> CPUFilter->SetNumberOfThreads( nThreads );
>>>>
>>>> CPUFilter->SetInput( reader->GetOutput() );
>>>>
>>>> CPUFilter->SetMaximumKernelWidth(10);
>>>>
>>>> CPUFilter->SetUseImageSpacingOff();
>>>>
>>>> CPUFilter->SetVariance( variance );
>>>>
>>>> CPUFilter->Update();
>>>>
>>>> cputimer.Stop();
>>>>
>>>>
>>>> // typedef itk::MeanImageFilter< InputImageType, OutputImageType> CPUFilterType;
>>>>
>>>> // CPUFilterType::Pointer CPUFilter = CPUFilterType::New();
>>>>
>>>> // itk::TimeProbe cputimer;
>>>>
>>>> // cputimer.Start();
>>>>
>>>> // CPUFilter->SetNumberOfThreads( nThreads );
>>>>
>>>> // CPUFilter->SetInput( reader->GetOutput() );
>>>>
>>>> //// CPUFilter->SetMaximumKernelWidth(10);
>>>>
>>>> //// CPUFilter->SetUseImageSpacingOff();
>>>>
>>>> // CPUFilter->SetRadius( variance );
>>>>
>>>> // CPUFilter->Update();
>>>>
>>>> // cputimer.Stop();
>>>>
>>>>
>>>> std::cout << "CPU Gaussian Filter took " << cputimer.GetMean() << " seconds with "
>>>>
>>>> << CPUFilter->GetNumberOfThreads() << " threads.\n" << std::endl;
>>>>
>>>>
>>>> // -------
>>>>
>>>>
>>>> typedef itk::GPUDiscreteGaussianImageFilter< InputImageType, OutputImageType> GPUFilterType;
>>>>
>>>> GPUFilterType::Pointer GPUFilter = GPUFilterType::New();
>>>>
>>>> itk::TimeProbe gputimer;
>>>>
>>>> gputimer.Start();
>>>>
>>>> GPUFilter->SetInput( reader->GetOutput() );
>>>>
>>>> GPUFilter->SetVariance( variance );
>>>>
>>>> GPUFilter->SetMaximumKernelWidth(10);
>>>>
>>>> GPUFilter->SetUseImageSpacingOff();
>>>>
>>>> // GPUFilter->DebugOn();
>>>>
>>>> // GPUFilter->GPUEnabledOff();
>>>>
>>>> GPUFilter->Print(std::cout);
>>>>
>>>> GPUFilter->Update();
>>>>
>>>> GPUFilter->GetOutput()->UpdateBuffers(); // synchronization point (GPU->CPU memcpy)
>>>>
>>>> gputimer.Stop();
>>>>
>>>> std::cout << "GPU Gaussian Filter took " << gputimer.GetMean() << " seconds.\n" << std::endl;
>>>>
>>>>
>>>> // typedef itk::GPUMeanImageFilter< InputImageType, OutputImageType> GPUFilterType;
>>>>
>>>> // GPUFilterType::Pointer GPUFilter = GPUFilterType::New();
>>>>
>>>> // itk::TimeProbe gputimer;
>>>>
>>>> // gputimer.Start();
>>>>
>>>> // GPUFilter->SetInput( reader->GetOutput() );
>>>>
>>>> //// GPUFilter->SetVariance( variance );
>>>>
>>>> //// GPUFilter->SetMaximumKernelWidth(10);
>>>>
>>>> //// GPUFilter->SetUseImageSpacingOff();
>>>>
>>>> //// GPUFilter->DebugOn();
>>>>
>>>> //// GPUFilter->Print(std::cout);
>>>>
>>>> // GPUFilter->SetRadius( variance );
>>>>
>>>> // GPUFilter->Update();
>>>>
>>>> // GPUFilter->GetOutput()->UpdateBuffers(); // synchronization point (GPU->CPU memcpy)
>>>>
>>>> // gputimer.Stop();
>>>>
>>>> // std::cout << "GPU Gaussian Filter took " << gputimer.GetMean() << " seconds.\n" << std::endl;
>>>>
>>>>
>>>> // ---------------
>>>>
>>>> // RMS Error check
>>>>
>>>> // ---------------
>>>>
>>>>
>>>> double diff = 0;
>>>>
>>>> unsigned int nPix = 0;
>>>>
