def test_02(self):
lmbda = 1e-1
opt = cbpdndl.ConvBPDNDictLearn.Options({'MaxMainIter': 10})
try:
b = cbpdndl.ConvBPDNDictLearn(self.D0, self.S, lmbda, opt=opt)
b.solve()
except Exception as e:
print(e)
assert 0
def nn_csc(input_, d_size, lmbda, Iter, visualize = False):
D0 = np.random.uniform(0, 1.0, d_size);
opt = cbpdndl.ConvBPDNDictLearn.Options({'Verbose': True, 'MaxMainIter': Iter,
'CBPDN': {'rho': 50.0*lmbda + 0.5, 'NonNegCoef': True},
'CCMOD': {'rho': 10.0, 'ZeroMean': True}},
dmethod='cns');
d = cbpdndl.ConvBPDNDictLearn(D0, input_, lmbda, opt, dmethod='cns');
D1 = d.solve();
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'));
return d, D1, d.getcoef();
def sporcosolve(cri, Dr0, Sr, maxitr=200):
Dr0 = Dr0.copy()
Sr = Sr.copy()
lmbda = 0.2
opt = cbpdndl.ConvBPDNDictLearn.Options({'Verbose': True, 'MaxMainIter': maxitr,
'CBPDN': {'rho': 50.0*lmbda + 0.5},
'CCMOD': {'rho': 10.0, 'ZeroMean': True}},
dmethod='cns')
d = cbpdndl.ConvBPDNDictLearn(Dr0.squeeze(), Sr.squeeze(), lmbda, opt, dmethod='cns')
Dr = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
return Dr
def test_05(self):
N = 16
Nc = 3
Nd = 5
M = 4
K = 3
D0 = np.random.randn(Nd, Nd, Nc, M)
S = np.random.randn(N, N, Nc, K)
lmbda = 1e-1
opt = cbpdndl.ConvBPDNDictLearn.Options({'MaxMainIter': 10})
try:
b = cbpdndl.ConvBPDNDictLearn(D0, S, lmbda, opt=opt)
b.solve()
except Exception as e:
print(e)
assert 0
def test_09(self):
lmbda = 1e-1
opt = cbpdndl.ConvBPDNDictLearn.Options({'MaxMainIter': 10},
xmethod='fista',
dmethod='cns')
try:
b = cbpdndl.ConvBPDNDictLearn(self.D0,
self.S,
lmbda,
opt=opt,
xmethod='fista',
dmethod='cns')
b.solve()
except Exception as e:
print(e)
assert 0
def test_05(self):
lmbda = 1e-1
Nit = 10
opts = cbpdndl.ConvBPDNDictLearn.Options(
{
'MaxMainIter': Nit,
'AccurateDFid': True,
'CBPDN': {
'RelaxParam': 1.0,
'AutoRho': {
'Enabled': False
}
},
'CCMOD': {
'RelaxParam': 1.0,
'AutoRho': {
'Enabled': False
}
}
},
dmethod='cns')
bs = cbpdndl.ConvBPDNDictLearn(self.D0,
self.S,
lmbda,
opt=opts,
dmethod='cns')
Ds = bs.solve()
optp = prlcnscdl.ConvBPDNDictLearn_Consensus.Options({
'MaxMainIter': Nit,
'CBPDN': {
'RelaxParam': 1.0
},
'CCMOD': {
'RelaxParam': 1.0
}
})
bp = prlcnscdl.ConvBPDNDictLearn_Consensus(self.D0,
self.S,
lmbda,
opt=optp,
nproc=2)
Dp = bp.solve()
assert np.linalg.norm(Ds - Dp) < 1e-7
assert np.abs(
bs.getitstat().ObjFun[-1] - bp.getitstat().ObjFun[-1] < 1e-7)
def findDictionary1(self, epochs):
#Creazione di un dizionario random
