def Main(vocab, vocab_inv):
i = 0
arret = False # peut-être mis à True par le programme (étape 3)
contexte = None
# préparation GRU
enc = Encoder(len(vocab), 100, 100, 2, 'cuda', vocab[pad])
dec = Decoder(len(vocab), 100, 100, 2, 'cuda', vocab[pad], vocab[sos],
vocab[eos], vocab[unk])
enc.to('cuda')
dec.to('cuda')
# chargement des poids
path_enc = "encoder_9.pkl"
path_dec = "decoder_9.pkl"
encoder_state = torch.load(path_enc)
decoder_state = torch.load(path_dec)
enc.load_states(encoder_state)
enc.eval()
dec.load_states(dict(decoder_state))
dec.eval()
# paramétrage de la taille de la prédiction
taille = int(input("nombre de mots prédis à la fois ? : "))
while (not arret):
phrase = takeInput(i)
exit_c, contexte = callGRU(enc, dec, phrase, vocab, contexte, taille)
sortie = posttreatment(exit_c, vocab_inv)
# sortie = "David Albert Huffman est un petit garçon de 10 ans des plus intelligents. Cependant, son monde cours à sa perte lorsque Poupoune décide de s'emparer de l'Europe, alors en pleine crise politique, pour y imposer son monde rose et fleurissant.Avec son ami Lamy, David va devoir lutter contre des adversaires redoutables pour sauver le monde, entrer au MIT et repousser la plus grande menace du Siècle (pour le moment) pour rétablir l'équilibre dans le rapport de Force." #test enchaînement
printResult(sortie)
# contexte = exit_c
i += 1
num_layers = parameters_dict['num_layers']
bidirectional = parameters_dict['bidirectional']
use_lstm = parameters_dict['use_lstm']
use_cuda = False
batch_size = 1
dropout_p = 0.0
encoder = Encoder(en_embedding_dim, hidden_dim, en_vocab.n_items,
num_layers, dropout_p, bidirectional, use_lstm, use_cuda)
decoder = Decoder(de_embedding_dim, hidden_dim, de_vocab.n_items,
num_layers, dropout_p, bidirectional, use_lstm, use_cuda)
encoder.load_state_dict(torch.load(encoder_model_file, map_location='cpu'))
decoder.load_state_dict(torch.load(decoder_model_file, map_location='cpu'))
encoder.eval()
decoder.eval()
f_en_test = open('input.txt', 'r', encoding='utf-8')
f_de_pred = open('output.txt', 'w', encoding='utf-8')
while True:
en_sent = f_en_test.readline()
if not en_sent: break
sent = en_sent.strip()
en_seq, en_seq_len = trf.trans_input(sent)
en_seq = torch.LongTensor(en_seq)
encoder_input = en_seq
class Text2song(object):
def __init__(self):
def Load_Vocab(file):
with open(file, 'rb') as fd:
_vocab = pickle.load(fd)
return _vocab
def Load_Parameters(file):
with open(file, 'rb') as fd:
parameters_dict = pickle.load(fd)
return parameters_dict
torch.manual_seed(1)
torch.set_num_threads(4)
en_vocab_dur_file = './en_vocab_dur.pkl'
de_vocab_dur_file = './de_vocab_dur.pkl'
encoder_dur_model_file = './encoder_dur.10.pt'
decoder_dur_model_file = './decoder_dur.10.pt'
en_vocab_key_file = './en_vocab.pkl'
de_vocab_key_file = './de_vocab.pkl'
encoder_key_model_file = './encoder.10.pt'
decoder_key_model_file = './decoder.10.pt'
hyper_parameters_file = './parameters_dict.pkl'
self.en_vocab_key = Load_Vocab(en_vocab_key_file)
self.de_vocab_key = Load_Vocab(de_vocab_key_file)
self.en_vocab_dur = Load_Vocab(en_vocab_dur_file)
self.de_vocab_dur = Load_Vocab(de_vocab_dur_file)
self.trf_key = Transfrom(self.en_vocab_key)
self.trf_dur = Transfrom(self.en_vocab_dur)
self.parameters_dict = Load_Parameters(hyper_parameters_file)
en_embedding_dim = self.parameters_dict['en_embedding_dim']
de_embedding_dim = self.parameters_dict['de_embedding_dim']
hidden_dim = self.parameters_dict['hidden_dim']
num_layers = self.parameters_dict['num_layers']
bidirectional = self.parameters_dict['bidirectional']
use_lstm = self.parameters_dict['use_lstm']
self.use_cuda_dur = self.use_cuda_key = False
batch_size = 1
dropout_p = 0.0
self.encoder_key = Encoder(en_embedding_dim, hidden_dim,
self.en_vocab_key.n_items, num_layers,
dropout_p, bidirectional, use_lstm,
self.use_cuda_key)
self.decoder_key = Decoder(de_embedding_dim, hidden_dim,
self.de_vocab_key.n_items, num_layers,
dropout_p, bidirectional, use_lstm,
self.use_cuda_key)
self.encoder_dur = Encoder(en_embedding_dim, hidden_dim,
self.en_vocab_dur.n_items, num_layers,
dropout_p, bidirectional, use_lstm,
self.use_cuda_dur)
self.decoder_dur = Decoder(de_embedding_dim, hidden_dim,
self.de_vocab_dur.n_items, num_layers,
dropout_p, bidirectional, use_lstm,
self.use_cuda_dur)
self.encoder_key.load_state_dict(
torch.load(encoder_key_model_file, map_location='cpu'))
