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