def main(note_embedding_dim, metadata_embedding_dim, num_encoder_layers,
encoder_hidden_size, encoder_dropout_prob, latent_space_dim,
num_decoder_layers, decoder_hidden_size, decoder_dropout_prob,
has_metadata, batch_size, num_epochs, train, plot, log, lr):
dataset_manager = DatasetManager()
metadatas = [
BeatMarkerMetadata(subdivision=6),
TickMetadata(subdivision=6)
]
mvae_train_kwargs = {
'metadatas': metadatas,
'sequences_size': 32,
'num_bars': 16,
'train': True
}
mvae_test_kwargs = {
'metadatas': metadatas,
'sequences_size': 32,
'num_bars': 16,
'train': False
}
folk_dataset: FolkDataset = dataset_manager.get_dataset(
name='folk_4by4nbars_train', **mvae_train_kwargs)
folk_dataset_test: FolkDataset = dataset_manager.get_dataset(
name='folk_4by4nbars_train', **mvae_test_kwargs)
model = MeasureVAE(dataset=folk_dataset,
note_embedding_dim=note_embedding_dim,
metadata_embedding_dim=metadata_embedding_dim,
num_encoder_layers=num_encoder_layers,
encoder_hidden_size=encoder_hidden_size,
encoder_dropout_prob=encoder_dropout_prob,
latent_space_dim=latent_space_dim,
num_decoder_layers=num_decoder_layers,
decoder_hidden_size=decoder_hidden_size,
decoder_dropout_prob=decoder_dropout_prob,
has_metadata=has_metadata)
if train:
if torch.cuda.is_available():
model.cuda()
trainer = VAETrainer(dataset=folk_dataset, model=model, lr=lr)
trainer.train_model(batch_size=batch_size,
num_epochs=num_epochs,
plot=plot,
log=log)
else:
model.load()
model.cuda()
model.eval()
tester = VAETester(dataset=folk_dataset_test, model=model)
tester.test_model()
def main(note_embedding_dim, metadata_embedding_dim, num_encoder_layers,
encoder_hidden_size, encoder_dropout_prob, latent_space_dim,
num_decoder_layers, decoder_hidden_size, decoder_dropout_prob,
has_metadata, num_latent_rnn_layers, latent_rnn_hidden_size,
latent_rnn_dropout_prob, num_layers, lstm_hidden_size, dropout_lstm,
input_dropout, linear_hidden_size, batch_size, num_target,
num_models):
# init dataset
dataset_manager = DatasetManager()
metadatas = [
BeatMarkerMetadata(subdivision=6),
TickMetadata(subdivision=6)
]
mvae_train_kwargs = {
'metadatas': metadatas,
'sequences_size': 32,
'num_bars': 16,
'train': True
}
folk_dataset_vae: FolkDataset = dataset_manager.get_dataset(
name='folk_4by4nbars_train', **mvae_train_kwargs)
# init vae model
vae_model = MeasureVAE(dataset=folk_dataset_vae,
note_embedding_dim=note_embedding_dim,
metadata_embedding_dim=metadata_embedding_dim,
num_encoder_layers=num_encoder_layers,
encoder_hidden_size=encoder_hidden_size,
encoder_dropout_prob=encoder_dropout_prob,
latent_space_dim=latent_space_dim,
num_decoder_layers=num_decoder_layers,
decoder_hidden_size=decoder_hidden_size,
decoder_dropout_prob=decoder_dropout_prob,
has_metadata=has_metadata)
vae_model.load() # VAE model must be pre-trained
if torch.cuda.is_available():
vae_model.cuda()
folk_train_kwargs = {
'metadatas': metadatas,
'sequences_size': 32,
'num_bars': 16,
'train': True
}
folk_test_kwargs = {
'metadatas': metadatas,
'sequences_size': 32,
'num_bars': 16,
'train': False
}
folk_dataset_train: FolkDataset = dataset_manager.get_dataset(
name='folk_4by4nbars_train', **folk_train_kwargs)
folk_dataset_test: FolkDataset = dataset_manager.get_dataset(
name='folk_4by4nbars_train', **folk_test_kwargs)
# init latent_rnn model and latent_rnn_tester
latent_rnn_model = LatentRNN(dataset=folk_dataset_train,
vae_model=vae_model,
num_rnn_layers=num_latent_rnn_layers,
rnn_hidden_size=latent_rnn_hidden_size,
dropout=latent_rnn_dropout_prob,
rnn_class=torch.nn.GRU,
auto_reg=False,
teacher_forcing=True)
latent_rnn_model.load() # latent_rnn model must be pre-trained
if torch.cuda.is_available():
latent_rnn_model.cuda()
latent_rnn_tester = LatentRNNTester(dataset=folk_dataset_test,
model=latent_rnn_model)
# inti arnn model and arnn_testes
arnn_model = ConstraintModelGaussianReg(
dataset=folk_dataset_train,
note_embedding_dim=note_embedding_dim,
metadata_embedding_dim=metadata_embedding_dim,
num_layers=num_layers,
num_lstm_constraints_units=lstm_hidden_size,
num_lstm_generation_units=lstm_hidden_size,
linear_hidden_size=linear_hidden_size,
dropout_prob=dropout_lstm,
dropout_input_prob=input_dropout,
unary_constraint=True,
teacher_forcing=True)
arnn_model.load() # ARNN model must be pre-trained
if torch.cuda.is_available():
arnn_model.cuda()
arnn_tester = AnticipationRNNTester(dataset=folk_dataset_test,
model=arnn_model)
arnn_baseline_model = AnticipationRNNBaseline(
dataset=folk_dataset_train,
note_embedding_dim=note_embedding_dim,
metadata_embedding_dim=metadata_embedding_dim,
num_layers=num_layers,
num_lstm_constraints_units=lstm_hidden_size,
num_lstm_generation_units=lstm_hidden_size,
linear_hidden_size=linear_hidden_size,
dropout_prob=dropout_lstm,
dropout_input_prob=input_dropout,
unary_constraint=True,
teacher_forcing=True)
arnn_baseline_model.load() # ARNN model must be pre-trained
if torch.cuda.is_available():
arnn_baseline_model.cuda()
arnn_baseline_tester = AnticipationRNNTester(dataset=folk_dataset_test,
model=arnn_baseline_model)
# create test dataloader
(_, _,
test_dataloader) = folk_dataset_test.data_loaders(batch_size=batch_size,
split=(0.01, 0.01))
# test
print('Num Test Batches: ', len(test_dataloader))
latent_rnn_mean_loss, latent_rnn_mean_accuracy, \
arnn_mean_loss, arnn_mean_accuracy, \
arnn_baseline_mean_loss, arnn_baseline_mean_accuracy = loss_and_acc_test(
data_loader=test_dataloader,
latent_rnn_tester=latent_rnn_tester,
arnn_tester=arnn_tester,
arnn_baseline_tester=arnn_baseline_tester,
num_target_measures=num_target,
num_models=num_models
)
print('Test Epoch:')
print('latent_rnn Test Loss: ', latent_rnn_mean_loss, '\n'
'latent_rnn Test Accuracy: ', latent_rnn_mean_accuracy * 100, '\n'
'ARNN Test Loss: ', arnn_mean_loss, '\n'
'ARNN Test Accuracy: ', arnn_mean_accuracy * 100, '\n'
'ARNN Baseline Test Loss: ', arnn_baseline_mean_loss, '\n'
'ARNN Baseline Test Accuracy: ', arnn_baseline_mean_accuracy * 100,
'\n')