carpet/lista_analysis.py
def _initialize_prox_tv_per_layer(self, layer_id, layer_params,
                                      initial_parameters_prox=None):
        """Create a Lista network to solve the proxTV sub problem.

        Make sure to register correctly its parameter so that the training is
        done properly for all net_solver_type values.
        """
        layer_prox_tv = ListaLASSO(
            A=self.I_k, n_layers=self.n_inner_layers, learn_th=True,
            initial_parameters=initial_parameters_prox,
            name=f"Prox-TV-Lista[layer={layer_id}]", device=self.device
        )
        self.prox_tv[layer_id] = layer_prox_tv
        for k, p in ravel_group_params(
                layer_prox_tv.parameter_groups).items():
            layer_params[f'prox:{k}'] = p
        return layer_params
Create a Lista network to solve the proxTV sub problem.

        Make sure to register correctly its parameter so that the training is
        done properly for all net_solver_type values.
        
Create a Lista network to solve the proxTV sub problem.
Make sure to register correctly its parameter so that the training is
done properly for all net_solver_type values.
Create a Lista network to solve the proxTV sub problem . Make sure to register correctly its parameter so that the training is done properly for all net_solver_type values .
def _initialize_prox_tv_per_layer(self, layer_id, layer_params,
                                      initial_parameters_prox=None):
        layer_prox_tv = ListaLASSO(
            A=self.I_k, n_layers=self.n_inner_layers, learn_th=True,
            initial_parameters=initial_parameters_prox,
            name=f"Prox-TV-Lista[layer={layer_id}]", device=self.device
        )
        self.prox_tv[layer_id] = layer_prox_tv
        for k, p in ravel_group_params(
                layer_prox_tv.parameter_groups).items():
            layer_params[f'prox:{k}'] = p
        return layer_params
def _initialize_prox_tv_per_layer ( self , layer_id , layer_params , initial_parameters_prox = None ) : layer_prox_tv = ListaLASSO ( A = self . I_k , n_layers = self . n_inner_layers , learn_th = True , initial_parameters = initial_parameters_prox , name = f"Prox-TV-Lista[layer={layer_id}]" , device = self . device ) self . prox_tv [ layer_id ] = layer_prox_tv for k , p in ravel_group_params ( layer_prox_tv . parameter_groups ) . items ( ) : layer_params [ f'prox:{k}' ] = p return layer_params
Create a Lista network to solve the proxTV sub problem.
Create a Lista network to solve the proxTV sub problem .
"""Create a Lista network to solve the proxTV sub problem.

        Make sure to register correctly its parameter so that the training is
        done properly for all net_solver_type values.
        """
self
layer_id
layer_params
initial_parameters_prox