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bdce880 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_
from model.Embedding import timestep_embedding
import numpy as np
from model.Physics_Attention import Physics_Attention_Irregular_Mesh
ACTIVATION = {'gelu': nn.GELU, 'tanh': nn.Tanh, 'sigmoid': nn.Sigmoid, 'relu': nn.ReLU, 'leaky_relu': nn.LeakyReLU(0.1),
'softplus': nn.Softplus, 'ELU': nn.ELU, 'silu': nn.SiLU}
class MLP(nn.Module):
def __init__(self, n_input, n_hidden, n_output, n_layers=1, act='gelu', res=True):
super(MLP, self).__init__()
if act in ACTIVATION.keys():
act = ACTIVATION[act]
else:
raise NotImplementedError
self.n_input = n_input
self.n_hidden = n_hidden
self.n_output = n_output
self.n_layers = n_layers
self.res = res
self.linear_pre = nn.Sequential(nn.Linear(n_input, n_hidden), act())
self.linear_post = nn.Linear(n_hidden, n_output)
self.linears = nn.ModuleList([nn.Sequential(nn.Linear(n_hidden, n_hidden), act()) for _ in range(n_layers)])
def forward(self, x):
x = self.linear_pre(x)
for i in range(self.n_layers):
if self.res:
x = self.linears[i](x) + x
else:
x = self.linears[i](x)
x = self.linear_post(x)
return x
class Transolver_block(nn.Module):
"""Transformer encoder block."""
def __init__(
self,
num_heads: int,
hidden_dim: int,
dropout: float,
act='gelu',
mlp_ratio=4,
last_layer=False,
out_dim=1,
slice_num=32,
):
super().__init__()
self.last_layer = last_layer
self.ln_1 = nn.LayerNorm(hidden_dim)
self.Attn = Physics_Attention_Irregular_Mesh(hidden_dim, heads=num_heads, dim_head=hidden_dim // num_heads,
dropout=dropout, slice_num=slice_num)
self.ln_2 = nn.LayerNorm(hidden_dim)
self.mlp = MLP(hidden_dim, hidden_dim * mlp_ratio, hidden_dim, n_layers=0, res=False, act=act)
if self.last_layer:
self.ln_3 = nn.LayerNorm(hidden_dim)
self.mlp2 = nn.Linear(hidden_dim, out_dim)
def forward(self, fx):
fx = self.Attn(self.ln_1(fx)) + fx
fx = self.mlp(self.ln_2(fx)) + fx
if self.last_layer:
return self.mlp2(self.ln_3(fx))
else:
return fx
class Model(nn.Module):
def __init__(self,
space_dim=1,
n_layers=5,
n_hidden=256,
dropout=0.0,
n_head=8,
Time_Input=False,
act='gelu',
mlp_ratio=1,
fun_dim=1,
out_dim=1,
slice_num=32,
ref=8,
unified_pos=False
):
super(Model, self).__init__()
self.__name__ = 'Transolver_1D'
self.ref = ref
self.unified_pos = unified_pos
self.Time_Input = Time_Input
self.n_hidden = n_hidden
self.space_dim = space_dim
if self.unified_pos:
self.preprocess = MLP(fun_dim + self.ref * self.ref, n_hidden * 2, n_hidden, n_layers=0, res=False, act=act)
else:
self.preprocess = MLP(fun_dim + space_dim, n_hidden * 2, n_hidden, n_layers=0, res=False, act=act)
if Time_Input:
self.time_fc = nn.Sequential(nn.Linear(n_hidden, n_hidden), nn.SiLU(), nn.Linear(n_hidden, n_hidden))
self.blocks = nn.ModuleList([Transolver_block(num_heads=n_head, hidden_dim=n_hidden,
dropout=dropout,
act=act,
mlp_ratio=mlp_ratio,
out_dim=out_dim,
slice_num=slice_num,
last_layer=(_ == n_layers - 1))
for _ in range(n_layers)])
self.initialize_weights()
self.placeholder = nn.Parameter((1 / (n_hidden)) * torch.rand(n_hidden, dtype=torch.float))
def initialize_weights(self):
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm1d)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_grid(self, x, batchsize=1):
# x: B N 2
# grid_ref
gridx = torch.tensor(np.linspace(0, 1, self.ref), dtype=torch.float)
gridx = gridx.reshape(1, self.ref, 1, 1).repeat([batchsize, 1, self.ref, 1])
gridy = torch.tensor(np.linspace(0, 1, self.ref), dtype=torch.float)
gridy = gridy.reshape(1, 1, self.ref, 1).repeat([batchsize, self.ref, 1, 1])
grid_ref = torch.cat((gridx, gridy), dim=-1).cuda().reshape(batchsize, self.ref * self.ref, 2) # B H W 8 8 2
pos = torch.sqrt(torch.sum((x[:, :, None, :] - grid_ref[:, None, :, :]) ** 2, dim=-1)). \
reshape(batchsize, x.shape[1], self.ref * self.ref).contiguous()
return pos
def forward(self, x, fx, T=None):
if self.unified_pos:
x = self.get_grid(x, x.shape[0])
if fx is not None:
fx = torch.cat((x, fx), -1)
fx = self.preprocess(fx)
else:
fx = self.preprocess(x)
fx = fx + self.placeholder[None, None, :]
if T is not None:
Time_emb = timestep_embedding(T, self.n_hidden).repeat(1, x.shape[1], 1)
Time_emb = self.time_fc(Time_emb)
fx = fx + Time_emb
for block in self.blocks:
fx = block(fx)
return fx
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