np.random.seed(12345)
D0 = np.random.randn(self.dimFilter, self.dimFilter, self.nFilters)
lmbda = 0.2
L_sc = 360.0
L_du = 50.0
opt = cbpdndl.ConvBPDNDictLearn.Options(
{
'Verbose': True,
'MaxMainIter': epochs,
'DictSize': D0.shape,
'CBPDN': {
'BackTrack': {
'Enabled': True
},
'L': L_sc
},
'CCMOD': {
'BackTrack': {
'Enabled': True
},
'L': L_du
}
},
xmethod='fista',
dmethod='fista')
d = cbpdndl.ConvBPDNDictLearn(D0,
self.imgs,
lmbda,
opt,
xmethod='fista',
dmethod='fista')
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
D1 = D1.squeeze()
self.D = D1
self.coef = d.getcoef()
self.rec = d.reconstruct().squeeze()
self.d = d #riferimento alla libreria
def findDictionary(self, epochs):
# #Filtro di tikhonov
# npd = 16
# fltlmbd = 5
# sl, sh = util.tikhonov_filter(self.imgs, fltlmbd, npd)
# #plot.imview(sl[:,:,1],title='IMG low');
#Creazione di un dizionario random
np.random.seed(12345)
D0 = np.random.randn(self.dimFilter, self.dimFilter, self.nFilters)
#Opzioni per l'algoritmo DL e Soluzione del problema
# lmbda = 0.1
lmbda = 0.1
opt = cbpdndl.ConvBPDNDictLearn.Options(
{
'Verbose': True,
'MaxMainIter': epochs,
'CBPDN': {
'rho': 50.0 * lmbda + 0.5
},
'CCMOD': {
'rho': 10.0,
'ZeroMean': True
}
},
dmethod='cns')
d = cbpdndl.ConvBPDNDictLearn(D0, self.imgs, lmbda, opt, dmethod='cns')
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
D1 = D1.squeeze()
self.D = D1
self.coef = d.getcoef()
self.rec = d.reconstruct().squeeze()
self.d = d #riferimento alla libreria
def train_batch_model(D0, train_blob, opt, args):
"""Train for batch dictlrn model."""
logger = logging.getLogger(__name__)
dname = args.dataset if not args.use_gray else args.dataset + '.gray'
if os.path.exists(os.path.join(args.output_path, 'iter_record.mat')):
iter_record = sio.loadmat(
os.path.join(args.output_path, 'iter_record.mat'))['iter_record']
selected = list(set(iter_record.ravel().tolist()))
if args.batch_max_samples > 0 and args.batch_max_samples < len(
selected):
selected = selected[:args.batch_max_samples]
if len(selected) < train_blob.shape[-1]:
logger.info(
'Selected %d -> %d train samples for training batch model',
train_blob.shape[-1], len(selected))
train_blob = train_blob[..., selected]
if not args.no_tikhonov_filter:
# fix lambda to be 5
_, train_blob = su.tikhonov_filter(train_blob, 5.)
path = os.path.join(args.output_path, 'ConvBPDNDictLearn')
if not os.path.exists(path):
os.makedirs(path)
def _callback(d):
"""Snapshot dictionaries for every iteration."""
_D = d.getdict().squeeze()
np.save(os.path.join(path, '{}.{}.npy'.format(dname, d.j)), _D)
return 0
opt['Callback'] = _callback
solver = cbpdndl.ConvBPDNDictLearn(D0,
train_blob,
args.lmbda,
opt=opt,
xmethod='admm',
dmethod='cns')
solver.solve()
return solver
def csc(input_, d_size, lmbda, Iter, visualize=False):
D0 = np.random.uniform(-1.0, 1.0, d_size)
opt = cbpdndl.ConvBPDNDictLearn.Options(
{
'Verbose': True,
'MaxMainIter': Iter,
'CBPDN': {
'rho': 50.0 * lmbda + 0.5
},
'CCMOD': {
'rho': 10.0,
'ZeroMean': True
}
},
dmethod='cns')
d = cbpdndl.ConvBPDNDictLearn(D0, input_, lmbda, opt, dmethod='cns')
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
if visualize:
plot_2([util.tiledict(D0.squeeze()),
util.tiledict(D1.squeeze())],
["initial dictionary", "learned dictionary"])
return d, D1, d.getcoef()
def learn_dict(signal, atom_length, n_atoms, rng, penalty=PENALTY):
"""
Return the learned dictionaty
:param signal: 2d_array with shape (n_time_steps, n_dims)
:param atom_length: int, length of an atom
:param n_atoms: int, number of atoms
:param rng: random number generator
:param penalty: float, sparsity penalty (Convolutional dictionary learning)
"""
opt_dl = get_opt_dl(penalty)
if signal.ndim == 1:
signal = atleast_2d(signal)
dimK = signal.shape[1]
return cbpdndl.ConvBPDNDictLearn(
D0=rng.randn(atom_length, dimK, n_atoms), # random init; set to 2 here for multidimensionality
S=signal, # signal at hand
dimK=dimK, # set to 2 here for multidimensionality
lmbda=penalty, # sparsity penalty
opt=opt_dl, # options for the optimizations
xmethod="admm", # optimization method (sparse coding)
dmethod="cns", # optimization method (dict learnin)
).solve()
D0 = np.random.randn(10, 10, 32)
lmbda = 0.1
opt = cbpdndl.ConvBPDNDictLearn.Options(
{
'Verbose': True,
'MaxMainIter': 200,
'CBPDN': {
'rho': 50.0 * lmbda + 0.5
},
'CCMOD': {
'rho': 10.0,
'ZeroMean': True
}
},
dmethod='cns')
d = cbpdndl.ConvBPDNDictLearn(D0, sh, lmbda, opt, dmethod='cns')
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), title='D0', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1), title='D1', fig=fig)
fig.show()
its = d.getitstat()
fig = plot.figure(figsize=(20, 5))
plot.subplot(1, 3, 1)
plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)
plot.subplot(1, 3, 2)
plot.plot(np.vstack((its.XPrRsdl, its.XDlRsdl, its.DPrRsdl, its.DDlRsdl)).T,
'Enabled': True
}
},
'CCMOD': {
'rho': 1e2,
'AutoRho': {
'Enabled': True
}
}
},
dmethod='cns')
"""
Create solver object and solve.
"""
d = cbpdndl.ConvBPDNDictLearn(D0, vh, lmbda, opt, dimK=0, dimN=3)
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
"""
Display initial and final dictionaries: central temporal slice
"""
D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0[..., 2, :]), fig=fig, title='D0')
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1[..., 2, :]), fig=fig, title='D1')
fig.show()
"""
Display initial and final dictionaries: central spatial vertical slice
lmbda = 0.2
L_sc = 360.0
L_du = 50.0
dsz = ((8, 8, 3, 32), (12, 12, 3, 32), (16, 16, 3, 32))
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': True, 'MaxMainIter': 200, 'DictSize': dsz,
'CBPDN': {'BackTrack': {'Enabled': True }, 'L': L_sc},
'CCMOD': {'BackTrack': {'Enabled': True }, 'L': L_du}},
xmethod='fista', dmethod='fista')
"""
Create solver object and solve.
"""
d = cbpdndl.ConvBPDNDictLearn(D0, sh, lmbda, opt, xmethod='fista',
dmethod='fista')
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
"""
Display initial and final dictionaries.
"""
D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), title='D0', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1, dsz), title='D1', fig=fig)
fig.show()
def gengraphs(pth, nopyfftw):
"""
Generate call graph images when necessary. Parameter pth is the path
to the directory in which images are to be created. Parameter nopyfftw
is a flag indicating whether it is necessary to avoid using pyfftw.
