def test_02(self):
rho = 1e-1
opt = ccmod.ConvCnstrMOD_CG.Options({
'Verbose': False,
'MaxMainIter': 500,
'LinSolveCheck': True,
'ZeroMean': True,
'RelStopTol': 1e-5,
'rho': rho,
'AutoRho': {
'Enabled': False
},
'CG': {
'StopTol': 1e-5
}
})
Xr = self.X.reshape(self.X.shape[0:2] + (
1,
1,
) + self.X.shape[2:])
Sr = self.S.reshape(self.S.shape + (1, ))
c = ccmod.ConvCnstrMOD_CG(Xr, Sr, self.D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.Y, self.D0.shape).squeeze()
assert rrs(self.D0, D1) < 1e-4
assert np.array(c.getitstat().XSlvRelRes).max() < 1e-3
def test_18(self):
N = 64
M = 4
Nd = 8
D0 = cr.normalise(cr.zeromean(np.random.randn(Nd, Nd, M), (Nd, Nd, M),
dimN=2),
dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(ifftn(
fftn(D0, (N, N), (0, 1)) * fftn(X, None, (0, 1)), None,
(0, 1)).real,
axis=2)
L = 50.0
opt = ccmod.ConvCnstrMOD.Options({
'Verbose': False,
'MaxMainIter': 3000,
'ZeroMean': True,
'RelStopTol': 0.,
'L': L,
'Monotone': True
})
Xr = X.reshape(X.shape[0:2] + (
1,
1,
) + X.shape[2:])
Sr = S.reshape(S.shape + (1, ))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().Rsdl)[-1] < 1e-5
def test_13(self):
N = 64
M = 4
Nd = 8
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0, 1)) *
sl.fftn(X, None, (0, 1)), None, (0, 1)).real,
axis=2)
L = 0.5
opt = ccmod.ConvCnstrMOD.Options(
{'Verbose': False, 'MaxMainIter': 3000, 'ZeroMean': True,
'RelStopTol': 0., 'L': L, 'BackTrack': {'Enabled': True}})
Xr = X.reshape(X.shape[0:2] + (1, 1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert sl.rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().Rsdl)[-1] < 1e-5
def test_13(self):
dsz = ((8, 8, 16), (12, 12, 32))
u = np.zeros((24, 24, 48))
u[0:8, 0:8, 0:16] = 1.0
u[0:12, 0:12, 16:] = 1.0
v = cnvrep.bcrop(u, dsz)
assert v.shape == (12, 12, 48)
def test_13(self):
dsz = ((8, 8, 16), (12, 12, 32))
u = np.zeros((24, 24, 48))
u[0:8, 0:8, 0:16] = 1.0
u[0:12, 0:12, 16:] = 1.0
v = cnvrep.bcrop(u, dsz)
assert v.shape == (12, 12, 48)
def dictionary_learning_by_L1(X, S, dsz, G1, H1, G0, H0, parameter_rho_dic,
iteration, thr, cri):
Xf = con.convert_to_Xf(X, S, cri)
bar_D = tqdm(total=iteration, desc='D', leave=False)
for i in range(iteration):
D, G0, H0, XD = dictionary_learning_by_L1_Dstep(
Xf, S, G1, H1, G0, H0, parameter_rho_dic, i, cri, dsz)
Dr = np.asarray(D.reshape(cri.shpD), dtype=S.dtype)
H1r = np.asarray(H1.reshape(cri.shpD), dtype=S.dtype)
Pcn = cr.getPcn(dsz, cri.Nv, cri.dimN, cri.dimCd)
G1r = Pcn(Dr + H1r)
G1 = cr.bcrop(G1r, dsz, cri.dimN).squeeze()
H1 = H1 + D - G1
Est = sp.norm_l1(XD - S)
if (i == 0):
pre_Est = 1.1 * Est
if ((pre_Est - Est) / pre_Est <= thr):
bar_D.update(iteration - i)
break
pre_Est = Est
bar_D.update(1)
bar_D.close()
return D, G0, G1, H0, H1
def test_02(self):
N = 32
M = 4
Nd = 5
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0,1)) *
sl.fftn(X, None, (0,1)), None, (0,1)).real, axis=2)
rho = 1e-1
opt = ccmod.ConvCnstrMOD_CG.Options({'Verbose': False,
'MaxMainIter': 500, 'LinSolveCheck': True,
'ZeroMean': True, 'RelStopTol': 1e-5, 'rho': rho,
'AutoRho': {'Enabled': False},
'CG': {'StopTol': 1e-5}})
Xr = X.reshape(X.shape[0:2] + (1,1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD_CG(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.Y, D0.shape).squeeze()
assert(sl.rrs(D0, D1) < 1e-4)
assert(np.array(c.getitstat().XSlvRelRes).max() < 1e-3)
def test_02(self):
N = 32
M = 4
Nd = 5
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0, 1)) *
sl.fftn(X, None, (0, 1)), None, (0, 1)).real, axis=2)
rho = 1e-1
opt = ccmod.ConvCnstrMOD_CG.Options({'Verbose': False,
'MaxMainIter': 500, 'LinSolveCheck': True,
'ZeroMean': True, 'RelStopTol': 1e-5, 'rho': rho,
'AutoRho': {'Enabled': False},
'CG': {'StopTol': 1e-5}})
Xr = X.reshape(X.shape[0:2] + (1, 1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD_CG(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.Y, D0.shape).squeeze()
assert sl.rrs(D0, D1) < 1e-4
assert np.array(c.getitstat().XSlvRelRes).max() < 1e-3
def test_14(self):
dsz = (((5, 5, 2, 8), (7, 7, 1, 8)), ((9, 9, 2, 16), (10, 10, 1, 16)))
u = np.zeros((16, 16, 3, 24))
u[0:5, 0:5, 0:2, 0:8] = 1.0
u[0:7, 0:7, 2, 0:8] = 1.0
u[0:9, 0:9, 0:2, 8:] = 1.0
u[0:10, 0:10, 2, 8:] = 1.0
v = cnvrep.bcrop(u, dsz)
assert v.shape == (10, 10, 3, 24)
def getdict(self, crop=True):
"""Get final dictionary. If ``crop`` is ``True``, apply
:func:`.cnvrep.bcrop` to returned array.
