def example_3D():
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]
pyplot.imshow(numpy.abs(image[:, :, 32]),
label='original signal',
cmap=gray)
pyplot.show()
Nd = (64, 64, 64) # time grid, tuple
Kd = (64, 64, 64) # frequency grid, tuple
Jd = (1, 1, 1) # interpolator
# om= numpy.load(DATA_PATH+'om3D.npz')['arr_0']
om = numpy.random.randn(15120, 3)
print(om.shape)
from pynufft import NUFFT_cpu, NUFFT_hsa
NufftObj = NUFFT_cpu()
NufftObj.plan(om, Nd, Kd, Jd)
kspace = NufftObj.forward(image)
restore_image = NufftObj.solve(kspace, 'cg', maxiter=200)
# restore_image1 = NufftObj.solve(kspace,'L1TVLAD', maxiter=200,rho=0.1)
#
restore_image2 = NufftObj.solve(kspace, 'L1TVOLS', maxiter=200, rho=0.1)
pyplot.subplot(2, 2, 1)
pyplot.imshow(numpy.abs(image[:, :, 32]),
label='original signal',
cmap=gray)
pyplot.title('original')
# pyplot.subplot(2,2,2)
# pyplot.imshow(numpy.abs(restore_image1[:,:,32]), label='L1TVLAD',cmap=gray)
# pyplot.title('L1TVLAD')
pyplot.subplot(2, 2, 3)
pyplot.imshow(numpy.abs(restore_image2[:, :, 32]),
label='L1TVOLS',
cmap=gray)
pyplot.title('L1TVOLS')
pyplot.subplot(2, 2, 4)
pyplot.imshow(numpy.abs(restore_image[:, :, 32]), label='CG', cmap=gray)
pyplot.title('CG')
# pyplot.legend([im1, im im4])
pyplot.show()
def example_1D():
om = numpy.random.randn(1512, 1)
# print(om)
# print(om.shape)
# pyplot.hist(om)
# pyplot.show()
Nd = (256, ) # time grid, tuple
Kd = (512, ) # frequency grid, tuple
Jd = (7, ) # interpolator
from pynufft import NUFFT_cpu, NUFFT_hsa
NufftObj = NUFFT_cpu()
batch = 4
NufftObj.plan(om, Nd, Kd, Jd, batch=batch)
time_data = numpy.zeros((256, batch))
time_data[64:192, :] = 1.0
pyplot.plot(time_data)
pyplot.ylim(-1, 2)
pyplot.show()
nufft_freq_data = NufftObj.forward(time_data)
print('shape of y = ', nufft_freq_data.shape)
x2 = NufftObj.adjoint(nufft_freq_data)
restore_time = NufftObj.solve(nufft_freq_data, 'cg', maxiter=30)
restore_time1 = NufftObj.solve(nufft_freq_data,
'L1TVOLS',
maxiter=30,
rho=1)
#
# restore_time2 = NufftObj.solve(nufft_freq_data,'L1TVOLS', maxiter=30,rho=1)
#
# im1,=pyplot.plot(numpy.abs(time_data),'r',label='original signal')
# im3,=pyplot.plot(numpy.abs(restore_time2),'k--',label='L1TVOLS')
# im4,=pyplot.plot(numpy.abs(restore_time),'r:',label='conjugate_gradient_method')
# pyplot.legend([im1, im2, im3,im4])
for slice in range(0, batch):
pyplot.plot(numpy.abs(x2[:, slice]))
pyplot.plot(numpy.abs(restore_time[:, slice]))
pyplot.show()
def spiral_recon(data_path, ktraj, N, plot=0):
##
# Load the raw data
##
dat = sio.loadmat(data_path + 'rawdata_spiral')['dat']
##
# Acq parameters
##
Npoints = ktraj.shape[0]
Nshots = ktraj.shape[1]
Nchannels = dat.shape[-1]
if len(dat.shape) < 4:
Nslices = 1
dat = dat.reshape(Npoints, Nshots, 1, Nchannels)
else:
Nslices = dat.shape[-2]
if dat.shape[0] != ktraj.shape[0] or dat.shape[1] != ktraj.shape[1]:
raise ValueError('Raw data and k-space trajectory do not match!')
