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import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from onescience.modules.equivariant.group_conv import GroupEquivariantConv2d
from onescience.modules.fourier.group_spectral import GSpectralConv2d
from onescience.modules.mlp.GMLP import GroupEquivariantMLP2d
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
class GNorm(nn.Module):
def __init__(self, width, group_size):
super().__init__()
self.group_size = group_size
self.norm = torch.nn.InstanceNorm3d(width)
def forward(self, x):
# x shape: (B, C*Group, H, W) -> (B, C, Group, H, W)
x = x.view(x.shape[0], -1, self.group_size, x.shape[-2], x.shape[-1])
x = self.norm(x)
x = x.view(x.shape[0], -1, x.shape[-2], x.shape[-1])
return x
class Model(nn.Module):
"""
GFNO (Group Factorized Neural Operator) 模型。
"""
def __init__(self, args, device, s1=96, s2=96):
super(Model, self).__init__()
self.__name__ = "GFNO"
self.args = args
self.device = device
self.in_channels = args.fun_dim
self.out_channels = args.out_dim
self.modes = args.modes
self.width = args.n_hidden
reflection = False
# 1. Embedding & Preprocessing
if args.unified_pos and args.geotype != "unstructured":
self.pos = unified_pos_embedding(args.shapelist, args.ref, device=device)
in_dim = args.fun_dim + args.ref ** len(args.shapelist)
else:
in_dim = args.fun_dim + args.space_dim
self.preprocess = StandardMLP(
input_dim=in_dim,
output_dim=args.n_hidden,
hidden_dims=[args.n_hidden * 2],
activation=args.act,
use_bias=True
)
# 2. Time Embedding
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),
)
else:
self.time_fc = None
# 3. Geometry Projection & Output Heads
if args.geotype == "unstructured":
self.group_size = 4 * (1 + reflection)
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=self.width * self.group_size,
out_channels=self.width,
modes1=args.modes,
modes2=args.modes,
s1=s1,
s2=s2,
)
self.iphi = IPHI()
grid_h, grid_w = s1, s2
self.point_q = nn.Sequential(
nn.Linear(self.width, self.width * 4),
nn.GELU(),
nn.Linear(self.width * 4, self.out_channels),
)
else:
grid_h, grid_w = args.shapelist
# 结构化输出头 (Explicit instantiation)
self.q = GroupEquivariantMLP2d(
in_channels=self.width,
out_channels=self.out_channels,
mid_channels=self.width * 4,
reflection=reflection,
last_layer=True,
)
self.padding = [(16 - size % 16) % 16 for size in [grid_h, grid_w]]
# 4. GFNO Stem Layers
# Lifting Layer
self.p = GroupEquivariantConv2d(
in_channels=args.n_hidden,
out_channels=self.width,
kernel_size=1,
reflection=reflection,
first_layer=True,
)
# Spectral Layers
self.conv0 = GSpectralConv2d(in_channels=self.width, out_channels=self.width, modes=self.modes, reflection=reflection)
self.conv1 = GSpectralConv2d(in_channels=self.width, out_channels=self.width, modes=self.modes, reflection=reflection)
self.conv2 = GSpectralConv2d(in_channels=self.width, out_channels=self.width, modes=self.modes, reflection=reflection)
self.conv3 = GSpectralConv2d(in_channels=self.width, out_channels=self.width, modes=self.modes, reflection=reflection)
self.mlp0 = GroupEquivariantMLP2d(
in_channels=self.width,
out_channels=self.width,
mid_channels=self.width,
reflection=reflection
)
self.mlp1 = GroupEquivariantMLP2d(
in_channels=self.width,
out_channels=self.width,
mid_channels=self.width,
reflection=reflection
)
self.mlp2 = GroupEquivariantMLP2d(
in_channels=self.width,
out_channels=self.width,
mid_channels=self.width,
reflection=reflection
)
self.mlp3 = GroupEquivariantMLP2d(
in_channels=self.width,
out_channels=self.width,
mid_channels=self.width,
reflection=reflection
)
# Residual Weights (1x1 GConv)
self.w0 = GroupEquivariantConv2d(
in_channels=self.width,
out_channels=self.width,
kernel_size=1,
reflection=reflection
)
self.w1 = GroupEquivariantConv2d(
in_channels=self.width,
out_channels=self.width,
kernel_size=1,
reflection=reflection
)
self.w2 = GroupEquivariantConv2d(
in_channels=self.width,
out_channels=self.width,
kernel_size=1,
reflection=reflection
)
self.w3 = GroupEquivariantConv2d(
in_channels=self.width,
out_channels=self.width,
kernel_size=1,
reflection=reflection
)
self.norm = GNorm(self.width, group_size=4 * (1 + reflection))
def _gfno_stem_forward(self, x):
# x: (B, C, H, W)
x = self.p(x) # Lifting
# 4 Layers
x1 = self.norm(self.conv0(self.norm(x)))
x1 = self.mlp0(x1)
x = F.gelu(x1 + self.w0(x))
x1 = self.norm(self.conv1(self.norm(x)))
x1 = self.mlp1(x1)
x = F.gelu(x1 + self.w1(x))
x1 = self.norm(self.conv2(self.norm(x)))
x1 = self.mlp2(x1)
x = F.gelu(x1 + self.w2(x))
x1 = self.norm(self.conv3(self.norm(x)))
x1 = self.mlp3(x1)
x = x1 + self.w3(x)
return x
def structured_geo(self, x, fx, T=None):
B, N, _ = x.shape
if self.args.unified_pos:
pos = self.pos.repeat(B, 1, 1)
feats = torch.cat([pos, fx], dim=-1) if fx is not None else pos
else:
feats = torch.cat([x, fx], dim=-1) if fx is not None else x
feats = self.preprocess(feats)
if (T is not None) and (self.time_fc is not None):
t_emb = timestep_embedding(T, self.args.n_hidden)
t_emb = self.time_fc(t_emb)
if t_emb.ndim == 2:
t_emb = t_emb.unsqueeze(1)
feats = feats + t_emb # Broadcast
H, W = self.args.shapelist
xg = feats.permute(0, 2, 1).reshape(B, self.args.n_hidden, H, W)
if not all(p == 0 for p in self.padding):
xg = F.pad(xg, [0, self.padding[1], 0, self.padding[0]])
xg = self._gfno_stem_forward(xg)
if not all(p == 0 for p in self.padding):
xg = xg[..., : -self.padding[0], : -self.padding[1]]
xg = self.q(xg)
out = xg.reshape(B, self.out_channels, -1).permute(0, 2, 1)
return out
def unstructured_geo(self, x, fx, T=None):
B, N, _ = x.shape
feats = torch.cat([x, fx], dim=-1) if fx is not None else x
feats = self.preprocess(feats)
if (T is not None) and (self.time_fc is not None):
t_emb = timestep_embedding(T, self.args.n_hidden)
t_emb = self.time_fc(t_emb)
if t_emb.ndim == 2:
t_emb = t_emb.unsqueeze(1)
feats = feats + t_emb
xg = self.fftproject_in(
feats.permute(0, 2, 1), x_in=x, iphi=self.iphi, code=None
)
xg = self._gfno_stem_forward(xg)
xp = self.fftproject_out(xg, x_out=x, iphi=self.iphi, code=None).permute(
0, 2, 1
)
out = self.point_q(xp)
return out
def forward(self, x, fx=None, 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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