https://github.com/jyhmiinlin/pynufft
Revision bff018fde8fff46d7c3da71222f8191b89aa4628 authored by Jyh-Miin Lin on 23 August 2020, 06:24:06 UTC, committed by GitHub on 23 August 2020, 06:24:06 UTC
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Tip revision: bff018fde8fff46d7c3da71222f8191b89aa4628 authored by Jyh-Miin Lin on 23 August 2020, 06:24:06 UTC
Create codeql-analysis.yml
Create codeql-analysis.yml
Tip revision: bff018f
benchmark_3D_batch.py
__license__='''
The license of the 3D Shepp-Logan phantom:
Copyright (c) 2006, Matthias Schabel
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the distribution
* Neither the name of the University of Utah Department of Radiology nor the names
of its contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
'''
import numpy
import matplotlib.pyplot as pyplot
from matplotlib import cm
gray = cm.gray
import pkg_resources
DATA_PATH = pkg_resources.resource_filename('pynufft', './src/data/')
image = numpy.load(DATA_PATH +'phantom_3D_128_128_128.npz')['arr_0']#[0::2, 0::2, 0::2]
image = numpy.array(image, order='C')
# print(__license__)
# pyplot.imshow(numpy.abs(image[:,:,64]), label='original signal',cmap=gray)
# pyplot.show()
def benchmark(nufftobj, gx, maxiter):
import time
t0= time.time()
for pp in range(0, maxiter):
gy = nufftobj.forward(gx)
t1 = time.time()
for pp in range(0, maxiter):
gx2 = nufftobj.adjoint(gy)
t2 = time.time()
t_iter0 = time.time()
for pp in range(0, maxiter):
pass
t_iter1 = time.time()
t_delta = t_iter1 - t_iter0
return (t1 - t0 - t_delta)/maxiter, (t2 - t1 -t_delta)/maxiter, gy, gx2
Nd = (128,128,128) # time grid, tuple
Kd = (256,256,256) # frequency grid, tuple
Jd = (6,6,6) # interpolator
# om= numpy.load(DATA_PATH+'om3D.npz')['arr_0']
# om = numpy.random.randn(10000,3)*2
# for m in (1e+5, 2e+5, 3e+5, 4e+5, 5e+5, 6e+5, 7e+5, 8e+5, 9e+5, 1e+6, 2e+6, 3e+6, 4e+6, 5e+6,
# 6e+6, 7e+6, 8e+6, 9e+6, 10e+6, 11e+6, 12e+6, 13e+6, 14e+6, 15e+6,
# 16e+6, 17e+6, 18e+6, 19e+6, 20e+6, 30e+6, 40e+6, 50e+6, 60e+6, 70e+6, 80e+6, 90e+6, 100e+6):
for m in (1e+4, ):
om = numpy.random.randn(int(m),3)*2
# om = numpy.load('/home/sram/UCL/DATA/G/3D_Angio/greg_3D.npz')['arr_0'][0:int(m), :]
print(om.shape)
from pynufft import NUFFT_cpu, NUFFT_hsa#, NUFFT_memsave
# from pynufft import NUFFT_memsave
NufftObj_cpu = NUFFT_cpu()
# NufftObj_hsa = NUFFT_hsa()
NufftObj_hsa = NUFFT_hsa('cuda', 0,0)
import time
t0=time.time()
NufftObj_cpu.plan(om, Nd, Kd, Jd)
t1 = time.time()
# NufftObj_hsa.plan(om, Nd, Kd, Jd)
t12 = time.time()
RADIX = 1
NufftObj_hsa.plan(om, Nd, Kd, Jd, radix=RADIX)
t2 = time.time()
# proc = 0 # GPU
# proc = 1 # gpu
# NufftObj_hsa.offload(API = 'ocl', platform_number = proc, device_number = 0)
t22 = time.time()
# NufftObj_memsave.offload(API = 'ocl', platform_number = proc, device_number = 0)
# NufftObj_memsave.offload(API = 'cuda', platform_number = 0, device_number = 0)
t3 = time.time()
# if proc is 0:
# print('CPU')
# else:
# print('GPU')
print('Number of samples = ', om.shape[0])
# print('planning time of CPU = ', t1 - t0)
# print('planning time of HSA = ', t12 - t1)
print('planning time of MEM = ', t2 - t12)
# print('loading time of HSA = ', t22 - t2)
print('loading time of MEM = ', t3 - t22)
# gx_hsa = NufftObj_hsa.thr.to_device(image.astype(numpy.complex64))
gx_memsave = NufftObj_hsa.thr.to_device(image.astype(numpy.complex64).copy())
print('loading data')
maxiter = 1
tcpu_forward, tcpu_adjoint, ycpu, xcpu = benchmark(NufftObj_cpu, image, maxiter)
print('CPU time', int(m), tcpu_forward, tcpu_adjoint)
maxiter = 1
# thsa_forward, thsa_adjoint, yhsa, xhsa = benchmark(NufftObj_hsa, gx_hsa, maxiter)
# print('HSA', 9, m, thsa_forward, thsa_adjoint, )#numpy.linalg.norm(yhsa.get() - ycpu)/ numpy.linalg.norm( ycpu))
tmem_forward, tmem_adjoint, ymem, xmem = benchmark(NufftObj_hsa, gx_memsave, maxiter)
print('default radix = ', RADIX)
print('Hardware = ', NufftObj_hsa.device)
print('HSA' , int(m), tmem_forward, tmem_adjoint)
del NufftObj_hsa
print('Error between CPU and HSA', numpy.linalg.norm(ymem.get() - ycpu)/ numpy.linalg.norm( ycpu))
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