def test_01(self):
Nr = 16
Nc = 17
C = 3
Nd = 5
Md = 4
Mb = 4
D = cp.random.randn(Nd, Nd, Md)
B = cp.random.randn(C, Mb)
s = cp.random.randn(Nr, Nc, C)
lmbda = 1e-1
try:
opt = pdcsc.ConvProdDictBPDN.Options({'LinSolveCheck': True})
b = pdcsc.ConvProdDictBPDN(D, B, s, lmbda, opt=opt, dimK=0)
b.solve()
except Exception as e:
print(e)
assert 0
assert list2array(b.getitstat().XSlvRelRes).max() < 1e-4
'RelStopTol': 5e-3, 'AuxVarObj': False})
"""
Initialise and run CSC solver.
"""
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.ConvProdDictBPDN(np2cp(D), np2cp(B), np2cp(shc), lmbda, opt, dimK=0)
X = cp2np(b.solve())
print("ConvProdDictBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Compute partial and full reconstructions from sparse representation $X$ with respect to convolutional dictionary $D$ and standard dictionary $B$. The partial reconstructions are $DX$ and $XB$, and the full reconstruction is $DXB$.
"""
DX = fft.fftconv(D[..., np.newaxis, np.newaxis, :], X, axes=(0, 1))
XB = linalg.dot(B, X, axis=2)
shr = cp2np(b.reconstruct().squeeze())
imgr = slc + shr
print("Reconstruction PSNR: %.2fdB\n" % metric.psnr(img, imgr))