def load_training_data(params, config):
"""Load and return the training data."""
# Load data
if params['is_conditional']:
raise ValueError("Not supported yet.")
else:
labels = None
# Load data
LOGGER.info("Loading training data.")
data = load_data(config['data_source'], config['data_filename'])
LOGGER.info("Training data size: %d", len(data))
# Build dataset
LOGGER.info("Building dataset.")
dataset = get_dataset(data, labels, config['batch_size'],
params['data_shape'], config['use_random_transpose'],
config['n_jobs'])
# Create iterator
if params['is_conditional']:
train_x, train_y = dataset.make_one_shot_iterator().get_next()
else:
train_x, train_y = dataset.make_one_shot_iterator().get_next(), None
return train_x, train_y
def load_training_data(params, config):
"""Load and return the training data."""
# Load data
if params['is_conditional']: # greg: ¿Qué significa este error??
raise ValueError("Not supported yet.")
else:
labels = None
LOGGER.info("Loading training data.")
data = load_data(config['data_source'], config['data_filename'])
LOGGER.info("Training data size: %d", len(data))
# Build dataset
LOGGER.info("Building dataset.")
dataset = get_dataset(data, labels, config['batch_size'],
params['data_shape'], config['use_random_transpose'],
config['n_jobs'])
# Create iterator
if params[
'is_conditional']: # greg: ESTO NUNCA PASA, YA QUE is_conditional arroja Error Not supported yet en el método load_training_data
# greg: ¿Que hace make_one_shot_iterator.get_next()?
train_x, train_y = dataset.make_one_shot_iterator().get_next()
else:
train_x, train_y = dataset.make_one_shot_iterator().get_next(), None
return train_x, train_y
def load_or_create_samples(params, config):
"""Load or create the samples used as the sampler inputs."""
# greg: En caso de no estar condicionando, solamente se sacan muestras de z.
# Load sample_z
LOGGER.info("Loading sample_z.")
sample_z_path = os.path.join(config['model_dir'], 'sample_z.npy')
if os.path.exists(sample_z_path):
sample_z = np.load(sample_z_path)
if sample_z.shape[1] != params['latent_dim']:
LOGGER.info("Loaded sample_z has wrong shape")
resample = True
else:
resample = False
else:
LOGGER.info("File for sample_z not found")
resample = True
# Draw new sample_z
if resample:
LOGGER.info("Drawing new sample_z.")
sample_z = scipy.stats.truncnorm.rvs(
-2, 2, size=(np.prod(config['sample_grid']), params['latent_dim']))
make_sure_path_exists(config['model_dir'])
np.save(sample_z_path, sample_z)
# greg: Solo si estamos condicionando se extraen muestras de sample_x
if params.get('is_accompaniment'):
# Load sample_x
LOGGER.info("Loading sample_x.")
sample_x_path = os.path.join(config['model_dir'], 'sample_x.npy')
if os.path.exists(sample_x_path):
sample_x = np.load(sample_x_path)
if sample_x.shape[1:] != params['data_shape']:
LOGGER.info("Loaded sample_x has wrong shape")
resample = True
else:
resample = False
else:
LOGGER.info("File for sample_x not found")
resample = True
# Draw new sample_x
if resample:
LOGGER.info("Drawing new sample_x.")
data = load_data(config['data_source'], config['data_filename'])
sample_x = get_samples(
np.prod(config['sample_grid']),
data,
use_random_transpose=config['use_random_transpose'])
make_sure_path_exists(config['model_dir'])
np.save(sample_x_path, sample_x)
else:
sample_x = None
return sample_x, None, sample_z
def load_or_create_samples(params, config):
"""Load or create the samples used as the sampler inputs."""
