def test_10cplx(self):
N = 64
M = 4
Nd = 8
D = np.random.randn(Nd, Nd, M) + 1j * np.random.randn(Nd, Nd, M)
X0 = np.zeros((N, N, M)) + 1j * np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X0[xp] = (np.random.randn(X0[xp].size) +
1j * np.random.randn(X0[xp].size))
S = np.sum(fftconv(D, X0, axes=(0, 1)), axis=2)
lmbda = 1e-4
rho = 1e-1
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 500,
'RelStopTol': 1e-3,
'rho': rho,
'AutoRho': {
'Enabled': False
}
})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.Y.squeeze()
assert rrs(X0, X1) < 5e-5
Sr = b.reconstruct().squeeze()
assert rrs(S, Sr) < 1e-4
def test_11(self):
N = 63
M = 4
Nd = 8
D = np.random.randn(Nd, Nd, M)
X0 = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X0[xp] = np.random.randn(X0[xp].size)
S = np.sum(sl.ifftn(
sl.fftn(D, (N, N), (0, 1)) * sl.fftn(X0, None, (0, 1)), None,
(0, 1)).real,
axis=2)
lmbda = 1e-4
rho = 1e-1
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 500,
'RelStopTol': 1e-3,
'rho': rho,
'AutoRho': {
'Enabled': False
}
})
b = cbpdn.ConvBPDN(D, S, lmbda, opt)
b.solve()
X1 = b.Y.squeeze()
assert sl.rrs(X0, X1) < 5e-5
Sr = b.reconstruct().squeeze()
assert sl.rrs(S, Sr) < 1e-4
def xstep(self, S, lmbda, dimK):
"""Solve CSC problem for training data `S`."""
if self.opt['CUDA_CBPDN']:
Z = np.stack([
cucbpdn.cbpdn(self.D.squeeze(), S[..., i], lmbda,
self.opt['CBPDN']) for i in range(S.shape[-1])
], axis=-2)
Z = Z.reshape(self.cri.Nv + (1, self.cri.K, self.cri.M,))
self.Z[:] = np.asarray(Z, dtype=self.dtype)
self.Zf = sl.rfftn(self.Z, self.cri.Nv, self.cri.axisN)
self.Sf = sl.rfftn(S.reshape(self.cri.shpS), self.cri.Nv,
self.cri.axisN)
self.xstep_itstat = None
elif self.opt['PAR_CBPDN']:
popt = parcbpdn.ParConvBPDN.Options(dict(self.opt['CBPDN']))
xstep = parcbpdn.ParConvBPDN(self.D.squeeze(), S, lmbda, opt=popt,
dimK=dimK, dimN=self.cri.dimN)
xstep.solve()
self.Sf = xstep.Sf
self.setcoef(xstep.getcoef())
self.xstep_itstat = xstep.itstat[-1] if len(xstep.itstat) > 0 \
else None
else:
# Create X update object (external representation is expected!)
xstep = cbpdn.ConvBPDN(self.D.squeeze(), S, lmbda,
self.opt['CBPDN'], dimK=dimK,
dimN=self.cri.dimN)
xstep.solve()
self.Sf = xstep.Sf
self.setcoef(xstep.getcoef())
self.xstep_itstat = xstep.itstat[-1] if len(xstep.itstat) > 0 \
else None
def xstep(self, S, lmbda, dimK):
"""Solve CSC problem for training data `S`."""
if self.opt['CUDA_CBPDN']:
Z = cuda.cbpdn(self.D.squeeze(), S[..., 0], lmbda,
self.opt['CBPDN'])
Z = Z.reshape(self.cri.Nv + (
1,
1,
self.cri.M,
))
self.Z[:] = np.asarray(Z, dtype=self.dtype)
self.Zf = sl.rfftn(self.Z, self.cri.Nv, self.cri.axisN)
self.Sf = sl.rfftn(S.reshape(self.cri.shpS), self.cri.Nv,
self.cri.axisN)
self.xstep_itstat = None
else:
# Create X update object (external representation is expected!)
xstep = cbpdn.ConvBPDN(self.D.squeeze(),
S,
lmbda,
self.opt['CBPDN'],
dimK=dimK,
dimN=self.cri.dimN)
xstep.solve()
self.Sf = xstep.Sf
self.setcoef(xstep.getcoef())
self.xstep_itstat = xstep.itstat[-1] if xstep.itstat else None
def test_22(self):
N = 32
M = 4
Nd = 8
D = np.random.randn(Nd, Nd, M)
D /= np.sqrt(np.sum(D**2, axis=(0, 1)))
X0 = np.zeros((N, N, M))
xr = np.random.randn(N, N, M)
xp = np.abs(xr) > 3
X0[xp] = np.random.randn(X0[xp].size)
S = np.sum(sl.fftconv(D, X0), axis=2)
lmbda = 1e-3
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 500,
'RelStopTol': 1e-5, 'rho': 5e-1,
'AutoRho': {'Enabled': False}})
bp = cbpdn.ConvBPDN(D, S, lmbda, opt)
Xp = bp.solve()
epsilon = np.linalg.norm(bp.reconstruct(Xp).squeeze() - S)
opt = cbpdn.ConvMinL1InL2Ball.Options({'Verbose': False,
'MaxMainIter': 500, 'RelStopTol': 1e-5,
'rho': 2e2, 'RelaxParam': 1.0,
'AutoRho': {'Enabled': False}})
bc = cbpdn.ConvMinL1InL2Ball(D, S, epsilon=epsilon, opt=opt)
Xc = bc.solve()
assert np.linalg.norm(Xp - Xc) / np.linalg.norm(Xp) < 1e-3
assert(np.abs(np.linalg.norm(Xp.ravel(), 1) -
np.linalg.norm(Xc.ravel(), 1)) < 1e-3)
def learn_codes(signal, atom_dictionary, penalty=PENALTY):
"""Return the sparse codes"""
opt_sc = get_opt_sc()
return (
cbpdn.ConvBPDN(
D=atom_dictionary, # learned dictionary
S=atleast_2d(signal), # signal at hand
lmbda=penalty, # sparsity penalty
opt=opt_sc, # options for the optimizations
).solve().squeeze())
def xinit(self, S):
init = cbpdn.ConvBPDN(self.getdict().squeeze(), S, self.lmbda,
self.opt['CBPDN'])
init.solve()
# (H, W, 1, K, M)
X = init.getcoef()
assert X.shape[2] == 1
X = X.reshape(-1, X.shape[-2], X.shape[-1])
X = X.transpose(1, 2, 0)
return X
def test_01(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda, dimK=0)
assert b.cri.dimC == 1
assert b.cri.dimK == 0
def test_03(self):
N = 16
Nd = 5
Cd = 3
M = 4
D = np.random.randn(Nd, Nd, Cd, M)
s = np.random.randn(N, N, Cd)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert b.cri.dimC == 1
assert b.cri.dimK == 0
def test_05(self):
N = 16
Nd = 5
K = 2
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, K)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert b.cri.dimC == 0
assert b.cri.dimK == 1
def test_09(self):
N = 16
Nd = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N)
try:
b = cbpdn.ConvBPDN(D, s)
b.solve()
except Exception as e:
print(e)
assert 0
def test_04(self):
N = 16
Nd = 5
Cd = 3
K = 5
M = 4
D = np.random.randn(Nd, Nd, Cd, M)
s = np.random.randn(N, N, Cd, K)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert(b.cri.dimC == 1)
assert(b.cri.dimK == 1)
def test_02(self):
N = 16
Nd = 5
Cs = 3
K = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs, K)
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda)
assert b.cri.dimC == 1
assert b.cri.dimK == 1
def test_08(self):
N = 16
Nd = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N)
lmbda = 1e-1
try:
b = cbpdn.ConvBPDN(D, s, lmbda)
b.solve()
except Exception as e:
print(e)
assert (0)
def test_01(self):
Nr = 32
Nc = 31
Nd = 5
M = 4
D = np.random.randn(Nd, Nd, M).astype(np.float32)
s = np.random.randn(Nr, Nc).astype(np.float32)
lmbda = 1e-1
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 50,
'AutoRho': {'Enabled': False}})
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
X1 = b.solve()
X2 = cucbpdn.cbpdn(D, s, lmbda, opt)
assert(sm.mse(X1, X2) < 1e-10)
def test_12(self):
N = 16
Nd = 5
Cs = 3
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, Cs)
lmbda = 1e-1
try:
b = cbpdn.ConvBPDN(D, s, lmbda, dimK=0)
b.solve()
except Exception as e:
print(e)
assert (0)
def test_34(self):
N = 16
Nd = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N)
lmbda = 1e-1
opt = cbpdn.ConvBPDN.Options({'Verbose': False, 'MaxMainIter': 10})
b = cbpdn.ConvBPDN(D, s, lmbda, opt)
bp = pickle.dumps(b)
c = pickle.loads(bp)
Xb = b.solve()
Xc = c.solve()
assert np.linalg.norm(Xb - Xc) == 0.0
def calcXr(cri, Dr, Sr, lmbda=5e-2):
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 200,
'RelStopTol': 5e-3,
'AuxVarObj': False
})
b = cbpdn.ConvBPDN(Dr.squeeze(),
Sr.squeeze(),
lmbda,
opt,
dimK=cri.dimK,
dimN=cri.dimN)
Xr = b.solve()
return Xr
def test_08(self):
N = 16
Nd = 5
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N)
lmbda = 1e-1
try:
opt = cbpdn.ConvBPDN.Options({'LinSolveCheck' : True})
b = cbpdn.ConvBPDN(D, s, lmbda, opt=opt)
b.solve()
except Exception as e:
print(e)
assert(0)
assert(np.array(b.getitstat().XSlvRelRes).max() < 1e-5)
def findHeatmaps(self, img):
#Opzioni e soluzione dell'algoritmo di CSC
lmbda = 5e-2
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 100,
'RelStopTol': 5e-3,
'AuxVarObj': False
})
csc = cbpdn.ConvBPDN(self.D, img, lmbda, opt, dimK=0)
X = csc.solve()
print(X.shape)
print("ConvBPDN solve time: %.2fs" % csc.timer.elapsed('solve'))
#rec = csc.reconstruct().squeeze()
return X
def run_cbpdn(D, sl, sh, lmbda, test_blob=None, opt=None):
"""Run ConvBPDN solver and compute statistics."""
opt = cbpdn.ConvBPDN.Options(opt)
if test_blob is None:
test_blob = sl + sh
solver = cbpdn.ConvBPDN(D, sh, lmbda, opt=opt)
solver.solve()
fnc = solver.getitstat().ObjFun[-1]
shr = solver.reconstruct().reshape(sl.shape)
imgr = sl + shr
psnr = 0.
for idx in range(sh.shape[-1]):
psnr += sm.psnr(test_blob[..., idx], imgr[..., idx], rng=1.)
psnr /= test_blob.shape[-1]
if _cfg.VERBOSE:
print('.', end='', flush=True)
return fnc, psnr
def test_06(self):
N = 16
Nd = 5
K = 2
M = 4
D = np.random.randn(Nd, Nd, M)
s = np.random.randn(N, N, K)
dt = np.float32
opt = cbpdn.ConvBPDN.Options({'Verbose' : False, 'MaxMainIter' : 20,
'AutoRho' : {'Enabled' : True},
'DataType' : dt})
lmbda = 1e-1
b = cbpdn.ConvBPDN(D, s, lmbda, opt=opt)
b.solve()
assert(b.X.dtype == dt)
assert(b.Y.dtype == dt)
assert(b.U.dtype == dt)
def recons(name):
if not os.path.exists(os.path.join(out_path, name)):
impath = os.path.join(im_path, name)
im = get_im(impath)
sl, sh = util.tikhonov_filter(im, fltlmbd, npd)
opt = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': 200,
'RelStopTol': 5e-3,
'AuxVarObj': False
})
b = cbpdn.ConvBPDN(basis, sh, lmbda, opt)
X = b.solve()
print("ConvBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
shr = b.reconstruct().squeeze()
imgr = sl + shr
cv2.imwrite(os.path.join(out_path, name), 255 * imgr / imgr.max())
else:
# print(name)
pass
},
'rho': 4e2 * lmbda
})
opt3 = cbpdntv.ConvBPDNVectorTV.Options({
'Verbose': True,
'MaxMainIter': 250,
'HighMemSolve': True,
'RelStopTol': 3e-3,
'AuxVarObj': True,
'L1Weight': W,
'AutoRho': {
'Enabled': False
},
'rho': 4e2 * lmbda
})
a = cbpdn.ConvBPDN(D, pad(imgnh), lmbda, opt1, dimK=0)
b = cbpdntv.ConvBPDNScalarTV(D, pad(imgnh), lmbda, mu, opt2)
c = cbpdntv.ConvBPDNVectorTV(D, pad(imgnh), lmbda, mu, opt3)
X1 = a.solve()
X2 = b.solve()
X3 = c.solve()
imgdp1 = a.reconstruct().squeeze()
imgd1 = np.clip(crop(imgdp1) + imgnl, 0, 1)
imgdp2 = b.reconstruct().squeeze()
imgd2 = np.clip(crop(imgdp2) + imgnl, 0, 1)
imgdp3 = c.reconstruct().squeeze()
imgd3 = np.clip(crop(imgdp3) + imgnl, 0, 1)
print("ConvBPDN solve time: %5.2f s" % b.timer.elapsed('solve'))