"""
srcmodflt = '^sporco.admm'
srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
fnmsub = ('^sporco.admm.', '')
grpflt = r'^[^\.]*.[^\.]*'
lnkpfx = '../../modules/'
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.admm.\1.html#sporco.admm.\1.\2\4')
fntsz = 9
fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans'
kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True}
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
# Make destination directory if it doesn't exist
if not os.path.exists(pth):
os.makedirs(pth, exist_ok=True)
# Handle environment in which pyfftw is unavailable
if nopyfftw:
import numpy.fft as npfft
import sporco.linalg as spl
def empty(shape, dtype, order='C', n=None):
return np.zeros(shape, dtype=dtype)
empty.__doc__ = spl.pyfftw_empty_aligned.__doc__
spl.pyfftw_empty_aligned = empty
def rfftn_empty(shape, axes, dtype, order='C', n=None):
ashp = list(shape)
raxis = axes[-1]
ashp[raxis] = ashp[raxis] // 2 + 1
cdtype = spl.complex_dtype(dtype)
return np.zeros(ashp, dtype=cdtype)
rfftn_empty.__doc__ = spl.pyfftw_rfftn_empty_aligned.__doc__
spl.pyfftw_rfftn_empty_aligned = rfftn_empty
npfft.fftn.__doc__ = spl.fftn.__doc__
spl.fftn = npfft.fftn
npfft.ifftn.__doc__ = spl.ifftn.__doc__
spl.ifftn = npfft.ifftn
npfft.rfftn.__doc__ = spl.rfftn.__doc__
spl.rfftn = npfft.rfftn
npfft.irfftn.__doc__ = spl.irfftn.__doc__
spl.irfftn = npfft.irfftn
import numpy as np
np.random.seed(12345)
#### bpdn module
from sporco.admm import bpdn
mdnm = 'sporco.admm.bpdn'
D = np.random.randn(8, 16)
s = np.random.randn(8, 1)
lmbda = 0.1
## BPDN class
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs):
b = bpdn.BPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs):
b.solve()
## BPDNJoint class
opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs):
b = bpdn.BPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs):
b.solve()
## ElasticNet class
opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs):
b = bpdn.ElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs):
b.solve()
# BPDNProjL1 class
opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 2.0
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs):
b = bpdn.BPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs):
b.solve()
## MinL1InL2Ball class
opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 1.0
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs):
b = bpdn.MinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs):
b.solve()
#### cbpdn module
from sporco.admm import cbpdn
mdnm = 'sporco.admm.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs):
b.solve()
## ConvBPDNJoint class
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs):
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs):
b.solve()
## ConvElasticNet class
opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs):
b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs):
b.solve()
## ConvBPDNGradReg class
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs):
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs):
b.solve()
## ConvBPDNProjL1 class
opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs):
b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs):
b.solve()
## ConvMinL1InL2Ball class
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs):
b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcpl class
opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1})
W = np.ones(s.shape)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs):
b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs):
b.solve()
## ConvL1L1Grd class
opt = cbpdn.ConvL1L1Grd.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 1e-2
with CallGraph(ct, mdnm, pth, 'cl1l1grd_init.svg', **kwargs):
b = cbpdn.ConvL1L1Grd(D, s, lmbda, mu, W, opt)
with CallGraph(ct, mdnm, pth, 'cl1l1grd_solve.svg', **kwargs):
b.solve()
#### cbpdntv module
from sporco.admm import cbpdntv
mdnm = 'sporco.admm.cbpdntv'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
## ConvBPDNScalarTV class
opt = cbpdntv.ConvBPDNScalarTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs):
b.solve()
## ConvBPDNVectorTV class
opt = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs):
b.solve()