# Load sample_z
LOGGER.info("Loading sample_z.")
sample_z_path = os.path.join(config['model_dir'], 'sample_z.npy')
if os.path.exists(sample_z_path):
sample_z = np.load(sample_z_path)
if sample_z.shape[1] != params['latent_dim']:
LOGGER.info("Loaded sample_z has wrong shape")
resample = True
else:
resample = False
else:
LOGGER.info("File for sample_z not found")
resample = True
# Draw new sample_z
if resample:
LOGGER.info("Drawing new sample_z.")
sample_z = scipy.stats.truncnorm.rvs(
-2, 2, size=(np.prod(config['sample_grid']), params['latent_dim']))
make_sure_path_exists(config['model_dir'])
np.save(sample_z_path, sample_z)
if params['is_accompaniment']:
# Load sample_x
LOGGER.info("Loading sample_x.")
sample_x_path = os.path.join(config['model_dir'], 'sample_x.npy')
if os.path.exists(sample_x_path):
sample_x = np.load(sample_x_path)
if sample_x.shape[1:] != params['data_shape']:
LOGGER.info("Loaded sample_x has wrong shape")
resample = True
else:
resample = False
else:
LOGGER.info("File for sample_x not found")
resample = True
# Draw new sample_x
if resample:
LOGGER.info("Drawing new sample_x.")
data = load_data(config['data_source'], config['data_filename'])
sample_x = get_samples(
np.prod(config['sample_grid']), data,
use_random_transpose = config['use_random_transpose'])
make_sure_path_exists(config['model_dir'])
np.save(sample_x_path, sample_x)
else:
sample_x = None
return sample_x, None, sample_z
def load_training_data(params, config):
"""Load and return the training data."""
# Load data
if params['is_conditional']:
raise ValueError("Not supported yet.")
else:
labels = None
LOGGER.info("Loading training data.")
data = load_data(config['data_source'], config['data_filename'])
LOGGER.info("Training data size: %d", len(data))
# Build dataset
LOGGER.info("Building dataset.")
dataset = get_dataset(
data, labels, config['batch_size'], params['data_shape'],
config['use_random_transpose'], config['n_jobs'])
# Create iterator
if params['is_conditional']:
train_x, train_y = dataset.make_one_shot_iterator().get_next()
else:
train_x, train_y = dataset.make_one_shot_iterator().get_next(), None
return train_x, train_y
def main():
"""Main function."""
# Setup
logging.basicConfig(level=LOGLEVEL, format=LOG_FORMAT)
params, config = setup()
LOGGER.info("Using parameters:\n%s", pformat(params))
LOGGER.info("Using configurations:\n%s", pformat(config))
# ============================== Placeholders ==============================
placeholder_x = tf.placeholder(tf.float32,
shape=([None] + params['data_shape']))
placeholder_z = tf.placeholder(tf.float32,
shape=(None, params['latent_dim']))
placeholder_c = tf.placeholder(tf.float32,
shape=([None] + params['data_shape'][:-1] +
[1]))
placeholder_suffix = tf.placeholder(tf.string)
# ================================= Model ==================================
# Create sampler configurations
sampler_config = {
'result_dir': config['result_dir'],
'image_grid': (config['rows'], config['columns']),
'suffix': placeholder_suffix,
'midi': config['midi'],
'colormap': np.array(config['colormap']).T,
'collect_save_arrays_op': config['save_array_samples'],
'collect_save_images_op': config['save_image_samples'],
'collect_save_pianorolls_op': config['save_pianoroll_samples']
}
# Build model
model = Model(params)
if params['is_accompaniment']:
_ = model(x=placeholder_x,
c=placeholder_c,
z=placeholder_z,
mode='train',
params=params,
config=config)
predict_nodes = model(c=placeholder_c,
z=placeholder_z,
mode='predict',
params=params,
config=sampler_config)
else:
_ = model(x=placeholder_x,
z=placeholder_z,
mode='train',
params=params,
config=config)
predict_nodes = model(z=placeholder_z,
mode='predict',
params=params,
config=sampler_config)
# Get sampler op
sampler_op = tf.group([
predict_nodes[key]
for key in ('save_arrays_op', 'save_images_op', 'save_pianorolls_op')
if key in predict_nodes
])
# ================================== Data ==================================
if params['is_accompaniment']:
data = load_data(config['data_source'], config['data_filename'])
# ========================== Session Preparation ===========================
# Get tensorflow session config
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
# Create saver to restore variables
saver = tf.train.Saver()
# =========================== Tensorflow Session ===========================
with tf.Session(config=tf_config) as sess:
# Restore the latest checkpoint
LOGGER.info("Restoring the latest checkpoint.")
