import torch 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)