def test_05(self):
D = cp.random.randn(4, 32)
s = cp.random.randn(64)
Wg = np.concatenate((cp.eye(16), cp.eye(16)), axis=-1)
lmbda = 0.1
mu = 0.01
gamma = 0.01
# ConvBPDNInhib class
opt = cbpdnin.ConvBPDNInhib.Options({
'Verbose': False,
'MaxMainIter': 10
})
try:
b = cbpdnin.ConvBPDNInhib(D,
s,
Wg=Wg,
lmbda=lmbda,
mu=mu,
gamma=gamma,
opt=opt,
dimN=1)
b.solve()
except Exception as e:
print(e)
assert 0
def test_01(self):
D = cp.random.randn(4, 4, 32)
s = cp.random.randn(8, 8)
# ConvBPDNInhib class
opt = cbpdnin.ConvBPDNInhib.Options({
'Verbose': False,
'MaxMainIter': 10
})
try:
b = cbpdnin.ConvBPDNInhib(D, s, opt=opt)
b.solve()
except Exception as e:
print(e)
assert 0
def test_02(self):
D = cp.random.randn(4, 4, 32)
s = cp.random.randn(8, 8)
Wg = cp.concatenate((cp.eye(16), cp.eye(16)), axis=-1)
lmbda = 0.1
# ConvBPDNInhib class
opt = cbpdnin.ConvBPDNInhib.Options({
'Verbose': False,
'MaxMainIter': 10
})
try:
b = cbpdnin.ConvBPDNInhib(D, s, Wg=Wg, lmbda=lmbda, opt=opt)
b.solve()
except Exception as e:
print(e)
assert 0
'rho': 100, 'AutoRho': {'Enabled': False}})
"""
Initialise and run CSC solver.
"""
Wg = np.eye(24)
Wg = np.repeat(Wg, M, axis=1)
if CUPY:
D = np2cp(D)
Wg = np2cp(Wg)
audio = np2cp(audio)
b = cbpdnin.ConvBPDNInhib(D.T, audio, Wg, int(
0.20*16000), None, lmbda, mu, gamma, opt, dimK=None, dimN=1)
X = b.solve()
if CUPY:
X = cp2np(X)
print("ConvBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Reconstruct image from sparse representation.
"""
recon = b.reconstruct().squeeze()
if CUPY: