def test_solver_comparison(self):
"""
Test that all solvers return the same and correct solution.
"""
# Convex functions.
y = [1, 0, 0.1, 8, -6.5, 0.2, 0.004, 0.01]
sol = [0.75, 0, 0, 7.75, -6.25, 0, 0, 0]
w1, w2 = .8, .4
f1 = functions.norm_l2(y=y, lambda_=w1 / 2.) # Smooth.
f2 = functions.norm_l1(lambda_=w2 / 2.) # Non-smooth.
# Solvers.
L = w1 # Lipschitz continuous gradient.
step = 1. / L
lambda_ = 0.5
params = {'step': step, 'lambda_': lambda_}
slvs = []
slvs.append(
solvers.forward_backward(accel=acceleration.dummy(), step=step))
slvs.append(solvers.douglas_rachford(**params))
slvs.append(solvers.generalized_forward_backward(**params))
# Compare solutions.
params = {'rtol': 1e-14, 'verbosity': 'NONE', 'maxit': 1e4}
niters = [2, 61, 26]
for solver, niter in zip(slvs, niters):
x0 = np.zeros(len(y))
ret = solvers.solve([f1, f2], x0, solver, **params)
nptest.assert_allclose(ret['sol'], sol)
self.assertEqual(ret['niter'], niter)
self.assertIs(ret['sol'], x0) # The initial value was modified.
def test_douglas_rachford(self):
"""
Test douglas-rachford solver with L1-norm, L2-norm and dummy functions.
"""
y = [4, 5, 6, 7]
solver = solvers.douglas_rachford()
param = {'solver': solver, 'verbosity': 'NONE'}
# L2-norm prox and dummy prox.
f1 = functions.norm_l2(y=y)
f2 = functions.dummy()
ret = solvers.solve([f1, f2], np.zeros(len(y)), **param)
nptest.assert_allclose(ret['sol'], y)
self.assertEqual(ret['crit'], 'RTOL')
self.assertEqual(ret['niter'], 35)
# L2-norm prox and L1-norm prox.
f1 = functions.norm_l2(y=y)
f2 = functions.norm_l1(y=y)
ret = solvers.solve([f1, f2], np.zeros(len(y)), **param)
nptest.assert_allclose(ret['sol'], y)
self.assertEqual(ret['crit'], 'RTOL')
self.assertEqual(ret['niter'], 4)
# Sanity checks
x0 = np.zeros((4, ))
solver.lambda_ = 2.
self.assertRaises(ValueError, solver.pre, [f1, f2], x0)
solver.lambda_ = -2.
self.assertRaises(ValueError, solver.pre, [f1, f2], x0)
self.assertRaises(ValueError, solver.pre, [f1, f2, f1], x0)
def test_solver_comparison(self):
"""
Test that all solvers return the same and correct solution.
"""
# Convex functions.
y = [1, 0, 0.1, 8, -6.5, 0.2, 0.004, 0.01]
sol = [0.75, 0, 0, 7.75, -6.25, 0, 0, 0]
w1, w2 = .8, .4
f1 = functions.norm_l2(y=y, lambda_=w1 / 2.) # Smooth.
f2 = functions.norm_l1(lambda_=w2 / 2.) # Non-smooth.
# Solvers.
L = w1 # Lipschitz continuous gradient.
step = 1. / L
lambda_ = 0.5
params = {'step': step, 'lambda_': lambda_}
slvs = []
slvs.append(solvers.forward_backward(accel=acceleration.dummy(),
step=step))
slvs.append(solvers.douglas_rachford(**params))
slvs.append(solvers.generalized_forward_backward(**params))
# Compare solutions.
params = {'rtol': 1e-14, 'verbosity': 'NONE', 'maxit': 1e4}
niters = [2, 61, 26]
for solver, niter in zip(slvs, niters):
x0 = np.zeros(len(y))
ret = solvers.solve([f1, f2], x0, solver, **params)
nptest.assert_allclose(ret['sol'], sol)
self.assertEqual(ret['niter'], niter)
self.assertIs(ret['sol'], x0) # The initial value was modified.
def test_douglas_rachford(self):
"""
Test douglas-rachford solver with L1-norm, L2-norm and dummy functions.