##
# Arrange data for pyNUFFT
##
om = np.zeros((Npoints * Nshots, 2))
om[:, 0] = np.real(ktraj).flatten()
om[:, 1] = np.imag(ktraj).flatten()
NufftObj = NUFFT_cpu() # Create a pynufft object
Nd = (N, N) # image size
Kd = (2 * N, 2 * N) # k-space size
Jd = (6, 6) # interpolation size
NufftObj.plan(om, Nd, Kd, Jd)
##
# Recon
##
im = np.zeros((N, N, Nslices, Nchannels), dtype=complex)
for ch in range(Nchannels):
for sl in range(Nslices):
im[:, :, sl, ch] = NufftObj.solve(dat[:, :, sl, ch].flatten(),
solver='cg',
maxiter=50)
sos = np.sum(np.abs(im), 2)
sos = np.divide(sos, np.max(sos))
if plot:
plt.imshow(np.rot90(np.abs(sos[:, :, 0]), -1), cmap='gray')
plt.axis('off')
plt.title('Uncorrected Image')
plt.show()
return
def example_1D():
om = numpy.random.randn(1512,1)
# print(om)
# print(om.shape)
# pyplot.hist(om)
# pyplot.show()
Nd = (256,) # time grid, tuple
Kd = (512,) # frequency grid, tuple
Jd = (7,) # interpolator
from pynufft import NUFFT_cpu, NUFFT_hsa
NufftObj = NUFFT_cpu()
NufftObj.plan(om, Nd, Kd, Jd)
time_data = numpy.zeros(256, )
time_data[64:192] = 1.0
pyplot.plot(time_data)
pyplot.ylim(-1,2)
pyplot.show()
nufft_freq_data =NufftObj.forward(time_data)
restore_time = NufftObj.solve(nufft_freq_data,'cg', maxiter=30)
restore_time2 = NufftObj.solve(nufft_freq_data,'L1TVOLS', maxiter=30,rho=1)
im1,=pyplot.plot(numpy.abs(time_data),'r',label='original signal')
# im2,=pyplot.plot(numpy.abs(restore_time1),'b:',label='L1TVLAD')
im3,=pyplot.plot(numpy.abs(restore_time2),'k--',label='L1TVOLS')
im4,=pyplot.plot(numpy.abs(restore_time),'r:',label='conjugate_gradient_method')
pyplot.legend([im1, im3,im4])
pyplot.show()
def non_uniform_fft(pos_stack,pos_wavefun,solver,interp_size):
assert len(pos_wavefun.shape) == 2
NufftObj = NUFFT_cpu()
om = pos_stack
Nd = (len(pos_stack[0]),len(pos_stack[1]))
Kd = Nd
Jd = (interp_size,interp_size)
NufftObj.plan(om,Nd,Kd,Jd)
y = NufftObj.forward(pos_wavefun)
mom_wavefun_1 = NufftObj.solve(y,solver=solver )
#mom_wavefun_2 = NufftObj.adjoint(y)
return mom_wavefun_1 #, mom_wavefun_2
image = scipy.misc.ascent()
image = scipy.misc.imresize(image, (256, 256))
image = image * 1.0 / numpy.max(image[...])
print('loading image...')
matplotlib.pyplot.imshow(image.real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.show()
y = NufftObj.forward(image)
print('setting non-uniform data')
print('y is an (M,) list', type(y), y.shape)
matplotlib.pyplot.subplot(2, 2, 1)
image0 = NufftObj.solve(y, solver='cg', maxiter=50)
matplotlib.pyplot.title('Restored image (cg)')
matplotlib.pyplot.imshow(image0.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
matplotlib.pyplot.subplot(2, 2, 2)
image2 = NufftObj.adjoint(y)
matplotlib.pyplot.imshow(image2.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0, vmax=5))
matplotlib.pyplot.title('Adjoint transform')
matplotlib.pyplot.subplot(2, 2, 3)
image3 = NufftObj.solve(y, solver='L1TVOLS', maxiter=50, rho=0.1)
matplotlib.pyplot.title('L1TV OLS')
def test_2D():
import pkg_resources
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
# PHANTOM_FILE = pkg_resources.resource_filename('pynufft', 'data/phantom_256_256.txt')
import numpy
import matplotlib.pyplot
from pynufft import NUFFT_cpu
# load example image
# image = numpy.loadtxt(DATA_PATH +'phantom_256_256.txt')
image = scipy.misc.ascent()[::2, ::2]
image = image.astype(numpy.float) / numpy.max(image[...])
#numpy.save('phantom_256_256',image)
matplotlib.pyplot.imshow(image, cmap=matplotlib.cm.gray)
matplotlib.pyplot.show()
print('loading image...')
Nd = (256, 256) # image size
print('setting image dimension Nd...', Nd)
Kd = (512, 512) # k-space size
print('setting spectrum dimension Kd...', Kd)
Jd = (6, 6) # interpolation size
print('setting interpolation size Jd...', Jd)
# load k-space points
# om = numpy.loadtxt(DATA_PATH+'om.txt')
om = numpy.load(DATA_PATH + 'om2D.npz')['arr_0']
print('setting non-uniform coordinates...')
matplotlib.pyplot.plot(om[::10, 0], om[::10, 1], 'o')
matplotlib.pyplot.title('non-uniform coordinates')
matplotlib.pyplot.xlabel('axis 0')
matplotlib.pyplot.ylabel('axis 1')
matplotlib.pyplot.show()
NufftObj = NUFFT_cpu()
NufftObj.plan(om, Nd, Kd, Jd)
y = NufftObj.forward(image)
print('setting non-uniform data')
print('y is an (M,) list', type(y), y.shape)
# kspectrum = NufftObj.xx2k( NufftObj.solve(y,solver='bicgstab',maxiter = 100))
image_restore = NufftObj.solve(y, solver='cg', maxiter=10)
shifted_kspectrum = numpy.fft.fftshift(
numpy.fft.fftn(numpy.fft.fftshift(image_restore)))
print('getting the k-space spectrum, shape =', shifted_kspectrum.shape)
print('Showing the shifted k-space spectrum')
matplotlib.pyplot.imshow(shifted_kspectrum.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=-100,
vmax=100))
matplotlib.pyplot.title('shifted k-space spectrum')
matplotlib.pyplot.show()
image4 = NufftObj.solve(y, 'L1TVOLS', maxiter=100, rho=1)
image2 = NufftObj.solve(y, 'dc', maxiter=25)
image3 = NufftObj.solve(y, 'cg', maxiter=25)
matplotlib.pyplot.subplot(1, 3, 1)
matplotlib.pyplot.imshow(image2.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0,
vmax=1))
matplotlib.pyplot.title('dc')
matplotlib.pyplot.subplot(1, 3, 2)
matplotlib.pyplot.imshow(image3.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0,
vmax=1))
matplotlib.pyplot.title('cg')
matplotlib.pyplot.subplot(1, 3, 3)
matplotlib.pyplot.imshow(image4.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0,
vmax=1))
matplotlib.pyplot.title('L1TVOLS')
matplotlib.pyplot.show()
# matplotlib.pyplot.imshow(image2.real, cmap=matplotlib.cm.gray, norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
# matplotlib.pyplot.show()
maxiter = 25
counter = 1
for solver in ('dc', 'bicg', 'bicgstab', 'cg', 'gmres', 'lgmres', 'lsqr'):
print(counter, solver)
if 'lsqr' == solver:
image2 = NufftObj.solve(y, solver, iter_lim=maxiter)
else:
image2 = NufftObj.solve(y, solver, maxiter=maxiter)
# image2 = NufftObj.solve(y, solver='bicgstab',maxiter=30)
matplotlib.pyplot.subplot(2, 4, counter)
matplotlib.pyplot.imshow(image2.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0,
vmax=1))
matplotlib.pyplot.title(solver)
# print(counter, solver)
counter += 1
matplotlib.pyplot.show()
def test_cuda():
import numpy
import matplotlib.pyplot
# load example image
import pkg_resources
## Define the source of data
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
# PHANTOM_FILE = pkg_resources.resource_filename('pynufft', 'data/phantom_256_256.txt')
import scipy
image = scipy.misc.ascent()
image = scipy.misc.imresize(image, (256, 256))
image = image.astype(numpy.float) / numpy.max(image[...])
Nd = (256, 256) # image space size
Kd = (512, 512) # k-space size
Jd = (6, 6) # interpolation size
# load k-space points as M * 2 array
om = numpy.load(DATA_PATH + 'om2D.npz')['arr_0']
# Show the shape of om
print('the shape of om = ', om.shape)
# initiating NUFFT_cpu object
nfft = NUFFT_cpu() # CPU NUFFT class
# Plan the nfft object
nfft.plan(om, Nd, Kd, Jd)
# initiating NUFFT_hsa object
NufftObj = NUFFT_hsa('cuda', 0, 0)
# Plan the NufftObj (similar to NUFFT_cpu)
NufftObj.plan(om, Nd, Kd, Jd)
import time
t0 = time.time()
for pp in range(0, 10):
y = nfft.forward(image)
t_cpu = (time.time() - t0) / 10.0
## Moving image to gpu
## gx is an gpu array, dtype = complex64
gx = NufftObj.to_device(image)
t0 = time.time()
for pp in range(0, 100):
gy = NufftObj.forward(gx)
t_cu = (time.time() - t0) / 100
print('t_cpu = ', t_cpu)
print('t_cuda =, ', t_cu)
print('gy close? = ',
numpy.allclose(y, gy.get(), atol=numpy.linalg.norm(y) * 1e-3))
print("acceleration=", t_cpu / t_cu)
maxiter = 100
import time
t0 = time.time()
x_cpu_cg = nfft.solve(y, 'cg', maxiter=maxiter)
# x2 = nfft.solve(y2, 'L1TVLAD',maxiter=maxiter, rho = 2)
t1 = time.time() - t0
# gy=NufftObj.thr.copy_array(NufftObj.thr.to_device(y2))
t0 = time.time()
x_cuda_cg = NufftObj.solve(gy, 'cg', maxiter=maxiter)
# x = NufftObj.solve(gy,'L1TVLAD', maxiter=maxiter, rho=2)
t2 = time.time() - t0
print(t1, t2)
print('acceleration of cg=', t1 / t2)
t0 = time.time()
x_cpu_TV = nfft.solve(y, 'L1TVOLS', maxiter=maxiter, rho=2)
t1 = time.time() - t0
t0 = time.time()
x_cuda_TV = NufftObj.solve(gy, 'L1TVOLS', maxiter=maxiter, rho=2)
t2 = time.time() - t0
print(t1, t2)
print('acceleration of TV=', t1 / t2)
matplotlib.pyplot.subplot(2, 2, 1)
matplotlib.pyplot.imshow(x_cpu_cg.real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('CG_cpu')
matplotlib.pyplot.subplot(2, 2, 2)
matplotlib.pyplot.imshow(x_cuda_cg.get().real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('CG_cuda')
matplotlib.pyplot.subplot(2, 2, 3)
matplotlib.pyplot.imshow(x_cpu_TV.real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('TV_cpu')
matplotlib.pyplot.subplot(2, 2, 4)
matplotlib.pyplot.imshow(x_cuda_TV.get().real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('TV_cuda')
matplotlib.pyplot.show()
NufftObj.release()
del NufftObj
Jd = (7, ) # interpolator
NufftObj = NUFFT_cpu()
NufftObj.plan(om, Nd, Kd, Jd)
time_data = numpy.zeros(256, )
time_data[64:192] = 1.0
pyplot.plot(time_data)
pyplot.ylim(-1, 2)
pyplot.show()
nufft_freq_data = NufftObj.forward(time_data)
pyplot.plot(om, nufft_freq_data.real, '.', label='real')
pyplot.plot(om, nufft_freq_data.imag, 'r.', label='imag')
pyplot.legend()
pyplot.show()
restore_time = NufftObj.solve(nufft_freq_data, 'cg', maxiter=30)
restore_time1 = NufftObj.solve(nufft_freq_data, 'L1TVLAD', maxiter=30, rho=1)
restore_time2 = NufftObj.solve(nufft_freq_data, 'L1TVOLS', maxiter=30, rho=1)
im1, = pyplot.plot(numpy.abs(time_data), 'r', label='original signal')
im2, = pyplot.plot(numpy.abs(restore_time1), 'b:', label='L1TVLAD')
im3, = pyplot.plot(numpy.abs(restore_time2), 'k--', label='L1TVOLS')
im4, = pyplot.plot(numpy.abs(restore_time),
'r:',
label='conjugate_gradient_method')
pyplot.legend([im1, im2, im3, im4])
pyplot.show()
om[512 * index:512 * (index + 1), 1] = spoke_y
#plt.plot(om[:,0], om[:,1],'.')