## ConvBPDNRecTV class
opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs):
b.solve()
#### cmod module
from sporco.admm import cmod
mdnm = 'sporco.admm.cmod'
X = np.random.randn(8, 16)
S = np.random.randn(8, 16)
## CnstrMOD class
opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs):
b = cmod.CnstrMOD(X, S, opt=opt)
with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs):
b.solve()
#### ccmod module
from sporco.admm import ccmod
mdnm = 'sporco.admm.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD_IterSM class
opt = ccmod.ConvCnstrMOD_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_CG class
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_Consensus class
opt = ccmod.ConvCnstrMOD_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs):
b.solve()
#### ccmodmd module
from sporco.admm import ccmodmd
mdnm = 'sporco.admm.ccmodmd'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
W = np.array([1.0])
dsz = (4, 4, 1)
## ConvCnstrMODMaskDcpl_IterSM class
opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_CG class
opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_Consensus class
opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs):
b.solve()
#### tvl1 module
from sporco.admm import tvl1
mdnm = 'sporco.admm.tvl1'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL1Denoise class
opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs):
b = tvl1.TVL1Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs):
b.solve()
## TVL1Deconv class
opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs):
b = tvl1.TVL1Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs):
b.solve()
#### tvl2 module
from sporco.admm import tvl2
mdnm = 'sporco.admm.tvl2'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL2Denoise class
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs):
b = tvl2.TVL2Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs):
b.solve()
## TVL2Deconv class
opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs):
b = tvl2.TVL2Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.fista'
fnmsub = ('^sporco.fista.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.fista.\1.html#sporco.fista.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### fista.cbpdn module
from sporco.fista import cbpdn
mdnm = 'sporco.fista.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs):
b.solve()
#### fista.ccmod module
from sporco.fista import ccmod
mdnm = 'sporco.fista.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD class
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMask class
opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.dictlrn'
fnmsub = ('^sporco.dictlrn.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.dictlrn.\1.html#sporco.dictlrn.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### bpdndl module
from sporco.dictlrn import bpdndl
mdnm = 'sporco.dictlrn.bpdndl'
D0 = np.random.randn(8, 8)
S = np.random.randn(8, 16)
lmbda = 0.1
## BPDNDictLearn class
opt = bpdndl.BPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs):
b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndl module
from sporco.dictlrn import cbpdndl
mdnm = 'sporco.dictlrn.cbpdndl'
D0 = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8, 10)
lmbda = 0.1
## ConvBPDNDictLearn class
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndlmd module
from sporco.dictlrn import cbpdndlmd
mdnm = 'sporco.dictlrn.cbpdndlmd'
## ConvBPDNMaskDcplDictLearn class
W = np.array([1.0])
opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs):
b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs):
b.solve()
def gengraphs(pth):
"""
Generate call graph images when necessary. Parameter pth is the path
to the directory in which images are to be created.
"""
srcmodflt = '^sporco.admm'
srcqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
dstqnmflt = r'^((?!<locals>|__new|_name_nested).)*$'
fnmsub = ('^sporco.admm.', '')
grpflt = r'^[^\.]*.[^\.]*'
lnkpfx = '../../modules/'
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.admm.\1.html#sporco.admm.\1.\2\4')
fntsz = 9
fntfm = 'Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans'
kwargs = {'fntsz': fntsz, 'fntfm': fntfm, 'rmsz': True}
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
