o z“©išã@spddlmZmZddlmZddlmZddlmZm Z ddl m Z ddl m Z ddlmZGd d „d e ƒZd S) é)ÚCallableÚUnion)ÚTensor)ÚModule)ÚvjpÚjacrevé)ÚSDE)ÚSLIC)ÚDEVICEc sbeZdZ    ddededeeefdede f ‡fdd „ Z d ed e ed fd efdd„Z ‡Z S)Ú KernelSLICNçü©ñÒMbP?ÚkernelÚ forward_modelÚmodelÚsdeÚtikhonov_regularisationc  s@tƒj|f||dœ|¤Žt|jƒdksJdƒ‚||_||_dS)aË This clas overwrites DSM loss to include correlation from the forward model. We use the approximation that the forward model can be represented as a convolution. The kerel of this convolution is provided by the user. This approximation is useful because it can be used in the case where the Jacobian is quite large or does not fit into memory. It allows us to use the convolution theorem to simplify the Gaussian transition score function used as target for DSM. In this class, I assume that kernel has the same shape as the output of the forward model. See the method to construct an effective kernel from the forward model. )rÚcheckpoints_directoryéz.Kernel should be an image with channels first.N)ÚsuperÚ__init__ÚlenÚshaperr) ÚselfrÚinput_dimensionsrrrrrÚhyperparameters©Ú __class__©úL/home/ubuntu/.local/lib/python3.10/site-packages/score_models/kernel_slic.pyr s zKernelSLIC.__init__ÚsamplesÚargs.ÚreturncGsdS)Nr)rr r!rrrÚloss_fn'szKernelSLIC.loss_fn)NNNr )Ú__name__Ú __module__Ú __qualname__rrrÚstrrr ÚfloatrÚlistr#Ú __classcell__rrrrr s"øþü ûúø&r N)ÚtypingrrÚtorchrÚtorch.nnrÚ torch.funcrrrr Ú score_modelr Úutilsr r rrrrÚs