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())
def initClBuffers(self):
mf = cl.mem_flags
# self.Et_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf = self.Et)
self.Et_cla = cla.to_device(self.q, self.Et)
self.Esig_t_tau = np.zeros((self.N, self.N), self.dtype_c)
# self.Esig_t_tau_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf = self.Esig_t_tau)
self.Esig_t_tau_cla = cla.to_device(self.q, self.Esig_t_tau)
self.Esig_w_tau = np.zeros((self.N, self.N), self.dtype_c)
self.Esig_w_tau_cla = cla.to_device(self.q, self.Esig_w_tau)
self.Esig_w_tau_fft = FFT(self.ctx, self.q, (self.Esig_t_tau_cla,), (self.Esig_w_tau_cla,), axes=[1])
self.I_w_tau_cla = cla.to_device(self.q, self.I_w_tau)
self.Esig_t_tau_p = np.zeros((self.N, self.N), self.dtype_c)
self.Esig_t_tau_p_cla = cla.to_device(self.q, self.Esig_t_tau_p)
self.Esig_t_tau_p_fft = FFT(self.ctx, self.q, (self.Esig_w_tau_cla,), (self.Esig_t_tau_p_cla,), axes=[1])
self.initClBuffersGP()
class FrogCalculation(object):
def __init__(self, useCPU=False):
self.useCPU = useCPU
self.useCL = True
self.rollFFT = True # There is a peculiarity in the fft calculation of a set of vectors
# so that the first fft end up in the end... Set this switch to roll
# back the last line of the Esig_w_tau fft matrix
self.dtype_c = np.complex64
self.dtype_r = np.float32
self.Esignal_w = None
self.Esignal_t = None
self.Et_cla = None
self.initCl(useCPU=useCPU)
def initCl(self, useCPU=False):
root.debug("Initializing opencl")
pl = cl.get_platforms()
d = None
v = None
root.debug("".join(("Found ", str(pl.__len__()), " platforms")))
vendorDict = {"amd": 3, "nvidia": 2, "intel": 1}
if useCPU == False:
for p in pl:
root.debug(p.vendor.lower())
if "amd" in p.vendor.lower():
vTmp = "amd"
elif "nvidia" in p.vendor.lower():
vTmp = "nvidia"
else:
vTmp = "intel"
if v == None:
d = p.get_devices()
v = vTmp
else:
if vendorDict[vTmp] > vendorDict[v]:
d = p.get_devices()
v = vTmp
else:
for p in pl:
if "amd" in p.vendor.lower():
vTmp = "amd"
elif "nvidia" in p.vendor.lower():
vTmp = "nvidia"
else:
vTmp = "intel"
d = p.get_devices(device_type=cl.device_type.CPU)
if d != []:
v = vTmp
break
root.debug("".join(("Using device ", str(d), " from ", v)))
self.ctx = cl.Context(devices=d)
self.q = cl.CommandQueue(self.ctx)
self.progs = FrogClKernels.FrogClKernels(self.ctx)
def initClBuffers(self):
mf = cl.mem_flags
# self.Et_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf = self.Et)
self.Et_cla = cla.to_device(self.q, self.Et)
self.Esig_t_tau = np.zeros((self.N, self.N), self.dtype_c)
# self.Esig_t_tau_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf = self.Esig_t_tau)
self.Esig_t_tau_cla = cla.to_device(self.q, self.Esig_t_tau)
self.Esig_w_tau = np.zeros((self.N, self.N), self.dtype_c)
self.Esig_w_tau_cla = cla.to_device(self.q, self.Esig_w_tau)
self.Esig_w_tau_fft = FFT(self.ctx, self.q, (self.Esig_t_tau_cla,), (self.Esig_w_tau_cla,), axes=[1])
self.I_w_tau_cla = cla.to_device(self.q, self.I_w_tau)
self.Esig_t_tau_p = np.zeros((self.N, self.N), self.dtype_c)
self.Esig_t_tau_p_cla = cla.to_device(self.q, self.Esig_t_tau_p)
self.Esig_t_tau_p_fft = FFT(self.ctx, self.q, (self.Esig_w_tau_cla,), (self.Esig_t_tau_p_cla,), axes=[1])
self.initClBuffersGP()
def initClBuffersGP(self):
# Gradient vector for the functional distance in the generalized projection
self.dZ_cla = cla.zeros(self.q, (self.N), self.dtype_c)
# Vector for intermediate results for the error minimization calculation
self.X0_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.X1_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.X2_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.X3_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.X4_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.X5_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.X6_cla = cla.zeros(self.q, (self.N), self.dtype_r)