# Make destination directory if it doesn't exist
if not os.path.exists(pth):
os.makedirs(pth, exist_ok=True)
import numpy as np
np.random.seed(12345)
#### bpdn module
from sporco.admm import bpdn
mdnm = 'sporco.admm.bpdn'
D = np.random.randn(8, 16)
s = np.random.randn(8, 1)
lmbda = 0.1
## BPDN class
opt = bpdn.BPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'bpdn_init.svg', **kwargs):
b = bpdn.BPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdn_solve.svg', **kwargs):
b.solve()
## BPDNJoint class
opt = bpdn.BPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'bpdnjnt_init.svg', **kwargs):
b = bpdn.BPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'bpdnjnt_solve.svg', **kwargs):
b.solve()
## ElasticNet class
opt = bpdn.ElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'elnet_init.svg', **kwargs):
b = bpdn.ElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'elnet_solve.svg', **kwargs):
b.solve()
# BPDNProjL1 class
opt = bpdn.BPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 2.0
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_init.svg', **kwargs):
b = bpdn.BPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'bpdnprjl1_solve.svg', **kwargs):
b.solve()
## MinL1InL2Ball class
opt = bpdn.MinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 1.0
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_init.svg', **kwargs):
b = bpdn.MinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'bpdnml1l2_solve.svg', **kwargs):
b.solve()
#### cbpdn module
from sporco.admm import cbpdn
mdnm = 'sporco.admm.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdn_solve.svg', **kwargs):
b.solve()
## ConvBPDNJoint class
opt = cbpdn.ConvBPDNJoint.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 0.01
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_init.svg', **kwargs):
b = cbpdn.ConvBPDNJoint(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnjnt_solve.svg', **kwargs):
b.solve()
## ConvElasticNet class
opt = cbpdn.ConvElasticNet.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'celnet_init.svg', **kwargs):
b = cbpdn.ConvElasticNet(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'celnet_solve.svg', **kwargs):
b.solve()
## ConvBPDNGradReg class
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdngrd_init.svg', **kwargs):
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdngrd_solve.svg', **kwargs):
b.solve()
## ConvBPDNProjL1 class
opt = cbpdn.ConvBPDNProjL1.Options({'Verbose': False, 'MaxMainIter': 1})
gamma = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_init.svg', **kwargs):
b = cbpdn.ConvBPDNProjL1(D, s, gamma, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnprjl1_solve.svg', **kwargs):
b.solve()
## ConvMinL1InL2Ball class
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False, 'MaxMainIter': 1})
epsilon = 0.5
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_init.svg', **kwargs):
b = cbpdn.ConvMinL1InL2Ball(D, s, epsilon, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnml1l2_solve.svg', **kwargs):
b.solve()
## ConvBPDNMaskDcpl class
opt = cbpdn.ConvBPDNMaskDcpl.Options({'Verbose': False, 'MaxMainIter': 1})
W = np.ones(s.shape)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_init.svg', **kwargs):
b = cbpdn.ConvBPDNMaskDcpl(D, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmd_solve.svg', **kwargs):
b.solve()
## ConvL1L1Grd class
opt = cbpdn.ConvL1L1Grd.Options({'Verbose': False, 'MaxMainIter': 1})
mu = 1e-2
with CallGraph(ct, mdnm, pth, 'cl1l1grd_init.svg', **kwargs):
b = cbpdn.ConvL1L1Grd(D, s, lmbda, mu, W, opt)
with CallGraph(ct, mdnm, pth, 'cl1l1grd_solve.svg', **kwargs):
b.solve()
#### cbpdntv module
from sporco.admm import cbpdntv
mdnm = 'sporco.admm.cbpdntv'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
## ConvBPDNScalarTV class
opt = cbpdntv.ConvBPDNScalarTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnstv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNScalarTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnstv_solve.svg', **kwargs):
b.solve()
## ConvBPDNVectorTV class
opt = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNVectorTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnvtv_solve.svg', **kwargs):
b.solve()