"""
y = [4, 5, 6, 7]
solver = solvers.douglas_rachford()
param = {'solver': solver, 'verbosity': 'NONE'}
# L2-norm prox and dummy prox.
f1 = functions.norm_l2(y=y)
f2 = functions.dummy()
ret = solvers.solve([f1, f2], np.zeros(len(y)), **param)
nptest.assert_allclose(ret['sol'], y)
self.assertEqual(ret['crit'], 'RTOL')
self.assertEqual(ret['niter'], 35)
# L2-norm prox and L1-norm prox.
f1 = functions.norm_l2(y=y)
f2 = functions.norm_l1(y=y)
ret = solvers.solve([f1, f2], np.zeros(len(y)), **param)
nptest.assert_allclose(ret['sol'], y)
self.assertEqual(ret['crit'], 'RTOL')
self.assertEqual(ret['niter'], 4)
# Sanity checks
x0 = np.zeros((4,))
solver.lambda_ = 2.
self.assertRaises(ValueError, solver.pre, [f1, f2], x0)
solver.lambda_ = -2.
self.assertRaises(ValueError, solver.pre, [f1, f2], x0)
self.assertRaises(ValueError, solver.pre, [f1, f2, f1], x0)
def Experiment(iterable):
M = 0
Omega = np.random.permutation(N)
x_0 = np.zeros(N)
x_0[Omega[0:k]] = np.random.standard_normal(k)
psi = np.ones(N)
Psi = np.diag(psi)
Phi = np.random.randn(n, N)
A = np.dot(Phi, Psi)
y = np.dot(A, x_0)
x = np.zeros(N)
f1 = functions.norm_l1()
f2 = functions.proj_b2(epsilon=0, y=y, A=A, tight=False,\
nu=np.linalg.norm(A, ord=2)**2)
solver = solvers.douglas_rachford(step=1e-2)
ret = solvers.solve([f1, f2], x, solver, rtol=1e-4, maxit=300)
x = ret.get('sol')
residual = (float(LA.norm(x - x_0, 2)) / LA.norm(x_0, 2))**2
if residual <= tolerance:
M += 1
return M
def main_tv(hparams):
## === Set up=== ##
# Printer setup
#sys.stdout = open(hparams.text_file_path, 'w')
# Get inputs
if hparams.image_mode == '1D':
x_real = np.array(load_1D(hparams.path, hparams.img_name)).astype(
np.float32) #[4096,1]
elif hparams.image_mode == '2D':
x_real = np.array(load_2D(hparams.path, hparams.img_name)).astype(
np.float32) #[64,64]
elif hparams.image_mode == '3D':
x_real = np.array(
load_img(hparams.path,
hparams.img_name, hparams.decoder_type)).astype(
np.float32) #[178,218,3] / [224,224,3]
# Initialization
#np.random.seed(7)
sig_shape = x_real.shape[0] * x_real.shape[
1] #n = 4096*1 or 64*64 or 178*218 or 224*224
random_vector = None #initialization
A = None #initialization
selection_mask = None #initialization
random_arr = random_flip(sig_shape) #initialization #[n,]
mask = None #initialization
# Get measurement matirx
if hparams.model_type == 'denoising' or hparams.model_type == 'compressing':
if hparams.type_measurements == 'random': #compressed sensing
if hparams.image_mode != '3D':
A = np.random.randn(hparams.num_measurements,
sig_shape).astype(np.float32) #[m,n]
noise_shape = [hparams.num_measurements, 1] #[m,1]
else:
A = np.random.randn(int(hparams.num_measurements / 3),
sig_shape).astype(np.float32) #[m,n]
noise_shape = [int(hparams.num_measurements / 3), 1] #[m,1]
elif hparams.type_measurements == 'identity': #denoising
A = np.identity(sig_shape).astype(np.float32) #[n,n]
noise_shape = [sig_shape, 1] #[n,1]
observ_noise = hparams.noise_level * np.random.randn(
noise_shape[0], noise_shape[1]) #[n,1]
elif hparams.type_measurements == 'circulant': #compressed sensing
if hparams.image_mode != '3D':