self.Esig_t_tau_norm = np.zeros((self.N, self.N), self.dtype_r)
self.Esig_t_tau_norm_cla = cla.to_device(self.q, self.Esig_t_tau_norm)
def initPulseFieldRandom(self, N, t_res, l0, seed=0):
""" Initiate signal field with parameters:
t_res: time resolution of the reconstruction
N: number of points in time and wavelength axes
l0: center wavelength
Creates the following variables:
self.w
self.dw
self.t
self.dt
self.tau
self.Et
"""
np.random.seed(seed)
self.N = np.int32(N)
t_span = N * t_res
# Now we calculate the frequency resolution required by the
# time span
w_res = 2 * np.pi / t_span
# Frequency span is given by the time resolution
f_max = 1 / (2 * t_res)
w_span = f_max * 2 * 2 * np.pi
c = 299792458.0
w0 = 2 * np.pi * c / l0
# w_spectrum = np.linspace(w0-w_span/2, w0+w_span/2, n_t)
w_spectrum = np.linspace(-w_span / 2, -w_span / 2 + w_res * N, N)
self.dw = w_res
self.w = w_spectrum
self.w0 = w0
# Create time vector
self.dt = t_res
self.t = np.linspace(-t_span / 2, t_span / 2, N)
self.tau_start_ind = 0
self.tau_stop_ind = N - 1
self.tau = self.t
root.info("".join(("t_span ", str(t_span))))
root.info("".join(("t_res ", str(t_res))))
# Finally calculate a gaussian E-field from the
self.Et = (np.exp(1j * 2 * np.pi * np.random.rand(N))).astype(self.dtype_c)
root.info("Finished")
def initPulseFieldGaussian(self, N, t_res, l0, tau_pulse):
""" Initiate signal field with parameters:
t_res: time resolution of the reconstruction
N: number of points in time and wavelength axes
l0: center wavelength
Creates the following variables:
self.w
self.dw
self.t
self.dt
self.tau
self.Et
"""
t_span = N * t_res
self.N = np.int32(N)
# Now we calculate the frequency resolution required by the
# time span
w_res = 2 * np.pi / t_span
# Frequency span is given by the time resolution
f_max = 1 / (2 * t_res)
w_span = f_max * 2 * 2 * np.pi
c = 299792458.0
w0 = 2 * np.pi * c / l0
# w_spectrum = np.linspace(w0-w_span/2, w0+w_span/2, n_t)
w_spectrum = np.linspace(-w_span / 2, -w_span / 2 + w_res * N, N)
self.dw = w_res
self.w = w_spectrum
self.w0 = w0
# Create time vector
self.dt = t_res
self.t = np.linspace(-t_span / 2, t_span / 2, N)
p = sp.SimulatedFrogTrace(N, self.dt, tau=tau_pulse, l0=l0)
p.pulse.generateGaussian(tau_pulse)
self.tau_start_ind = 0
self.tau_stop_ind = N - 1
self.tau = self.t
root.info("".join(("t_span ", str(t_span))))
root.info("".join(("t_res ", str(t_res))))
# Finally calculate a gaussian E-field from the
self.Et = (np.abs(p.pulse.Et) * np.exp(1j * 2 * np.pi * np.random.rand(N))).astype(self.dtype_c)
self.t = p.pulse.t
self.p = p
root.info("Finished")
def loadFrogTrace(self, filename, thr=0.0, lStartPixel=0, lStopPixel=-1, tStartPixel=0, tStopPixel=-1):
fNameRoot = "_".join((filename.split("_")[0:3]))
tData = np.loadtxt("".join((fNameRoot, "_timevector.txt")))
tData = tData - tData.mean()
lData = np.loadtxt("".join((fNameRoot, "_wavelengthvector.txt"))) * 1e-9
pic = np.float32(imread("".join((fNameRoot, "_image.png"))))
picN = pic / pic.max()
if tStopPixel == -1:
tStopPixel = picN.shape[0] - 1
if lStopPixel == -1:
lStopPixel = picN.shape[1] - 1
picF = self.filterFrogTrace(picN, 3, thr)
self.conditionFrogTrace(
picF[tStartPixel:tStopPixel, lStartPixel:lStopPixel],
lData[lStartPixel],
lData[lStopPixel],
tData[tStartPixel],
tData[tStopPixel],
)
def conditionFrogTrace(self, Idata, l_start, l_stop, tau_start, tau_stop):
""" Take the measured intensity data and interpolate it to the
internal w, tau grid.
