def test_13(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)
frc = 0.5
msk = su.rndmask(s.shape, frc, dtype=np.float32)
s *= msk
lmbda = 1e-1
mu = 1e-2
Wl1 = np.random.randn(1, 1, M).astype(np.float32)
Wl1i = np.concatenate((Wl1, np.ones(Wl1.shape[0:-1] + (1,))),
axis=-1)
Wgrdi = np.hstack((np.ones(M,), np.zeros((1,))))
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False,
'MaxMainIter': 50, 'AutoRho': {'Enabled': False}})
opt['L1Weight'] = Wl1i
opt['GradWeight'] = Wgrdi
b = cbpdn.AddMaskSim(cbpdn.ConvBPDNGradReg, D, s, msk, lmbda, mu, opt)
X1 = b.solve()
opt['L1Weight'] = Wl1
opt['GradWeight'] = 1.0
X2 = cucbpdn.cbpdngrdmsk(D, s, msk, lmbda, mu, opt)
assert(sm.mse(X1, X2) < 1e-10)
def test_12(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)
frc = 0.5
msk = su.rndmask(s.shape, frc, dtype=np.float32)
s *= msk
lmbda = 1e-1
mu = 1e-2
# Since cucbpdn.cbpdngrdmsk automatically ensures that the ℓ2 of
# gradient term is not applied to the AMS impulse filter, while
# cbpdn.AddMaskSim does not, we have to pass a GradWeight array
# with a zero entry corresponding to the AMS impulse filter to
# cbpdn.AddMaskSim
Wgrdi = np.hstack((np.ones(M,), np.zeros((1,))))
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False,
'MaxMainIter': 50, 'AutoRho': {'Enabled': False}})
opt['GradWeight'] = Wgrdi
b = cbpdn.AddMaskSim(cbpdn.ConvBPDNGradReg, D, s, msk, lmbda, mu, opt)
X1 = b.solve()
opt['GradWeight'] = 1.0
X2 = cucbpdn.cbpdngrdmsk(D, s, msk, lmbda, mu, opt)
assert(sm.mse(X1, X2) < 1e-10)
def test_14(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)
frc = 0.5
msk = su.rndmask(s.shape, frc, dtype=np.float32)
s *= msk
lmbda = 1e-1
mu = 1e-2
# Create a random ℓ2 of gradient term weighting array. There is no
# need to extend this array to account for the AMS impulse filter
# since this is taken care of automatically by cucbpdn.cbpdngrdmsk
Wgrd = np.random.randn(M).astype(np.float32)
# Append a zero entry to the GradWeight array, corresponding to
# the impulse filter appended to the dictionary by cbpdn.AddMaskSim,
# since this is not done automatically by cbpdn.AddMaskSim
Wgrdi = np.hstack((Wgrd, np.zeros((1,))))
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False,
'MaxMainIter': 50, 'AutoRho': {'Enabled': False}})
opt['GradWeight'] = Wgrdi
b = cbpdn.AddMaskSim(cbpdn.ConvBPDNGradReg, D, s, msk, lmbda, mu, opt)
X1 = b.solve()
opt['GradWeight'] = Wgrd
X2 = cucbpdn.cbpdngrdmsk(D, s, msk, lmbda, mu, opt)
assert(sm.mse(X1, X2) < 1e-10)
def test_13(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)
frc = 0.5
msk = su.rndmask(s.shape, frc, dtype=np.float32)
s *= msk
lmbda = 1e-1
mu = 1e-2
# Create a random ℓ1 term weighting array. There is no need to
# extend this array to account for the AMS impulse filter since
# this is taken care of automatically by cucbpdn.cbpdngrdmsk
Wl1 = np.random.randn(1, 1, M).astype(np.float32)
# Append a zero entry to the L1Weight array, corresponding to
# the impulse filter appended to the dictionary by cbpdn.AddMaskSim,
# since this is not done automatically by cbpdn.AddMaskSim
Wl1i = np.concatenate((Wl1, np.zeros(Wl1.shape[0:-1] + (1, ))),
axis=-1)
# Since cucbpdn.cbpdngrdmsk automatically ensures that the ℓ2 of
# gradient term is not applied to the AMS impulse filter, while
# cbpdn.AddMaskSim does not, we have to pass a GradWeight array
# with a zero entry corresponding to the AMS impulse filter to
# cbpdn.AddMaskSim
Wgrdi = np.hstack((np.ones(M, ), np.zeros((1, ))))
opt = cbpdn.ConvBPDNGradReg.Options({
'Verbose': False,
'MaxMainIter': 50,
'AutoRho': {
'Enabled': False
}
})
opt['L1Weight'] = Wl1i
opt['GradWeight'] = Wgrdi
b = cbpdn.AddMaskSim(cbpdn.ConvBPDNGradReg, D, s, msk, lmbda, mu, opt)
X1 = b.solve()
opt['L1Weight'] = Wl1
opt['GradWeight'] = 1.0
X2 = cucbpdn.cbpdngrdmsk(D, s, msk, lmbda, mu, opt)
assert (sm.mse(X1, X2) < 1e-10)
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': True, 'MaxMainIter': 20,
'HighMemSolve': True, 'LinSolveCheck': False,
'RelStopTol': 2e-3, 'AuxVarObj': False,
'AutoRho': {'Enabled': False}})
# Initialise and run AddMaskSim/ConvBPDNGradReg object
opt['GradWeight'] = wgrd
b = cbpdn.AddMaskSim(cbpdn.ConvBPDNGradReg, D, shw, msk, lmbda, mu, opt)
X1 = b.solve()
print("AddMaskSim/ConvBPDNGradReg solve time: %.2fs" %
b.timer.elapsed('solve'))
# Time CUDA AddMaskSim/ConvBPDNGradReg solve
opt['GradWeight'] = 1.0
t = util.Timer()
with util.ContextTimer(t):
X2 = cucbpdn.cbpdngrdmsk(D, shw, msk, lmbda, mu, opt)
print("GPU AddMaskSim/ConvBPDNGradReg solve time: %.2fs" % t.elapsed())
print("GPU time improvement factor: %.1f" % (b.timer.elapsed('solve') /
t.elapsed()))
# Compare CPU and GPU solutions
print("CPU solution: min: %.4e max: %.4e l1: %.4e" %
(X1.min(), X1.max(), np.sum(np.abs(X1))))
print("GPU solution: min: %.4e max: %.4e l1: %.4e" %
(X2.min(), X2.max(), np.sum(np.abs(X2))))
print("CPU/GPU MSE: %.2e SNR: %.2f dB" % (sm.mse(X1, X2), sm.snr(X1, X2)))