## ConvBPDNRecTV class
opt = cbpdntv.ConvBPDNRecTV.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_init.svg', **kwargs):
b = cbpdntv.ConvBPDNRecTV(D, s, lmbda, mu, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnrtv_solve.svg', **kwargs):
b.solve()
#### cbpdnin module
from sporco.admm import cbpdnin
mdnm = 'sporco.admm.cbpdnin'
D = np.random.randn(4, 4, 32)
s = np.random.randn(8, 8)
lmbda = 0.1
mu = 0.01
Wg = np.append(np.eye(16), np.eye(16), axis=-1)
## ConvBPDNInhib class
opt = cbpdnin.ConvBPDNInhib.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cbpdnin_init.svg', **kwargs):
b = cbpdnin.ConvBPDNInhib(D,
s,
Wg,
Whn=4,
lmbda=lmbda,
mu=mu,
gamma=None,
opt=opt)
with CallGraph(ct, mdnm, pth, 'cbpdnin_solve.svg', **kwargs):
b.solve()
#### cmod module
from sporco.admm import cmod
mdnm = 'sporco.admm.cmod'
X = np.random.randn(8, 16)
S = np.random.randn(8, 16)
## CnstrMOD class
opt = cmod.CnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'cmod_init.svg', **kwargs):
b = cmod.CnstrMOD(X, S, opt=opt)
with CallGraph(ct, mdnm, pth, 'cmod_solve.svg', **kwargs):
b.solve()
#### ccmod module
from sporco.admm import ccmod
mdnm = 'sporco.admm.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD_IterSM class
opt = ccmod.ConvCnstrMOD_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodism_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_IterSM(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_CG class
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodcg_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_CG(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMOD_Consensus class
opt = ccmod.ConvCnstrMOD_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD_Consensus(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodcnsns_solve.svg', **kwargs):
b.solve()
#### ccmodmd module
from sporco.admm import ccmodmd
mdnm = 'sporco.admm.ccmodmd'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
W = np.array([1.0])
dsz = (4, 4, 1)
## ConvCnstrMODMaskDcpl_IterSM class
opt = ccmodmd.ConvCnstrMODMaskDcpl_IterSM.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdism_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_IterSM(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdism_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_CG class
opt = ccmodmd.ConvCnstrMODMaskDcpl_CG.Options({
'Verbose': False,
'MaxMainIter': 1,
'CG': {
'MaxIter': 1
}
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_CG(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcg_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMaskDcpl_Consensus class
opt = ccmodmd.ConvCnstrMODMaskDcpl_Consensus.Options({
'Verbose': False,
'MaxMainIter': 1
})
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_init.svg', **kwargs):
b = ccmodmd.ConvCnstrMODMaskDcpl_Consensus(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdcnsns_solve.svg', **kwargs):
b.solve()
#### tvl1 module
from sporco.admm import tvl1
mdnm = 'sporco.admm.tvl1'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL1Denoise class
opt = tvl1.TVL1Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl1den_init.svg', **kwargs):
b = tvl1.TVL1Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1den_solve.svg', **kwargs):
b.solve()
## TVL1Deconv class
opt = tvl1.TVL1Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_init.svg', **kwargs):
b = tvl1.TVL1Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl1dcn_solve.svg', **kwargs):
b.solve()
#### tvl2 module
from sporco.admm import tvl2
mdnm = 'sporco.admm.tvl2'
s = np.random.randn(16, 16)
lmbda = 0.1
## TVL2Denoise class
opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'tvl2den_init.svg', **kwargs):
b = tvl2.TVL2Denoise(s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2den_solve.svg', **kwargs):
b.solve()
## TVL2Deconv class
opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 1})
h = np.random.randn(3, 3)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_init.svg', **kwargs):
b = tvl2.TVL2Deconv(h, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'tvl2dcn_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.fista'
fnmsub = ('^sporco.fista.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.fista.\1.html#sporco.fista.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### fista.cbpdn module
