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2d48951 | 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 | import torch
import esm,math
import torch.nn as nn
import torch.nn.functional as F
from DeepMFPP.utils import PositionalEncoding,FAN_encode
from DeepMFPP.data_helper import index_alignment
class DeepMFPP(nn.Module):
def __init__(self, vocab_size: int, embedding_size: int, fan_layer_num: int=1, num_heads: int=8, encoder_layer_num: int = 1,
output_size: int = 21, layer_idx=None, esm_path=None, dropout: float = 0.6, max_pool=5, Contrastive_Learning=False):
super(DeepMFPP,self).__init__()
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.output_size = output_size
self.dropout = dropout
self.dropout_layer = nn.Dropout(self.dropout)
self.encoder_layer_num = encoder_layer_num
self.fan_layer_num = fan_layer_num
self.num_heads = num_heads
self.max_pool = max_pool
self.ctl = Contrastive_Learning
self.ffn_size = self.embedding_size*2
self.dropout_layer1 = nn.Dropout(0.4)
self.ESMmodel,_ = esm.pretrained.load_model_and_alphabet_local(esm_path)
self.ESMmodel.eval()
self.layer_idx = layer_idx
self.out_chs = 64
final_feats_shape = self.out_chs*50
self.embedding = nn.Embedding(self.vocab_size, self.embedding_size)
self.pos_encoding = PositionalEncoding(num_hiddens=self.embedding_size,dropout=self.dropout)
# self.attention_encode = AttentionEncode(self.dropout, self.embedding_size, self.num_heads,ffn=False)
self.ffn = nn.Sequential(
nn.Linear(self.embedding_size, self.embedding_size*2, bias=True),
nn.GELU(),
# nn.LeakyReLU(),
nn.Linear(self.embedding_size*2, self.embedding_size, bias=True),
)
self.ln1 = nn.LayerNorm(self.embedding_size)
self.softmax = nn.Softmax(dim=-1)
self.W_o = nn.Linear(self.embedding_size,self.embedding_size)
self.kernel_sizes = [3,5,7,11,15]
self.MaxPool1d = nn.MaxPool1d(kernel_size=self.max_pool)
self.all_conv = nn.ModuleList([
nn.Sequential(
nn.Conv1d(self.embedding_size,out_channels=self.out_chs,kernel_size=self.kernel_sizes[i],padding=(self.kernel_sizes[i]-1)//2),
nn.BatchNorm1d(self.out_chs),
nn.LeakyReLU()
)
for i in range(len(self.kernel_sizes))
])
# self.project_layer =nn.Linear(self.embedding_size,64)
self.fan = FAN_encode(self.dropout, final_feats_shape)
self.proj_layer = nn.Sequential( nn.Linear(final_feats_shape,1280),
nn.BatchNorm1d(1280),
nn.LeakyReLU(),
nn.Linear(1280,128)
)
self.fc = nn.Sequential(
nn.BatchNorm1d(128),
nn.LeakyReLU(),
nn.Linear(128,self.output_size)
)
def CNN1DNet(self,x):
for i in range(len(self.kernel_sizes)):
conv = self.all_conv[i]
conv_x = conv(x)
conv_x = self.MaxPool1d(conv_x)
if i == 0:
all_feats = conv_x
else:
all_feats = torch.cat([all_feats,conv_x],dim=-1)
return all_feats
def forward(self, x):
B,S = x.shape
H = self.embedding_size
# --- ESM layer ----
with torch.no_grad():
results = self.ESMmodel(x, repr_layers=[self.layer_idx], return_contacts=False)
esm_x = results["representations"][self.layer_idx] #* 50 480 /640 /1280
# --- feature A Embedding+PE layer ----
index_ali_x = index_alignment(x,condition_num=1,subtraction_num1=3,subtraction_num2=1)
embedding_x = self.embedding(index_ali_x) # [batch_size,seq_len,embedding_size]
pos_x = self.pos_encoding(embedding_x * math.sqrt(self.embedding_size)) # [batch_size,seq_len,embedding_size]
feats1 = pos_x
# feats1 = embedding_x
# feats_fuse = feats1
# for _ in range(self.encoder_layer_num):
# feats1 = self.attention_encode(feats1)
# feats1 += embedding_x # B,S,H
# feats1 += esm_x
feats2 = esm_x
# feats_fuse = feats2
# # --- Self-attention feature fuse ---
d = feats1.size(-1)
q,k = feats1, feats2
v = feats1 + feats2 #+ esm_x
feats_qk = q @ k.transpose(-1, -2)*math.sqrt(d)
feats_qk = self.softmax(feats_qk)
feats_v = feats_qk @ v
# 线性变换投影到输出向量空间
feats_v = self.W_o(feats_v) # [B,S,H]
ffn_y = self.ffn(self.ln1(feats_v)) # 这两行的结构好像只能这样写
feats_fuse = v + self.dropout_layer(ffn_y)
# feats_fuse = feats1 + feats2
# feats_final = self.dropout_layer(self.project_layer(feats_fuse))
# # --- 1DCNN layer ---
cnn_input = feats_fuse
cnn_input = cnn_input.permute(0, 2, 1) # [B,H,S]
feats3 = self.CNN1DNet(cnn_input) # [B,F,S] F:out_chas
feats3 = self.dropout_layer(feats3)
feats_final = feats3
# --- FFN layer ---
fan_input = feats_final.view(x.size(0),-1) # B,seq_len*feat_dim:50*64
fan_input = fan_input.unsqueeze(1) # B,1,seq_len*feat_dim:50*64 AddNorm中的normalized=[1, shape]
for _ in range(self.fan_layer_num):
fan_encode = self.fan(fan_input)
fan_out = fan_encode.squeeze(1)
# fan_out = fan_input.squeeze(1)
# --- CLSFC layer ---
hidden = self.proj_layer(fan_out)
logits = self.fc(hidden)
# return feats1,feats2,feats_fuse,feats_final,fan_out,hidden,logits
return hidden,logits |