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hypernetwork_mlp_meta2_3.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 20 12:17:29 2022
@author: Shiyu
"""
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from dgl import DGLGraph
import networkx as nx
from dgl.nn.pytorch import GATConv
class HyperNetworkGAT(nn.Module):
def __init__(self, nhidmlp, nmeta, ngene, nattlayer_learn, nfeatin_learn, nmlplayer_learn, nheads_learn, nhidatt_learn, nhidmlp_learn):
super(HyperNetworkGAT, self).__init__()
"""
This is the implementation of hypernetwork.
# Parameters of hypernetwork:
nhidatt: hidden dimension of graph attention
nheads: number of heads of graph attention
nhidmlp: hidden dimension of MLP
nmeta: dimension of meta information
ngene: number of genes
nfeatin: dimention of input feature
# Dimension of the network to be learned
nattlayer_learn: number of graph attention layers to be learned
nfeatin_learn: dimension of input features
nmlplayer_learn: number of MLP layers to be learned
nheads_learn: dimention of heads of graph attention to be learned
nhidatt_learn: hidden dimension of graph attention to be learned
nhidmlp_learn: hidden dimension of MLP to be learned
# Dimenstion of parameters to be learned
## Graph attention:
### First layer
nheads_learn x (2 x nhidatt_learn)
nheads_learn x (nfeatin_learn x nhidatt_learn)
### Other layers
nheads_learn x nattlayer_learn x (2 x nhidatt_learn)
nheads_learn x nattlayer_learn x (nhidatt_learn x nhidatt_learn)
## MLP
### First layer
nhidatt_learn x nhidmlp_learn
### Other layers
nmlplayer_learn x (nhidmlp_learn x nhidmlp_learn)
### Last layer
nhidmlp_learn x 1
"""
# self.nhidatt = nhidatt
# self.nheads = nheads
self.nhidmlp = nhidmlp
self.nmeta = nmeta
self.ngene = ngene
self.nattlayer_learn = nattlayer_learn
self.nfeatin_learn = nfeatin_learn
self.nmlplayer_learn = nmlplayer_learn
self.nheads_learn = nheads_learn
self.nhidatt_learn = nhidatt_learn
self.nhidmlp_learn = nhidmlp_learn
# Graph attention layers to produce graph embedding
# self.attentions1 = GATConv(self.nfeatin, self.nhidatt, self.nheads).cuda()
# self.attentions2 = GATConv(self.nhidatt, self.nhidatt, self.nheads).cuda()
# self.out_attention1 = GATConv(self.nhidatt * self.nheads, self.nhidatt, 1).cuda()
# self.out_attention2 = GATConv(self.nhidatt * self.nheads, 1, 1).cuda()
# MLP to predict kernel
## First layer of graph attention parameter
### a
"""
Dimension of a: [nheads_learn] x [(self.nattlayer_learn + 1) * 2 * self.nhidatt_learn]
"""
self.pred_att_a1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nheads_learn * self.nmeta)
).cuda()
self.pred_att_a2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, (self.nattlayer_learn + 1) * 2 * self.nhidatt_learn)
).cuda()
### w
"""
Dimension of w: [nheads_learn] x [(nfeatin_learn + nhidatt_learn * nattlayer_learn) * self.nhidatt_learn]
"""
self.pred_att_w1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nheads_learn * self.nmeta)
).cuda()
self.pred_att_w2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, (self.nfeatin_learn + self.nhidatt_learn * self.nattlayer_learn) * self.nhidatt_learn)
).cuda()
### b
"""
Dimension of b: [1] x [nhidatt_learn * (nattlayer_learn + 1) + 1]
"""
self.pred_att_b1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nheads_learn * self.nmeta)
).cuda()
self.pred_att_b2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidatt_learn * (self.nattlayer_learn + 1))
).cuda()
### a_out
"""
Dimension of a: [self.nattlayer_learn + 1] x [2 * self.nhidatt_learn]
"""
self.pred_att_a_out1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, (self.nattlayer_learn + 1) * self.nmeta)
).cuda()
self.pred_att_a_out2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, 2 * self.nhidatt_learn)
).cuda()
### w_out
"""
Dimension of w: [self.nfeatin_learn + self.nhidatt_learn * self.nattlayer_learn] x [self.nhidatt_learn]
"""
self.pred_att_w_out1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, (self.nhidatt_learn * self.nheads_learn + self.nhidatt_learn * self.nheads_learn * self.nattlayer_learn) * self.nmeta)
).cuda()
self.pred_att_w_out2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidatt_learn)
