def main(unused_argv):
tf.logging.set_verbosity(FLAGS.log)
if not tf.gfile.Exists(FLAGS.logdir):
tf.gfile.MakeDirs(FLAGS.logdir)
with tf.Graph().as_default():
# If ps_tasks is 0, the local device is used. When using multiple
# (non-local) replicas, the ReplicaDeviceSetter distributes the variables
# across the different devices.
model = utils.get_module("baseline.models.%s" % FLAGS.model)
hparams = model.get_hparams(FLAGS.config)
# Run the Reader on the CPU
cpu_device = ("/job:worker/cpu:0" if FLAGS.ps_tasks else
"/job:localhost/replica:0/task:0/cpu:0")
with tf.device(cpu_device):
with tf.name_scope("Reader"):
batch = reader.NSynthDataset(
FLAGS.train_path, is_training=True).get_baseline_batch(hparams)
with tf.device(tf.train.replica_device_setter(ps_tasks=FLAGS.ps_tasks)):
train_op = model.train_op(batch, hparams, FLAGS.config)
# Run training
slim.learning.train(
train_op=train_op,
logdir=FLAGS.logdir,
master=FLAGS.master,
is_chief=FLAGS.task == 0,
number_of_steps=hparams.max_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
def get_batch(self, batch_size):
"""Summary
Parameters
----------
batch_size : TYPE
Description
Returns
-------
TYPE
Description
"""
assert self.train_path is not None
data_train = reader.NSynthDataset(self.train_path, is_training=True)
return data_train.get_wavenet_batch(batch_size, length=6144)
def get_batch(self, batch_size):
assert self.train_path is not None
data_train = reader.NSynthDataset(self.train_path, is_training=True)
return data_train.get_wavenet_batch(batch_size, length=6144)
def main(unused_argv):
tf.logging.set_verbosity(FLAGS.log)
if FLAGS.checkpoint_path:
checkpoint_path = FLAGS.checkpoint_path
else:
expdir = FLAGS.expdir
tf.logging.info("Will load latest checkpoint from %s.", expdir)
while not tf.gfile.Exists(expdir):
tf.logging.fatal("\tExperiment save dir '%s' does not exist!", expdir)
sys.exit(1)
try:
checkpoint_path = tf.train.latest_checkpoint(expdir)
except tf.errors.NotFoundError:
tf.logging.fatal("There was a problem determining the latest checkpoint.")
sys.exit(1)
if not tf.train.checkpoint_exists(checkpoint_path):
tf.logging.fatal("Invalid checkpoint path: %s", checkpoint_path)
sys.exit(1)
savedir = FLAGS.savedir
if not tf.gfile.Exists(savedir):
tf.gfile.MakeDirs(savedir)
# Make the graph
with tf.Graph().as_default():
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
model = utils.get_module("baseline.models.%s" % FLAGS.model)
hparams = model.get_hparams(FLAGS.config)
# Load the trained model with is_training=False
with tf.name_scope("Reader"):
batch = reader.NSynthDataset(
FLAGS.tfrecord_path,
is_training=False).get_baseline_batch(hparams)
_ = model.train_op(batch, hparams, FLAGS.config)
z = tf.get_collection("z")[0]
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
# Add ops to save and restore all the variables.
# Restore variables from disk.
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
tf.logging.info("Model restored.")
# Start up some threads
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
i = 0
z_val = []
try:
while True:
if coord.should_stop():
break
res_val = sess.run([z])
z_val.append(res_val[0])
tf.logging.info("Iter: %d" % i)
tf.logging.info("Z:{}".format(res_val[0].shape))
i += 1
if i + 1 % 1 == 0:
save_arrays(savedir, hparams, z_val)
# Report all exceptions to the coordinator, pylint: disable=broad-except
except Exception as e:
coord.request_stop(e)
# pylint: enable=broad-except
finally:
save_arrays(savedir, hparams, z_val)
# Terminate as usual. It is innocuous to request stop twice.
coord.request_stop()
coord.join(threads)