def test_14(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = cp.random.randn(Nd, Nd, M)
s = cp.random.randn(N, N, Cs)
lmbda = 1e-1
try:
opt = cbpdn.ConvBPDNJoint.Options({'LinSolveCheck': True})
b = cbpdn.ConvBPDNJoint(D, s, lmbda, opt=opt, dimK=0)
b.solve()
except Exception as e:
print(e)
assert 0
assert list2array(b.getitstat().XSlvRelRes).max() < 1e-5
def test_15(self):
N = 16
Nd = 5
K = 2
M = 4
D = cp.random.randn(Nd, Nd, M)
s = cp.random.randn(N, N, K)
dt = cp.float32
opt = cbpdn.ConvBPDNJoint.Options(
{'Verbose': False, 'MaxMainIter': 20, 'AutoRho': {'Enabled': True},
'DataType': dt})
lmbda = 1e-1
mu = 1e-2
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt=opt)
b.solve()
assert b.X.dtype == dt
assert b.Y.dtype == dt
assert b.U.dtype == dt
},
'rho': 1e3 * lmbda
})
"""
Initialise a ``sporco.cupy`` version of a :class:`.admm.cbpdn.ConvBPDNJoint` 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 = cbpdn.ConvBPDNJoint(np2cp(D), np2cp(pad(imgnh)), lmbda, mu, opt, dimK=0)
X = cp2np(b.solve())
"""
The denoised estimate of the image is just the reconstruction from the coefficient maps.
"""
imgdp = cp2np(b.reconstruct().squeeze())
imgd = np.clip(crop(imgdp) + imgnl, 0, 1)
"""
Display solve time and denoising performance.
"""
print("ConvBPDNJoint solve time: %5.2f s" % b.timer.elapsed('solve'))
print("Noisy image PSNR: %5.2f dB" % metric.psnr(img, imgn))
print("Denoised image PSNR: %5.2f dB" % metric.psnr(img, imgd))
"""
