lmbda = 2e-2
opt = cbpdn.ConvBPDN.Options({'Verbose': True, 'MaxMainIter': 200,
'HighMemSolve': True, 'RelStopTol': 5e-3,
'AuxVarObj': False, 'RelaxParam': 1.8,
'rho': 5e1*lmbda + 1e-1, 'AutoRho': {'Enabled': False,
'StdResiduals': False}})
"""
Construct :class:`.admm.cbpdn.AddMaskSim` wrapper for :class:`.admm.cbpdn.ConvBPDN` and solve via wrapper. This example could also have made use of :class:`.admm.cbpdn.ConvBPDNMaskDcpl` (see example `cbpdn_md_gry`), which has similar performance in this application, but :class:`.admm.cbpdn.AddMaskSim` has the advantage of greater flexibility in that the wrapper can be applied to a variety of CSC solver objects. If the ``sporco-cuda`` extension is installed and a GPU is available, use the CUDA implementation of this combination.
"""
if cuda.device_count() > 0:
ams = None
print('%s GPU found: running CUDA solver' % cuda.device_name())
tm = util.Timer()
with sys_pipes(), util.ContextTimer(tm):
X = cuda.cbpdnmsk(D, sh, mskp, lmbda, opt)
t = tm.elapsed()
imgr = crop(sl + np.sum(fftconv(D, X, axes=(0, 1)), axis=-1))
else:
ams = cbpdn.AddMaskSim(cbpdn.ConvBPDN, D, sh, mskp, lmbda, opt=opt)
X = ams.solve()
t = ams.timer.elapsed('solve')
imgr = crop(sl + ams.reconstruct().squeeze())
"""
Display solve time and reconstruction performance.
"""
"""
lmbda = 1e-2
mu = 5e-1
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': True, 'MaxMainIter': 250,
'HighMemSolve': True, 'RelStopTol': 5e-3,
'AuxVarObj': False, 'AutoRho': {'Enabled': False},
'rho': 0.5, 'L1Weight': wl1, 'GradWeight': wgr})
"""
If GPU available, run CUDA ConvBPDNGradReg 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.cbpdngrd(D, img, lmbda, mu, opt)
t = tm.elapsed()
else:
print('GPU not found: running Python solver')
c = cbpdn.ConvBPDNGradReg(D, img, lmbda, mu, opt)
X = c.solve().squeeze()
t = c.timer.elapsed('solve')
print('Solve time: %.2f s' % t)
"""
Reconstruct the image from the sparse representation.
"""
