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#
# SPDX-FileCopyrightText: Copyright (c) 2022 The torch-harmonics Authors. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# This module is adapted from the official Spherical Fourier Neural Operator
# reference implementation of Boris Bonev et al. (ICML 2023), published in the
# NVIDIA/torch-harmonics repository (BSD-3-Clause). Only a thin configurable
# wrapper is added so that a single YAML config can drive the model.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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import torch
import torch.nn as nn
from torch_harmonics.examples.models.sfno import SphericalFourierNeuralOperator
class SFNO(nn.Module):
"""
Configurable wrapper around the official SFNO (Spherical Fourier Neural
Operator, Bonev et al. 2023, ICML).
The underlying network is provided by ``torch-harmonics``
(``torch_harmonics.examples.models.sfno.SphericalFourierNeuralOperator``),
which replaces the plain FFT of a vanilla FNO by a Spherical Harmonic
Transform (SHT) so that the learned convolution respects the geometry of
the sphere.
Model inputs / outputs are deterministic global fields of shape
``(Batch, C, H, W)``: a single 6-hour state ``u_t`` is mapped to the next
state ``u_{t+1}`` (trained with weighted L2 losses and 1-2 step rollout).
"""
def __init__(
self,
img_size=(32, 64),
scale_factor=2,
in_chans=4,
out_chans=4,
embed_dim=16,
num_layers=2,
activation_function="gelu",
use_mlp=True,
mlp_ratio=2.0,
drop_rate=0.0,
drop_path_rate=0.0,
normalization_layer="instance_norm",
hard_thresholding_fraction=1.0,
residual_prediction=False,
pos_embed="none",
bias=False,
):
super().__init__()
self.img_size = tuple(img_size)
self.in_chans = int(in_chans)
self.out_chans = int(out_chans)
self.model = SphericalFourierNeuralOperator(
img_size=self.img_size,
scale_factor=int(scale_factor),
in_chans=self.in_chans,
out_chans=self.out_chans,
embed_dim=int(embed_dim),
num_layers=int(num_layers),
activation_function=activation_function,
use_mlp=use_mlp,
mlp_ratio=mlp_ratio,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
normalization_layer=normalization_layer,
hard_thresholding_fraction=hard_thresholding_fraction,
residual_prediction=residual_prediction,
pos_embed=pos_embed,
bias=bias,
)
def forward(self, x):
return self.model(x)
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