print("Noisy image PSNR: %5.2f dB" % sm.psnr(img, imgn))
S7 = sh7.reshape(N * N)
# S6 = tesS[0].reshape(N*N)
# S7 = tesS[1].reshape(N*N)
TESTS = [S6, S7]
# %%
# d_0 = np.random.normal(-1, 1, (M, d_size, d_size))
from sporco.admm import cbpdn
lmbda = 0.048
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 500,
'RelStopTol': 5e-3,
'AuxVarObj': False
})
b0 = cbpdn.ConvBPDN(D1, sh6, lmbda, opt, dimK=0)
X6 = b0.solve()
b1 = cbpdn.ConvBPDN(D1, sh7, lmbda, opt, dimK=0)
X7 = b1.solve()
shr6 = b0.reconstruct().squeeze()
imgr6 = sl6 + shr6
print("Reconstruction PSNR: %.2fdB\n" % sm.psnr(tesS[0], imgr6))
shr7 = b1.reconstruct().squeeze()
imgr7 = sl7 + shr7
print("Reconstruction PSNR: %.2fdB\n" % sm.psnr(tesS[1], imgr7))
# %%
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(np.sum(abs(X6), axis=b0.cri.axisM).squeeze(),
cmap=plot.cm.Blues,
title='Sparse representation',
'RelStopTol': 5e-3,
'AuxVarObj': False
})
"""
If GPU available, run CUDA ConvBPDN solver, otherwise run standard Python version.
"""
if cuda.device_count() > 0:
print('%s GPU found: running CUDA solver' % cuda.device_name())
tm = util.Timer()
with sys_pipes(), util.ContextTimer(tm):
X = cuda.cbpdn(D, sh, lmbda, opt)
t = tm.elapsed()
else:
print('GPU not found: running Python solver')
c = cbpdn.ConvBPDN(D, sh, lmbda, opt)
X = c.solve().squeeze()
t = c.timer.elapsed('solve')
print('Solve time: %.2f s' % t)
"""
Reconstruct the image from the sparse representation.
"""
shr = np.sum(spl.fftconv(D, X), axis=2)
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % spm.psnr(img, imgr))
"""
Display representation and reconstructed image.
"""
fig = plot.figure(figsize=(14, 14))
"""
Set :class:`.admm.cbpdn.ConvBPDN` solver options.
"""
lmbda = 5e-2
opt = cbpdn.ConvBPDN.Options({
'Verbose': True,
'MaxMainIter': 200,
'RelStopTol': 5e-3,
'AuxVarObj': False
})
"""
Initialise and run CSC solver.
"""
b = cbpdn.ConvBPDN(D, sh, lmbda, opt, dimK=0)
X = b.solve()
print("ConvBPDN solve time: %.2fs" % b.timer.elapsed('solve'))
"""
Reconstruct image from sparse representation.
"""
shr = b.reconstruct().squeeze()
imgr = sl + shr
print("Reconstruction PSNR: %.2fdB\n" % sm.psnr(img, imgr))
"""
Display low pass component and sum of absolute values of coefficient maps of highpass component.
"""
fig = plot.figure(figsize=(14, 7))
plot.subplot(1, 2, 1)
'Scaling': 2.0,
'RsdlTarget': 1.0
}
})
# Normalise dictionary according to Y update options
D0n = ccmod.getPcn0(optd['ZeroMean'], D0.shape, dimN=2, dimC=1)(D0)
# Update D update options to include initial values for Y and U
optd.update({
'Y0': ccmod.zpad(ccmod.stdformD(D0n, cri.C, cri.M), cri.Nv),
'U0': np.zeros(cri.shpD)
})
# Create X update object
xstep = cbpdn.ConvBPDN(D0n, sh, lmbda, optx)
# Create D update object
dstep = ccmod.ConvCnstrMOD(None, sh, D0.shape, optd)
# Create DictLearn object
opt = dictlrn.DictLearn.Options({'Verbose': True, 'MaxMainIter': 100})
d = dictlrn.DictLearn(xstep, dstep, opt)
D1 = d.solve()
print("DictLearn solve time: %.2fs" % d.runtime, "\n")
# Display dictionaries
D1 = D1.squeeze()
fig1 = plot.figure(1, figsize=(14, 7))
plot.subplot(1, 2, 1)
plot.imview(util.tiledict(D0), fgrf=fig1, title='D0')
#train_feature = nmf.transform(train_feature);
nmf_p.fit(train_feature_p)
train_feature_p = nmf_p.transform(train_feature_p)
#%%
# テスト用データセットの係数算出
print("### テスト用データセットの係数算出 ###")
test_opt0 = cbpdn.ConvBPDN.Options({
'Verbose': False,
'MaxMainIter': opt["Iter"],
'RelStopTol': 5e-3,
'AuxVarObj': False
})
b0 = cbpdn.ConvBPDN(np.float32(D0), np.float32(test_data), opt["lmbda"],
test_opt0)
print("第１層目:フォワードパスの計算中...")
test_coef0_ = b0.solve()
test_coef0 = np.zeros_like(test_coef0_)
for i in range(opt['test_amount']):
for j in range(test_coef0_.shape[4]):
test_coef0[:, :, 0, i, j] = power_spectrum(test_coef0_[:, :, 0, i, j])
#test_feature = test_coef0_.transpose(3, 0, 1, 2, 4).squeeze().reshape(opt["test_amount"], -1);
#test_feature = nmf.transform(test_feature);
test_feature_p = test_coef0.transpose(3, 0, 1, 2, 4).squeeze().reshape(
opt["test_amount"], -1)
test_feature_p = nmf_p.transform(test_feature_p)
#%%
def solve(self, S):
self.cri = cr.CSC_ConvRepIndexing(self.getdict(), S,
dimK=self.cri.dimK,
dimN=self.cri.dimN)
self.timer.start(['solve', 'solve_wo_eval'])
# Initialize with CBPDN
self.timer.start('xstep')
copt = copy.deepcopy(self.opt['CBPDN'])
if self.opt['OCDL', 'CUCBPDN']:
X = np.stack([
cucbpdn.cbpdn(self.getdict(), S[..., i].squeeze(),
self.lmbda, opt=copt) for i in range(S.shape[-1])
], axis=-2)
X = np.asarray(X.reshape(self.cri.shpX), dtype=self.dtype)
elif self.opt['OCDL', 'PARCBPDN']:
popt = parcbpdn.ParConvBPDN.Options(dict(self.opt['CBPDN']))
xstep = parcbpdn.ParConvBPDN(self.getdict(), S, self.lmbda,
opt=popt,
nproc=self.opt['OCDL', 'nproc'])
X = xstep.solve()
X = np.asarray(X.reshape(self.cri.shpX), dtype=self.dtype)
else:
xstep = cbpdn.ConvBPDN(self.getdict(), S, self.lmbda, opt=copt)
xstep.solve()
X = np.asarray(xstep.getcoef().reshape(self.cri.shpX),
dtype=self.dtype)
self.timer.stop('xstep')
# X = np.asarray(xstep.getcoef().reshape(self.cri.shpX), dtype=self.dtype)
S = np.asarray(S.reshape(self.cri.shpS), dtype=self.dtype)
# update At and Bt
# (H, W, 1, K, M) -> (H, W, Hc, Wc, 1, K, M)
self.timer.start('hessian')
Xe = self.extend_code(X)
self.update_At(Xe)
self.update_Bt(Xe, S)
self.timer.stop('hessian')
self.Lmbda = self.dtype.type(self.alpha*self.Lmbda+1)
# update dictionary with FISTA
fopt = copy.deepcopy(self.opt['CCMOD'])
fopt['X0'] = self.D
if self.opt['OCDL', 'DiminishingTol']:
if self.opt['OCDL', 'MinTol'] is None:
min_tol = 0.
else:
min_tol = self.opt['OCDL', 'MinTol']
fopt['RelStopTol'] = max(
self.dtype.type(self.opt['CCMOD', 'RelStopTol']/(1.+self.j)),
min_tol
)
self.timer.start('dstep')
dstep = SpatialFISTA(self.At, self.Bt, opt=fopt)
dstep.solve()
self.timer.stop('dstep')
# set dictionary
self.setdict(dstep.getmin())
self.timer.stop('solve_wo_eval')
evl = self.evaluate(S, X)
self.timer.start('solve_wo_eval')
t = self.timer.elapsed(self.opt['IterTimer'])
if self.opt['OCDL', 'CUCBPDN']:
# this requires a slight modification of dictlrn
itst = self.isc.iterstats(self.j, t, None, dstep.itstat[-1], evl)
else:
itst = self.isc.iterstats(self.j, t, xstep.itstat[-1],
dstep.itstat[-1], evl)
self.itstat.append(itst)
if self.opt['Verbose']:
self.isc.printiterstats(itst)
self.j += 1
self.timer.stop(['solve', 'solve_wo_eval'])
if 0:
import matplotlib.pyplot as plt
plt.imshow(su.tiledict(self.getdict().squeeze()))
plt.show()
return self.getdict()
