def test_robust_sampling(conf, training_result):
"""
Sampling rate vs Reconstruction Quality at fixed noise std.
Parameters
----------
conf : conf_loader.Conf
Experiment parameters
training_result : touple
Retrun values of function `training`
Returns
-------
srange : np.array
sampling rate range used
sampling_rate : float
sampling rate used
(bk_ser, fa_ser, rc_ser) : (np.array, np.array, np.array)
SER for `k-best`, `f_avg` and `LSC` methods
(bk_mse, fa_mse, rc_mse) : (np.array, np.array, np.array)
mse for `k-best`, `f_avg` and `LSC` methods
"""
(sort, tros), (energy, comulated, bands), codebooks = training_result
n_bands = conf.nbands
# matrxi to vector (m2v) and vector to matrix (v2m) functions
m2v, v2m = conf.vect_functions()
subcfg = conf.testing['robust_sampling']
# Load testing set
testing, Testing = common.load_dataset(conf.testingset_path(),
conf.fformat, conf.size())
FlatTst = m2v(Testing)[:, sort]
# f_avg sampling pattern
Omega = energy.argsort()[::-1]
# lsc sampling pattern
Omegas = [c.sampling_pattern() for c in codebooks]
shape = testing[0].shape
n = np.prod(shape)
N = len(testing)
srange = np.logspace(*subcfg['sampling_range'])
sigma = subcfg['noise_rate']
bk_ser = np.zeros(len(srange))
fa_ser = np.zeros(len(srange))
rc_ser = np.zeros(len(srange))
bk_mse = np.zeros(len(srange))
fa_mse = np.zeros(len(srange))
rc_mse = np.zeros(len(srange))
print('Sampling rate at fixed noise test:')
for i, rate in enumerate(srange):
print(f'\r {i+1:3d}/{len(srange)}', flush=True, end='')
M = int(round(n * rate))
m = int(round(M / n_bands))
ms = lsc.num_samples(bands, m)
M = np.sum(ms)
smalls = [omega[:y] for omega, y in zip(Omegas, ms)]
for idx in range(N):
reference = common.norm(testing[idx])
X = FlatTst[idx] + common.noise(sigma, n)
Xsbs = lsc.split(X, bands)
Ysbs = lsc.sub_sample(Xsbs, Omegas, m)
recovered = [
codebooks[b].reconstruct(Ysbs[b], smalls[b])
for b in range(len(bands))
]
Y = v2m((lsc.union(recovered))[tros], shape)
y = common.norm(common.pos(common.ifft2(Y).real))
BK = X.copy()[tros]
O = np.abs(BK).argsort()[::-1]
BK[O[M:]] = 0
BK = v2m(BK, shape)
bK = common.norm(common.pos(common.ifft2(BK).real))
FA = X.copy()[tros]
FA[Omega[M:]] = 0
FA = v2m(FA, shape)
fA = common.norm(common.pos(common.ifft2(FA).real))
fa_ser[i] += common.SER(reference, fA) / N
bk_ser[i] += common.SER(reference, bK) / N
rc_ser[i] += common.SER(reference, y) / N
fa_mse[i] += mse(reference, fA) / N
bk_mse[i] += mse(reference, bK) / N
rc_mse[i] += mse(reference, y) / N
print(' [done]')
return srange, sigma, (bk_ser, fa_ser, rc_ser), (bk_mse, fa_mse, rc_mse)
def test_robust_visual(conf, training_result, idx):
"""
Noise std vs Reconstruction Quality at fixed sampling rate visual test.
Parameters
----------
conf : conf_loader.Conf
Experiment parameters
training_result : touple
Retrun values of function `training`
idx : int
Id of image to test
Returns
-------
srange : np.array
noise std range used
sampling_rate : float
sampling rate used
(bk_ser, fa_ser, rc_ser) : (np.array, np.array, np.array)
SER for `k-best`, `f_avg` and `LSC` methods at different noise std
(bk_mse, fa_mse, rc_mse) : (np.array, np.array, np.array)
mse for `k-best`, `f_avg` and `LSC` methods at different noise std
"""
(sort, tros), (energy, comulated, bands), codebooks = training_result
n_bands = conf.nbands
# matrxi to vector (m2v) and vector to matrix (v2m) functions
m2v, v2m = conf.vect_functions()
subcfg = conf.testing['robust_reconstruction_visual']
testing, Testing = common.load_dataset(conf.testingset_path(),
conf.fformat, conf.size())
FlatTst = m2v(Testing)[:, sort]
# f_avg sampling pattern
Omega = energy.argsort()[::-1]
# lsc sampling pattern
Omegas = [c.sampling_pattern() for c in codebooks]
shape = testing[0].shape
n = np.prod(shape)
srange = np.logspace(*subcfg['noise_range'])
sampling_rate = subcfg['sampling_rate']
W, H = shape
Wt = W * 3
Ht = H * len(srange)
res = np.zeros((Wt, Ht))
print(f'Robust Reconstruction Quality Visual Test (img {idx}):')
for i, sigma in enumerate(srange):
print(f'\r {i+1:3d}/{len(srange)}', flush=True, end='')
X = FlatTst[idx] + common.noise(sigma, n)
M = int(round(sampling_rate * n))
m = int(round(M / n_bands))
ms = lsc.num_samples(bands, m)
M = np.sum(ms)
smalls = [omega[:y] for omega, y in zip(Omegas, ms)]
Xsbs = lsc.split(X, bands)
Ysbs = lsc.sub_sample(Xsbs, Omegas, m)
recovered = [
codebooks[b].reconstruct(Ysbs[b], smalls[b])
for b in range(len(bands))
]
Y = v2m((lsc.union(recovered))[tros], shape)
y = common.norm(common.pos(common.ifft2(Y).real))
BK = X.copy()[tros]
O = np.abs(BK).argsort()[::-1]
BK[O[M:]] = 0
BK = v2m(BK, shape)
bK = common.norm(common.pos(common.ifft2(BK).real))
FA = X.copy()[tros]
FA[Omega[M:]] = 0
FA = v2m(FA, shape)
fA = common.norm(common.pos(common.ifft2(FA).real))
res[:W, H * i:H * (i + 1)] = bK
res[W:2 * W, H * i:H * (i + 1)] = fA
res[2 * W:3 * W, H * i:H * (i + 1)] = y
print('\t[done]')
return srange, res