def __init__(self,
ctx,
queue,
par,
kwidth=3,
overgridfactor=2,
fft_dim=(1, 2),
klength=200,
DTYPE=np.complex64,
DTYPE_real=np.float32):
print("Setting up PyOpenCL NUFFT.")
self.DTYPE = DTYPE
self.DTYPE_real = DTYPE_real
self.fft_shape = (par["NScan"] * par["NC"] * par["NSlice"], par["N"],
par["N"])
self.traj = par["traj"]
self.dcf = par["dcf"]
self.Nproj = par["Nproj"]
self.ctx = ctx
self.queue = queue
self.overgridfactor = overgridfactor
self.kerneltable, self.kerneltable_FT, self.u = calckbkernel(
kwidth, overgridfactor, par["N"], klength)
self.kernelpoints = self.kerneltable.size
self.fft_scale = DTYPE_real(
np.sqrt(np.prod(self.fft_shape[fft_dim[0]:])))
self.deapo = 1 / self.kerneltable_FT.astype(DTYPE_real)
self.kwidth = kwidth / 2
self.cl_kerneltable = cl.Buffer(
self.ctx,
cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf=self.kerneltable.astype(DTYPE_real).data)
self.deapo_cl = cl.Buffer(self.ctx,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=self.deapo.data)
self.dcf = clarray.to_device(self.queue, self.dcf)
self.traj = clarray.to_device(self.queue, self.traj)
self.tmp_fft_array = (clarray.empty(self.queue, (self.fft_shape),
dtype=DTYPE))
self.check = np.ones(par["N"], dtype=DTYPE_real)
self.check[1::2] = -1
self.check = clarray.to_device(self.queue, self.check)
self.par_fft = int(self.fft_shape[0] / par["NScan"])
self.fft = FFT(ctx,
queue,
self.tmp_fft_array[0:int(self.fft_shape[0] /
par["NScan"]), ...],
out_array=self.tmp_fft_array[0:int(self.fft_shape[0] /
par["NScan"]), ...],
axes=fft_dim)
self.gridsize = par["N"]
self.fwd_NUFFT = self.NUFFT
self.adj_NUFFT = self.NUFFTH
self.prg = Program(
self.ctx,
open(
resource_filename('rrsg_cgreco',
'kernels/opencl_nufft_kernels.c')).read())
import numpy as np import pyopencl as cl import pyopencl.array as cla from gpyfft.fft import FFT context = cl.create_some_context() queue = cl.CommandQueue(context) data_host = np.zeros((4, 1024, 1024), dtype=np.complex64) #data_host[:] = some_useful_data data_gpu = cla.to_device(queue, data_host) transform = FFT(context, queue, data_gpu, axes=(2, 1)) event, = transform.enqueue() event.wait() result_host = data_gpu.get()
