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import math
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from onescience.modules.decoder.unet_decoder import (
UNetDecoder1D,
UNetDecoder2D,
UNetDecoder3D,
)
from onescience.modules.encoder.unet_encoder import (
UNetEncoder1D,
UNetEncoder2D,
UNetEncoder3D,
)
from onescience.modules.fourier.geo_spectral import GeoSpectralConv2d, IPHI
from onescience.modules.head.unet_head import UNetHead1D, UNetHead2D, UNetHead3D
from onescience.modules.mlp.MLP import StandardMLP
from onescience.modules.embedding import timestep_embedding, unified_pos_embedding
EncoderList = [None, UNetEncoder1D, UNetEncoder2D, UNetEncoder3D]
DecoderList = [None, UNetDecoder1D, UNetDecoder2D, UNetDecoder3D]
HeadList = [None, UNetHead1D, UNetHead2D, UNetHead3D]
class Model(nn.Module):
"""
多尺度物理场 U-Net 模型。
该模型支持结构化网格（1D/2D/3D）和非结构化网格（通过 GeoFNO 的几何投影）的物理场预测。
利用编码器和解码器实现多尺度特征提取与融合。
Args:
args: 包含模型配置的参数命名空间 (如 task, geotype, n_hidden 等)。
device: 运行设备。
bilinear (bool, optional): U-Net 上采样是否使用双线性插值。默认值: True。
s1 (int, optional): 非结构化网格投影的潜在空间高度。默认值: 96。
s2 (int, optional): 非结构化网格投影的潜在空间宽度。默认值: 96。
形状:
输入 x: 坐标张量 (B, N, space_dim)。
输入 fx: 物理场特征张量 (B, N, fun_dim)。
输入 T: 可选的时间步张量。
输出: (B, N, out_dim)
"""
def __init__(self, args, device, bilinear=True, s1=96, s2=96):
super(Model, self).__init__()
self.__name__ = "U-Net"
self.args = args
self.bilinear = bilinear
normtype = "bn" if args.task == "steady" else "in"
# ==========================================
# 1. 位置编码与预处理 (Embedding)
# ==========================================
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,
)
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. 几何投影 (Geometry Projection)
# ==========================================
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()
patch_size = [(size + (16 - size % 16) % 16) // 16 for size in [s1, s2]]
self.padding = [(16 - size % 16) % 16 for size in [s1, s2]]
else:
patch_size = [(size + (16 - size % 16) % 16) // 16 for size in args.shapelist]
self.padding = [(16 - size % 16) % 16 for size in args.shapelist]
# ==========================================
# 3. U-Net 核心 (Multiscale modules)
# ==========================================
dim = len(patch_size)
num_stages = 4
self.encoder = EncoderList[dim](
in_channels=args.n_hidden,
base_channels=args.n_hidden,
num_stages=num_stages,
bilinear=bilinear,
normtype=normtype
)
self.decoder = DecoderList[dim](
base_channels=args.n_hidden,
num_stages=num_stages,
bilinear=bilinear,
normtype=normtype
)
self.outc = HeadList[dim](
in_channels=args.n_hidden,
out_channels=args.n_hidden
)
# 最终投影
self.fc1 = nn.Linear(args.n_hidden, args.n_hidden)
self.fc2 = nn.Linear(args.n_hidden, args.out_dim)
def multiscale(self, x):
"""完全解耦的 U-Net 前向计算"""
# 提取多尺度特征
features = self.encoder(x)
# 融合上采样
x = self.decoder(features)
# 预测头输出
return self.outc(x)
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]])
# 极简调用多尺度 U-Net
x = self.multiscale(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 = 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).repeat(1, x.shape[1], 1)
Time_emb = self.time_fc(Time_emb)
fx = fx + Time_emb
# GeoFNO: 坐标映射与入场变换
x = self.fftproject_in(
fx.permute(0, 2, 1), x_in=original_pos, iphi=self.iphi, code=None
)
# 极简调用多尺度 U-Net
x = self.multiscale(x)
# GeoFNO: 出场逆变换
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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