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from math import floor
import re
from typing import Literal
import numpy as np
import torch.nn as nn
import torch
import torch.nn.functional as F
def conv(in_channels, out_channels, kernel_size, conv_dim, stride=1):
conv_layer = None
match conv_dim:
case 1:
conv_layer = nn.Conv1d
case 2:
conv_layer = nn.Conv2d
case 3:
conv_layer = nn.Conv3d
return conv_layer(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=floor(kernel_size / 2), bias=False)
def batch_norm(out_channels, conv_dim):
bn_layer = None
match conv_dim:
case 1:
bn_layer = nn.BatchNorm1d
case 2:
bn_layer = nn.BatchNorm2d
case 3:
bn_layer = nn.BatchNorm3d
return bn_layer(out_channels)
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
def conv5x5(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=5,
stride=stride, padding=2, bias=False)
def conv1x1(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=1,
stride=stride, padding=0, bias=False)
# Residual block
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, conv_dim, stride=1, downsample=None):
super().__init__()
# self.conv1 = conv5x5(in_channels, out_channels, stride)
self.conv1 = conv(in_channels, out_channels, kernel_size=5, conv_dim=conv_dim, stride=stride)
self.bn1 = batch_norm(out_channels, conv_dim=conv_dim)
self.elu = nn.ELU(inplace=True)
# self.conv2 = conv3x3(out_channels, out_channels)
self.conv2 = conv(out_channels, out_channels, kernel_size=3, conv_dim=conv_dim, stride=stride)
self.bn2 = batch_norm(out_channels, conv_dim=conv_dim)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.elu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.elu(out)
return out
class DrugVQA(nn.Module):
"""
The class is an implementation of the DrugVQA model including regularization and without pruning.
Slight modifications have been done for speedup
"""
def __init__(
self,
conv_dim: Literal[1, 2, 3],
lstm_hid_dim: int,
d_a: int,
r: int,
n_chars_smi: int,
n_chars_seq: int,
dropout: float,
in_channels: int,
cnn_channels: int,
cnn_layers: int,
emb_dim: int,
dense_hid: int,
):
"""
lstm_hid_dim: {int} hidden dimension for lstm
d_a : {int} hidden dimension for the dense layer
r : {int} attention-hops or attention heads
n_chars_smi : {int} voc size of smiles
n_chars_seq : {int} voc size of protein sequence
dropout : {float}
in_channels : {int} channels of CNN block input
cnn_channels: {int} channels of CNN block
cnn_layers : {int} num of layers of each CNN block
emb_dim : {int} embeddings dimension
dense_hid : {int} hidden dim for the output dense
"""
super().__init__()
self.conv_dim = conv_dim
self.lstm_hid_dim = lstm_hid_dim
self.r = r
self.in_channels = in_channels
# rnn
self.embeddings = nn.Embedding(n_chars_smi, emb_dim)
# self.seq_embed = nn.Embedding(n_chars_seq, emb_dim)
self.lstm = nn.LSTM(emb_dim, self.lstm_hid_dim, 2, batch_first=True, bidirectional=True,
dropout=dropout)
self.linear_first = nn.Linear(2 * self.lstm_hid_dim, d_a)
self.linear_second = nn.Linear(d_a, r)
self.linear_first_seq = nn.Linear(cnn_channels, d_a)
self.linear_second_seq = nn.Linear(d_a, self.r)
# cnn
# self.conv = conv3x3(1, self.in_channels)
self.conv = conv(1, self.in_channels, kernel_size=3, conv_dim=conv_dim)
self.bn = batch_norm(in_channels, conv_dim=conv_dim)
self.elu = nn.ELU(inplace=False)
self.layer1 = self.make_layer(cnn_channels, cnn_layers)
self.layer2 = self.make_layer(cnn_channels, cnn_layers)
self.linear_final_step = nn.Linear(self.lstm_hid_dim * 2 + d_a, dense_hid)
