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Encoder.py

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+import torch
+from torch import nn
+from attention import MultiHeadAttention
+import config
+from Feed_Forward import PoswiseFeedForwardNet
+from torch_geometric.nn import GCNConv
+import math
+
+def get_attn_pad_mask(seq_q, seq_k):
+    batch_size, len_q = seq_q.size()
+    batch_size, len_k = seq_k.size()
+    # eq(zero) is PAD token
+    pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # batch_size x 1 x len_k(=len_q), one is masking
+    # 扩展成多维度
+    return pad_attn_mask.expand(batch_size, len_q, len_k)  # batch_size x len_q x len_k
+
+
+def get_sinusoid_encoding_table(max_len, d_model):
+    # 创建一个位置编码表，大小为 [max_len, d_model]
+    position_enc = torch.zeros(max_len, d_model)
+    # 为每个位置生成编码
+    for pos in range(max_len):
+        for i in range(0, d_model, 2):
+            position_enc[pos, i] = math.sin(pos / (10000 ** (2 * i / d_model)))
+            position_enc[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1)) / d_model)))
+    return position_enc
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self):
+        super(EncoderLayer, self).__init__()
+        self.conv = GCNConv(config.embedding_dim, config.embedding_dim, normalize=True,bias=config.bias,aggr='mean')
+        self.conv1 = GCNConv(config.embedding_dim, config.embedding_dim, normalize=True, bias=config.bias, aggr='mean')
+        self.conv2 = GCNConv(config.embedding_dim, config.embedding_dim, normalize=True, bias=config.bias, aggr='mean')
+        self.enc_feed_forward1 = PoswiseFeedForwardNet()
+        self.enc_feed_forward2=PoswiseFeedForwardNet()
+        self.Model_list=nn.ModuleList([MultiHeadAttention() for _ in range(4)])
+    def forward(self, enc_inputs,enc2,enc_self_attn_mask,edge_index):
+
+        enc_outputs=self.conv(enc_inputs,edge_index)
+        enc_outputs=self.enc_feed_forward2(enc_outputs)
+        enc_outputs=self.conv1(enc_outputs,edge_index)
+        enc_outputs=self.enc_feed_forward2(enc_outputs)
+        enc_outputs=self.conv2(enc_outputs,edge_index)
+        enc_outputs=self.enc_feed_forward2(enc_outputs)
+
+        attn=0
+        for i in self.Model_list:
+            enc2,attn=i(enc2,enc2,enc2,enc_self_attn_mask)
+            enc2 = self.enc_feed_forward1(enc2)
+        return enc_outputs,enc2, attn
+class Encoder(nn.Module):
+    def __init__(self):
+        super(Encoder, self).__init__()
+        self.embedding = nn.Embedding(config.vocab_size, config.embedding_dim)
+        self.embedding1 = nn.Embedding(config.sm_size, config.embedding_dim)
+        self.attention = MultiHeadAttention()
+        self.pos_ffn = PoswiseFeedForwardNet()
+        self.layers = nn.ModuleList([EncoderLayer() for _ in range(config.Encoder_n_layers)])
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, enc_inputs,edge_index,enc):
+        enc=self.embedding1(enc)
+        enc_outputs=self.embedding(enc_inputs)
+        atoms_enc_self_attns1 = []
+        enc_self_attn_mask = get_attn_pad_mask(enc.squeeze(0),
+                                               enc.squeeze(0))
+        enc_outputs=enc_outputs.squeeze(0)
+        edge_index=edge_index.squeeze(0)
+        enc_outputs=enc_outputs.unsqueeze(0)
+
+        for layer in self.layers:
+            enc_outputs,enc, attn = layer(enc_outputs,enc,enc_self_attn_mask,edge_index)
+            atoms_enc_self_attns1.append(attn)
+
+        return enc_outputs,enc
+