random_vector = np.random.normal(size=sig_shape) #[n,]
selection_mask = create_A_selection(
sig_shape, hparams.num_measurements) #[1,n]
else:
random_vector = np.random.normal(size=sig_shape) #[n,]
selection_mask = create_A_selection(
sig_shape, int(hparams.num_measurements / 3)) #[1,n]
def circulant_np(signal_vector,
random_arr_p=random_arr.reshape(-1, 1),
random_vector_p=random_vector.reshape(-1, 1),
selection_mask_p=selection_mask.reshape(-1, 1)):
#step 0: Flip
signal_vector = signal_vector * random_arr_p #[n,1] * [n,1] -> [n,1]
#step 1: F^{-1} @ x
r1 = ifft(signal_vector) #[n,1]
#step 2: Diag() @ F^{-1} @ x
Ft = fft(random_vector_p) #[n,1]
r2 = np.multiply(r1, Ft) #[n,1] * [n,1] -> [n,1]
#step 3: F @ Diag() @ F^{-1} @ x
compressive = fft(r2) #[n,1]
#step 4: R_{omega} @ C_{t} @ D){epsilon}
compressive = compressive.real #[n,1]
select_compressive = compressive * selection_mask_p #[n,1] * [n,1] -> [n,1]
return select_compressive
elif hparams.model_type == 'inpainting':
if hparams.image_mode == '1D':
mask = load_mask('Masks', hparams.mask_name_1D, hparams.image_mode,
hparams.decoder_type) #[n,1]
elif hparams.image_mode == '2D' or hparams.image_mode == '3D':
mask = load_mask('Masks', hparams.mask_name_2D, hparams.image_mode,
hparams.decoder_type) #[n,n]
## === TV norm === ##
if hparams.decoder_type == 'tv_norm':
# Construct observation and perform reconstruction
if hparams.model_type == 'inpainting':
# measurements and observation
g = lambda x: mask * x #[4096,1] * [4096,1] / [178,218,3] * [178,218,3]
y_real = g(x_real) #[4096,1] / [178,218,3]
# tv norm
if hparams.image_mode == '1D':
f1 = functions.norm_tv(dim=1)
elif hparams.image_mode == '2D':
f1 = functions.norm_tv(dim=2)
elif hparams.image_mode == '3D':
f1 = functions.norm_tv(dim=3)
# L2 norm
tau = hparams.tau
f2 = functions.norm_l2(y=y_real, A=g, lambda_=tau)
# optimisation
solver = solvers.forward_backward(step=0.5 / tau)
x0 = np.array(y_real) # Make a copy to preserve im_masked.
ret = solvers.solve([f1, f2], x0, solver,
maxit=3000) #output = ret['sol']
# output
out_img = ret['sol'] #[4096,1] / [178,218,3]
elif hparams.model_type == 'denoising':
assert hparams.type_measurements == 'identity'
if hparams.image_mode == '3D':
out_img_list = []
for i in range(x_real.shape[-1]):
# measurements and observation
y_real = np.matmul(A, x_real[:, :, i].reshape(
-1, 1)) + observ_noise # [n,n] * [n,1] -> [n,1]
# tv norm
f1 = functions.norm_tv(dim=1)
# epsilon
N = math.sqrt(sig_shape)
epsilon = N * hparams.noise_level
# L2 ball
y = np.reshape(y_real, -1) #[n,1] -> [n,]
f = functions.proj_b2(y=y, epsilon=epsilon)
f2 = functions.func()
# Indicator functions
f2._eval = lambda x: 0
def prox(x, step):
return np.reshape(f.prox(np.reshape(x, -1), 0),
y_real.shape)
f2._prox = prox
# solver
solver = solvers.douglas_rachford(step=0.1)
x0 = np.array(y_real) #[n,1]
ret = solvers.solve([f1, f2], x0, solver)
# output
out_img_piece = ret['sol'].reshape(
x_real.shape[0], x_real.shape[1]) #[178,218]
out_img_list.append(out_img_piece)
out_img = np.transpose(np.array(out_img_list), (1, 2, 0))
else:
# measurements and observation
y_real = np.matmul(A, x_real.reshape(