#plt.title("Radial Kspace Trajectory")
#plt.show()
numProjections = kspace.shape[1]
numReadouts = kspace.shape[0]
print('Number of Projections = ', numProjections)
print('Number of Readout Values = ', numReadouts)
myNufft = NUFFT_cpu()
myNufft.plan(om=om, Nd=(256, 256), Kd=(numReadouts, numReadouts), Jd=(2, 2))
y = kspace.flatten(order='C')
image = myNufft.adjoint(y)
#y = myNufft.forward(image)
#ipdb.set_trace()
plt.subplot(2, 2, 1)
image0 = myNufft.solve(y, solver='cg', maxiter=50)
#img = image0.real/image0.real.max()
plt.title('Restored image (cg)')
plt.imshow(image0.real,
cmap=matplotlib.cm.gray,
norm=matplotlib.colors.Normalize(vmin=0.0, vmax=1))
plt.show()
pyplot.show()
Nd = (64, 64, 64) # time grid, tuple
Kd = (64, 64, 64) # frequency grid, tuple
Jd = (1, 1, 1) # interpolator
# om= numpy.load(DATA_PATH+'om3D.npz')['arr_0']
om = numpy.random.randn(151200, 3) * 2
print(om.shape)
from pynufft import NUFFT_cpu, NUFFT_hsa
NufftObj = NUFFT_cpu()
NufftObj.plan(om, Nd, Kd, Jd)
kspace = NufftObj.forward(image)
restore_image = NufftObj.solve(kspace, 'cg', maxiter=500)
restore_image1 = NufftObj.solve(kspace, 'L1TVLAD', maxiter=500, rho=0.1)
#
restore_image2 = NufftObj.solve(kspace, 'L1TVOLS', maxiter=500, rho=0.1)
pyplot.subplot(2, 2, 1)
pyplot.imshow(numpy.real(image[:, :, 32]), label='original signal', cmap=gray)
pyplot.title('original')
pyplot.subplot(2, 2, 2)
pyplot.imshow(numpy.real(restore_image1[:, :, 32]), label='L1TVLAD', cmap=gray)
pyplot.title('L1TVLAD')
pyplot.subplot(2, 2, 3)
pyplot.imshow(numpy.real(restore_image2[:, :, 32]), label='L1TVOLS', cmap=gray)
pyplot.title('L1TVOLS')
Jd = (7, ) # interpolator
NufftObj = NUFFT_cpu()
NufftObj.plan(om, Nd, Kd, Jd)
time_data = numpy.zeros(256, )
time_data[64:192] = 1.0
pyplot.plot(time_data)
pyplot.ylim(-1, 2)
pyplot.show()
nufft_freq_data = NufftObj.forward(time_data)
pyplot.plot(om, nufft_freq_data.real, '.', label='real')
pyplot.plot(om, nufft_freq_data.imag, 'r.', label='imag')
pyplot.legend()
pyplot.show()
restore_time = NufftObj.solve(nufft_freq_data, 'cg', maxiter=30)
#restore_time1 = NufftObj.solve(nufft_freq_data, 'L1TVLAD', maxiter=30, rho=1)
#restore_time2 = NufftObj.solve(nufft_freq_data, 'L1TVOLS', maxiter=30, rho=1)
im1, = pyplot.plot(numpy.abs(time_data), 'r', label='original signal')
#im2, = pyplot.plot(numpy.abs(restore_time1), 'b:', label='L1TVLAD')
#im3, = pyplot.plot(numpy.abs(restore_time2), 'k--', label='L1TVOLS')
im4, = pyplot.plot(numpy.abs(restore_time),
'r:',
label='conjugate_gradient_method')
#pyplot.legend([im1, im2, im3, im4])
pyplot.show()
def test_opencl_multicoils():
import numpy
import matplotlib.pyplot
# load example image
import pkg_resources
## Define the source of data
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
# PHANTOM_FILE = pkg_resources.resource_filename('pynufft', 'data/phantom_256_256.txt')
import scipy
image = scipy.misc.ascent()[::2, ::2]
image = image.astype(numpy.float) / numpy.max(image[...])
