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}')