o i@sdZddlZddlZddlZddlmZddlZddl m Z ddl Z ddl m Z e dZGdddejZedGdd d eZdS) z Abstract SDE classes, Reverse SDE, and VE/VP SDEs. Taken and adapted from https://github.com/yang-song/score_sde_pytorch/blob/1618ddea340f3e4a2ed7852a0694a809775cf8d0/sde_lib.py N)batch_broadcast)RegistrySDEcseZdZdZfddZeejddZejddZ ejdd Z ejd d Z ejd d Z e ejddZddZdddZejddZZS)rzFSDE abstract class. Functions are designed for a mini-batch of inputs.cst||_dS)z]Construct an SDE. Args: N: number of discretization time steps. N)super__init__N)selfr __class__=/home/ubuntu/.local/lib/python3.10/site-packages/geco/sdes.pyrs  z SDE.__init__cCdS)zEnd time of the SDE.Nr rr r r T szSDE.TcGdSNr rxtargsr r r sde&zSDE.sdecGr )zLParameters to determine the marginal distribution of the SDE, $p_t(x|args)$.Nr rr r r marginal_prob*zSDE.marginal_probcGr )zRGenerate one sample from the prior distribution, $p_T(x|args)$ with shape `shape`.Nr )rshaperr r r prior_sampling/rzSDE.prior_samplingcCr )zCompute log-density of the prior distribution. Useful for computing the log-likelihood via probability flow ODE. Args: z: latent code Returns: log probability density Nr rzr r r prior_logp4s zSDE.prior_logpcCr )zp Add the necessary arguments for instantiation of this SDE class to an argparse ArgumentParser. Nr ) parent_parserr r r add_argparse_argsAszSDE.add_argparse_argsc Cs@|}||||\}}||}|ttj||jd} || fS)aiDiscretize the SDE in the form: x_{i+1} = x_i + f_i(x_i) + G_i z_i. Useful for reverse diffusion sampling and probabiliy flow sampling. Defaults to Euler-Maruyama discretization. Args: x: a torch tensor t: a torch float representing the time step (from 0 to `self.T`) Returns: f, G device)rtorchsqrttensorr") rrrystepsizedtdrift diffusionfGr r r discretizeIs  zSDE.discretizeFcs<|j|j|j}|jGfddd|j}|S)zCreate the reverse-time SDE/ODE. Args: score_model: A function that takes x, t and y and returns the score. probability_flow: If `True`, create the reverse-time ODE used for probability flow sampling. cs@eZdZfddZefddZddZfddZd S) zSDE.reverse..RSDEcs|_|_dSrrprobability_flowrr.r r rks z"SDE.reverse..RSDE.__init__csSrr rrr r rorzSDE.reverse..RSDE.TcWs.|j||g|R}|d|d}}||fS)zACreate the drift and diffusion functions for the reverse SDE/ODE. total_driftr*) rsde_parts)rrrrr2r1r*r r r rsszSDE.reverse..RSDE.sdec sv||||\}}t|r|j}||dddddfd|||||jr)dnd}|jr5t|n|} || fS)zECreate discretized iteration rules for the reverse diffusion sampler.Ng?g?)r# is_compleximagr/ zeros_like) rrrr&mr'r+r,rev_frev_G) discretize_fn score_modelr r r-ys  8z$SDE.reverse..RSDE.discretizeN)__name__ __module__ __qualname__rpropertyrrr-r rrr:r/r;r r RSDEjs rA)rrrr-r )oselfr;r/sde_fnrAr r@r reverse]s z SDE.reversecCrrr rr r r copyrzSDE.copy)F)r<r=r>__doc__rr?abcabstractmethodrrrrr staticmethodr r-rDrE __classcell__r r r r rs*          'bbedcsreZdZeddZdfdd Zd d Zd d Zd dZddZ ddZ ddZ ddZ ddZ ddZZS)BBEDcCsL|jdtddd|jdtddd|jdtd d d|jd td d d|S)Nz--sde-nz@The number of timesteps in the SDE discretization. 30 by default)typedefaulthelpz --T_sampling+?z6The T so that t < T during sampling in the train step.z--k@zbase factor for diffusion termz--thetap= ף?z%root scale factor for diffusion term.) add_argumentintfloat)parserr r r r s zBBED.add_argparse_argsrQrRrSc sNt|||_t|j|_||_||_t d|j|_ ||_ d|_ dS)zConstruct an Brownian Bridge with Exploding Diffusion Coefficient SDE with parameterization as in the paper. dx = (y-x)/(Tc-t) dt + sqrt(theta)*k^t dw N) rrknploglogkthetarscexpiEilogrTc)r T_samplingr[r_rkwargsr r r rs  z BBED.__init__cCst|j|j|j|jdS)N)r)rLrr[r_rrr r r rEz BBED.copycC|jSrr0rr r r rzBBED.TcCrgrrcrr r r rcrhzBBED.TccCs4|||j|}|j|}|t|j}||fSr)rcr[r\r$r_)rrrr&r)sigmar*r r r rs zBBED.sdecCs2||jdddddf}|d|||}|S)NrZri)rx0rr&timemeanr r r _meansz BBED._meancCs|}td|d|j|j}d|jd|j}|jd|d||d||}t |j |j dd||j }t |S)Nr3rZr!)cpudetachnumpyr`rar^rbr[r#r%tor"r_r$)rrt_npEishvarr r r _stds &" z BBED._stdcCs||||||fSr)rnrw)rrkrr&r r r rrfzBBED.marginal_probcCsv||jkrtd|d|jd||jtj|jdf|jd}t|}|||dddddf}||fS)Nz Target shape z does not match shape of y z! Ignoring target shape.rr!) rwarningswarnrwrr#onesr" randn_like)rrr&stdrx_Tr r r rs $ zBBED.prior_samplingcCstd)Nz(prior_logp for BBED not yet implemented!)NotImplementedErrorrr r r rszBBED.prior_logp)rQrRrSrX)r<r=r>rIr rrErrcrrnrwrrrrJr r r r rLs rL)rFrGrxmath scipy.specialspecialr`rqr\&solospeech.corrector.geco.util.tensorsrr#'solospeech.corrector.geco.util.registryr SDERegistryABCrregisterrLr r r r s   u