with open(os.path.join(config['checkpoint_dir'], 'checkpoint')) as f:
checkpoint_name = os.path.basename(
f.readline().split()[1].strip('"'))
checkpoint_path = os.path.realpath(
os.path.join(config['checkpoint_dir'], checkpoint_name))
saver.restore(sess, checkpoint_path)
# Run sampler op
for i in range(config['runs']):
feed_dict_sampler = {
placeholder_z:
scipy.stats.truncnorm.rvs(config['lower'],
config['upper'],
size=((config['rows'] *
config['columns']),
params['latent_dim'])),
placeholder_suffix:
str(i)
}
if params['is_accompaniment']:
sample_x = get_samples(
(config['rows'] * config['columns']),
data,
use_random_transpose=config['use_random_transpose'])
feed_dict_sampler[placeholder_c] = np.expand_dims(
sample_x[..., params['condition_track_idx']], -1)
sess.run(sampler_op, feed_dict=feed_dict_sampler)
def main():
"""Main function."""
# Setup
logging.basicConfig(level=LOGLEVEL, format=LOG_FORMAT)
params, config = setup()
LOGGER.info("Using parameters:\n%s", pformat(params))
LOGGER.info("Using configurations:\n%s", pformat(config))
# ============================== Placeholders ==============================
placeholder_x = tf.placeholder(
tf.float32, shape=([None] + params['data_shape']))
placeholder_z = tf.placeholder(
tf.float32, shape=(None, params['latent_dim']))
placeholder_c = tf.placeholder(
tf.float32, shape=([None] + params['data_shape'][:-1] + [1]))
placeholder_suffix = tf.placeholder(tf.string)
# ================================= Model ==================================
# Create sampler configurations
sampler_config = {
'result_dir': config['result_dir'],
'image_grid': (config['rows'], config['columns']),
'suffix': placeholder_suffix, 'midi': config['midi'],
'colormap': np.array(config['colormap']).T,
'collect_save_arrays_op': config['save_array_samples'],
'collect_save_images_op': config['save_image_samples'],
'collect_save_pianorolls_op': config['save_pianoroll_samples']}
# Build model
model = Model(params)
if params['is_accompaniment']:
_ = model(
x=placeholder_x, c=placeholder_c, z=placeholder_z, mode='train',
params=params, config=config)
predict_nodes = model(
c=placeholder_c, z=placeholder_z, mode='predict', params=params,
config=sampler_config)
else:
_ = model(
x=placeholder_x, z=placeholder_z, mode='train', params=params,
config=config)
predict_nodes = model(
z=placeholder_z, mode='predict', params=params,
config=sampler_config)
# Get sampler op
sampler_op = tf.group([
predict_nodes[key] for key in (
'save_arrays_op', 'save_images_op', 'save_pianorolls_op')
if key in predict_nodes])
# ================================== Data ==================================
if params['is_accompaniment']:
data = load_data(config['data_source'], config['data_filename'])
# ========================== Session Preparation ===========================
# Get tensorflow session config
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
# Create saver to restore variables
saver = tf.train.Saver()
# =========================== Tensorflow Session ===========================
with tf.Session(config=tf_config) as sess:
# Restore the latest checkpoint
LOGGER.info("Restoring the latest checkpoint.")
with open(os.path.join(config['checkpoint_dir'], 'checkpoint')) as f:
checkpoint_name = os.path.basename(
f.readline().split()[1].strip('"'))
checkpoint_path = os.path.realpath(
os.path.join(config['checkpoint_dir'], checkpoint_name))
saver.restore(sess, checkpoint_path)
# Run sampler op
for i in range(config['runs']):
feed_dict_sampler = {
placeholder_z: get_input_z(config, params),
placeholder_suffix: str(i)}
if params['is_accompaniment']:
sample_x = get_samples(
1, data,
use_random_transpose=config['use_random_transpose'])
sample_c = np.expand_dims(
sample_x[..., params['condition_track_idx']], -1)
feed_dict_sampler[placeholder_c] = np.repeat(
sample_c, (config['rows'] * config['columns']), axis=0)
sess.run(sampler_op, feed_dict=feed_dict_sampler)