Nd = (256, 256) # image space size
Kd = (512, 512) # k-space size
Jd = (6, 6) # interpolation size
# load k-space points as M * 2 array
om = numpy.load(DATA_PATH + 'om2D.npz')['arr_0']
# Show the shape of om
print('the shape of om = ', om.shape)
batch = 8
# initiating NUFFT_cpu object
nfft = NUFFT_cpu() # CPU NUFFT class
# Plan the nfft object
nfft.plan(om, Nd, Kd, Jd, batch=batch)
# initiating NUFFT_hsa object
try:
NufftObj = NUFFT_hsa('cuda', 0, 0)
except:
try:
NufftObj = NUFFT_hsa('ocl', 1, 0)
except:
NufftObj = NUFFT_hsa('ocl', 0, 0)
# Plan the NufftObj (similar to NUFFT_cpu)
NufftObj.plan(om, Nd, Kd, Jd, batch=batch, radix=2)
coil_sense = numpy.ones(Nd + (batch, ), dtype=numpy.complex64)
for cc in range(0, batch, 2):
coil_sense[int(256 / batch) * cc:int(256 / batch) * (cc + 1), :,
cc].real *= 0.1
coil_sense[:, int(256 / batch) * cc:int(256 / batch) * (cc + 1),
cc].imag *= -0.1
NufftObj.set_sense(coil_sense)
nfft.set_sense(coil_sense)
y = nfft.forward_one2many(image)
import time
t0 = time.time()
for pp in range(0, 2):
xx = nfft.adjoint_many2one(y)
t_cpu = (time.time() - t0) / 2
## Moving image to gpu
## gx is an gpu array, dtype = complex64
gx = NufftObj.to_device(image)
gy = NufftObj.forward_one2many(gx)
t0 = time.time()
for pp in range(0, 10):
gxx = NufftObj.adjoint_many2one(gy)
t_cu = (time.time() - t0) / 10
print(y.shape, gy.get().shape)
print('t_cpu = ', t_cpu)
print('t_cuda =, ', t_cu)
print('gy close? = ',
numpy.allclose(y, gy.get(), atol=numpy.linalg.norm(y) * 1e-6))
print('gy error = ',
numpy.linalg.norm(y - gy.get()) / numpy.linalg.norm(y))
print('gxx close? = ',
numpy.allclose(xx, gxx.get(), atol=numpy.linalg.norm(xx) * 1e-6))
print('gxx error = ',
numpy.linalg.norm(xx - gxx.get()) / numpy.linalg.norm(xx))
# for bb in range(0, batch):
matplotlib.pyplot.subplot(1, 2, 1)
matplotlib.pyplot.imshow(xx[...].real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('Adjoint_cpu_coil')
matplotlib.pyplot.subplot(1, 2, 2)
matplotlib.pyplot.imshow(gxx.get()[...].real, cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('Adjoint_hsa_coil')
# matplotlib.pyplot.subplot(2, 2, 3)
# matplotlib.pyplot.imshow( x_cpu_TV.real, cmap= matplotlib.cm.gray)
# matplotlib.pyplot.title('TV_cpu')# x_cuda_TV = NufftObj.solve(gy,'L1TVOLS', maxiter=maxiter, rho=2)
# matplotlib.pyplot.subplot(2, 2, 4)
# matplotlib.pyplot.imshow(x_cuda_TV.get().real, cmap= matplotlib.cm.gray)
# matplotlib.pyplot.title('TV_cuda')
matplotlib.pyplot.show(block=False)
matplotlib.pyplot.pause(1)
matplotlib.pyplot.close()
print("acceleration=", t_cpu / t_cu)
maxiter = 100
import time
t0 = time.time()
x_cpu_cg = nfft.solve(y, 'cg', maxiter=maxiter)
# x2 = nfft.solve(y2, 'L1TVLAD',maxiter=maxiter, rho = 2)
t1 = time.time() - t0
