def __init__(self,
D0,
S,
lmbda=None,
opt=None,
xmethod=None,
dmethod=None,
dimK=1,
dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/cbpdndl_init.svg
:width: 20%
:target: ../_static/jonga/cbpdndl_init.svg
|
Parameters
----------
D0 : array_like
Initial dictionary array
S : array_like
Signal array
lmbda : float
Regularisation parameter
opt : :class:`ConvBPDNDictLearn.Options` object
Algorithm options
xmethod : string, optional (default 'admm')
String selecting sparse coding solver. Valid values are
documented in function :func:`.ConvBPDN`.
dmethod : string, optional (default 'fista')
String selecting dictionary update solver. Valid values are
documented in function :func:`.ConvCnstrMOD`.
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 = ConvBPDNDictLearn.Options(xmethod=xmethod, dmethod=dmethod)
if xmethod is None:
xmethod = opt.xmethod
if dmethod is None:
dmethod = opt.dmethod
if opt.xmethod != xmethod or opt.dmethod != dmethod:
raise ValueError('Parameters xmethod and dmethod must have the '
'same values used to initialise the Options '
'object')
self.opt = opt
self.xmethod = xmethod
self.dmethod = dmethod
# 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 value for Y
optname = 'X0' if dmethod == 'fista' else 'Y0'
opt['CCMOD'].update(
{optname: cr.zpad(cr.stdformD(D0, cri.Cd, cri.M, dimN), cri.Nv)})
# Create X update object
xstep = ConvBPDN(D0,
S,
lmbda,
opt['CBPDN'],
method=xmethod,
dimK=dimK,
dimN=dimN)
# Create D update object
dstep = ConvCnstrMOD(None,
S,
dsz,
opt['CCMOD'],
method=dmethod,
dimK=dimK,
dimN=dimN)
# Configure iteration statistics reporting
isc = dictlrn.IterStatsConfig(isfld=dc.isfld(xmethod, dmethod, opt),
isxmap=dc.isxmap(xmethod, opt),
isdmap=dc.isdmap(dmethod),
evlmap=dc.evlmap(opt['AccurateDFid']),
hdrtxt=dc.hdrtxt(xmethod, dmethod, opt),
hdrmap=dc.hdrmap(xmethod, dmethod, opt),
fmtmap={
'It_X': '%4d',
'It_D': '%4d'
})
# Call parent constructor
super(ConvBPDNDictLearn, self).__init__(xstep, dstep, opt, isc)
def __init__(self, Z, S, dsz, opt=None, dimK=1, dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/ccmodcnsns_init.svg
:width: 20%
:target: ../_static/jonga/ccmodcnsns_init.svg
"""
# Set default options if none specified
if opt is None:
opt = ConvCnstrMOD_Consensus.Options()
# Infer problem dimensions and set relevant attributes of self
self.cri = cr.CDU_ConvRepIndexing(dsz, S, dimK=dimK, dimN=dimN)
# Handle possible reshape of channel axis onto multiple image axis
# (see comment below)
Nb = self.cri.K if self.cri.C == self.cri.Cd else \
self.cri.C * self.cri.K
admm.ADMMConsensus.__init__(self, Nb, self.cri.shpD, S.dtype, opt)
# Set penalty parameter
self.set_attr('rho', opt['rho'], dval=self.cri.K, dtype=self.dtype)
# Reshape S to standard layout (Z, i.e. X in cbpdn, is assumed
# to be taken from cbpdn, and therefore already in standard
# form). If the dictionary has a single channel but the input
# (and therefore also the coefficient map array) has multiple
# channels, the channel index and multiple image index have
# the same behaviour in the dictionary update equation: the
# simplest way to handle this is to just reshape so that the
# channels also appear on the multiple image index.
if self.cri.Cd == 1 and self.cri.C > 1:
self.S = S.reshape(self.cri.Nv + (1,) +
(self.cri.C*self.cri.K,) + (1,))
else:
self.S = S.reshape(self.cri.shpS)
self.S = np.asarray(self.S, dtype=self.dtype)
# Compute signal S in DFT domain
self.Sf = sl.rfftn(self.S, None, self.cri.axisN)
# Create constraint set projection function
self.Pcn = cr.getPcn(dsz, self.cri.Nv, self.cri.dimN, self.cri.dimCd,
zm=opt['ZeroMean'])
if Z is not None:
self.setcoef(Z)
self.X = sl.pyfftw_empty_aligned(self.xshape, dtype=self.dtype)
# See comment on corresponding test in xstep method
if self.cri.Cd > 1:
self.YU = sl.pyfftw_empty_aligned(self.yshape, dtype=self.dtype)
else:
self.YU = sl.pyfftw_empty_aligned(self.xshape, dtype=self.dtype)
self.Xf = sl.pyfftw_rfftn_empty_aligned(self.xshape, self.cri.axisN,
self.dtype)
def __init__(self, Z, S, dsz, opt=None, dimK=1, dimN=2):
"""
This class supports an arbitrary number of spatial dimensions,
`dimN`, with a default of 2. The input coefficient map array `Z`
(usually labelled X, but renamed here to avoid confusion with
the X and Y variables in the ADMM base class) is expected to
be in standard form as computed by the ConvBPDN class.
