def xstep(self, S, lmbda, dimK):
"""Solve CSC problem for training data `S`."""
if self.opt['CUDA_CBPDN']:
Z = cuda.cbpdn(self.D.squeeze(), S[..., 0], lmbda,
self.opt['CBPDN'])
Z = Z.reshape(self.cri.Nv + (
1,
1,
self.cri.M,
))
self.Z[:] = np.asarray(Z, dtype=self.dtype)
self.Zf = sl.rfftn(self.Z, self.cri.Nv, self.cri.axisN)
self.Sf = sl.rfftn(S.reshape(self.cri.shpS), self.cri.Nv,
self.cri.axisN)
self.xstep_itstat = None
else:
# Create X update object (external representation is expected!)
xstep = cbpdn.ConvBPDN(self.D.squeeze(),
S,
lmbda,
self.opt['CBPDN'],
dimK=dimK,
dimN=self.cri.dimN)
xstep.solve()
self.Sf = xstep.Sf
self.setcoef(xstep.getcoef())
self.xstep_itstat = xstep.itstat[-1] if xstep.itstat else None
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 250,
'HighMemSolve': True,
'RelStopTol': 5e-3,
'AuxVarObj': False
})
"""
If GPU available, run CUDA ConvBPDN solver, otherwise run standard Python version.
"""
if cuda.device_count() > 0:
print('%s GPU found: running CUDA solver' % cuda.device_name())
tm = util.Timer()
with sys_pipes(), util.ContextTimer(tm):
X = cuda.cbpdn(D, sh, lmbda, opt)
t = tm.elapsed()
else:
print('GPU not found: running Python solver')
c = cbpdn.ConvBPDN(D, sh, lmbda, opt)
X = c.solve().squeeze()
t = c.timer.elapsed('solve')
print('Solve time: %.2f s' % t)
"""
Reconstruct the image from the sparse representation.
"""
shr = np.sum(spl.fftconv(D, X), axis=2)
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % spm.psnr(img, imgr))
"""
lmbda = 1e-2
opt = cbpdn.ConvBPDN.Options({'Verbose': True, 'MaxMainIter': 250,
'HighMemSolve': True, 'RelStopTol': 5e-3,
'AuxVarObj': False})
"""
If GPU available, run CUDA ConvBPDN solver, otherwise run standard Python version.
"""
if cuda.device_count() > 0:
print('%s GPU found: running CUDA solver' % cuda.device_name())
tm = util.Timer()
with sys_pipes(), util.ContextTimer(tm):
X = cuda.cbpdn(D, sh, lmbda, opt)
t = tm.elapsed()
else:
print('GPU not found: running Python solver')
c = cbpdn.ConvBPDN(D, sh, lmbda, opt)
X = c.solve().squeeze()
t = c.timer.elapsed('solve')
print('Solve time: %.2f s' % t)
"""
Reconstruct the image from the sparse representation.
"""
shr = np.sum(spl.fftconv(D, X), axis=2)
imgr = sl + shr