-1, 1)) + observ_noise # [n,n] * [n,1] -> [n,1]
# tv norm
f1 = functions.norm_tv(dim=1)
# epsilon
N = math.sqrt(sig_shape)
epsilon = N * hparams.noise_level
# L2 ball
y = np.reshape(y_real, -1) #[n,1] -> [n,]
f = functions.proj_b2(y=y, epsilon=epsilon)
f2 = functions.func()
# Indicator functions
f2._eval = lambda x: 0
def prox(x, step):
return np.reshape(f.prox(np.reshape(x, -1), 0),
y_real.shape)
f2._prox = prox
# solver
solver = solvers.douglas_rachford(step=0.1)
x0 = np.array(y_real) #[n,1]
ret = solvers.solve([f1, f2], x0, solver)
# output
out_img = ret['sol'] #[n,1]
elif hparams.model_type == 'compressing':
assert hparams.type_measurements == 'circulant'
if hparams.image_mode == '3D':
out_img_list = []
for i in range(x_real.shape[-1]):
# construct observation
g = circulant_np
y_real = g(x_real[:, :, i].reshape(-1, 1)) #[n,1] -> [n,1]
# tv norm
f1 = functions.norm_tv(dim=1)
# L2 norm
tau = hparams.tau
f2 = functions.norm_l2(y=y_real, A=g, lambda_=tau)
# optimisation solver
A_real = np.random.normal(
size=(int(hparams.num_measurements / 3), sig_shape))
step = 0.5 / np.linalg.norm(A_real, ord=2)**2
solver = solvers.forward_backward(
step=step
) #solver = solvers.forward_backward(step=0.5/tau)
# initialisation
x0 = np.array(y_real) #[n,1]
# output
ret = solvers.solve([f1, f2],
x0,
solver,
rtol=1e-4,
maxit=3000) #output = ret['sol']
out_img_piece = ret['sol'].reshape(
x_real.shape[0], x_real.shape[1]) #[178,218]
out_img_list.append(out_img_piece)
out_img = np.transpose(np.array(out_img_list), (1, 2, 0))
else:
# construct observation
g = circulant_np
y_real = g(x_real.reshape(-1, 1)) #[n,1] -> [n,1]
# tv norm
f1 = functions.norm_tv(dim=1)
# L2 norm
tau = hparams.tau
f2 = functions.norm_l2(y=y_real, A=g, lambda_=tau)
# optimisation solver
A_real = np.random.normal(size=(hparams.num_measurements,
sig_shape))
step = 0.5 / np.linalg.norm(A_real, ord=2)**2
solver = solvers.forward_backward(
step=step
) #solver = solvers.forward_backward(step=0.5/tau)
# initialisation
x0 = np.array(y_real) #[n,1]
# output
ret = solvers.solve([f1, f2],
x0,
solver,
rtol=1e-4,
maxit=3000) #output = ret['sol']
out_img = ret['sol'] #[n,1]
# ## === Lasso wavelet === ##
elif hparams.decoder_type == 'lasso_wavelet':
# Construct lasso wavelet functions
def solve_lasso(A_val, y_val, hparams): #(n,m), (1,m)
if hparams.lasso_solver == 'sklearn':
lasso_est = Lasso(alpha=hparams.lmbd)
lasso_est.fit(A_val.T, y_val.reshape(hparams.num_measurements))
x_hat = lasso_est.coef_
x_hat = np.reshape(x_hat, [-1])
elif hparams.lasso_solver == 'cvxopt':
A_mat = matrix(A_val.T) #[m,n]
y_mat = matrix(y_val.T) ###
x_hat_mat = l1regls(A_mat, y_mat)
x_hat = np.asarray(x_hat_mat)
x_hat = np.reshape(x_hat, [-1]) #[n, ]
elif hparams.lasso_solver == 'pyunlocbox':
tau = hparams.tau
f1 = functions.norm_l1(lambda_=tau)
f2 = functions.norm_l2(y=y_val.T, A=A_val.T)
if hparams.model_type == 'compressing':
if hparams.image_mode == '3D':
A_real = np.random.normal(
size=(int(hparams.num_measurements / 3),
sig_shape))
else:
A_real = np.random.normal(
size=(hparams.num_measurements, sig_shape))
step = 0.5 / np.linalg.norm(A_real, ord=2)**2
else:
step = 0.5 / np.linalg.norm(A_val, ord=2)**2
solver = solvers.forward_backward(step=step)
x0 = np.zeros((sig_shape, 1))
ret = solvers.solve([f1, f2],
x0,
solver,
rtol=1e-4,
maxit=3000)
x_hat_mat = ret['sol']
x_hat = np.asarray(x_hat_mat)
x_hat = np.reshape(x_hat, [-1]) #[n, ]
return x_hat
#generate basis
def generate_basis(size):
"""generate the basis"""
x = np.zeros((size, size))
coefs = pywt.wavedec2(x, 'db1')
n_levels = len(coefs)
basis = []
for i in range(n_levels):
coefs[i] = list(coefs[i])
n_filters = len(coefs[i])
for j in range(n_filters):
for m in range(coefs[i][j].shape[0]):
try:
for n in range(coefs[i][j].shape[1]):
coefs[i][j][m][n] = 1
temp_basis = pywt.waverec2(coefs, 'db1')
basis.append(temp_basis)
coefs[i][j][m][n] = 0
except IndexError:
coefs[i][j][m] = 1
temp_basis = pywt.waverec2(coefs, 'db1')
basis.append(temp_basis)
coefs[i][j][m] = 0
basis = np.array(basis)
return basis
def wavelet_basis(path_):
if path_ == 'Ieeg_signal':
W_ = generate_basis(32)
W_ = W_.reshape((1024, 1024))
elif path_ == 'Celeb_signal':
W_ = generate_basis(128)
W_ = W_.reshape((16384, 16384))
else:
W_ = generate_basis(64)
W_ = W_.reshape((4096, 4096))
return W_
def lasso_wavelet_estimator(A_val, y_val, hparams): #(n,m), (1,m)
W = wavelet_basis(hparams.path) #[n,n]
if not callable(A_val):
WA = np.dot(W, A_val) #[n,n] * [n,m] = [n,m]
else:
WA = np.array([
A_val(W[i, :].reshape(-1, 1)).reshape(-1)
for i in range(len(W))
]) #[n,n] -> [n,n]
z_hat = solve_lasso(WA, y_val, hparams) # [n, ]
x_hat = np.dot(z_hat, W) #[n, ] * [n,n] = [n, ]
x_hat_max = np.abs(x_hat).max()
x_hat = x_hat / (1.0 * x_hat_max)
return x_hat
# Construct inpainting masks
def get_A_inpaint(mask_p):
mask = mask_p.reshape(1, -1)
A = np.eye(np.prod(mask.shape)) * np.tile(mask,
[np.prod(mask.shape), 1])
A = np.asarray([a for a in A if np.sum(a) != 0])
A = np.sqrt(
sig_shape
) * A # Make sure that the norm of each row of A is sig_shape
assert all(np.abs(np.sum(A**2, 1) - sig_shape) < 1e-6)
return A.T
# Perofrm reconstruction
if hparams.model_type == 'inpainting':
# measurements and observation
A_val = get_A_inpaint(mask) #(n,m)
if hparams.image_mode == '3D':
out_img_list = []
for i in range(x_real.shape[-1]):
y_real = np.matmul(x_real[:, :, i].reshape(1, -1),
A_val) #(1,m)
out_img_piece = lasso_wavelet_estimator(
A_val, y_real, hparams)
out_img_piece = out_img_piece.reshape(
x_real.shape[0], x_real.shape[1])
out_img_list.append(out_img_piece)
out_img = np.transpose(np.array(out_img_list), (1, 2, 0))
elif hparams.image_mode == '1D':
y_real = np.matmul(x_real.reshape(1, -1), A_val) #(1,m)
out_img = lasso_wavelet_estimator(A_val, y_real, hparams)
out_img = out_img.reshape(-1, 1)
elif hparams.model_type == 'denoising':
assert hparams.type_measurements == 'identity'
A_val = A #(n,n)
if hparams.image_mode == '3D':
out_img_list = []
for i in range(x_real.shape[-1]):
y_real = x_real[:, :, i].reshape(1, -1) + observ_noise.T
out_img_piece = lasso_wavelet_estimator(
A_val, y_real, hparams)
out_img_piece = out_img_piece.reshape(
x_real.shape[0], x_real.shape[1])
out_img_list.append(out_img_piece)
out_img = np.transpose(np.array(out_img_list), (1, 2, 0))
