def run_sampling(self, model):
"""Test sampling from the model."""
config = self.run_training_one_step("fivo", "pianoroll", 88,
"tiny_pianoroll.pkl",
"test-sampling", "multinomial",
model)
config.prefix_length = 3
config.sample_length = 6
config.split = "train"
config.sample_out_dir = None
runners.run_sample(config)
unused_samples = np.load(os.path.join(config.logdir, "samples.npz"))
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.model in ["vrnn", "srnn"]:
if FLAGS.data_dimension is None:
if FLAGS.dataset_type == "pianoroll":
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
elif FLAGS.dataset_type == "speech":
FLAGS.data_dimension = SPEECH_DEFAULT_DATA_DIMENSION
if FLAGS.mode == "train":
runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
runners.run_eval(FLAGS)
elif FLAGS.mode == "sample":
runners.run_sample(FLAGS)
elif FLAGS.model == "ghmm":
if FLAGS.mode == "train":
ghmm_runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
ghmm_runners.run_eval(FLAGS)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.model in ["vrnn", "srnn"]:
if FLAGS.data_dimension is None:
if FLAGS.dataset_type == "pianoroll":
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
elif FLAGS.dataset_type == "speech":
FLAGS.data_dimension = SPEECH_DEFAULT_DATA_DIMENSION
if FLAGS.mode == "train":
runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
runners.run_eval(FLAGS)
elif FLAGS.mode == "sample":
runners.run_sample(FLAGS)
elif FLAGS.model == "ghmm":
if FLAGS.mode == "train":
ghmm_runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
ghmm_runners.run_eval(FLAGS)
def test_sampling_with_custom_fn(self):
config = self.run_training_one_step(
"fivo",
"pianoroll",
3,
"tiny_pianoroll.pkl",
"test-sample-custom-fn",
"multinomial",
"vrnn",
batch_size=5,
create_dataset_and_model_fn=self.dummmy_dataset_and_model_fn)
config.prefix_length = 2
config.sample_length = 3
config.split = "train"
config.sample_out_dir = None
runners.run_sample(
config,
create_dataset_and_model_fn=self.dummmy_dataset_and_model_fn)
unused_samples = np.load(os.path.join(config.logdir, "samples.npz"))