def test_05(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
mu = 1e-2
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': False,
'MaxMainIter': 50, 'AutoRho': {'Enabled': False}})
b = cbpdn.ConvBPDNGradReg(D, s, lmbda, mu, opt)
X1 = b.solve()
X2 = cucbpdn.cbpdngrd(D, s, lmbda, mu, opt)
assert(sm.mse(X1, X2) < 1e-10)
'Verbose': True,
'MaxMainIter': 20,
'HighMemSolve': True,
'LinSolveCheck': False,
'RelStopTol': 2e-3,
'AuxVarObj': False,
'AutoRho': {
'Enabled': False
}
})
# Initialise and run ConvBPDNGradReg object
b = cbpdn.ConvBPDNGradReg(D, sh, lmbda, mu, opt)
X1 = b.solve()
print("ConvBPDNGradReg solve time: %.2fs" % b.timer.elapsed('solve'))
# Time CUDA ConvBPDNGradReg solve
t = util.Timer()
with util.ContextTimer(t):
X2 = cucbpdn.cbpdngrd(D, sh, lmbda, mu, opt)
print("GPU 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)))
wgr[0] = 1.0
# Set up ConvBPDNGradReg options
lmbda = 1e-2
mu = 1e1
opt = cbpdn.ConvBPDNGradReg.Options({'Verbose': True, 'MaxMainIter': 200,
'HighMemSolve': True, 'RelStopTol': 5e-3,
'AuxVarObj': False, 'AutoRho': {'Enabled': False},
'rho': 0.2, 'L1Weight': wl1, 'GradWeight': wgr})
# Time CUDA cbpdn solve
t = util.Timer()
with util.ContextTimer(t):
X = cucbpdn.cbpdngrd(D, img, lmbda, mu, opt)
print("Image size: %d x %d" % img.shape)
print("GPU ConvBPDNGradReg solve time: %.2fs" % t.elapsed())
# Reconstruct the image from the sparse representation
imgr = np.sum(spl.fftconv(D, X), axis=2)
#Display representation and reconstructed image.
fig = plot.figure(figsize=(14, 14))
plot.subplot(2, 2, 1)
plot.imview(X[..., 0].squeeze(), fig=fig, title='Lowpass component')
plot.subplot(2, 2, 2)
plot.imview(np.sum(abs(X[..., 1:]), axis=2).squeeze(), fig=fig,
cmap=plot.cm.Blues, title='Main representation')
