datasets

model_custom.py

@@ -42,7 +42,7 @@ class Model_Common_Transformer(nn.Module):
     def __init__(self, drop_rate=0.1, embedding_dim=128, nb_aa=23,
                  regressor_layer_size_rt=512, regressor_layer_size_int=512, decoder_rt_ff=512, decoder_int_ff=512,
                  n_head=1, seq_length=25,
-                 charge_max=4, charge_frag_max=3, encoder_ff=512, encoder_num_layer=1, decoder_rt_num_layer=1,
+                 charge_max=5, charge_frag_max=3, encoder_ff=512, encoder_num_layer=1, decoder_rt_num_layer=1,
                  decoder_int_num_layer=1, acti='relu', norm=False):
         self.charge_max = charge_max
         self.seq_length = seq_length
@@ -101,14 +101,9 @@ class Model_Common_Transformer(nn.Module):
                                                 d_model=self.embedding_dim)
 
     def forward(self, seq, charge):
-        print(seq.size(),charge.size())
-        print(seq, charge)
         print(torch.cuda.mem_get_info())
         meta_ohe = torch.nn.functional.one_hot(charge - 1, self.charge_max).float()
         seq_emb = torch.nn.functional.one_hot(seq, self.nb_aa).float()
-        print(seq_emb.shape)
-        print(self.nb_aa, self.embedding_dim)
-        print(seq_emb)
         emb = self.pos_embedding(self.emb(seq_emb))
         meta_enc = self.meta_enc(meta_ohe)