# gy=NufftObj.thr.copy_array(NufftObj.thr.to_device(y2))
t0 = time.time()
x_cuda_cg = NufftObj.solve(gy, 'cg', maxiter=maxiter)
# x = NufftObj.solve(gy,'L1TVLAD', maxiter=maxiter, rho=2)
print('shape of cg = ', x_cuda_cg.get().shape, x_cpu_cg.shape)
t2 = time.time() - t0
print(t1, t2)
print('acceleration of cg=', t1 / t2)
t0 = time.time()
# x_cpu_TV = nfft.solve(y, 'L1TVOLS',maxiter=maxiter, rho = 2)
t1 = time.time() - t0
t0 = time.time()
# x_cuda_TV = NufftObj.solve(gy,'L1TVOLS', maxiter=maxiter, rho=2)
t2 = time.time() - t0
print(t1, t2)
# print('acceleration of TV=', t1/t2 )
# try:
for bb in range(0, batch):
matplotlib.pyplot.subplot(2, batch, 1 + bb)
matplotlib.pyplot.imshow(x_cpu_cg[..., bb].real,
cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('CG_cpu_coil_' + str(bb))
matplotlib.pyplot.subplot(2, batch, 1 + batch + bb)
matplotlib.pyplot.imshow(x_cuda_cg.get()[..., bb].real,
cmap=matplotlib.cm.gray)
matplotlib.pyplot.title('CG_hsa_coil_' + str(bb))
# matplotlib.pyplot.subplot(2, 2, 3)
# matplotlib.pyplot.imshow( x_cpu_TV.real, cmap= matplotlib.cm.gray)
# matplotlib.pyplot.title('TV_cpu')# x_cuda_TV = NufftObj.solve(gy,'L1TVOLS', maxiter=maxiter, rho=2)
# matplotlib.pyplot.subplot(2, 2, 4)
# matplotlib.pyplot.imshow(x_cuda_TV.get().real, cmap= matplotlib.cm.gray)
# matplotlib.pyplot.title('TV_cuda')
matplotlib.pyplot.show()
# except:
# print('no matplotlib')
NufftObj.release()
del NufftObj
def test_init():
# cm = matplotlib.cm.gray
# load example image
import pkg_resources
DATA_PATH = pkg_resources.resource_filename('pynufft', 'src/data/')
# PHANTOM_FILE = pkg_resources.resource_filename('pynufft', 'data/phantom_256_256.txt')
import numpy
# import matplotlib.pyplot
import scipy
image = scipy.misc.ascent()[::2,::2]
image=image.astype(numpy.float)/numpy.max(image[...])
Nd = (256, 256) # image space size
Kd = (512, 512) # k-space size
Jd = (6,6) # interpolation size
# load k-space points
om = numpy.load(DATA_PATH+'om2D.npz')['arr_0']
nfft = NUFFT_cpu() # CPU
nfft.plan(om, Nd, Kd, Jd)
try:
NufftObj = NUFFT_hsa('cuda',0,0)
except:
NufftObj = NUFFT_hsa('ocl',0,0)
# NufftObj2 = NUFFT_hsa('cuda',0,0)
NufftObj.debug = 1
NufftObj.plan(om, Nd, Kd, Jd, radix=2)
# NufftObj2.plan(om, Nd, Kd, Jd)
# NufftObj.offload(API = 'cuda', platform_number = 0, device_number = 0)
# NufftObj2.offload(API = 'cuda', platform_number = 0, device_number = 0)
# NufftObj2.offload('cuda')
# NufftObj.offload(API = 'cuda', platform_number = 0, device_number = 0)
# print('api=', NufftObj.thr.api_name())
# NufftObj.offload(API = 'ocl', platform_number = 0, device_number = 0)
y = nfft.k2y(nfft.xx2k(nfft.x2xx(image)))
NufftObj.x_Nd = NufftObj.thr.to_device( image.astype(dtype))
gx = NufftObj.thr.copy_array(NufftObj.x_Nd)