The input signal set `S` is either `dimN` dimensional (no
channels, only one signal), `dimN` +1 dimensional (either
multiple channels or multiple signals), or `dimN` +2 dimensional
(multiple channels and multiple signals). Parameter `dimK`, with
a default value of 1, indicates the number of multiple-signal
dimensions in `S`:
::
Default dimK = 1, i.e. assume input S is of form
S(N0, N1, C, K) or S(N0, N1, K)
If dimK = 0 then input S is of form
S(N0, N1, C, K) or S(N0, N1, C)
The internal data layout for S, D (X here), and X (Z here) is:
::
dim<0> - dim<Nds-1> : Spatial dimensions, product of N0,N1,... is N
dim<Nds> : C number of channels in S and D
dim<Nds+1> : K number of signals in S
dim<Nds+2> : M number of filters in D
sptl. chn sig flt
S(N0, N1, C, K, 1)
D(N0, N1, C, 1, M) (X here)
X(N0, N1, 1, K, M) (Z here)
The `dsz` parameter indicates the desired filter supports in the
output dictionary, since this cannot be inferred from the
input variables. The format is the same as the `dsz` parameter
of :func:`.cnvrep.bcrop`.
Parameters
----------
Z : array_like
Coefficient map array
S : array_like
Signal array
dsz : tuple
Filter support size(s)
opt : ccmod.Options object
Algorithm options
dimK : int, optional (default 1)
Number of dimensions for multiple signals in input S
dimN : int, optional (default 2)
Number of spatial dimensions
"""
# Set default options if none specified
if opt is None:
opt = ConvCnstrMODBase.Options()
# Infer problem dimensions and set relevant attributes of self
self.cri = cr.CDU_ConvRepIndexing(dsz, S, dimK=dimK, dimN=dimN)
# Call parent class __init__
super(ConvCnstrMODBase, self).__init__(self.cri.shpD, S.dtype, opt)
# Set penalty parameter
self.set_attr('rho', opt['rho'], dval=self.cri.K, dtype=self.dtype)
# Reshape S to standard layout (Z, i.e. X in cbpdn, is assumed
# to be taken from cbpdn, and therefore already in standard
# form). If the dictionary has a single channel but the input
# (and therefore also the coefficient map array) has multiple
# channels, the channel index and multiple image index have
# the same behaviour in the dictionary update equation: the
# simplest way to handle this is to just reshape so that the
# channels also appear on the multiple image index.
if self.cri.Cd == 1 and self.cri.C > 1:
self.S = S.reshape(self.cri.Nv + (1,) +
(self.cri.C*self.cri.K,) + (1,))
else:
self.S = S.reshape(self.cri.shpS)
self.S = np.asarray(self.S, dtype=self.dtype)
# Compute signal S in DFT domain
self.Sf = sl.rfftn(self.S, None, self.cri.axisN)
# Create constraint set projection function
self.Pcn = cr.getPcn(dsz, self.cri.Nv, self.cri.dimN, self.cri.dimCd,
zm=opt['ZeroMean'])
# Create byte aligned arrays for FFT calls
self.YU = sl.pyfftw_empty_aligned(self.Y.shape, dtype=self.dtype)
self.Xf = sl.pyfftw_rfftn_empty_aligned(self.Y.shape, self.cri.axisN,
self.dtype)
if Z is not None:
self.setcoef(Z)
def __init__(self,
D0,
S,
lmbda,
W,
opt=None,
xmethod=None,
dmethod=None,
dimK=1,
dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/cbpdnmddl_init.svg
:width: 20%
:target: ../_static/jonga/cbpdnmddl_init.svg
|
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:`ConvBPDNMaskDictLearn.Options` object
Algorithm options
xmethod : string, optional (default 'admm')
String selecting sparse coding solver. Valid values are
documented in function :func:`.ConvBPDNMask`.
dmethod : string, optional (default 'fista')
String selecting dictionary update solver. Valid values are
documented in function :func:`.ConvCnstrMODMask`.
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 = ConvBPDNMaskDictLearn.Options(xmethod=xmethod,
dmethod=dmethod)
if xmethod is None:
xmethod = opt.xmethod
if dmethod is None:
dmethod = opt.dmethod
if opt.xmethod != xmethod or opt.dmethod != dmethod:
raise ValueError('Parameters xmethod and dmethod must have the '
'same values used to initialise the Options '
'object')
self.opt = opt
self.xmethod = xmethod
self.dmethod = dmethod
# 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 Y
if cri.C == cri.Cd:
Y0b0 = np.zeros(cri.Nv + (cri.C, 1, cri.K))
else:
Y0b0 = np.zeros(cri.Nv + (1, 1, cri.C * cri.K))
Y0b1 = cr.zpad(cr.stdformD(D0, cri.Cd, cri.M, dimN), cri.Nv)
if dmethod == 'fista':
opt['CCMOD'].update({'X0': Y0b1})
else:
if dmethod == 'cns':
Y0 = Y0b1
else:
Y0 = np.concatenate((Y0b0, Y0b1), axis=cri.axisM)
opt['CCMOD'].update({'Y0': Y0})
# Create X update object
xstep = ConvBPDNMask(D0,
S,
lmbda,
W,
opt['CBPDN'],
method=xmethod,
dimK=dimK,
dimN=dimN)
# Create D update object
dstep = ConvCnstrMODMask(None,
S,
W,
dsz,
opt['CCMOD'],
method=dmethod,
dimK=dimK,
dimN=dimN)
# Configure iteration statistics reporting
isc = dictlrn.IterStatsConfig(isfld=dc.isfld(xmethod, dmethod, opt),
isxmap=dc.isxmap(xmethod, opt),
isdmap=dc.isdmap(dmethod),
evlmap=dc.evlmap(opt['AccurateDFid']),
hdrtxt=dc.hdrtxt(xmethod, dmethod, opt),
hdrmap=dc.hdrmap(xmethod, dmethod, opt),
fmtmap={
'It_X': '%4d',
'It_D': '%4d'
})
# Call parent constructor
super(ConvBPDNMaskDictLearn, self).__init__(xstep, dstep, opt, isc)
def __init__(self, Z, S, W, dsz, opt=None, dimK=None, dimN=2):
"""
|
**Call graph**
.. image:: ../_static/jonga/ccmodmdfista_init.svg
:width: 20%
:target: ../_static/jonga/ccmodmdfista_init.svg
|
Parameters
----------
Z : array_like
Coefficient map array
S : array_like
Signal array
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).
dsz : tuple
Filter support size(s)
opt : :class:`ConvCnstrMODMasked.Options` object
Algorithm options
dimK : 0, 1, or None, optional (default None)
Number of dimensions in input signal corresponding to multiple
independent signals
dimN : int, optional (default 2)
Number of spatial dimensions
"""
# Set default options if none specified
if opt is None:
opt = ConvCnstrMODMask.Options()
# Infer problem dimensions and set relevant attributes of self
self.cri = cr.CDU_ConvRepIndexing(dsz, S, dimK=dimK, dimN=dimN)
# Append singleton dimensions to W if necessary
if hasattr(W, 'ndim'):
W = sl.atleast_nd(self.cri.dimN + 3, W)
# Reshape W if necessary (see discussion of reshape of S in
# ccmod base class)
if self.cri.Cd == 1 and self.cri.C > 1 and hasattr(W, 'ndim'):