self.decoder_key.load_state_dict(
torch.load(decoder_key_model_file, map_location='cpu'))
self.encoder_dur.load_state_dict(
torch.load(encoder_dur_model_file, map_location='cpu'))
self.decoder_dur.load_state_dict(
torch.load(decoder_dur_model_file, map_location='cpu'))
self.encoder_key.eval()
self.decoder_key.eval()
self.encoder_dur.eval()
self.decoder_dur.eval()
""" __init__ return the parameters: {self.trf_dur,self.trf_key;
self.encoder_dur,self.encoder_key;
self.decoder_dur,self.decoder_key;
self.en_vocab_dur,self.en_vocab_key;
self.de_vocab_dur,self.de_vocab_key;
self.use_cuda_dur,self,self.use_cuda_key.}"""
def get_song(self, lyric):
def stop_before_eos(li, length):
if '_EOS_' in li:
i = li.index('_EOS_')
li = li[:i]
while (li.__len__() < length):
li.append(li[-1])
return li
def important_function_in_while_loop(trf, sent, encoder, decoder,
de_vocab, use_cuda, en_sent):
en_seq, en_seq_len = trf.trans_input(sent)
en_seq = torch.LongTensor(en_seq)
encoder_input = en_seq
encoder_output, encoder_state = encoder(encoder_input, en_seq_len)
# initial decoder hidden
decoder_state = decoder.init_state(encoder_state)
# Start decoding
decoder_inputs = torch.LongTensor([de_vocab.item2index['_START_']])
pred_char = ''
if use_cuda: decoder_inputs = decoder_inputs.cuda()
decoder_outputs, decoder_state = decoder(decoder_inputs,
encoder_output,
decoder_state)
max_len = len(en_sent.split())
return (pred_char, encoder_output, decoder_outputs, decoder_state,
max_len)
f_en_test = io.StringIO(lyric)
pred_list = []
while True:
en_sent = f_en_test.readline()
if not en_sent: break
sent = en_sent.strip()
pred_sent_dur = []
pred_sent_key = []
pred_char_key, encoder_output_key, decoder_outputs_key, decoder_state_key, max_len_key = \
important_function_in_while_loop(self.trf_key, sent, self.encoder_key, self.decoder_key, self.de_vocab_key, self.use_cuda_key,
en_sent)
pred_char_dur, encoder_output_dur, decoder_outputs_dur, decoder_state_dur, max_len_dur = \
important_function_in_while_loop(self.trf_dur, sent, self.encoder_dur, self.decoder_dur, self.de_vocab_dur, self.use_cuda_dur,
en_sent)
# Greedy search
while pred_char_key != '_EOS_' and pred_char_dur != '_EOS_':
log_prob_key, v_idx_key = decoder_outputs_key.data.topk(1)
pred_char_key = self.de_vocab_key.index2item[v_idx_key.item()]
pred_sent_key.append(pred_char_key)
log_prob_dur, v_idx_dur = decoder_outputs_dur.data.topk(1)
pred_char_dur = self.de_vocab_dur.index2item[v_idx_dur.item()]
pred_sent_dur.append(pred_char_dur)
if (len(pred_sent_dur) > max_len_dur
or len(pred_sent_dur) > max_len_key):
break
decoder_inputs_dur = torch.LongTensor([v_idx_dur.item()])
if self.use_cuda_dur:
decoder_inputs_dur = decoder_inputs_dur.cuda()
decoder_outputs_dur, decoder_state_dur = self.decoder_dur(
decoder_inputs_dur, encoder_output_dur, decoder_state_dur)
decoder_inputs_key = torch.LongTensor([v_idx_key.item()])
if self.use_cuda_key:
decoder_inputs_key = decoder_inputs_key.cuda()
decoder_outputs_key, decoder_state_key = self.decoder_key(
decoder_inputs_key, encoder_output_key, decoder_state_key)
length = len(sent.split())
pred_list.append({
'lyrics': sent,
'key': stop_before_eos(pred_sent_key, length),
'duration': stop_before_eos(pred_sent_dur, length)
})
# pred_list.append({'lyrics': sent, 'key': pred_sent_key, 'duration': pred_sent_dur})
return pred_list
epoch_loss += loss.item()
print(f'Batch loss : {loss.item()}')
nn.utils.clip_grad_norm_(enc.parameters(), 5)
nn.utils.clip_grad_norm_(dec.parameters(), 5)
trn_src_t.detach()
trn_tgt_t.detach()
opt_dec.step()
opt_enc.step()
opt_enc.zero_grad()
opt_dec.zero_grad()
torch.save(enc.state_dict(), f'encoder_{e}.pkl')
torch.save(dec.state_dict(), f'decoder_{e}.pkl')
print(f'Epoch training loss : {epoch_loss / n_batch}')
enc.eval()
dec.eval()
test_loss = 0
for i in range(len(tst_tgt_t) // batch_size):
lengths = torch.LongTensor(l_tst_src[batch_size * i:batch_size *
(i + 1)])
out, h_n = enc(tst_src_t[batch_size * i:batch_size * (i + 1)], lengths)
output = dec.teacher_force(
tst_tgt_t[batch_size * i:batch_size * (i + 1)].reshape(
[batch_size, tgt_max, 1]), h_n,
torch.LongTensor(l_tst_tgt[batch_size * i:batch_size * (i + 1)]))
for o, l, t in zip(output,
l_tst_tgt[batch_size * i:batch_size * (i + 1)],
tst_tgt_t[batch_size * i:batch_size * (i + 1)]):
test_loss += torch.nn.functional.cross_entropy(
o[:(l - 1)], t[1:l].to('cuda')).detach()