Idata.shape[0] = number of tau points
Idata.shape[1] = number of spectrum points
"""
tau_data = np.linspace(tau_start, tau_stop, Idata.shape[0])
l_data = np.linspace(l_start, l_stop, Idata.shape[1])
if l_start > l_stop:
w_start = 2 * np.pi * 299792458.0 / l_start
w_stop = 2 * np.pi * 299792458.0 / l_stop
w0 = 2 * np.pi * 299792458.0 / ((l_stop + l_start) / 2)
Idata_i = Idata.copy()
else:
w_start = 2 * np.pi * 299792458.0 / l_stop
w_stop = 2 * np.pi * 299792458.0 / l_start
w0 = 2 * np.pi * 299792458.0 / ((l_stop + l_start) / 2)
Idata_i = np.fliplr(Idata).copy()
root.debug("".join(("w_start: ", str(w_start))))
root.debug("".join(("w_stop: ", str(w_stop))))
w_data = np.linspace(w_start, w_stop, Idata.shape[1]) - w0
# w_data = 2*np.pi*299792458.0/l_data[::-1].copy()
Idata_i = np.flipud(Idata_i).copy()
Idata_i[0:2, :] = 0.0
Idata_i[-2:, :] = 0.0
Idata_i[:, 0:2] = 0.0
Idata_i[:, -2:] = 0.0
Idata_i = Idata_i / Idata_i.max()
root.info("Creating interpolator")
t0 = time.clock()
Idata_interp = si.RectBivariateSpline(tau_data, w_data, Idata_i)
# Idata_interp = interp2d(tau_mat, w_mat, Idata, kind='linear', fill_value = 0.0, bounds_error = False)
root.info("".join(("Time spent: ", str(time.clock() - t0))))
root.info("".join(("Interpolating frog trace to ", str(self.tau.shape[0]), "x", str(self.w.shape[0]))))
t0 = time.clock()
self.I_w_tau = np.fft.fftshift(np.maximum(Idata_interp(self.tau, self.w), 0.0), axes=1).astype(self.dtype_r)
# self.I_w_tau = np.maximum(Idata_interp(self.tau, self.w), 0.0)
if self.rollFFT == True:
self.I_w_tau = np.roll(self.I_w_tau, 1, axis=0)
root.info("".join(("Time spent: ", str(time.clock() - t0))))
return Idata_i, w_data, tau_data
def filterFrogTrace(self, Idata, kernel=5, thr=0.1):
Idata_f = medfilt2d(Idata, kernel) - thr
Idata_f[Idata_f < 0.0] = 0.0
return Idata_f
def generateEsig_t_tau_SHG(self):
""" Generate the time shifted E-field matrix for the SHG process.
Output:
self.Esig_t_tau, a n_tau x n_t matrix where each row is Esig(t,tau)
"""
root.debug("Generating new Esig_t_tau from SHG")
t0 = time.clock()
krn = self.progs.progs["generateEsig_t_tau_SHG"].generateEsig_t_tau_SHG
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Et_cla.data, self.Esig_t_tau_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_t_tau.shape, None)
ev.wait()
root.debug("".join(("Time spent: ", str(time.clock() - t0))))
def generateEsig_t_tau_SD(self):
""" Generate the time shifted E-field matrix for the SD process.
Output:
self.Esig_t_tau, a n_tau x n_t matrix where each row is Esig(t,tau)
"""
root.debug("Generating new Esig_t_tau from SD")
t0 = time.clock()
krn = self.progs.progs["generateEsig_t_tau_SD"].generateEsig_t_tau_SD
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Et_cla.data, self.Esig_t_tau_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_t_tau.shape, None)
ev.wait()
root.debug("".join(("Time spent: ", str(time.clock() - t0))))
def generateEsig_w_tau(self):
""" Generate the fft of the time shifted E(t)
"""
root.debug("Generating Esig_w_tau")
rollFFT = False # There is a peculiarity in the fft calculation of a set of vectors
# so that the first fft end up in the end... Set this switch to roll
# back the last line of the Esig_w_tau fft matrix
tic = time.clock()
# transform = FFT(self.ctx, self.q, (self.Esig_t_tau_cla,) , (self.Esig_w_tau_cla,) , axes = [1])
# events = transform.enqueue()
if self.useCL == True:
events = self.Esig_w_tau_fft.enqueue()
for e in events:
e.wait()
# if self.rollFFT == True:
# krn = self.progs.progs['rollEsigWTau'].rollEsigWTau
# krn.set_scalar_arg_dtypes((None, np.int32))
# krn.set_args(self.Esig_w_tau_cla.data, self.N)
# ev = cl.enqueue_nd_range_kernel(self.q, krn, [self.Esig_w_tau.shape[0]], None)
# ev.wait()
else:
Esig_t_tau = self.Esig_t_tau_cla.get()
if self.rollFFT == True:
Esig_w_tau = np.roll(np.fft.fft(Esig_t_tau, axis=1).astype(self.dtype_c), 1, axis=0)
else:
Esig_w_tau = np.fft.fft(Esig_t_tau, axis=1).astype(self.dtype_c)
self.Esig_w_tau_cla.set(Esig_w_tau.copy())
toc = time.clock()
root.debug("".join(("Time spent: ", str(toc - tic))))
def applyIntensityData(self, I_w_tau=None):
root.debug("Applying intensity data from experiment")
t0 = time.clock()
krn = self.progs.progs["applyIntensityData"].applyIntensityData
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_w_tau_cla.data, self.I_w_tau_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_w_tau.shape, None)
ev.wait()
# if self.useCL == True:
# krn = self.progs.progs['applyIntensityData'].applyIntensityData
# krn.set_scalar_arg_dtypes((None, None, np.int32))
# krn.set_args(self.Esig_w_tau_cla.data, self.I_w_tau_cla.data, self.N)
# ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_w_tau.shape, None)
# ev.wait()
# else:
# eps = 0.00
# Esig_w_tau = self.Esig_w_tau_cla.get()
# Esig_mag = np.abs(Esig_w_tau)
#
# Esig_w_tau_p = np.zeros_like(Esig_w_tau)
# good_ind = np.where(Esig_mag > eps)
# Esig_w_tau_p[good_ind[0], good_ind[1]] = np.sqrt(self.I_w_tau_cla.get()[good_ind[0], good_ind[1]])*Esig_w_tau[good_ind[0], good_ind[1]]/Esig_mag[good_ind[0], good_ind[1]]
root.debug("".join(("Time spent: ", str(time.clock() - t0))))
def updateEt_vanilla(self, algo="SHG"):
root.debug("Updating Et using vanilla algorithm")
t0 = time.clock()
# transform = FFT(self.ctx, self.q, (self.Esig_w_tau_cla,) , (self.Esig_t_tau_p_cla,) , axes = [1])
# events = transform.enqueue(forward = False)
# self.Esig_t_tau_p_cla.set(np.fft.ifft(self.Esig_w_tau_cla.get(), axis=1).astype(self.dtype_c).copy())
if self.useCL == True:
events = self.Esig_t_tau_p_fft.enqueue(forward=False)
for e in events:
e.wait()
if algo == "SD":
krn = self.progs.progs["updateEtVanillaSumSD"].updateEtVanillaSumSD
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
Et = self.Et_cla.get()
self.Et_cla.set(-np.conj(Et).astype(self.dtype_c).copy())
# Esig_w_tau = self.Esig_w_tau_cla.get()
# Gm = np.conj(Esig_w_tau.sum(axis=1))[::-1]
# self.Et_cla.set(Gm.copy())
else:
krn = self.progs.progs["updateEtVanillaSumSHG"].updateEtVanillaSumSHG
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
krn = self.progs.progs["updateEtVanillaNorm"].updateEtVanillaNorm
krn.set_scalar_arg_dtypes((None, np.int32))
krn.set_args(self.Et_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, [1], None)
ev.wait()
else:
self.Esig_t_tau_p_cla.set(np.fft.ifft(self.Esig_w_tau_cla.get(), axis=1).astype(self.dtype_c).copy())
Esig_t_tau_p = self.Esig_t_tau_p_cla.get()
if algo == "SD":
Et = np.sqrt(Esig_t_tau_p.sum(axis=0))
# Et = (Esig_t_tau_p.sum(axis=0))
else:
Et = Esig_t_tau_p.sum(axis=0)
Et = Et / np.abs(Et).max()
self.Et_cla.set(Et)
root.debug("".join(("Time spent: ", str(time.clock() - t0))))
def gradZSHG_naive(self):
root.debug("Calculating dZ for SHG using for loops")
Et = self.Et_cla.get()
Esigp = self.Esig_t_tau_p_cla.get()
dZ = np.zeros_like(Et)
N = Esigp.shape[0]
sz = N * N
for t0 in range(N):
T = 0.0 + 1j * 0.0
for tau in range(N):
tp = t0 - (tau - N / 2)
if tp >= 0 and tp < N:
T += (Et[t0] * Et[tp] - Esigp[tau, t0]) * np.conj(Et[tp])
tp = t0 + (tau - N / 2)
if tp >= 0 and tp < N:
T += (Et[t0] * Et[tp] - Esigp[tau, tp]) * np.conj(Et[tp])
dZ[t0] = -T / sz
self.dZ_cla.set(dZ.copy())
def gradZSHG_gpu(self):
root.debug("Calculating dZ for SHG using gpu")
krn = self.progs.progs["gradZSHG"].gradZSHG
krn.set_scalar_arg_dtypes((None, None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.dZ_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
def gradZSD_naive(self):
# Todo: fix this algorithm
root.debug("Calculating dZ for SD using for loops")
Et = self.Et_cla.get()
Esigp = self.Esig_t_tau_p_cla.get()
dZ = np.zeros_like(Et)
N = Esigp.shape[0]
sz = N * N
for t0 in range(N):
T = 0.0 + 1j * 0.0
for tau in range(N):
tp = t0 - (tau - N / 2)
if tp >= 0 and tp < N:
EtEtp = np.conj(Et[t0]) * Et[tp]
T += 4 * (Et[t0] * np.conj(EtEtp) - Esigp[tau, t0]) * EtEtp
tp = t0 + (tau - N / 2)
if tp >= 0 and tp < N:
EtpEtp = Et[tp] * Et[tp]
T += 2 * (Et[t0] * np.conj(EtpEtp) - np.conj(Esigp[tau, tp])) * EtpEtp
dZ[t0] = -T / sz
self.dZ_cla.set(dZ.copy())
def gradZSD_gpu(self):
root.debug("Calculating dZ for SD using gpu")
krn = self.progs.progs["gradZSD"].gradZSD
krn.set_scalar_arg_dtypes((None, None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.dZ_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
def minZerrKernSHG_naive(self):
Et0 = self.Et_cla.get()
Esig = self.Esig_t_tau_p_cla.get()
dZ = self.dZ_cla.get()
N = Esig.shape[0]
mx = 0.0
X = np.zeros(5)
for t in range(N):
for tau in range(N):
T = np.abs(Esig[tau, t]) ** 2
if mx < T:
mx = T
tp = t - (tau - N / 2)
if tp >= 0 and tp < N:
dZdZ = dZ[t] * dZ[tp]
dZE = dZ[t] * Et0[tp] + dZ[tp] * Et0[t]
DEsig = Et0[t] * Et0[tp] - Esig[tau, t]
X[0] += np.abs(dZdZ) ** 2
X[1] += 2.0 * np.real(dZE * np.conj(dZdZ))
X[2] += 2.0 * np.real(DEsig * np.conj(dZdZ)) + np.abs(dZE) ** 2
X[3] += 2.0 * np.real(DEsig * np.conj(dZE))
X[4] += np.abs(DEsig) ** 2
T = N * N * mx
X[0] = X[0] / T
X[1] = X[1] / T
X[2] = X[2] / T
X[3] = X[3] / T
X[4] = X[4] / T
root.debug("".join(("Esig_t_tau_p norm max: ", str(mx))))
return X
def minZerrKernSHG_gpu(self):
krn = self.progs.progs["minZerrSHG"].minZerrSHG
krn.set_scalar_arg_dtypes((None, None, None, None, None, None, None, None, np.int32))
krn.set_args(
self.Esig_t_tau_p_cla.data,
self.Et_cla.data,
self.dZ_cla.data,
self.X0_cla.data,
self.X1_cla.data,
self.X2_cla.data,
self.X3_cla.data,
self.X4_cla.data,
self.N,
)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
krn = self.progs.progs["normEsig"].normEsig
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Esig_t_tau_norm_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_t_tau_p.shape, None)
ev.wait()
mx = cla.max(self.Esig_t_tau_norm_cla).get() * self.N * self.N
# Esig_t_tau = self.Esig_t_tau_p_cla.get()
# mx = ((Esig_t_tau*Esig_t_tau.conj()).real).max() * self.N*self.N
X0 = cla.sum(self.X0_cla, queue=self.q).get() / mx
X1 = cla.sum(self.X1_cla, queue=self.q).get() / mx
X2 = cla.sum(self.X2_cla, queue=self.q).get() / mx
X3 = cla.sum(self.X3_cla, queue=self.q).get() / mx
X4 = cla.sum(self.X4_cla, queue=self.q).get() / mx
root.debug("".join(("X0=", str(X0), ", type ", str(type(X0)))))
root.debug(
"".join(("Poly: ", str(X4), " x^4 + ", str(X3), " x^3 + ", str(X2), " x^2 + ", str(X1), " x + ", str(X0)))
)
# Polynomial in dZ (expansion of differential)
X = np.array([X0, X1, X2, X3, X4]).astype(np.double)
root.debug("".join(("Esig_t_tau_p norm max: ", str(mx / (self.N * self.N)))))
return X
def minZerrKernSD_naive(self):
# Todo: fix this algorithm
Et0 = self.Et_cla.get()
Esig = self.Esig_t_tau_p_cla.get()
dZ = self.dZ_cla.get()
N = Esig.shape[0]
mx = 0.0
X = np.zeros(7)
for t in range(N):
for tau in range(N):
T = np.abs(Esig[tau, t]) ** 2
if mx < T:
mx = T
tp = t - (tau - N / 2)
if tp >= 0 and tp < N:
a0 = Esig[tau, t] - Et0[t] * Et0[t] * np.conj(Et0[tp])
a1 = -(2 * Et0[t] * dZ[t] * np.conj(Et0[tp]) + Et0[t] * Et0[t] * np.conj(dZ[tp]))
a2 = -(dZ[t] * dZ[t] * np.conj(Et0[tp]) + 2 * Et0[t] * np.conj(dZ[tp]) * dZ[t])
a3 = -dZ[t] * dZ[t] * np.conj(dZ[tp])
X[0] += np.real(a3 * np.conj(a3))
X[1] += np.real(a2 * np.conj(a3) + a3 * np.conj(a2))
X[2] += np.real(a1 * np.conj(a3) + a3 * np.conj(a1) + a2 * np.conj(a2))
X[3] += np.real(a0 * np.conj(a3) + a3 * np.conj(a0) + a1 * np.conj(a2) + a2 * np.conj(a1))
X[4] += np.real(a0 * np.conj(a2) + a2 * np.conj(a0) + a1 * np.conj(a1))
X[5] += np.real(a0 * np.conj(a1) + a1 * np.conj(a0))
X[6] += np.real(a0 * np.conj(a0))
T = N * N * mx
X = X / T
root.debug("".join(("Esig_t_tau_p norm max: ", str(mx))))
return X
def minZerrKernSD_gpu(self):
krn = self.progs.progs["minZerrSD"].minZerrSD
krn.set_scalar_arg_dtypes((None, None, None, None, None, None, None, None, None, None, np.int32))
krn.set_args(
self.Esig_t_tau_p_cla.data,
self.Et_cla.data,
self.dZ_cla.data,
self.X0_cla.data,
self.X1_cla.data,
self.X2_cla.data,
self.X3_cla.data,
self.X4_cla.data,
self.X5_cla.data,
self.X6_cla.data,
self.N,
)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
krn = self.progs.progs["normEsig"].normEsig
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Esig_t_tau_norm_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_t_tau_p.shape, None)
ev.wait()
mx = cla.max(self.Esig_t_tau_norm_cla).get() * self.N * self.N
# Esig_t_tau = self.Esig_t_tau_p_cla.get()
# mx = ((Esig_t_tau*Esig_t_tau.conj()).real).max() * self.N*self.N
X0 = cla.sum(self.X0_cla, queue=self.q).get() / mx
X1 = cla.sum(self.X1_cla, queue=self.q).get() / mx
X2 = cla.sum(self.X2_cla, queue=self.q).get() / mx
X3 = cla.sum(self.X3_cla, queue=self.q).get() / mx
X4 = cla.sum(self.X4_cla, queue=self.q).get() / mx
X5 = cla.sum(self.X5_cla, queue=self.q).get() / mx
X6 = cla.sum(self.X6_cla, queue=self.q).get() / mx
root.debug("".join(("X0=", str(X0), ", type ", str(type(X0)))))
root.debug(
"".join(
(
"Poly: ",
str(X6),
" x^6 + ",
str(X5),
" x^5 + ",
str(X4),
" x^4 + ",
str(X3),
" x^3 + ",
str(X2),
" x^2 + ",
str(X1),
" x + ",
str(X0),
)
)
)
# Polynomial in dZ (expansion of differential)
X = np.array([X0, X1, X2, X3, X4, X5, X6]).astype(np.double)
root.debug("".join(("Esig_t_tau_p norm max: ", str(mx / (self.N * self.N)))))
return X
def updateEt_gp(self, algo="SHG"):
root.debug("Updating Et using GP algorithm")
tic = time.clock()
events = self.Esig_t_tau_p_fft.enqueue(forward=False)
for e in events:
e.wait()
if algo == "SHG":
# Calculate the gradient of the functional distance:
if self.useCL == True:
self.gradZSHG_gpu()
else:
self.gradZSHG_naive()
# Calculate error minimization polynomial
if self.useCL == True:
p1 = self.minZerrKernSHG_gpu()
else:
p1 = self.minZerrKernSHG_naive()
root.debug(
"".join(
(
"Poly: ",
str(p1[4]),
" x^4 + ",
str(p1[3]),
" x^3 + ",
str(p1[2]),
" x^2 + ",
str(p1[1]),
" x + ",
str(p1[0]),
)
)
)
elif algo == "SD":
# Calculate the gradient of the functional distance:
if self.useCL == True:
self.gradZSD_gpu()
else:
self.gradZSD_naive()
# Calculate error minimization polynomial
if self.useCL == True:
p1 = self.minZerrKernSD_gpu()
else:
p1 = self.minZerrKernSD_naive()
root.debug(
"".join(
(
"Poly: ",
str(p1[4]),
" x^4 + ",
str(p1[3]),
" x^3 + ",
str(p1[2]),
" x^2 + ",
str(p1[1]),
" x + ",
str(p1[0]),
)
)
)
# Root finding of the polynomial in the gradient expansion
p = np.polyder(p1)
r = np.roots(p)
X = r[np.abs(r.imag) < 1e-9].real
root.debug("".join(("Real roots: ", str(X))))
Z1 = np.polyval(p1, X)
minZInd = Z1.argmin()
Z = np.maximum(3e-16 * X[-1], Z1[minZInd])
Z = np.sqrt(Z)
X = X[minZInd].astype(self.dtype_r)
# Update Et
if self.useCL == True:
krn = self.progs.progs["updateEtGP"].updateEtGP
krn.set_scalar_arg_dtypes((None, None, self.dtype_r, np.int32))
krn.set_args(self.Et_cla.data, self.dZ_cla.data, X, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
else:
root.debug("".join(("Moving distance X=", str(X))))
Et = self.Et_cla.get()
Et_new = Et + X * self.dZ_cla.get()
self.Et_cla.set(Et_new.copy())
toc = time.clock()
root.debug("".join(("Time spent: ", str(toc - tic))))
return Z
def centerPeakTime(self):
Et = self.Et_cla.get()
ind = np.argmax(abs(Et))
shift = Et.shape[0] / 2 - ind
Et = np.roll(Et, shift)
self.Et_cla.set(Et)
def calcReconstructionError(self):
root.debug("Calculating reconstruction error")
tic = time.clock()
Esig_w_tau = self.Esig_w_tau_cla.get()
I_rec_w_tau = np.real(Esig_w_tau * np.conj(Esig_w_tau))
I_w_tau = self.I_w_tau_cla.get()
