def _test_sample_vae():
train_dataset = LyricsRawDataset(os.path.join('local_data', 'data'), TRAIN_SET, genre=Genre.Rock)
vae = SentenceVAE(
vocab_size=train_dataset.vocab_size,
sos_idx=train_dataset.sos_idx,
eos_idx=train_dataset.eos_idx,
pad_idx=train_dataset.pad_idx,
unk_idx=train_dataset.unk_idx,
max_sequence_length=50,
embedding_size=300,
rnn_type='gru',
hidden_dim=64,
latent_size=32).cuda()
# vae: SentenceVAE
datamanager = DataManager("./local_data/results/2019-09-26_19.18.29")
loaded = datamanager.load_python_obj("models/model_best")
state_dict = 0
for state_dict in loaded.values():
state_dict = state_dict
vae.load_state_dict(state_dict)
vae.eval()
y = vae.sample()
for sen in y:
string = ""
for num in sen:
string += (train_dataset.i2w[str(num.item())])
print(string)
def setup(self, data_manager: DataManager, set_name: str) -> List[Song]:
x: List[Song] = data_manager.load_python_obj(f'song_lyrics.{set_name}')
return x
def setup(self, data_manager: DataManager, set_name: str):
x = data_manager.load_python_obj(f'song_lyrics.{set_name}')
x: List[Song]
return [song for song in x if self.genre == song.genre]
def setup(self, data_manager: DataManager, set_name: str):
return data_manager.load_python_obj(f'song_lyrics.{set_name}')