From faee664f733cfb4ce1bf2c576968c2cc6b309ada Mon Sep 17 00:00:00 2001
From: lschneider <leo.schneider@univ-lyon1.fr>
Date: Thu, 19 Sep 2024 15:34:20 +0200
Subject: [PATCH] test cossim
---
common_dataset.py | 2 +-
model_custom.py | 7 -------
2 files changed, 1 insertion(+), 8 deletions(-)
diff --git a/common_dataset.py b/common_dataset.py
index 6e46845..5ab8a5f 100644
--- a/common_dataset.py
+++ b/common_dataset.py
@@ -95,7 +95,7 @@ def alphabetical_to_numerical(seq, vocab):
else :
for i in range(len(seq) - 2 * seq.count('-')):
if seq[i + dec] != '-':
- num.append(IUPAC_VOCAB[seq[i + dec]])
+ num.append(ALPHABET_UNMOD[seq[i + dec]])
else:
if seq[i + dec + 1:i + dec + 4] == 'CaC':
num.append(21)
diff --git a/model_custom.py b/model_custom.py
index d2888b4..96cfc64 100644
--- a/model_custom.py
+++ b/model_custom.py
@@ -25,17 +25,14 @@ class PositionalEncoding(nn.Module):
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
- print(d_model, max_len)
self.register_buffer('pe', pe)
def forward(self, x):
-
x = torch.permute(x, (1, 0, 2))
"""
Arguments:
x: Tensor, shape ``[seq_len, batch_size, embedding_dim]``
"""
-
x = x + self.pe[:x.size(0)]
return self.dropout(x)
@@ -106,8 +103,6 @@ class Model_Common_Transformer(nn.Module):
def forward(self, seq, charge):
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()
- seq_emb = torch.permute(seq_emb, (0, 2, 1))
- print(seq_emb.size())
emb = self.pos_embedding(self.emb(seq_emb))
meta_enc = self.meta_enc(meta_ohe)
@@ -121,7 +116,6 @@ class Model_Common_Transformer(nn.Module):
def forward_rt(self, seq):
seq_emb = torch.nn.functional.one_hot(seq, self.nb_aa).float()
- seq_emb = torch.permute(seq_emb, (0, 2, 1))
emb = self.pos_embedding(self.emb(seq_emb))
enc = self.encoder(emb)
out_rt = self.decoder_RT(enc)
@@ -131,7 +125,6 @@ class Model_Common_Transformer(nn.Module):
def forward_int(self, seq, charge):
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()
- seq_emb = torch.permute(seq_emb, (0, 2, 1))
emb = self.pos_embedding(self.emb(seq_emb))
meta_enc = self.meta_enc(meta_ohe)
enc = self.encoder(emb)
--
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