From c1370d0e815e12e66ca2d2de075eb8b7695288b1 Mon Sep 17 00:00:00 2001
From: hnu202409060624 <2804411502@qq.com>
Date: Mon, 30 Dec 2024 18:29:02 +0800
Subject: [PATCH] Delete 'Encoder.py'

---
 Encoder.py | 77 ------------------------------------------------------
 1 file changed, 77 deletions(-)
 delete mode 100644 Encoder.py

diff --git a/Encoder.py b/Encoder.py
deleted file mode 100644
index d142208..0000000
--- a/Encoder.py
+++ /dev/null
@@ -1,77 +0,0 @@
-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_inputs: [batch_size, src_len, d_model]
-        # 输入3个enc_inputs分别与W_q、W_k、W_v相乘得到Q、K、V
-        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(5, 1024)
-        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_self_attn_mask: [batch_size, src_len, src_len]
-        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)  # enc_outputs :   [batch_size, src_len, d_model],
-            atoms_enc_self_attns1.append(attn)
-
-        return enc_outputs,enc
-