DEQ_builder
- deepinv.unfolded.DEQ_builder(iteration, params_algo={'lambda': 1.0, 'stepsize': 1.0}, data_fidelity=L2( (d): L2Distance() ), prior=None, F_fn=None, g_first=False, bregman_potential=None, **kwargs)[source]
Helper function for building an instance of the
BaseDEQ()class.Note
Note
For now DEQ is only possible with PGD, HQS and GD optimization algorithms.
- Parameters:
iteration (str, deepinv.optim.OptimIterator) – either the name of the algorithm to be used, or directly an optim iterator. If an algorithm name (string), should be either
"PGD"(proximal gradient descent),"ADMM"(ADMM),"HQS"(half-quadratic splitting),"CP"(Chambolle-Pock) or"DRS"(Douglas Rachford).params_algo (dict) – dictionary containing all the relevant parameters for running the algorithm, e.g. the stepsize, regularisation parameter, denoising standard deviation. Each value of the dictionary can be either Iterable (distinct value for each iteration) or a single float (same value for each iteration). Default:
{"stepsize": 1.0, "lambda": 1.0}. See Parameters for more details.deepinv.optim.DataFidelity (list,) – data-fidelity term. Either a single instance (same data-fidelity for each iteration) or a list of instances of
deepinv.optim.DataFidelity()(distinct data-fidelity for each iteration). Default:None.prior (list, deepinv.optim.Prior) – regularization prior. Either a single instance (same prior for each iteration) or a list of instances of deepinv.optim.Prior (distinct prior for each iteration). Default:
None.F_fn (callable) – Custom user input cost function. default: None.
g_first (bool) – whether to perform the step on \(g\) before that on \(f\) before or not. default: False
bregman_potential (deepinv.optim.Bregman) – Bregman potential used for Bregman optimization algorithms such as Mirror Descent. Default: None, comes back to standart Euclidean optimization.
kwargs – additional arguments to be passed to the
BaseUnfold()class.
Examples using DEQ_builder:
Deep Equilibrium (DEQ) algorithms for image deblurring