File size: 8,177 Bytes
ff0fadf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import torch
import math
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from onescience.modules.fourier.fno_layers import (
SpectralConv1d,
SpectralConv2d,
SpectralConv3d,
)
from onescience.modules.fourier.geo_spectral import GeoSpectralConv2d, IPHI
from onescience.modules.mlp.MLP import StandardMLP
from onescience.modules.embedding import timestep_embedding, unified_pos_embedding
ConvList = [None, nn.Conv1d, nn.Conv2d, nn.Conv3d]
class Model(nn.Module):
"""
傅里叶神经算子 (Fourier Neural Operator, FNO)。
支持 1D/2D/3D 结构化网格，以及基于 Geo-FNO 的非结构化网格。
"""
def __init__(self, args, device, s1=96, s2=96):
super(Model, self).__init__()
self.__name__ = "FNO"
self.args = args
# ==========================================
# 1. Embedding & Preprocess
# ==========================================
if args.unified_pos and args.geotype != "unstructured": # structured mesh
self.pos = unified_pos_embedding(args.shapelist, args.ref, device=device)
input_dim = args.fun_dim + args.ref ** len(args.shapelist)
else:
input_dim = args.fun_dim + args.space_dim
self.preprocess = StandardMLP(
input_dim=input_dim,
hidden_dims=[args.n_hidden * 2],
output_dim=args.n_hidden,
activation=args.act,
n_layers=0,
res=False,
)
if args.time_input:
self.time_fc = nn.Sequential(
nn.Linear(args.n_hidden, args.n_hidden),
nn.SiLU(),
nn.Linear(args.n_hidden, args.n_hidden),
)
# ==========================================
# 2. Geometry Projection (GeoFNO 特有)
# ==========================================
if self.args.geotype == "unstructured":
# 明确传入具体参数
self.fftproject_in = GeoSpectralConv2d(
in_channels=args.n_hidden,
out_channels=args.n_hidden,
modes1=args.modes,
modes2=args.modes,
s1=s1,
s2=s2
)
self.fftproject_out = GeoSpectralConv2d(
in_channels=args.n_hidden,
out_channels=args.n_hidden,
modes1=args.modes,
modes2=args.modes,
s1=s1,
s2=s2
)
self.iphi = IPHI()
self.padding = [(16 - size % 16) % 16 for size in [s1, s2]]
else:
self.padding = [(16 - size % 16) % 16 for size in args.shapelist]
# ==========================================
# 3. FNO Blocks (显式参数实例化)
# ==========================================
dim = len(self.padding)
if dim == 1:
self.conv0 = SpectralConv1d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes)
self.conv1 = SpectralConv1d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes)
self.conv2 = SpectralConv1d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes)
self.conv3 = SpectralConv1d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes)
elif dim == 2:
self.conv0 = SpectralConv2d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes)
self.conv1 = SpectralConv2d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes)
self.conv2 = SpectralConv2d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes)
self.conv3 = SpectralConv2d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes)
elif dim == 3:
self.conv0 = SpectralConv3d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes, modes3=args.modes)
self.conv1 = SpectralConv3d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes, modes3=args.modes)
self.conv2 = SpectralConv3d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes, modes3=args.modes)
self.conv3 = SpectralConv3d(in_channels=args.n_hidden, out_channels=args.n_hidden, modes1=args.modes, modes2=args.modes, modes3=args.modes)
else:
raise ValueError(f"Unsupported dimension: {dim}. Only 1D, 2D, and 3D are supported.")
# 对应的 1x1 卷积通道混合层
self.w0 = ConvList[dim](args.n_hidden, args.n_hidden, 1)
self.w1 = ConvList[dim](args.n_hidden, args.n_hidden, 1)
self.w2 = ConvList[dim](args.n_hidden, args.n_hidden, 1)
self.w3 = ConvList[dim](args.n_hidden, args.n_hidden, 1)
# ==========================================
# 4. Projectors (输出层)
# ==========================================
self.fc1 = nn.Linear(args.n_hidden, args.n_hidden)
self.fc2 = nn.Linear(args.n_hidden, args.out_dim)
def structured_geo(self, x, fx, T=None):
B, N, _ = x.shape
if self.args.unified_pos:
x = self.pos.repeat(x.shape[0], 1, 1)
if fx is not None:
fx = torch.cat((x, fx), -1)
fx = self.preprocess(fx)
else:
fx = self.preprocess(x)
if T is not None:
Time_emb = timestep_embedding(T, self.args.n_hidden).repeat(1, x.shape[1], 1)
Time_emb = self.time_fc(Time_emb)
fx = fx + Time_emb
x = fx.permute(0, 2, 1).reshape(B, self.args.n_hidden, *self.args.shapelist)
# Padding
if not all(item == 0 for item in self.padding):
if len(self.args.shapelist) == 2:
x = F.pad(x, [0, self.padding[1], 0, self.padding[0]])
elif len(self.args.shapelist) == 3:
x = F.pad(x, [0, self.padding[2], 0, self.padding[1], 0, self.padding[0]])
# Spectral Convs + Res connections
x = F.gelu(self.conv0(x) + self.w0(x))
x = F.gelu(self.conv1(x) + self.w1(x))
x = F.gelu(self.conv2(x) + self.w2(x))
x = self.conv3(x) + self.w3(x)
# Unpadding
if not all(item == 0 for item in self.padding):
if len(self.args.shapelist) == 2:
x = x[..., : -self.padding[0], : -self.padding[1]]
elif len(self.args.shapelist) == 3:
x = x[..., : -self.padding[0], : -self.padding[1], : -self.padding[2]]
x = x.reshape(B, self.args.n_hidden, -1).permute(0, 2, 1)
x = F.gelu(self.fc1(x))
x = self.fc2(x)
return x
def unstructured_geo(self, x, fx, T=None):
original_pos = x
if fx is not None:
fx = torch.cat((x, fx), -1)
fx = self.preprocess(fx)
else:
fx = self.preprocess(x)
if T is not None:
Time_emb = timestep_embedding(T, self.args.n_hidden).repeat(1, x.shape[1], 1)
Time_emb = self.time_fc(Time_emb)
fx = fx + Time_emb
# 透传参数到 GeoSpectralConv2d
x = self.fftproject_in(
fx.permute(0, 2, 1), x_in=original_pos, iphi=self.iphi, code=None
)
x = F.gelu(self.conv0(x) + self.w0(x))
x = F.gelu(self.conv1(x) + self.w1(x))
x = F.gelu(self.conv2(x) + self.w2(x))
x = self.conv3(x) + self.w3(x)
# 透传参数到 GeoSpectralConv2d
x = self.fftproject_out(
x, x_out=original_pos, iphi=self.iphi, code=None
).permute(0, 2, 1)
x = F.gelu(self.fc1(x))
x = self.fc2(x)
return x
def forward(self, x, fx, T=None, geo=None):
if self.args.geotype == "unstructured":
return self.unstructured_geo(x, fx, T)
else:
return self.structured_geo(x, fx, T)
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