>>>> itk::ImageRegionIterator<OutputImageType> cit(CPUFilter->GetOutput(), CPUFilter->GetOutput()->GetLargestPossibleRegion());
>>>>
>>>> itk::ImageRegionIterator<OutputImageType> git(GPUFilter->GetOutput(), GPUFilter->GetOutput()->GetLargestPossibleRegion());
>>>>
>>>>
>>>> for(cit.GoToBegin(), git.GoToBegin(); !cit.IsAtEnd(); ++cit, ++git)
>>>>
>>>> {
>>>>
>>>> double err = (double)(cit.Get()) - (double)(git.Get());
>>>>
>>>> // if(err > 0.1 || (double)cit.Get() < 0.1) std::cout << "CPU : " << (double)(cit.Get()) << ", GPU : " << (double)(git.Get()) << std::endl;
>>>>
>>>> diff += err*err;
>>>>
>>>> nPix++;
>>>>
>>>> }
>>>>
>>>>
>>>> writer->SetInput( GPUFilter->GetOutput() );
>>>>
>>>> // writer->SetInput( CPUFilter->GetOutput() );
>>>>
>>>> writer->Update();
>>>>
>>>>
>>>> if (nPix > 0)
>>>>
>>>> {
>>>>
>>>> double RMSError = sqrt( diff / (double)nPix );
>>>>
>>>> std::cout << "RMS Error : " << RMSError << std::endl;
>>>>
>>>> // the CPU filter operator has type double
>>>>
>>>> // but the double precision is not well-supported on most GPUs
>>>>
>>>> // and by most drivers at this time. Therefore, the GPU filter
>>>>
>>>> // operator has type float
>>>>
>>>> // relax the RMS threshold here to allow for errors due to
>>>>
>>>> // differences in precision
>>>>
>>>> // NOTE:
>>>>
>>>> // a threshold of 1.2e-5 worked on linux and Mac, but not Windows
>>>>
>>>> // why?
>>>>
>>>> double RMSThreshold = 1.7e-5;
>>>>
>>>> if (vnl_math_isnan(RMSError))
>>>>
>>>> {
>>>>
>>>> std::cout << "RMS Error is NaN! nPix: " << nPix << std::endl;
>>>>
>>>> return EXIT_FAILURE;
>>>>
>>>> }
>>>>
>>>> if (RMSError > RMSThreshold)
>>>>
>>>> {
>>>>
>>>> std::cout << "RMS Error exceeds threshold (" << RMSThreshold << ")" << std::endl;
>>>>
>>>> return EXIT_FAILURE;
>>>>
>>>> }
>>>>
>>>> }
>>>>
>>>> else
>>>>
>>>> {
>>>>
>>>> std::cout << "No pixels in output!" << std::endl;
>>>>
>>>> return EXIT_FAILURE;
>>>>
>>>> }
>>>>
>>>>
>>>> }
>>>>
>>>>
>>>>
>>>> OUTPUT
>>>>
>>>>
>>>> Starting C:\DocsMaracuya\Build\Ejemplos\Gpu\GPUTest.exe...
>>>> Platform : AMD Accelerated Parallel Processing
>>>> Platform : AMD Accelerated Parallel Processing
>>>> Pitcairn
>>>> Maximum Work Item Sizes : { 256, 256, 256 }
>>>> Maximum Work Group Size : 256
>>>> Alignment in bits of the base address : 2048
>>>> Smallest alignment in bytes for any data type : 128
>>>> cl_khr_fp64 cl_amd_fp64 cl_khr_global_int32_base_atomics
>>>> cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics
>>>> cl_khr_local_int32_extended_atomics cl_khr_int64_base_atomics
>>>> cl_khr_int64_extended_atomics cl_khr_3d_image_writes
>>>> cl_khr_byte_addressable_store cl_khr_gl_sharing cl_ext_atomic_counters_32
>>>> cl_amd_device_attribute_query cl_amd_vec3 cl_amd_printf cl_amd_media_ops
>>>> cl_amd_media_ops2 cl_amd_popcnt cl_khr_d3d10_sharing
>>>> cl_amd_bus_addressable_memory cl_amd_c1x_atomics
>>>> CPU Gaussian Filter took 1.70355 seconds with 8 threads.
>>>>
>>>> Defines: #define DIM_3
>>>> #define INTYPE short
>>>> #define OUTTYPE short
>>>> #define OPTYPE short
>>>>
>>>> Defines: #define DIM_3
>>>> #define INTYPE short
>>>> #define OUTTYPE short
>>>> #define OPTYPE short
>>>>
>>>> Defines: #define DIM_3
>>>> #define INTYPE short
>>>> #define OUTTYPE short
>>>> #define OPTYPE short
>>>>
>>>> GPUDiscreteGaussianImageFilter (0000000002205DF0)
>>>> RTTI typeinfo: class itk::GPUDiscreteGaussianImageFilter<class
>>>> itk::GPUImage<short,3>,class itk::GPUImage<short,3> >
>>>> Reference Count: 1
>>>> Modified Time: 560
>>>> Debug: Off
>>>> Object Name:
>>>> Observers:
>>>> none
>>>> Inputs:
>>>> Primary: (000000000216E560) *
>>>> Indexed Inputs:
>>>> 0: Primary (000000000216E560)
>>>> Required Input Names: Primary
>>>> NumberOfRequiredInputs: 1
>>>> Outputs:
>>>> Primary: (000000000218A070)
>>>> Indexed Outputs:
>>>> 0: Primary (000000000218A070)
>>>> NumberOfRequiredOutputs: 1
>>>> Number Of Threads: 8
>>>> ReleaseDataFlag: Off
>>>> ReleaseDataBeforeUpdateFlag: Off
>>>> AbortGenerateData: Off
>>>> Progress: 0
>>>> Multithreader:
>>>> RTTI typeinfo: class itk::MultiThreader
>>>> Reference Count: 1
>>>> Modified Time: 499
>>>> Debug: Off
>>>> Object Name:
>>>> Observers:
>>>> none
>>>> Thread Count: 8
>>>> Global Maximum Number Of Threads: 128
>>>> Global Default Number Of Threads: 8
>>>> CoordinateTolerance: 1e-006
>>>> DirectionTolerance: 1e-006
>>>> Variance: [4, 4, 4]
>>>> MaximumError: [0.01, 0.01, 0.01]
>>>> MaximumKernelWidth: 10
>>>> FilterDimensionality: 3
>>>> UseImageSpacing: 0
>>>> InternalNumberOfStreamDivisions: 9
>>>> GPU: Enabled
>>>> GPU Gaussian Filter took 0.111351 seconds.
>>>>
>>>> RMS Error : 26.4279
>>>> RMS Error exceeds threshold (1.7e-005)
>>>> C:\DocsMaracuya\Build\Ejemplos\Gpu\GPUTest.exe exited with code 1
>>>>
>>>>
>>>> --
>>>> José Ignacio Prieto
>>>> celular(nuevo): 94348182
>>>>
>>>> _____________________________________
>>>> Powered by www.kitware.com
>>>>
>>>> Visit other Kitware open-source projects at
>>>> http://www.kitware.com/opensource/opensource.html
>>>>
>>>> Kitware offers ITK Training Courses, for more information visit:
>>>> http://www.kitware.com/products/protraining.php
>>>>
>>>> Please keep messages on-topic and check the ITK FAQ at:
>>>> http://www.itk.org/Wiki/ITK_FAQ
>>>>
>>>> Follow this link to subscribe/unsubscribe:
>>>> http://www.itk.org/mailman/listinfo/insight-users
>>>>
>>>>
>>>
>>>
>>> --
>>> José Ignacio Prieto
>>> celular(nuevo): 94348182
>>>
>>> _____________________________________
>>> Powered by www.kitware.com
>>>
>>> Visit other Kitware open-source projects at
>>> http://www.kitware.com/opensource/opensource.html
>>>
>>> Kitware offers ITK Training Courses, for more information visit:
>>> http://www.kitware.com/products/protraining.php
>>>
>>> Please keep messages on-topic and check the ITK FAQ at:
>>> http://www.itk.org/Wiki/ITK_FAQ
>>>
>>> Follow this link to subscribe/unsubscribe:
>>> http://www.itk.org/mailman/listinfo/insight-users
>>>
>>>
>>
>
>
> --
> Won-Ki Jeong, PhD
> Assistant Professor
> Electrical and Computer Engineering
> Ulsan National Institute of Science and Technology (UNIST)
> 100 Banyeon-ri Eonyang-eup, Ulju-gun
> Ulsan, Korea 689-798
> Tel: +82-52-217-2131
> http:// <http://home.unist.ac.kr/professor/wkjeong>hvcl.unist.ac.kr
>
>
>
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