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chem_trans.py

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+from Encoder import Encoder
+import torch
+import torch.nn as nn
+import config
+
+
+class Chem_trans(nn.Module):
+    def __init__(self,):
+        super(Chem_trans, self).__init__()
+        self.Encoder = Encoder()
+        self.projection = nn.Linear(2, 2, bias=True)
+        self.conv1d = nn.Conv1d(in_channels=config.input_dim, out_channels=2, kernel_size=config.kernal_size, stride=config.stride, padding=0)
+        
+
+    def forward(self,enc_inputs,CCO,enc):
+        enc_outputs,enc= self.Encoder(enc_inputs,CCO,enc)
+        dec=torch.cat((enc_outputs,enc),dim=1)
+
+        dec_logits=self.conv1d(dec.transpose(1,2))
+
+        dec_logits = self.projection(dec_logits.transpose(1,2))
+
+        return dec_logits.view(-1, dec_logits.size(-1))
+