SKRock#
- class deepinv.sampling.SKRock(prior, data_fidelity, step_size=1.0, inner_iter=10, eta=0.05, alpha=1.0, max_iter=1e3, burnin_ratio=0.2, thinning=10, clip=(-1.0, 2.0), thresh_conv=1e-3, save_chain=False, verbose=False, sigma=0.05)[source]#
Bases:
BaseSampling
Plug-and-Play SKROCK algorithm.
Obtains samples of the posterior distribution using an orthogonal Runge-Kutta-Chebyshev stochastic approximation to accelerate the standard Unadjusted Langevin Algorithm.
The algorithm was introduced by Pereyra et al.[1].
SKROCK assumes that the denoiser is \(L\)-Lipschitz differentiable
For convergence, SKROCK required step_size smaller than \(\frac{1}{L+\|A\|_2^2}\)
Warning
This a legacy class provided for convenience. See the example in Markov Chain Monte Carlo for details on how to build a SKRock sampler.
- Parameters:
prior (deepinv.optim.ScorePrior, torch.nn.Module) – negative log-prior based on a trained or model-based denoiser.
data_fidelity (deepinv.optim.DataFidelity, torch.nn.Module) – negative log-likelihood function linked with the noise distribution in the acquisition physics.
step_size (float) – Step size of the algorithm. Tip: use physics.lipschitz to compute the Lipschitz
eta (float) – \(\eta\) SKROCK damping parameter.
alpha (float) – regularization parameter \(\alpha\).
inner_iter (int) – Number of inner SKROCK iterations.
max_iter (int) – Number of outer iterations.
thinning (int) – Thins the Markov Chain by an integer \(\geq 1\) (i.e., keeping one out of
thinning
samples to compute posterior statistics).burnin_ratio (float) – percentage of iterations used for burn-in period. The burn-in samples are discarded constant with a numerical algorithm.
clip (tuple) – Tuple containing the box-constraints \([a,b]\). If
None
, the algorithm will not project the samples.verbose (bool) – prints progress of the algorithm.
sigma (float) – noise level used in the plug-and-play prior denoiser. A larger value of sigma will result in a more regularized reconstruction.
- References:
- forward(y, physics, seed=None, x_init=None, g_statistics=lambda d: ...)[source]#
Runs the chain to obtain the posterior mean and variance of the reconstruction of the measurements y.
- Parameters:
y (torch.Tensor) – Measurements
physics (deepinv.physics.Physics) – Forward operator associated with the measurements
seed (float) – Random seed for generating the Monte Carlo samples
g_statistics (List[Callable] | Callable) – List of functions for which to compute posterior statistics, or a single function. The sampler will compute the posterior mean and variance of each function in the list. Note the sampler outputs a dictionary so they must act on
d["x"]
. Default:lambda d: d["x"]
(identity function)
- Returns:
(tuple of torch.tensor) containing the posterior mean and variance.
- Return type: