datasets

model_custom.py

@@ -101,15 +101,24 @@ class Model_Common_Transformer(nn.Module):
                                                 d_model=self.embedding_dim)
 
     def forward(self, seq, charge):
+        print('seq', seq)
+        print('charge', charge)
         meta_ohe = torch.nn.functional.one_hot(charge - 1, self.charge_max).float()
+        print('meta_ohe', meta_ohe)
         seq_emb = torch.nn.functional.one_hot(seq, self.nb_aa).float()
+        print('seq_emb', seq_emb)
         emb = self.pos_embedding(self.emb(seq_emb))
-        meta_enc = self.meta_enc(meta_ohe)
         print('emb', emb)
+        meta_enc = self.meta_enc(meta_ohe)
+        print('meta_enc', meta_enc)
         enc = self.encoder(emb)
+        print('enc', enc)
         out_rt = self.decoder_RT(enc)
+        print('out_rt', out_rt)
         int_enc = torch.mul(enc, meta_enc)
+        print('int_enc', int_enc)
         out_int = self.decoder_int(int_enc)
+        print('out_int', out_int)
 
         return out_rt.flatten(), out_int