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import abc
import torch
import numpy as np
from sgmse.util.registry import Registry
PredictorRegistry = Registry("Predictor")
class Predictor(abc.ABC):
"""The abstract class for a predictor algorithm."""
def __init__(self, sde, score_fn, probability_flow=False):
super().__init__()
self.sde = sde
self.rsde = sde.reverse(score_fn)
self.score_fn = score_fn
self.probability_flow = probability_flow
@abc.abstractmethod
def update_fn(self, x, t, *args):
"""One update of the predictor.
Args:
x: A PyTorch tensor representing the current state
t: A Pytorch tensor representing the current time step.
*args: Possibly additional arguments, in particular `y` for OU processes
Returns:
x: A PyTorch tensor of the next state.
x_mean: A PyTorch tensor. The next state without random noise. Useful for denoising.
"""
pass
def debug_update_fn(self, x, t, *args):
raise NotImplementedError(f"Debug update function not implemented for predictor {self}.")
@PredictorRegistry.register('euler_maruyama')
class EulerMaruyamaPredictor(Predictor):
def __init__(self, sde, score_fn, probability_flow=False):
super().__init__(sde, score_fn, probability_flow=probability_flow)
def update_fn(self, x, t, *args):
dt = -1. / self.rsde.N
z = torch.randn_like(x)
f, g = self.rsde.sde(x, t, *args)
x_mean = x + f * dt
x = x_mean + g[:, None, None, None] * np.sqrt(-dt) * z
return x, x_mean
@PredictorRegistry.register('reverse_diffusion')
class ReverseDiffusionPredictor(Predictor):
def __init__(self, sde, score_fn, probability_flow=False):
super().__init__(sde, score_fn, probability_flow=probability_flow)
def update_fn(self, x, t, y, stepsize):
f, g = self.rsde.discretize(x, t, y, stepsize)
z = torch.randn_like(x)
x_mean = x - f
x = x_mean + g[:, None, None, None] * z
return x, x_mean
@PredictorRegistry.register('none')
class NonePredictor(Predictor):
"""An empty predictor that does nothing."""
def __init__(self, *args, **kwargs):
pass
def update_fn(self, x, t, *args):
return x, x
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