| # Copyright 2024 DeepMind Technologies Limited. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS-IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Base class for diffusion samplers.""" | |
| import abc | |
| from typing import Optional | |
| from . import denoisers_base | |
| import xarray | |
| class Sampler(abc.ABC): | |
| """A sampling algorithm for a denoising diffusion model. | |
| This is constructed with a denoising function, and uses it to draw samples. | |
| """ | |
| _denoiser: denoisers_base.Denoiser | |
| def __init__(self, denoiser: denoisers_base.Denoiser): | |
| """Constructs Sampler. | |
| Args: | |
| denoiser: A Denoiser which has been trained with an MSE loss to predict | |
| the noise-free targets. | |
| """ | |
| self._denoiser = denoiser | |
| def __call__( | |
| self, | |
| inputs: xarray.Dataset, | |
| targets_template: xarray.Dataset, | |
| forcings: Optional[xarray.Dataset] = None, | |
| **kwargs) -> xarray.Dataset: | |
| """Draws a sample using self._denoiser. Contract like Predictor.__call__.""" | |