'RelaxParam': 1.8,
'RelStopTol': 1e-7,
'MaxMainIter': 50,
'FastSolve': False,
'DataType': np.float32
}
})
lmbda = 0.2
if not cupy_enabled():
print(
'CuPy/GPU device not available: running without GPU acceleration\n'
)
else:
id = select_device_by_load()
info = gpu_info()
if info:
print('Running on GPU %d (%s)\n' % (id, info[id].name))
data_ra_new = []
for i in range(1, int(data_ra.shape[0] / 1000)):
data_ra_new.append(data_ra[(i - 1) * 1000:i * 1000, :])
data_ra = np.asarray(data_ra_new)
print(data_ra.shape, "New data_ra shape")
stimulus_ra_new = []
for i in range(1, int(stimulus_ra.shape[0] / 1000)):
stimulus_ra_new.append(stimulus_ra[(i - 1) * 1000:i * 1000, :])
stimulus_ra = np.asarray(stimulus_ra_new)
d = online_cdl.OnlineConvBPDNDictLearn(np2cp(np.load('Data_81sets/v_ra')),
opt = pdcsc.ConvProdDictL1L1Grd.Options(
{'Verbose': True, 'MaxMainIter': 100, 'RelStopTol': 5e-3,
'AuxVarObj': False, 'rho': 1e1, 'RelaxParam': 1.8,
'L1Weight': np2cp(wl1), 'GradWeight': np2cp(wgr)})
"""
Initialise the :class:`.admm.pdcsc.ConvProdDictL1L1Grd` object and call the ``solve`` method.
"""
if not cupy_enabled():
print('CuPy/GPU device not available: running without GPU acceleration\n')
else:
id = select_device_by_load()
info = gpu_info()
if info:
print('Running on GPU %d (%s)\n' % (id, info[id].name))
b = pdcsc.ConvProdDictL1L1Grd(np2cp(D), np2cp(B), np2cp(pad(imgn)),
lmbda, mu, opt=opt, dimK=0)
X = cp2np(b.solve())
"""
The denoised estimate of the image is just the reconstruction from all coefficient maps.
"""
imgdp = cp2np(b.reconstruct().squeeze())
imgd = crop(imgdp)