# In most cases broadcasting rules make it possible for W
# to have a singleton dimension corresponding to a
# non-singleton dimension in S. However, when S is
# reshaped to interleave axisC and axisK on the same axis,
# broadcasting is no longer sufficient unless axisC and
# axisK of W are either both singleton or both of the same
# size as the corresponding axes of S. If neither of these
# cases holds, it is necessary to replicate the axis of W
# (axisC or axisK) that does not have the same size as the
# corresponding axis of S.
shpw = list(W.shape)
swck = shpw[self.cri.axisC] * shpw[self.cri.axisK]
if swck > 1 and swck < self.cri.C * self.cri.K:
if W.shape[self.cri.axisK] == 1 and self.cri.K > 1:
shpw[self.cri.axisK] = self.cri.K
else:
shpw[self.cri.axisC] = self.cri.C
W = np.broadcast_to(W, shpw)
self.W = W.reshape(
W.shape[0:self.cri.dimN] +
(1, W.shape[self.cri.axisC] * W.shape[self.cri.axisK], 1))
else:
self.W = W
super(ConvCnstrMODMask, self).__init__(Z, S, dsz, opt, dimK, dimN)
# Create byte aligned arrays for FFT calls
self.WRy = sl.pyfftw_empty_aligned(self.S.shape, dtype=self.dtype)
self.Ryf = sl.pyfftw_rfftn_empty_aligned(self.S.shape, self.cri.axisN,
self.dtype)
def __init__(self, Z, S, W, dsz, opt=None, dimK=None, dimN=2):
"""
Parameters
----------
Z : array_like
Coefficient map array
S : array_like
Signal array
W : array_like
Mask array. The array shape must be such that the array is
compatible for multiplication with input array S (see
:func:`.cnvrep.mskWshape` for more details).
dsz : tuple
Filter support size(s)
opt : :class:`ConvCnstrMODMaskDcplBase.Options` object
Algorithm options
dimK : 0, 1, or None, optional (default None)
Number of dimensions in input signal corresponding to multiple
independent signals
dimN : int, optional (default 2)
Number of spatial dimensions
"""
# Set default options if none specified
if opt is None:
opt = ConvCnstrMODMaskDcplBase.Options()
# Infer problem dimensions and set relevant attributes of self
self.cri = cr.CDU_ConvRepIndexing(dsz, S, dimK=dimK, dimN=dimN)
# Convert W to internal shape
W = np.asarray(W.reshape(cr.mskWshape(W, self.cri)),
dtype=S.dtype)
# Reshape W if necessary (see discussion of reshape of S below)
if self.cri.Cd == 1 and self.cri.C > 1:
# In most cases broadcasting rules make it possible for W
# to have a singleton dimension corresponding to a non-singleton
# dimension in S. However, when S is reshaped to interleave axisC
# and axisK on the same axis, broadcasting is no longer sufficient
# unless axisC and axisK of W are either both singleton or both
# of the same size as the corresponding axes of S. If neither of
# these cases holds, it is necessary to replicate the axis of W
# (axisC or axisK) that does not have the same size as the
# corresponding axis of S.
shpw = list(W.shape)
swck = shpw[self.cri.axisC] * shpw[self.cri.axisK]
if swck > 1 and swck < self.cri.C * self.cri.K:
if W.shape[self.cri.axisK] == 1 and self.cri.K > 1:
shpw[self.cri.axisK] = self.cri.K
else:
shpw[self.cri.axisC] = self.cri.C
W = np.broadcast_to(W, shpw)
self.W = W.reshape(
W.shape[0:self.cri.dimN] +
(1, W.shape[self.cri.axisC] * W.shape[self.cri.axisK], 1))
else:
self.W = W
# Call parent class __init__
Nx = self.cri.N * self.cri.Cd * self.cri.M
CK = (self.cri.C if self.cri.Cd == 1 else 1) * self.cri.K
shpY = list(self.cri.shpX)
shpY[self.cri.axisC] = self.cri.Cd
shpY[self.cri.axisK] = 1
shpY[self.cri.axisM] += CK
super(ConvCnstrMODMaskDcplBase, self).__init__(
Nx, shpY, self.cri.axisM, CK, S.dtype, opt)
# Reshape S to standard layout (Z, i.e. X in cbpdn, is assumed
# to be taken from cbpdn, and therefore already in standard
# form). If the dictionary has a single channel but the input
# (and therefore also the coefficient map array) has multiple
# channels, the channel index and multiple image index have
# the same behaviour in the dictionary update equation: the
# simplest way to handle this is to just reshape so that the
# channels also appear on the multiple image index.
if self.cri.Cd == 1 and self.cri.C > 1:
self.S = S.reshape(self.cri.Nv + (1, self.cri.C*self.cri.K, 1))
else:
self.S = S.reshape(self.cri.shpS)
self.S = np.asarray(self.S, dtype=self.dtype)
# Create constraint set projection function
self.Pcn = cr.getPcn(dsz, self.cri.Nv, self.cri.dimN, self.cri.dimCd,
zm=opt['ZeroMean'])
# Initialise byte-aligned arrays for pyfftw
self.YU = sl.pyfftw_empty_aligned(self.Y.shape, dtype=self.dtype)
xfshp = list(self.cri.Nv + (self.cri.Cd, 1, self.cri.M))
self.Xf = sl.pyfftw_rfftn_empty_aligned(xfshp, self.cri.axisN,
self.dtype)
if Z is not None:
self.setcoef(Z)