my = I_w_tau.max() / I_rec_w_tau.max()
root.debug("".join(("My=", str(my))))
G = np.sqrt(((I_w_tau - my * I_rec_w_tau) ** 2).sum() / (I_rec_w_tau.shape[0] * I_rec_w_tau.shape[1]))
# G = np.sqrt(((self.I_w_tau-my*I_rec_w_tau)**2).sum()/(self.I_w_tau.sum()))
toc = time.clock()
root.debug("".join(("Time spent: ", str(toc - tic))))
return G
def getData(self):
root.debug("Retrieving data from opencl buffers")
tic = time.clock()
self.Esig_t_tau_cla.get()
self.Et_cla.get()
self.Esig_t_tau_p_cla.get()
self.Esig_w_tau_cla.get()
toc = time.clock()
root.debug("".join(("Time spent: ", str(toc - tic))))
def getTraceAbs(self):
self.centerPeakTime()
return np.abs(self.Et_cla.get())
def getTracePhase(self):
self.centerPeakTime()
Et = self.Et_cla.get()
ph0 = np.angle(Et[Et.shape[0] / 2])
return np.angle(Et) - ph0
def getT(self):
return self.t
def setupVanillaAlgorithm(self):
if self.Et_cla is None:
self.initClBuffers()
def runCycleVanilla(self, cycles=1, algo="SHG", useCL=None):
root.debug("Starting FROG reconstruction cycle using the vanilla algorithm")
if useCL is not None:
self.useCL = useCL
self.rollFFT = useCL
t0 = time.clock()
er = []
self.setupVanillaAlgorithm()
for c in range(cycles):
root.debug("".join(("Cycle ", str(c + 1), "/", str(cycles))))
if algo == "SD":
self.generateEsig_t_tau_SD()
else:
self.generateEsig_t_tau_SHG()
self.generateEsig_w_tau()
G = self.calcReconstructionError()
self.applyIntensityData()
self.updateEt_vanilla("SD")
# self.centerPeakTime()
root.debug("-------------------------------------------")
root.debug("".join(("Error G = ", str(G))))
root.debug("-------------------------------------------")
er.append(G)
deltaT = time.clock() - t0
root.debug("".join(("Total runtime ", str(deltaT))))
root.debug("".join((str(cycles / deltaT), " iterations/s")))
print "".join((str(cycles / deltaT), " iterations/s"))
return np.array(er)
def setupGPAlgorithm(self):
if self.Et_cla is None:
self.initClBuffers()
def runCycleGP(self, cycles=1, algo="SHG", useCL=None):
root.debug("Starting FROG reconstruction cycle using the GP algorithm")
if useCL is not None:
self.useCL = useCL
self.rollFFT = useCL
t0 = time.clock()
er = []
self.setupGPAlgorithm()
for c in range(cycles):
root.debug("".join(("Cycle ", str(c + 1), "/", str(cycles))))
if algo == "SD":
self.generateEsig_t_tau_SD()
else:
self.generateEsig_t_tau_SHG()
self.generateEsig_w_tau()
G = self.calcReconstructionError()
self.applyIntensityData()
self.updateEt_gp(algo)
# self.centerPeakTime()
root.debug("-------------------------------------------")
root.debug("".join(("Error G = ", str(G))))
root.debug("-------------------------------------------")
er.append(G)
deltaT = time.clock() - t0
root.debug("".join(("Total runtime ", str(deltaT))))
root.debug("".join((str(cycles / deltaT), " iterations/s")))
print "".join((str(cycles / deltaT), " iterations/s"))
return np.array(er)
def runComplete(self):
tic = time.clock()
er = self.runCycleVanilla(30)
oldEr = np.min(er)
er = self.runCycleGP(30)
newEr = np.min(er)
epochs = 0
while oldEr - newEr > 1e-5 and epochs < 20:
oldEr = newEr
er = self.runCycleGP(30)
newEr = np.min(er)
epochs += 1
print "Epoch ", epochs, ", error ", newEr
print "Epochs: ", epochs
toc = time.clock()
print "Total reconstruction time ", toc - tic, " s"
pl = cl.get_platforms()
d = pl[1].get_devices()
context = cl.Context(devices = d)
#context = cl.create_some_context()
queue = cl.CommandQueue(context)
dataRe = np.random.rand(512,512)
dataIm = np.random.rand(512,512)
nd_dataC = (dataRe + 1j*dataIm).astype(np.complex64)
#nd_dataC = np.random.rand((1024, 1024), dtype = np.complex64)
dataC = cla.to_device(queue, nd_dataC)
nd_result = np.zeros_like(nd_dataC, dtype = np.complex64)
resultC = cla.to_device(queue, nd_result)
transform = FFT(context, queue, (dataC,), (resultC,), axes = [1])