from sporco.fista import cbpdn
mdnm = 'sporco.fista.cbpdn'
D = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8)
lmbda = 0.1
## ConvBPDN class
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_init.svg', **kwargs):
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'fista_cbpdn_solve.svg', **kwargs):
b.solve()
#### fista.ccmod module
from sporco.fista import ccmod
mdnm = 'sporco.fista.ccmod'
X = np.random.randn(8, 8, 1, 2, 1)
S = np.random.randn(8, 8, 2)
dsz = (4, 4, 1)
## ConvCnstrMOD class
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMOD(X, S, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodfista_solve.svg', **kwargs):
b.solve()
## ConvCnstrMODMask class
opt = ccmod.ConvCnstrMODMask.Options({'Verbose': False, 'MaxMainIter': 1})
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_init.svg', **kwargs):
b = ccmod.ConvCnstrMODMask(X, S, W, dsz=dsz, opt=opt)
with CallGraph(ct, mdnm, pth, 'ccmodmdfista_solve.svg', **kwargs):
b.solve()
srcmodflt = '^sporco.dictlrn'
fnmsub = ('^sporco.dictlrn.', '')
lnksub = (r'^([^\.]*).([^\.]*)(?:(.__init__|.__call__)|(.[^\.]*))',
lnkpfx + r'sporco.dictlrn.\1.html#sporco.dictlrn.\1.\2\4')
ct = jonga.CallTracer(srcmodflt=srcmodflt,
srcqnmflt=srcqnmflt,
dstqnmflt=dstqnmflt,
fnmsub=fnmsub,
grpflt=grpflt,
lnksub=lnksub)
#### bpdndl module
from sporco.dictlrn import bpdndl
mdnm = 'sporco.dictlrn.bpdndl'
D0 = np.random.randn(8, 8)
S = np.random.randn(8, 16)
lmbda = 0.1
## BPDNDictLearn class
opt = bpdndl.BPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'bpdndl_init.svg', **kwargs):
b = bpdndl.BPDNDictLearn(D0, S, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'bpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndl module
from sporco.dictlrn import cbpdndl
mdnm = 'sporco.dictlrn.cbpdndl'
D0 = np.random.randn(4, 4, 16)
s = np.random.randn(8, 8, 10)
lmbda = 0.1
## ConvBPDNDictLearn class
opt = cbpdndl.ConvBPDNDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdndl_init.svg', **kwargs):
b = cbpdndl.ConvBPDNDictLearn(D0, s, lmbda, opt)
with CallGraph(ct, mdnm, pth, 'cbpdndl_solve.svg', **kwargs):
b.solve()
#### cbpdndlmd module
from sporco.dictlrn import cbpdndlmd
mdnm = 'sporco.dictlrn.cbpdndlmd'
## ConvBPDNMaskDcplDictLearn class
W = np.array([1.0])
opt = cbpdndlmd.ConvBPDNMaskDictLearn.Options({
'Verbose': False,
'MaxMainIter': 1,
'AccurateDFid': True
})
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_init.svg', **kwargs):
b = cbpdndlmd.ConvBPDNMaskDictLearn(D0, s, lmbda, W, opt)
with CallGraph(ct, mdnm, pth, 'cbpdnmddl_solve.svg', **kwargs):
b.solve()
# training, offline learning
np.random.seed(0)
s = np.transpose(imgs[..., 0], (1, 2, 0))
sl, sh = util.tikhonov_filter(s, args.lmbdaPre)
D0 = np.random.normal(size = args.kernelSize + [args.nChannels]).astype(np.float32)
opt = cbpdndl.ConvBPDNDictLearn.Options({'Verbose': True, 'MaxMainIter': args.nPreIter,
'CBPDN': {'rho': 100*args.lmbda + 1,
'AutoRho': {'Enabled': True},
'RelaxParam': 1.8,
'RelStopTol': 1e-7},
'CCMOD': {'rho': 10.0, 'ZeroMean': False}},
dmethod='cns')
learner = cbpdndl.ConvBPDNDictLearn(D0, sh, args.lmbda, opt, dmethod='cns')
learner.solve()
D1 = learner.getdict()
D1 = D1.squeeze()
if not os.path.exists(os.path.dirname(args.checkPoint)):
os.makedirs(os.path.dirname(args.checkPoint))
np.save(args.checkPoint, D1)
# In[21]:
if showPlots:
plt.imshow(sporco.util.tiledict(D1))
'CBPDN': {
'Backtrack': BacktrackStandard(gamma_u=1.1),
'L': L_sc
},
'CCMOD': {
'Backtrack': BacktrackStandard(),
'L': L_du
}
},
xmethod='pgm',
dmethod='pgm')
"""
Create solver object and solve.
"""
d = cbpdndl.ConvBPDNDictLearn(D0, sh, lmbda, opt, xmethod='pgm', dmethod='pgm')
D1 = d.solve()
print("ConvBPDNDictLearn solve time: %.2fs" % d.timer.elapsed('solve'))
"""
Display initial and final dictionaries.
"""
D1 = D1.squeeze()
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), title='D0', fig=fig)
plot.subplot(1, 2, 2)
plot.imview(util.tiledict(D1, dsz), title='D1', fig=fig)
fig.show()
"""
Get iterations statistics from solver object and plot functional value, residuals, and automatically adjusted gradient step parameters against the iteration number.
