def main(_):
model_path = os.path.join('./model/', FLAGS.name)
if os.path.exists(model_path) is False:
os.makedirs(model_path)
if os.path.isdir(model_path):
checkpoint_path =\
tf.train.latest_checkpoint(model_path)
dataset = load_dataset()
batch_gen = dataset.batches(FLAGS.num_seqs, FLAGS.num_steps, 10)
print(EventSeq.dim())
model = CharRNN(EventSeq.dim(),
ControlSeq.dim(),
num_seqs=FLAGS.num_seqs,
num_steps=FLAGS.num_steps,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
learning_rate=FLAGS.learning_rate,
train_keep_prob=FLAGS.train_keep_prob,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.sess.run(tf.global_variables_initializer())
model.load(checkpoint_path)
model.train(
batch_gen,
FLAGS.max_steps,
model_path,
FLAGS.save_every_n,
FLAGS.log_every_n,
)
def main(_):
if os.path.isfile(FLAGS.control) or os.path.isdir(FLAGS.control):
if os.path.isdir(FLAGS.control):
files = list(utils.find_files_by_extensions(FLAGS.control))
assert len(files) > 0, 'no file in "{control}"'.format(
control=FLAGS.control)
control = np.random.choice(files)
events, compressed_controls = torch.load(FLAGS.control)
controls = ControlSeq.recover_compressed_array(compressed_controls)
max_len = FLAGS.max_length
if FLAGS.max_length == 0:
max_len = controls.shape[0]
control = np.expand_dims(controls, 1).repeat(1, 1)
control = 'control sequence from "{control}"'.format(control=control)
assert max_len > 0, 'either max length or control sequence length should be given'
#FLAGS.start_string = FLAGS.start_string.decode('utf-8')
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(EventSeq.dim(),
ControlSeq.dim(),
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.sess.run(tf.global_variables_initializer())
model.load(FLAGS.checkpoint_path)
outputs = model.sample(1000,
prime=events[0:100],
vocab_size=EventSeq.dim())
outputs = outputs.reshape([-1, 1])
print(outputs)
name = 'output-{i:03d}.mid'.format(i=0)
path = os.path.join("output/", name)
n_notes = utils.event_indeces_to_midi_file(outputs[:, 0], path)
print('===> {path} ({n_notes} notes)'.format(path=path, n_notes=n_notes))
sess_path = options.sess_path
data_path = options.data_path
saving_interval = options.saving_interval
learning_rate = options.learning_rate
batch_size = options.batch_size
window_size = options.window_size
stride_size = options.stride_size
use_transposition = options.use_transposition
control_ratio = options.control_ratio
teacher_forcing_ratio = options.teacher_forcing_ratio
reset_optimizer = options.reset_optimizer
enable_logging = options.enable_logging
event_dim = EventSeq.dim()
control_dim = ControlSeq.dim()
model_config = config.model
model_params = utils.params2dict(options.model_params)
model_config.update(model_params)
device = config.device
print('-' * 70)
print('Session path:', sess_path)
print('Dataset path:', data_path)
print('Saving interval:', saving_interval)
print('-' * 70)
print('Hyperparameters:', utils.dict2params(model_config))
print('Learning rate:', learning_rate)
print('Batch size:', batch_size)
import torch
from sequence import EventSeq, ControlSeq
#pylint: disable=E1101
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = {
'init_dim': 32,
'event_dim': EventSeq.dim(),
'control_dim': ControlSeq.dim(),
'hidden_dim': 512,
'gru_layers': 3,
'gru_dropout': 0.3,
}
train = {
'learning_rate': 0.001,
'batch_size': 64,
'window_size': 200,
'stride_size': 10,
'use_transposition': False,
'control_ratio': 1.0,
'teacher_forcing_ratio': 1.0
}
