def test_24(self):
N = 16
M = 4
Nc = 3
Nd = 8
X = np.random.randn(N, N, Nc, 1, M)
S = np.random.randn(N, N, Nc)
W = np.random.randn(N, N)
L = 5e1
try:
opt = ccmod.ConvCnstrMODMask.Options({
'Verbose': False,
'MaxMainIter': 200,
'L': L,
'BackTrack': {
'Enabled': True,
'Robust': True
}
})
c = ccmod.ConvCnstrMODMask(X,
S,
W, (Nd, Nd, 1, M),
opt=opt,
dimK=0)
c.solve()
except Exception as e:
print(e)
assert 0
assert np.array(c.getitstat().Rsdl)[-1] < 5e-3
def test_15(self):
N = 16
M = 4
Nd = 8
X = np.random.randn(N, N, 1, 1, M)
S = np.random.randn(N, N)
W = np.random.randn(N, N)
try:
opt = ccmod.ConvCnstrMODMask.Options(
{'Verbose': False, 'MaxMainIter': 20})
c = ccmod.ConvCnstrMODMask(X, S, W,
(Nd, Nd, 1, M), opt=opt, dimK=0)
c.solve()
except Exception as e:
print(e)
assert(0)
def test_22(self):
N = 16
M = 4
K = 2
Nc = 3
Nd = 8
X = np.random.randn(N, N, Nc, K, M)
S = np.random.randn(N, N, Nc, K)
W = np.random.randn(N, N, Nc, K)
try:
opt = ccmod.ConvCnstrMODMask.Options(
{'Verbose': False, 'MaxMainIter': 20})
c = ccmod.ConvCnstrMODMask(X, S, W,
(Nd, Nd, Nc, M), opt=opt)
c.solve()
except Exception as e:
print(e)
assert(0)
def test_23(self):
N = 16
M = 4
Nc = 3
Nd = 8
X = np.random.randn(N, N, Nc, 1, M)
S = np.random.randn(N, N, Nc)
W = np.random.randn(N, N)
L = 5e3
try:
opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False,
'MaxMainIter': 200, 'L' : L})
c = ccmod.ConvCnstrMODMask(X, S, W, (Nd, Nd, 1, M),
opt=opt, dimK=0)
c.solve()
except Exception as e:
print(e)
assert(0)
assert(np.array(c.getitstat().Rsdl)[-1] < 5e-3)
def __init__(self, D0, S, lmbda, W, opt=None, dimK=1, dimN=2):
"""
Initialise a MixConvBPDNMaskDcplDictLearn object with problem
size and options.
Parameters
----------
D0 : array_like
Initial dictionary array
S : array_like
Signal array
lmbda : float
Regularisation parameter
W : array_like
Mask array. The array shape must be such that the array is
compatible for multiplication with the *internal* shape of
input array S (see :class:`.cnvrep.CDU_ConvRepIndexing` for a
discussion of the distinction between *external* and *internal*
data layouts).
opt : :class:`MixConvBPDNMaskDcplDictLearn.Options` object
Algorithm options
dimK : int, optional (default 1)
Number of signal dimensions. If there is only a single input
signal (e.g. if `S` is a 2D array representing a single image)
`dimK` must be set to 0.
dimN : int, optional (default 2)
Number of spatial/temporal dimensions
"""
if opt is None:
opt = MixConvBPDNMaskDcplDictLearn.Options()
self.opt = opt
# Get dictionary size
if self.opt['DictSize'] is None:
dsz = D0.shape
else:
dsz = self.opt['DictSize']
# Construct object representing problem dimensions
cri = cr.CDU_ConvRepIndexing(dsz, S, dimK, dimN)
# Normalise dictionary
D0 = cr.Pcn(D0,
dsz,
cri.Nv,
dimN,
cri.dimCd,
crp=True,
zm=opt['CCMOD', 'ZeroMean'])
# Modify D update options to include initial values for X
X0 = cr.zpad(cr.stdformD(D0, cri.Cd, cri.M, dimN), cri.Nv)
opt['CCMOD'].update({'X0': X0})
# Create X update object
xstep = Acbpdn.ConvBPDNMaskDcpl(D0,
S,
lmbda,
W,
opt['CBPDN'],
dimK=dimK,
dimN=dimN)
# Create D update object
dstep = ccmod.ConvCnstrMODMask(None,
S,
W,
dsz,
opt['CCMOD'],
dimK=dimK,
dimN=dimN)
# Configure iteration statistics reporting
if self.opt['AccurateDFid']:
isxmap = {
'XPrRsdl': 'PrimalRsdl',
'XDlRsdl': 'DualRsdl',
'XRho': 'Rho'
}
evlmap = {'ObjFun': 'ObjFun', 'DFid': 'DFid', 'RegL1': 'RegL1'}
else:
isxmap = {
'ObjFun': 'ObjFun',
'DFid': 'DFid',
'RegL1': 'RegL1',
'XPrRsdl': 'PrimalRsdl',
'XDlRsdl': 'DualRsdl',
'XRho': 'Rho'
}
evlmap = {}
if dstep.opt['BackTrack', 'Enabled']:
isfld = [
'Iter', 'ObjFun', 'DFid', 'RegL1', 'Cnstr', 'XPrRsdl',
'XDlRsdl', 'XRho', 'D_F_Btrack', 'D_Q_Btrack', 'D_ItBt', 'D_L',
'Time'
]
isdmap = {
'Cnstr': 'Cnstr',
'D_F_Btrack': 'F_Btrack',
'D_Q_Btrack': 'Q_Btrack',
'D_ItBt': 'IterBTrack',
'D_L': 'L'
}
hdrtxt = [
'Itn', 'Fnc', 'DFid',
u('ℓ1'), 'Cnstr', 'r_X', 's_X',
u('ρ_X'), 'F_D', 'Q_D', 'It_D', 'L_D'
]
hdrmap = {
'Itn': 'Iter',
'Fnc': 'ObjFun',
'DFid': 'DFid',
u('ℓ1'): 'RegL1',
'Cnstr': 'Cnstr',
'r_X': 'XPrRsdl',
's_X': 'XDlRsdl',
u('ρ_X'): 'XRho',
'F_D': 'D_F_Btrack',
'Q_D': 'D_Q_Btrack',
'It_D': 'D_ItBt',
'L_D': 'D_L'
}
else:
isfld = [
'Iter', 'ObjFun', 'DFid', 'RegL1', 'Cnstr', 'XPrRsdl',
'XDlRsdl', 'XRho', 'D_L', 'Time'
]
isdmap = {'Cnstr': 'Cnstr', 'D_L': 'L'}
hdrtxt = [
'Itn', 'Fnc', 'DFid',
u('ℓ1'), 'Cnstr', 'r_X', 's_X',
u('ρ_X'), 'L_D'
]
hdrmap = {
'Itn': 'Iter',
'Fnc': 'ObjFun',
'DFid': 'DFid',
u('ℓ1'): 'RegL1',
'Cnstr': 'Cnstr',
'r_X': 'XPrRsdl',
's_X': 'XDlRsdl',
u('ρ_X'): 'XRho',
'L_D': 'D_L'
}
isc = dictlrn.IterStatsConfig(isfld=isfld,
isxmap=isxmap,
isdmap=isdmap,
evlmap=evlmap,
hdrtxt=hdrtxt,
hdrmap=hdrmap)
# Call parent constructor
super(MixConvBPDNMaskDcplDictLearn,
self).__init__(xstep, dstep, opt, isc)