elif hparams.image_mode == '1D':
y_real = np.matmul(x_real.reshape(1, -1),
A_val) + observ_noise.T
out_img = lasso_wavelet_estimator(A_val, y_real, hparams)
out_img = out_img.reshape(-1, 1)
elif hparams.model_type == 'compressing':
assert hparams.type_measurements == 'circulant'
A_val = circulant_np
if hparams.image_mode == '3D':
out_img_list = []
for i in range(x_real.shape[-1]):
y_real = A_val(x_real[:, :, i].reshape(-1, 1)).reshape(
1, -1) #[n,1] -> [1,n]
out_img_piece = lasso_wavelet_estimator(
A_val, y_real, hparams)
out_img_piece = out_img_piece.reshape(
x_real.shape[0], x_real.shape[1])
out_img_list.append(out_img_piece)
out_img = np.transpose(np.array(out_img_list), (1, 2, 0))
elif hparams.image_mode == '1D':
y_real = A_val(x_real).reshape(1, -1) #[n,1] -> [1,n]
out_img = lasso_wavelet_estimator(A_val, y_real, hparams)
out_img = out_img.reshape(-1, 1)
## === Printer === ##
# Compute and print measurement and l2 loss
# if hparams.image_mode == '3D' and hparams.model_type != 'inpainting':
# x_real = x_real.reshape(-1,1)
l2_losses = get_l2_loss(out_img, x_real, hparams.image_mode,
hparams.decoder_type)
psnr = 10 * np.log10(1 * 1 / l2_losses) #PSNR
# Printer info
if hparams.model_type == 'inpainting':
if hparams.image_mode == '1D':
mask_info = hparams.mask_name_1D[8:-4]
elif hparams.image_mode == '2D' or hparams.image_mode == '3D':
mask_info = hparams.mask_name_2D[8:-4]
type_mea_info = 'NA'
num_mea_info = 'NA'
noise_level_info = 'NA'
elif hparams.model_type == 'compressing':
mask_info = 'NA'
type_mea_info = hparams.type_measurements
num_mea_info = str(hparams.num_measurements)
noise_level_info = 'NA'
elif hparams.model_type == 'denoising':
mask_info = 'NA'
type_mea_info = 'NA'
num_mea_info = 'NA'
noise_level_info = str(hparams.noise_level)
# Print result
print(
'Final representation PSNR for img_name:{}, model_type:{}, type_mea:{}, num_mea:{}, mask:{}, decoder:{} tau:{} noise:{} is {}'
.format(hparams.img_name, hparams.model_type, type_mea_info,
num_mea_info, mask_info, hparams.decoder_type, hparams.tau,
noise_level_info, psnr))
print('END')
print('\t')
#sys.stdout.close()
## == to pd frame == ##
if hparams.pickle == 1:
pickle_file_path = hparams.pickle_file_path
if not os.path.exists(pickle_file_path):
d = {
'img_name': [hparams.img_name],
'model_type': [hparams.model_type],
'type_mea': [type_mea_info],
'num_mea': [num_mea_info],
'mask_info': [mask_info],
'decoder_type': [hparams.decoder_type],
'tau': [hparams.tau],
'noise': [noise_level_info],
'psnr': [psnr]
}
df = pd.DataFrame(data=d)
df.to_pickle(pickle_file_path)
else:
d = {
'img_name': hparams.img_name,
'model_type': hparams.model_type,
'type_mea': type_mea_info,
'num_mea': num_mea_info,
'mask_info': mask_info,
'decoder_type': hparams.decoder_type,
'tau': hparams.tau,
'noise': noise_level_info,
'psnr': psnr
}
df = pd.read_pickle(pickle_file_path)
df = df.append(d, ignore_index=True)
df.to_pickle(pickle_file_path)
## === Save === ##
if hparams.save == 1:
save_out_img(out_img, 'result/', hparams.img_name,
hparams.decoder_type, hparams.model_type, num_mea_info,
mask_info, noise_level_info, hparams.image_mode)