def test_01(self):
lmbda = 1e-1
opt = onlinecdl.OnlineConvBPDNDictLearn.Options()
try:
b = onlinecdl.OnlineConvBPDNDictLearn(self.D0, lmbda, opt=opt)
for it in range(10):
img_index = np.random.randint(0, self.S.shape[-1])
b.solve(self.S[..., img_index])
except Exception as e:
print(e)
assert 0
def test_02(self):
lmbda = 1e-1
opts = onlinecdl.OnlineConvBPDNDictLearn.Options(
{'CBPDN': {
'MaxMainIter': 10,
'AutoRho': {
'Enabled': False
}
}})
try:
b = onlinecdl.OnlineConvBPDNDictLearn(self.D0, lmbda, opt=opts)
for it in range(10):
img_index = np.random.randint(0, self.S.shape[-1])
b.solve(self.S[..., [img_index]])
except Exception as e:
print(e)
assert 0
"""
lmbda = 0.2
opt = onlinecdl.OnlineConvBPDNDictLearn.Options({
'Verbose': True, 'ZeroMean': False, 'eta_a': 10.0,
'eta_b': 20.0, 'DataType': np.float32,
'CBPDN': {'rho': 5.0, 'AutoRho': {'Enabled': True},
'RelaxParam': 1.8, 'RelStopTol': 1e-7, 'MaxMainIter': 50,
'FastSolve': False, 'DataType': np.float32}})
"""
Create solver object and solve.
"""
d = onlinecdl.OnlineConvBPDNDictLearn(D0, lmbda, opt)
iter = 50
d.display_start()
for it in range(iter):
img_index = np.random.randint(0, sh.shape[-1])
d.solve(sh[..., [img_index]])
d.display_end()
D1 = d.getdict()
print("OnlineConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
"""
Display initial and final dictionaries.
"""