# self.linear_final = nn.Linear(dense_hid, n_classes)
self.softmax = nn.Softmax(dim=1)
# @staticmethod
# def softmax(input, axis=1):
# """
# Softmax applied to axis=n
# Args:
# input: {Tensor,Variable} input on which softmax is to be applied
# axis : {int} axis on which softmax is to be applied
#
# Returns:
# softmaxed tensors
# """
# input_size = input.size()
# trans_input = input.transpose(axis, len(input_size) - 1)
# trans_size = trans_input.size()
# input_2d = trans_input.contiguous().view(-1, trans_size[-1])
# soft_max_2d = F.softmax(input_2d)
# soft_max_nd = soft_max_2d.view(*trans_size)
# return soft_max_nd.transpose(axis, len(input_size) - 1)
def make_layer(self, out_channels, blocks, stride=1):
downsample = None
if (stride != 1) or (self.in_channels != out_channels):
downsample = nn.Sequential(
# conv3x3(self.in_channels, out_channels, stride=stride),
conv(self.in_channels, out_channels, kernel_size=3, conv_dim=self.conv_dim, stride=stride),
batch_norm(out_channels, conv_dim=self.conv_dim)
)
layers = [ResidualBlock(self.in_channels, out_channels,
conv_dim=self.conv_dim, stride=stride, downsample=downsample)]
self.in_channels = out_channels
for i in range(1, blocks):
layers.append(ResidualBlock(out_channels, out_channels, conv_dim=self.conv_dim))
return nn.Sequential(*layers)
def forward(self, enc_drug, enc_protein):
enc_drug, _ = enc_drug
enc_protein, _ = enc_protein
smile_embed = self.embeddings(enc_drug.long())
# self.hidden_state = tuple(hidden_state.to(smile_embed).detach() for hidden_state in self.hidden_state)
outputs, hidden_state = self.lstm(smile_embed)
sentence_att = F.tanh(self.linear_first(outputs))
sentence_att = self.linear_second(sentence_att)
sentence_att = self.softmax(sentence_att)
sentence_att = sentence_att.transpose(1, 2)
sentence_embed = sentence_att @ outputs
avg_sentence_embed = torch.sum(sentence_embed, 1) / self.r # multi head
pic = self.conv(enc_protein.float().unsqueeze(1))
pic = self.bn(pic)
pic = self.elu(pic)
pic = self.layer1(pic)
pic = self.layer2(pic)
pic_emb = torch.mean(pic, 2).unsqueeze(2)
pic_emb = pic_emb.permute(0, 2, 1)
seq_att = F.tanh(self.linear_first_seq(pic_emb))
seq_att = self.linear_second_seq(seq_att)
seq_att = self.softmax(seq_att)
seq_att = seq_att.transpose(1, 2)
seq_embed = seq_att @ pic_emb
avg_seq_embed = torch.sum(seq_embed, 1) / self.r
sscomplex = torch.cat([avg_sentence_embed, avg_seq_embed], dim=1)
sscomplex = F.relu(self.linear_final_step(sscomplex))
# if not bool(self.type):
# output = F.sigmoid(self.linear_final(sscomplex))
# return output, seq_att
# else:
# return F.log_softmax(self.linear_final(sscomplex)), seq_att
return sscomplex, seq_att
class AttentionL2Regularization(nn.Module):
def __init__(self):
super().__init__()
def forward(self, seq_att):
batch_size = seq_att.size(0)
identity = torch.eye(seq_att.size(1), device=seq_att.device)
identity = identity.unsqueeze(0).expand(batch_size, seq_att.size(1), seq_att.size(1))
loss = torch.mean(self.l2_matrix_norm(seq_att @ seq_att.transpose(1, 2) - identity))
return loss
@staticmethod
def l2_matrix_norm(m):
"""
m = ||A * A_T - I||
Missing from the original DrugVQA GitHub source code.
Opting to use the faster Frobenius norm rather than the induced L2 matrix norm (spectral norm)
proposed in the original research, because the goal is to minimize the difference between
the attention matrix and the identity matrix.
"""
return torch.linalg.norm(m, ord='fro', dim=(1, 2))
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