File size: 6,593 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 torch.nn as nn
import torch.nn.functional as F
import numpy as np
from onescience.modules.fourier.ffno_layers import (
SpectralConv1d,
SpectralConv2d,
SpectralConv3d,
)
from onescience.modules.mlp import StandardMLP
from onescience.modules.embedding import timestep_embedding, unified_pos_embedding
from onescience.modules.fourier.geo_spectral import GeoSpectralConv2d, IPHI
SpectralConvList = [None, SpectralConv1d, SpectralConv2d, SpectralConv3d]
class Model(nn.Module):
"""
Factorized Fourier Neural Operator (F-FNO) 模型。
"""
def __init__(self, args, device, s1=96, s2=96):
super(Model, self).__init__()
self.__name__ = "F-FNO"
self.args = args
self.device = device
# 1. Embedding & Preprocessing
input_dim = args.fun_dim
if args.unified_pos and args.geotype != "unstructured":
self.pos = unified_pos_embedding(args.shapelist, args.ref, device=device)
input_dim += args.ref ** len(args.shapelist)
else:
input_dim += args.space_dim
self.preprocess = StandardMLP(
input_dim=input_dim,
output_dim=args.n_hidden,
hidden_dims=[args.n_hidden * 2],
activation=args.act,
use_bias=True
)
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 & Spectral Layers
self.spectral_layers = nn.ModuleList([])
if self.args.geotype == "unstructured":
# --- 非结构化网格路径 (GeoFNO) ---
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]]
# 中间层使用 FFNO
spectral_class = SpectralConv2d
conv_args = {
"in_dim": args.n_hidden,
"out_dim": args.n_hidden,
"modes_x": args.modes,
"modes_y": args.modes
}
else:
# --- 结构化网格路径 (FFNO) ---
self.padding = [(16 - size % 16) % 16 for size in args.shapelist]
dim = len(self.padding)
spectral_class = SpectralConvList[dim]
conv_args = {
"in_dim": args.n_hidden,
"out_dim": args.n_hidden,
}
mode_names = ["modes_x", "modes_y", "modes_z"]
for i in range(dim):
if i < len(mode_names):
conv_args[mode_names[i]] = args.modes
for _ in range(args.n_layers):
self.spectral_layers.append(
spectral_class(**conv_args)
)
# 3. Projectors (Decoder)
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:
# 【修复】使用广播机制，避免 repeat 导致的维度错误
Time_emb = timestep_embedding(T, self.args.n_hidden) # (B, C)
Time_emb = self.time_fc(Time_emb)
if Time_emb.ndim == 2:
Time_emb = Time_emb.unsqueeze(1) # (B, 1, C)
fx = fx + Time_emb # Broadcasting: (B, N, C) + (B, 1, C)
x = fx.permute(0, 2, 1).reshape(B, self.args.n_hidden, *self.args.shapelist)
if not all(item == 0 for item in self.padding):
pad_arg = []
for p in reversed(self.padding):
pad_arg.extend([0, p])
x = F.pad(x, pad_arg)
for i in range(self.args.n_layers):
x = x + self.spectral_layers[i](x)
if not all(item == 0 for item in self.padding):
if len(self.args.shapelist) == 1:
x = x[..., :-self.padding[0]]
elif 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 = self.fc1(x)
x = F.gelu(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:
# 【修复】使用广播机制，避免 repeat 导致的维度错误
Time_emb = timestep_embedding(T, self.args.n_hidden) # (B, C)
Time_emb = self.time_fc(Time_emb)
if Time_emb.ndim == 2:
Time_emb = Time_emb.unsqueeze(1) # (B, 1, C)
fx = fx + Time_emb # Broadcasting: (B, N, C) + (B, 1, C)
x = self.fftproject_in(
fx.permute(0, 2, 1), x_in=original_pos, iphi=self.iphi, code=None
)
for i in range(self.args.n_layers):
x = x + self.spectral_layers[i](x)
x = self.fftproject_out(
x, x_out=original_pos, iphi=self.iphi, code=None
).permute(0, 2, 1)
x = self.fc1(x)
x = F.gelu(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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