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import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from onescience.modules.fourier.fno_layers import (
SpectralConv1d,
SpectralConv2d,
SpectralConv3d,
)
from onescience.modules.mlp.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]
from .U_Net import Model as U_Net
class Model(nn.Module):
"""
U-FNO 模型。
结合了 U-Net (用于多尺度特征提取) 和 FNO (用于全局谱特征提取)。
U-Net 作为 FNO 层的并联分支，增强了局部特征捕捉能力。
"""
def __init__(self, args, device, s1=96, s2=96):
super(Model, self).__init__()
self.__name__ = "U-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 & Padding Logic
# -----------------------------------------------------------
if self.args.geotype == "unstructured":
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
dim = len(self.padding)
# 辅助函数：构建 FNO 参数
def get_fno_layer(in_c, out_c):
kwargs = {
"in_channels": in_c,
"out_channels": out_c
}
# 动态添加 modes1, modes2, modes3
mode_names = ["modes1", "modes2", "modes3"]
for i in range(dim):
if i < len(mode_names):
kwargs[mode_names[i]] = args.modes
return SpectralConvList[dim](**kwargs)
self.conv0 = get_fno_layer(args.n_hidden, args.n_hidden)
self.conv1 = get_fno_layer(args.n_hidden, args.n_hidden)
self.conv2 = get_fno_layer(args.n_hidden, args.n_hidden)
self.conv3 = get_fno_layer(args.n_hidden, args.n_hidden)
ConvClass = [None, nn.Conv1d, nn.Conv2d, nn.Conv3d][dim]
self.w0 = ConvClass(args.n_hidden, args.n_hidden, 1)
self.w1 = ConvClass(args.n_hidden, args.n_hidden, 1)
self.w2 = ConvClass(args.n_hidden, args.n_hidden, 1)
self.w3 = ConvClass(args.n_hidden, args.n_hidden, 1)
# 4. U-Net Branches (Parallel)
# -----------------------------------------------------------
self.u_net2 = U_Net(args, device)
self.u_net3 = U_Net(args, device)
# 5. Projectors
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) # (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
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)
# Layer 0
x1 = self.conv0(x)
x2 = self.w0(x)
x = x1 + x2
x = F.gelu(x)
# Layer 1
x1 = self.conv1(x)
x2 = self.w1(x)
x = x1 + x2
x = F.gelu(x)
# Layer 2 (with U-Net)
x1 = self.conv2(x)
x2 = self.w2(x)
x3 = self.u_net2.multiscale(x)
x = x1 + x2 + x3
x = F.gelu(x)
# Layer 3 (with U-Net)
x1 = self.conv3(x)
x2 = self.w3(x)
x3 = self.u_net3.multiscale(x)
x = x1 + x2 + x3
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:
Time_emb = timestep_embedding(T, self.args.n_hidden)
Time_emb = self.time_fc(Time_emb)
if Time_emb.ndim == 2:
Time_emb = Time_emb.unsqueeze(1)
fx = fx + Time_emb
x = self.fftproject_in(
fx.permute(0, 2, 1), x_in=original_pos, iphi=self.iphi, code=None
)
# Layer 0
x1 = self.conv0(x)
x2 = self.w0(x)
x = x1 + x2
x = F.gelu(x)
# Layer 1
x1 = self.conv1(x)
x2 = self.w1(x)
x = x1 + x2
x = F.gelu(x)
# Layer 2
x1 = self.conv2(x)
x2 = self.w2(x)
x3 = self.u_net2.multiscale(x)
x = x1 + x2 + x3
x = F.gelu(x)
# Layer 3
x1 = self.conv3(x)
x2 = self.w3(x)
x3 = self.u_net3.multiscale(x)
x = x1 + x2 + x3
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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