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LSM.py
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import torch
import math
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from timm.models.layers import trunc_normal_
from layers.Basic import MLP
from layers.Embedding import timestep_embedding, unified_pos_embedding
from layers.Neural_Spectral_Block import NeuralSpectralBlock1D, NeuralSpectralBlock2D, NeuralSpectralBlock3D
from layers.UNet_Blocks import DoubleConv1D, Down1D, Up1D, OutConv1D, DoubleConv2D, Down2D, Up2D, OutConv2D, \
DoubleConv3D, Down3D, Up3D, OutConv3D
from layers.GeoFNO_Projection import SpectralConv2d_IrregularGeo, IPHI
ConvList = [None, DoubleConv1D, DoubleConv2D, DoubleConv3D]
DownList = [None, Down1D, Down2D, Down3D]
UpList = [None, Up1D, Up2D, Up3D]
OutList = [None, OutConv1D, OutConv2D, OutConv3D]
BlockList = [None, NeuralSpectralBlock1D, NeuralSpectralBlock2D, NeuralSpectralBlock3D]
class Model(nn.Module):
def __init__(self, args, bilinear=True, num_token=4, num_basis=12, s1=96, s2=96):
super(Model, self).__init__()
self.__name__ = 'LSM'
self.args = args
if args.task == 'steady':
normtype = 'bn'
else:
normtype = 'in' # when conducting dynamic tasks, use instance norm for stability
## embedding
if args.unified_pos and args.geotype != 'unstructured': # only for structured mesh
self.pos = unified_pos_embedding(args.shapelist, args.ref)
self.preprocess = MLP(args.fun_dim + args.ref ** len(args.shapelist), args.n_hidden * 2,
args.n_hidden, n_layers=0, res=False, act=args.act)
else:
self.preprocess = MLP(args.fun_dim + args.space_dim, args.n_hidden * 2, args.n_hidden,
n_layers=0, res=False, act=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))
# geometry projection
if self.args.geotype == 'unstructured':
self.fftproject_in = SpectralConv2d_IrregularGeo(args.n_hidden, args.n_hidden, args.modes, args.modes,
s1, s2)
self.fftproject_out = SpectralConv2d_IrregularGeo(args.n_hidden, args.n_hidden, args.modes, args.modes,
s1, 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]
# multiscale modules
self.inc = ConvList[len(patch_size)](args.n_hidden, args.n_hidden, normtype=normtype)
self.down1 = DownList[len(patch_size)](args.n_hidden, args.n_hidden * 2, normtype=normtype)
self.down2 = DownList[len(patch_size)](args.n_hidden * 2, args.n_hidden * 4, normtype=normtype)
self.down3 = DownList[len(patch_size)](args.n_hidden * 4, args.n_hidden * 8, normtype=normtype)
factor = 2 if bilinear else 1
self.down4 = DownList[len(patch_size)](args.n_hidden * 8, args.n_hidden * 16 // factor, normtype=normtype)
self.up1 = UpList[len(patch_size)](args.n_hidden * 16, args.n_hidden * 8 // factor, bilinear, normtype=normtype)
self.up2 = UpList[len(patch_size)](args.n_hidden * 8, args.n_hidden * 4 // factor, bilinear, normtype=normtype)
self.up3 = UpList[len(patch_size)](args.n_hidden * 4, args.n_hidden * 2 // factor, bilinear, normtype=normtype)
self.up4 = UpList[len(patch_size)](args.n_hidden * 2, args.n_hidden, bilinear, normtype=normtype)
self.outc = OutList[len(patch_size)](args.n_hidden, args.n_hidden)
# Patchified Neural Spectral Blocks
self.process1 = BlockList[len(patch_size)](args.n_hidden, num_basis, patch_size, num_token, args.n_heads)
self.process2 = BlockList[len(patch_size)](args.n_hidden * 2, num_basis, patch_size, num_token, args.n_heads)
self.process3 = BlockList[len(patch_size)](args.n_hidden * 4, num_basis, patch_size, num_token, args.n_heads)
self.process4 = BlockList[len(patch_size)](args.n_hidden * 8, num_basis, patch_size, num_token, args.n_heads)
self.process5 = BlockList[len(patch_size)](args.n_hidden * 16 // factor, num_basis, patch_size, num_token,
args.n_heads)
# projectors
self.fc1 = nn.Linear(args.n_hidden, args.n_hidden * 2)
self.fc2 = nn.Linear(args.n_hidden * 2, 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)
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]])
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(self.process5(x5), self.process4(x4))
x = self.up2(x, self.process3(x3))
x = self.up3(x, self.process2(x2))
x = self.up4(x, self.process1(x1))
x = self.outc(x)
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
x = self.fftproject_in(fx.permute(0, 2, 1), x_in=original_pos, iphi=self.iphi, code=None)
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(self.process5(x5), self.process4(x4))
x = self.up2(x, self.process3(x3))
x = self.up3(x, self.process2(x2))
x = self.up4(x, self.process1(x1))
x = self.outc(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)