"""
D = self.Y
if crop:
D = cr.bcrop(D, self.cri.dsz, self.cri.dimN)
return D
def getdict(self, crop=True):
"""Get final dictionary. If ``crop`` is ``True``, apply
:func:`.cnvrep.bcrop` to returned array.
"""
D = self.Y
if crop:
D = cr.bcrop(D, self.cri.dsz, self.cri.dimN)
return D
def test_14(self):
dsz = (((5, 5, 2, 8), (7, 7, 1, 8)),
((9, 9, 2, 16), (10, 10, 1, 16)))
u = np.zeros((16, 16, 3, 24))
u[0:5, 0:5, 0:2, 0:8] = 1.0
u[0:7, 0:7, 2, 0:8] = 1.0
u[0:9, 0:9, 0:2, 8:] = 1.0
u[0:10, 0:10, 2, 8:] = 1.0
v = cnvrep.bcrop(u, dsz)
assert v.shape == (10, 10, 3, 24)
def getdict(dsz):
if type(dsz[0]) is int:
return np.random.standard_normal(dsz)
else:
num_filt = 0
tmp = [0 for i in range(len(dsz[0]))]
for i in dsz:
i = list(i)
num_filt = num_filt + i[len(i) - 1]
if i[0] > tmp[0]:
tmp = i
tmp[len(tmp) - 1] = num_filt
return cnvrep.bcrop(np.random.standard_normal(tuple(tmp)), dsz)
def dictionary_learning_by_L2(X, S, dsz, G, H, parameter_rho_dic, iteration,
thr, cri):
Xf = con.convert_to_Xf(X, S, cri)
bar_D = tqdm(total=iteration, desc='D', leave=False)
for i in range(iteration):
Gf = con.convert_to_Df(G, S, cri)
Hf = con.convert_to_Df(H, S, cri)
GH = Gf - Hf
Sf = con.convert_to_Sf(S, cri)
b = parameter_rho_dic * GH + sl.inner(np.conj(Xf), Sf, cri.axisK)
Df = sl.solvemdbi_ism(Xf, parameter_rho_dic, b, cri.axisM, cri.axisK)
D = con.convert_to_D(Df, dsz, cri)
XfDf = np.sum(Xf * Df, axis=cri.axisM)
XD = con.convert_to_S(XfDf, cri)
Dr = np.asarray(D.reshape(cri.shpD), dtype=S.dtype)
Hr = np.asarray(H.reshape(cri.shpD), dtype=S.dtype)
Pcn = cr.getPcn(dsz, cri.Nv, cri.dimN, cri.dimCd)
Gr = Pcn(Dr + Hr)
G = cr.bcrop(Gr, dsz, cri.dimN).squeeze()
H = H + D - G
Est = sp.norm_2l2(XD - S)
if (i == 0):
pre_Est = 1.1 * Est
if ((pre_Est - Est) / pre_Est <= thr):
bar_D.update(iteration - i)
break
pre_Est = Est
bar_D.update(1)
bar_D.close()
return D, G, H
def test_02(self):
N = 32
M = 4
Nd = 5
D0 = cr.normalise(cr.zeromean(
np.random.randn(Nd, Nd, M), (Nd, Nd, M), dimN=2), dimN=2)
X = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X[xp] = np.random.randn(X[xp].size)
S = np.sum(sl.ifftn(sl.fftn(D0, (N, N), (0,1)) *
sl.fftn(X, None, (0,1)), None, (0,1)).real, axis=2)
L = 1e1
opt = ccmod.ConvCnstrMOD.Options({'Verbose': False,
'MaxMainIter': 3000,
'ZeroMean': True, 'RelStopTol': 1e-6, 'L': L,
'BackTrack': {'Enabled': True}})
Xr = X.reshape(X.shape[0:2] + (1,1,) + X.shape[2:])
Sr = S.reshape(S.shape + (1,))
c = ccmod.ConvCnstrMOD(Xr, Sr, D0.shape, opt)
c.solve()
D1 = cr.bcrop(c.X, D0.shape).squeeze()
assert(sl.rrs(D0, D1) < 1e-4)
assert(np.array(c.getitstat().Rsdl)[-1] < 1e-5)
def convert_to_D(Df, dsz, cri):
D = sl.irfftn(Df, cri.Nv, cri.axisN)
D = cr.bcrop(D, dsz, cri.dimN).squeeze()
return D
def test_12(self):
dsz = (8, 8, 32)
u = np.zeros((16, 16, 32))
u[0:8, 0:8, 0:16] = 1.0
v = cnvrep.bcrop(u, dsz)
assert v.shape == dsz
def test_12(self):
dsz = (8, 8, 32)
u = np.zeros((16, 16, 32))
u[0:8, 0:8, 0:16] = 1.0
v = cnvrep.bcrop(u, dsz)
assert v.shape == dsz