).cuda()
### b_out
"""
Dimension of b: [self.nattlayer_learn + 1] x [self.nhidatt_learn]
"""
self.pred_att_b_out1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, (self.nattlayer_learn + 1) * self.nmeta)
).cuda()
self.pred_att_b_out2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidatt_learn)
).cuda()
def forward(self, metadata):
# Embedding of meta information
# z = self.attentions1(A, X)
# z = z.view(z.shape[0], -1)
# z = F.elu(self.out_attention1(A, z))
# z = self.attentions2(A, z)
# z = z.view(z.shape[0], -1)
# z = F.elu(self.out_attention2(A, z))
# z = z.view(1, -1)
# z = torch.cat((z, metadata), 1)
z = metadata
# Predict graph attention parameters
a = self.pred_att_a1(z)
a = a.view(self.nheads_learn, -1)
a = self.pred_att_a2(a)
w = self.pred_att_w1(z)
w = w.view(self.nheads_learn, -1)
w = self.pred_att_w2(w)
b = self.pred_att_b1(z)
b = b.view(self.nheads_learn, -1)
b = self.pred_att_b2(b)
a_out = self.pred_att_a_out1(z)
a_out= a_out.view(self.nattlayer_learn + 1, -1)
a_out = self.pred_att_a_out2(a_out)
w_out = self.pred_att_w_out1(z)
w_out = w_out.view(self.nhidatt_learn * self.nheads_learn + self.nhidatt_learn * self.nheads_learn * self.nattlayer_learn, -1)
w_out = self.pred_att_w_out2(w_out)
b_out = self.pred_att_b_out1(z)
b_out = b_out.view(self.nattlayer_learn + 1, -1)
b_out = self.pred_att_b_out2(b_out)
return a, w, b, a_out, w_out, b_out
class HyperNetworkMLP(nn.Module):
def __init__(self, nhidmlp, nmeta, ngene, nattlayer_learn, nfeatin_learn, nmlplayer_learn, nheads_learn, nhidatt_learn, nhidmlp_learn):
super(HyperNetworkMLP, self).__init__()
"""
This is the implementation of hypernetwork.
# Parameters of hypernetwork:
nhidatt: hidden dimension of graph attention
nheads: number of heads of graph attention
nhidmlp: hidden dimension of MLP
nmeta: dimension of meta information
ngene: number of genes
nfeatin: dimention of input feature
# Dimension of the network to be learned
nattlayer_learn: number of graph attention layers to be learned
nfeatin_learn: dimension of input features
nmlplayer_learn: number of MLP layers to be learned
nheads_learn: dimention of heads of graph attention to be learned
nhidatt_learn: hidden dimension of graph attention to be learned
nhidmlp_learn: hidden dimension of MLP to be learned
# Dimenstion of parameters to be learned
## Graph attention:
### First layer
nheads_learn x (2 x nhidatt_learn)
nheads_learn x (nfeatin_learn x nhidatt_learn)
### Other layers
nheads_learn x nattlayer_learn x (2 x nhidatt_learn)
nheads_learn x nattlayer_learn x (nhidatt_learn x nhidatt_learn)
## MLP
### First layer
nhidatt_learn x nhidmlp_learn
### Other layers
nmlplayer_learn x (nhidmlp_learn x nhidmlp_learn)
### Last layer
nhidmlp_learn x 1
"""
# self.nhidatt = nhidatt
# self.nheads = nheads
self.nhidmlp = nhidmlp
self.nmeta = nmeta
self.ngene = ngene
self.nattlayer_learn = nattlayer_learn
self.nfeatin_learn = nfeatin_learn
self.nmlplayer_learn = nmlplayer_learn
self.nheads_learn = nheads_learn
self.nhidatt_learn = nhidatt_learn
self.nhidmlp_learn = nhidmlp_learn
# MLP to predict kernel
# MLP parameter
### w
"""
Dimension of w: [nhidmlp_learn] x [nhidatt_learn + nmlplayer_learn * nhidmlp_learn + 1]
"""
self.pred_mlp_w1 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp_learn * self.nmeta)
).cuda()
self.pred_mlp_w2 = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidatt_learn + self.nmlplayer_learn * self.nhidmlp_learn + 1)
).cuda()
### b
"""
Dimension of b: [1] x [nhidmlp_learn * (nmlplayer_learn + 1) + 1]
"""
self.pred_mlp_b = nn.Sequential(
nn.Linear(self.nmeta, 32),
nn.ELU(),
nn.Linear(32, 64),
nn.ELU(),
nn.Linear(64, 256),
nn.ELU(),
nn.Linear(256, 512),
nn.ELU(),
nn.Linear(512, 1024),
nn.ELU(),
nn.Linear(1024, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp),
nn.ELU(),
nn.Linear(self.nhidmlp, self.nhidmlp_learn * (self.nmlplayer_learn + 1) + 1)
).cuda()
def forward(self, metadata):
z = metadata
# Predict MLP parameters
w_mlp = self.pred_mlp_w1(z)
w_mlp = w_mlp.view(self.nhidmlp_learn, -1)
w_mlp = self.pred_mlp_w2(w_mlp)
b_mlp = self.pred_mlp_b(z)
return w_mlp, b_mlp