tic = time.clock()
events = transform.enqueue()
for e in events:
e.wait()
toc = time.clock()
clTime = toc-tic
print 'clTime: ', clTime
tic = time.clock()
resultCl = resultC.get()
toc = time.clock()
print "transfer time: ", toc-tic
ticNp = time.clock()
resultNp = np.fft.fft(nd_dataC, axis=1).astype(np.complex64)
tocNp = time.clock()
class PyOpenCLNUFFT:
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())
def __del__(self):
del self.traj
del self.dcf
del self.tmp_fft_array
del self.cl_kerneltable
del self.fft
del self.deapo_cl
del self.check
del self.queue
del self.ctx
def NUFFTH(self, sg, s, wait_for=[]):
# Zero tmp arrays
self.tmp_fft_array.add_event(
self.prg.zero_tmp(self.queue, (self.tmp_fft_array.size, ),
None,
self.tmp_fft_array.data,
wait_for=(s.events + sg.events +
self.tmp_fft_array.events + wait_for)))
# Grid k-space
self.tmp_fft_array.add_event(
self.prg.grid_lut(self.queue, (s.shape[0], s.shape[1] * s.shape[2],
s.shape[-2] * self.gridsize),
None,
self.tmp_fft_array.data,
s.data,
self.traj.data,
np.int32(self.gridsize),
self.DTYPE_real(self.kwidth / self.gridsize),
self.dcf.data,
self.cl_kerneltable,
np.int32(self.kernelpoints),
wait_for=(wait_for + sg.events + s.events +
self.tmp_fft_array.events)))
# FFT
self.tmp_fft_array.add_event(
self.prg.fftshift(
self.queue,
(self.fft_shape[0], self.fft_shape[1], self.fft_shape[2]),
None, self.tmp_fft_array.data, self.check.data))
for j in range(s.shape[0]):
self.tmp_fft_array.add_event(
self.fft.enqueue_arrays(
data=self.tmp_fft_array[j * self.par_fft:(j + 1) *
self.par_fft, ...],
result=self.tmp_fft_array[j * self.par_fft:(j + 1) *
self.par_fft, ...],
forward=False)[0])
self.tmp_fft_array.add_event(
self.prg.fftshift(
self.queue,
(self.fft_shape[0], self.fft_shape[1], self.fft_shape[2]),
None, self.tmp_fft_array.data, self.check.data))
return self.prg.deapo_adj(self.queue,
(sg.shape[0] * sg.shape[1] * sg.shape[2],
sg.shape[3], sg.shape[4]),
None,
sg.data,
self.tmp_fft_array.data,
self.deapo_cl,
np.int32(self.tmp_fft_array.shape[-1]),
self.DTYPE_real(self.fft_scale),
self.DTYPE_real(self.overgridfactor),
wait_for=wait_for + sg.events + s.events +
self.tmp_fft_array.events)
def NUFFT(self, s, sg, wait_for=[]):
# Zero tmp arrays
self.tmp_fft_array.add_event(
self.prg.zero_tmp(self.queue, (self.tmp_fft_array.size, ),
None,
self.tmp_fft_array.data,
wait_for=(s.events + sg.events +
self.tmp_fft_array.events + wait_for)))
# Deapodization and Scaling
self.tmp_fft_array.add_event(
self.prg.deapo_fwd(
self.queue, (sg.shape[0] * sg.shape[1] * sg.shape[2],
sg.shape[3], sg.shape[4]),
None,
self.tmp_fft_array.data,
sg.data,
self.deapo_cl,
np.int32(self.tmp_fft_array.shape[-1]),
self.DTYPE_real(1 / self.fft_scale),
self.DTYPE_real(self.overgridfactor),
wait_for=wait_for + sg.events + self.tmp_fft_array.events))
# FFT
self.tmp_fft_array.add_event(
self.prg.fftshift(
self.queue,
(self.fft_shape[0], self.fft_shape[1], self.fft_shape[2]),
None, self.tmp_fft_array.data, self.check.data))
for j in range(s.shape[0]):
self.tmp_fft_array.add_event(
self.fft.enqueue_arrays(
data=self.tmp_fft_array[j * self.par_fft:(j + 1) *
self.par_fft, ...],
result=self.tmp_fft_array[j * self.par_fft:(j + 1) *
self.par_fft, ...],
forward=True)[0])
self.tmp_fft_array.add_event(
self.prg.fftshift(
self.queue,
(self.fft_shape[0], self.fft_shape[1], self.fft_shape[2]),
None, self.tmp_fft_array.data, self.check.data))
# Resample on Spoke
return self.prg.invgrid_lut(
self.queue,
(s.shape[0], s.shape[1] * s.shape[2], s.shape[-2] * self.gridsize),
None,
s.data,
self.tmp_fft_array.data,
self.traj.data,
np.int32(self.gridsize),
self.DTYPE_real(self.kwidth / self.gridsize),
self.dcf.data,
self.cl_kerneltable,
np.int32(self.kernelpoints),
wait_for=s.events + wait_for + self.tmp_fft_array.events)
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()
