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import torch
import torch.nn as nn
from torch.nn import functional as F
import math
class MLP(nn.Module):
def __init__(self, in_feat, hid_feat=None, out_feat=None,
dropout=0.):
super().__init__()
if not hid_feat:
hid_feat = in_feat
if not out_feat:
out_feat = in_feat
self.fc1 = nn.Linear(in_feat, hid_feat)
self.act = torch.nn.ReLU()
self.fc2 = nn.Linear(hid_feat,out_feat)
self.droprateout = nn.Dropout(dropout)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return self.droprateout(x)
class Attention_new(nn.Module):
def __init__(self, dim, heads, attention_dropout=0.):
super().__init__()
assert dim % heads == 0
self.heads = heads
self.scale = 1./dim**0.5
self.q = nn.Linear(dim, dim)
self.k = nn.Linear(dim, dim)
self.v = nn.Linear(dim, dim)
self.e = nn.Linear(dim, dim)
#self.attention_dropout = nn.Dropout(attention_dropout)
self.d_k = dim // heads
self.heads = heads
self.out_e = nn.Linear(dim,dim)
self.out_n = nn.Linear(dim, dim)
def forward(self, node, edge):
b, n, c = node.shape
q_embed = self.q(node).view(-1, n, self.heads, c//self.heads)
k_embed = self.k(node).view(-1, n, self.heads, c//self.heads)
v_embed = self.v(node).view(-1, n, self.heads, c//self.heads)
e_embed = self.e(edge).view(-1, n, n, self.heads, c//self.heads)
q_embed = q_embed.unsqueeze(2)
k_embed = k_embed.unsqueeze(1)
attn = q_embed * k_embed
attn = attn/ math.sqrt(self.d_k)
attn = attn * (e_embed + 1) * e_embed
edge = self.out_e(attn.flatten(3))
attn = F.softmax(attn, dim=2)
v_embed = v_embed.unsqueeze(1)
v_embed = attn * v_embed
v_embed = v_embed.sum(dim=2).flatten(2)
node = self.out_n(v_embed)
return node, edge
class Encoder_Block(nn.Module):
def __init__(self, dim, heads,act, mlp_ratio=4, drop_rate=0.):
super().__init__()
self.ln1 = nn.LayerNorm(dim)
self.attn = Attention_new(dim, heads, drop_rate)
self.ln3 = nn.LayerNorm(dim)
self.ln4 = nn.LayerNorm(dim)
self.mlp = MLP(dim, dim*mlp_ratio, dim, dropout=drop_rate)
self.mlp2 = MLP(dim, dim*mlp_ratio, dim, dropout=drop_rate)
self.ln5 = nn.LayerNorm(dim)
self.ln6 = nn.LayerNorm(dim)
def forward(self, x,y):
x1 = self.ln1(x)
x2,y1 = self.attn(x1,y)
x2 = x1 + x2
y2 = y1 + y
x2 = self.ln3(x2)
y2 = self.ln4(y2)
x = self.ln5(x2 + self.mlp(x2))
y = self.ln6(y2 + self.mlp2(y2))
return x, y
class TransformerEncoder(nn.Module):
def __init__(self, dim, depth, heads, act, mlp_ratio=4, drop_rate=0.1):
super().__init__()
self.Encoder_Blocks = nn.ModuleList([
Encoder_Block(dim, heads, act, mlp_ratio, drop_rate)
for i in range(depth)])
def forward(self, x,y):
for Encoder_Block in self.Encoder_Blocks:
x, y = Encoder_Block(x,y)
return x, y
class enc_dec_attention(nn.Module):
def __init__(self, dim, heads, attention_dropout=0., proj_dropout=0.):
super().__init__()
self.dim = dim
self.heads = heads
self.scale = 1./dim**0.5
"query is molecules"
"key is prot"
"values is again molecule"
self.q_mx = nn.Linear(dim,dim)
self.k_px = nn.Linear(dim,dim)
self.v_mx = nn.Linear(dim,dim)
self.k_pa = nn.Linear(dim,dim)
self.v_ma = nn.Linear(dim,dim)
#self.dropout_dec = nn.Dropout(proj_dropout)
self.out_nd = nn.Linear(dim, dim)
self.out_ed = nn.Linear(dim,dim)
def forward(self, mol_annot, prot_annot, mol_adj, prot_adj):
b, n, c = mol_annot.shape
_, m, _ = prot_annot.shape
query_mol_annot = self.q_mx(mol_annot).view(-1,m, self.heads, c//self.heads)
key_prot_annot = self.k_px(prot_annot).view(-1,n, self.heads, c//self.heads)
value_mol_annot = self.v_mx(mol_annot).view(-1,m, self.heads, c//self.heads)
mol_e = self.v_ma(mol_adj).view(-1,m,m, self.heads, c//self.heads)
prot_e = self.k_pa(prot_adj).view(-1,m,m, self.heads, c//self.heads)
query_mol_annot = query_mol_annot.unsqueeze(2)
key_prot_annot = key_prot_annot.unsqueeze(1)
#attn = torch.einsum('bnchd,bmahd->bnahd', query_mol_annot, key_prot_annot)
attn = query_mol_annot * key_prot_annot
attn = attn/ math.sqrt(self.dim)
attn = attn * (prot_e + 1) * mol_e
mol_e_new = attn.flatten(3)
mol_adj = self.out_ed(mol_e_new)
attn = F.softmax(attn, dim=2)
value_mol_annot = value_mol_annot.unsqueeze(1)
value_mol_annot = attn * value_mol_annot
value_mol_annot = value_mol_annot.sum(dim=2).flatten(2)
mol_annot = self.out_nd(value_mol_annot)
return mol_annot, prot_annot, mol_adj, prot_adj
class Decoder_Block(nn.Module):
def __init__(self, dim, heads, mlp_ratio=4, drop_rate=0.):
super().__init__()
self.ln1_ma = nn.LayerNorm(dim)
self.ln1_pa = nn.LayerNorm(dim)
self.ln1_mx = nn.LayerNorm(dim)
self.ln1_px = nn.LayerNorm(dim)
self.attn2 = Attention_new(dim, heads, drop_rate)
self.ln2_pa = nn.LayerNorm(dim)
self.ln2_px = nn.LayerNorm(dim)
self.dec_attn = enc_dec_attention(dim, heads, drop_rate, drop_rate)
self.ln3_ma = nn.LayerNorm(dim)
self.ln3_mx = nn.LayerNorm(dim)
self.mlp_ma = MLP(dim, dim, dropout=drop_rate)
self.mlp_mx = MLP(dim, dim, dropout=drop_rate)
self.ln4_ma = nn.LayerNorm(dim)
self.ln4_mx = nn.LayerNorm(dim)
def forward(self,mol_annot, prot_annot, mol_adj, prot_adj):
mol_annot = self.ln1_mx(mol_annot)
mol_adj = self.ln1_ma(mol_adj)
prot_annot = self.ln1_px(prot_annot)
prot_adj = self.ln1_pa(prot_adj)
px1, pa1= self.attn2(prot_annot, prot_adj)
prot_annot = prot_annot + px1
prot_adj = prot_adj + pa1
prot_annot = self.ln2_px(prot_annot)
prot_adj = self.ln2_pa(prot_adj)
mx1, prot_annot, ma1, prot_adj = self.dec_attn(mol_annot,prot_annot,mol_adj,prot_adj)
ma1 = mol_adj + ma1
mx1 = mol_annot + mx1
ma2 = self.ln3_ma(ma1)
mx2 = self.ln3_mx(mx1)
ma3 = self.mlp_ma(ma2)
mx3 = self.mlp_mx(mx2)
ma = ma3 + ma2
mx = mx3 + mx2
mol_adj = self.ln4_ma(ma)
mol_annot = self.ln4_mx(mx)
return mol_annot, prot_annot, mol_adj, prot_adj
class TransformerDecoder(nn.Module):
def __init__(self, dim, depth, heads, mlp_ratio=4, drop_rate=0.):
super().__init__()
self.Decoder_Blocks = nn.ModuleList([
Decoder_Block(dim, heads, mlp_ratio, drop_rate)
for i in range(depth)])
def forward(self, mol_annot, prot_annot, mol_adj, prot_adj):
for Decoder_Block in self.Decoder_Blocks:
mol_annot, prot_annot, mol_adj, prot_adj = Decoder_Block(mol_annot, prot_annot, mol_adj, prot_adj)
return mol_annot, prot_annot,mol_adj, prot_adj
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