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import torch.nn as nn
import copy, math
import torch
import numpy as np
import torch.nn.functional as F
from transformers import AutoModelForMaskedLM, AutoConfig
from bertmodel import make_bert, make_bert_without_emb
from utils import ContraLoss
def load_pretrained_model():
model_checkpoint = "Rostlab/prot_bert"
config = AutoConfig.from_pretrained(model_checkpoint)
model = AutoModelForMaskedLM.from_config(config)
return model
class ConoEncoder(nn.Module):
def __init__(self, encoder):
super(ConoEncoder, self).__init__()
self.encoder = encoder
self.trainable_encoder = make_bert_without_emb()
for param in self.encoder.parameters():
param.requires_grad = False
def forward(self, x, mask): # x:(128,54) mask:(128,54)
feat = self.encoder(x, attention_mask=mask) # (128,54,128)
feat = list(feat.values())[0] # (128,54,128)
feat = self.trainable_encoder(feat, mask) # (128,54,128)
return feat
class MSABlock(nn.Module):
def __init__(self, in_dim, out_dim, vocab_size):
super(MSABlock, self).__init__()
self.embedding = nn.Embedding(vocab_size, in_dim)
self.mlp = nn.Sequential(
nn.Linear(in_dim, out_dim),
nn.LeakyReLU(),
nn.Linear(out_dim, out_dim)
)
self.init()
def init(self):
for layer in self.mlp.children():
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
# nn.init.xavier_uniform_(self.embedding.weight)
def forward(self, x): # x: (128,3,54)
x = self.embedding(x) # x: (128,3,54,128)
x = self.mlp(x) # x: (128,3,54,128)
return x
class ConoModel(nn.Module):
def __init__(self, encoder, msa_block, decoder):
super(ConoModel, self).__init__()
self.encoder = encoder
self.msa_block = msa_block
self.feature_combine = nn.Conv2d(in_channels=4, out_channels=1, kernel_size=1)
self.decoder = decoder
def forward(self, input_ids, msa, attn_idx=None):
encoder_output = self.encoder.forward(input_ids, attn_idx) # (128,54,128)
msa_output = self.msa_block(msa) # (128,3,54,128)
# msa_output = torch.mean(msa_output, dim=1)
encoder_output = encoder_output.view(input_ids.shape[0], 54, -1).unsqueeze(1) # (128,1,54,128)
output = torch.cat([encoder_output*5, msa_output], dim=1) # (128,4,54,128)
output = self.feature_combine(output) # (128,1,54,128)
output = output.squeeze(1) # (128,54,128)
logits = self.decoder(output) # (128,54,85)
return logits
class ContraModel(nn.Module):
def __init__(self, cono_encoder):
super(ContraModel, self).__init__()
self.contra_loss = ContraLoss()
self.encoder1 = cono_encoder
self.encoder2 = make_bert(404, 6, 128)
# contrastive decoder
self.lstm = nn.LSTM(16, 16, batch_first=True)
self.contra_decoder = nn.Sequential(
nn.Linear(128, 64),
nn.LeakyReLU(),
nn.Linear(64, 32),
nn.LeakyReLU(),
nn.Linear(32, 16),
nn.LeakyReLU(),
nn.Dropout(0.1),
)
# classifier
self.pre_classifer = nn.LSTM(128, 64, batch_first=True)
self.classifer = nn.Sequential(
nn.Linear(128, 32),
nn.LeakyReLU(),
nn.Linear(32, 6),
nn.Softmax(dim=-1)
)
self.init()
def init(self):
for layer in self.contra_decoder.children():
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
for layer in self.classifer.children():
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
for layer in self.pre_classifer.children():
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
for layer in self.lstm.children():
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform_(layer.weight)
def compute_class_loss(self, feat1, feat2, labels):
_, cls_feat1= self.pre_classifer(feat1)
_, cls_feat2 = self.pre_classifer(feat2)
cls_feat1 = torch.cat([cls_feat1[0], cls_feat1[1]], dim=-1).squeeze(0)
cls_feat2 = torch.cat([cls_feat2[0], cls_feat2[1]], dim=-1).squeeze(0)
cls1_dis = self.classifer(cls_feat1)
cls2_dis = self.classifer(cls_feat2)
cls1_loss = F.cross_entropy(cls1_dis, labels.to('cuda:0'))
cls2_loss = F.cross_entropy(cls2_dis, labels.to('cuda:0'))
return cls1_loss, cls2_loss
def compute_contrastive_loss(self, feat1, feat2):
contra_feat1 = self.contra_decoder(feat1)
contra_feat2 = self.contra_decoder(feat2)
_, feat1 = self.lstm(contra_feat1)
_, feat2 = self.lstm(contra_feat2)
feat1 = torch.cat([feat1[0], feat1[1]], dim=-1).squeeze(0)
feat2 = torch.cat([feat2[0], feat2[1]], dim=-1).squeeze(0)
ctr_loss = self.contra_loss(feat1, feat2)
return ctr_loss
def forward(self, x1, x2, labels=None):
loss = dict()
idx1, attn1 = x1
idx2, attn2 = x2
feat1 = self.encoder1(idx1.to('cuda:0'), attn1.to('cuda:0'))
feat2 = self.encoder2(idx2.to('cuda:0'), attn2.to('cuda:0'))
cls1_loss, cls2_loss = self.compute_class_loss(feat1, feat2, labels)
ctr_loss = self.compute_contrastive_loss(feat1, feat2)
loss['cls1_loss'] = cls1_loss
loss['cls2_loss'] = cls2_loss
loss['ctr_loss'] = ctr_loss
return loss |