print('x close? = ', numpy.allclose(image, gx.get() , atol=1e-4))
gxx = NufftObj.x2xx(gx)
print('xx close? = ', numpy.allclose(nfft.x2xx(image), gxx.get() , atol=1e-4))
gk = NufftObj.xx2k(gxx)
k = nfft.xx2k(nfft.x2xx(image))
print('k close? = ', numpy.allclose(nfft.xx2k(nfft.x2xx(image)), gk.get(), atol=1e-3*numpy.linalg.norm(k)))
gy = NufftObj.k2y(gk)
k2 = NufftObj.y2k(gy)
print('y close? = ', numpy.allclose(y, gy.get() , atol=1e-3*numpy.linalg.norm(y)), numpy.linalg.norm((y - gy.get())/numpy.linalg.norm(y)))
y2 = y
print('k2 close? = ', numpy.allclose(nfft.y2k(y2), k2.get(), atol=1e-3*numpy.linalg.norm(nfft.y2k(y2)) ), numpy.linalg.norm(( nfft.y2k(y2)- k2.get())/numpy.linalg.norm(nfft.y2k(y2))))
gxx2 = NufftObj.k2xx(k2)
# print('xx close? = ', numpy.allclose(nfft.k2xx(nfft.y2k(y2)), NufftObj.xx_Nd.get(queue=NufftObj.queue, async=False) , atol=0.1))
gx2 = NufftObj.xx2x(gxx2)
print('x close? = ', numpy.allclose(nfft.adjoint(y2), gx2.get() , atol=1e-3*numpy.linalg.norm(nfft.adjoint(y2))))
image3 = gx2.get()
import time
t0 = time.time()
# k = nfft.xx2k(nfft.x2xx(image))
for pp in range(0,50):
# y = nfft.k2y(nfft.xx2k(nfft.x2xx(image)))
y = nfft.forward(image)
# y = nfft.k2y(k)
# k = nfft.y2k(y)
# x = nfft.adjoint(y)
# y = nfft.forward(image)
# y2 = NufftObj.y.get( NufftObj.queue, async=False)
t_cpu = (time.time() - t0)/50.0
print(t_cpu)
# del nfft
gy2=NufftObj.forward(gx)
# gk = NufftObj.xx2k(NufftObj.x2xx(gx))
t0= time.time()
for pp in range(0,20):
# pass
gy2 = NufftObj.forward(gx)
# gy2 = NufftObj.k2y(gk)
# gx2 = NufftObj.adjoint(gy2)
# gk2 = NufftObj.y2k(gy2)
# del gy2
# c = gx2.get()
# gy=NufftObj.forward(gx)
NufftObj.thr.synchronize()
t_cl = (time.time() - t0)/20
print(t_cl)
print('gy close? = ', numpy.allclose(y, gy.get(), atol=numpy.linalg.norm(y)*1e-3))
print("acceleration=", t_cpu/t_cl)
maxiter =100
import time
t0= time.time()
# x2 = nfft.solve(y2, 'cg',maxiter=maxiter)
x2 = nfft.solve(y2, 'L1TVOLS',maxiter=maxiter, rho = 2)
t1 = time.time()-t0
# gy=NufftObj.thr.copy_array(NufftObj.thr.to_device(y2))
t0= time.time()
# x = NufftObj.solve(gy,'cg', maxiter=maxiter)
x = NufftObj.solve(gy,'L1TVOLS', maxiter=maxiter, rho=2)
t2 = time.time() - t0
print(t1, t2)
print('acceleration=', t1/t2 )
# k = x.get()
# x = nfft.k2xx(k)/nfft.st['sn']
# return
try:
import matplotlib.pyplot
matplotlib.pyplot.subplot(1, 2, 1)
matplotlib.pyplot.imshow( x.get().real, cmap= matplotlib.cm.gray, vmin = 0, vmax = 1)
matplotlib.pyplot.title("HSA reconstruction")
matplotlib.pyplot.subplot(1, 2,2)
matplotlib.pyplot.imshow(x2.real, cmap= matplotlib.cm.gray)
matplotlib.pyplot.title("CPU reconstruction")
matplotlib.pyplot.show(block = False)
matplotlib.pyplot.pause(3)
matplotlib.pyplot.close()
# del NufftObj.thr
# del NufftObj
except:
print("no graphics")
