def test_get_batch_size(self):
config = {'num_resolutions': 5, 'batch_size_schedule': [8, 4, 2]}
# batch_size_schedule is expanded to [8, 8, 8, 4, 2]
# At stage level it is [8, 8, 8, 8, 8, 4, 4, 2, 2]
for i, expected_batch_size in enumerate([8, 8, 8, 8, 8, 4, 4, 2, 2]):
self.assertEqual(train.get_batch_size(i, **config),
expected_batch_size)
def test_train_success(self):
train_root_dir = self._config['train_root_dir']
if not tf.gfile.Exists(train_root_dir):
tf.gfile.MakeDirs(train_root_dir)
for stage_id in train.get_stage_ids(**self._config):
batch_size = train.get_batch_size(stage_id, **self._config)
tf.reset_default_graph()
real_images = provide_random_data(batch_size=batch_size)
model = train.build_model(stage_id, batch_size, real_images,
**self._config)
train.add_model_summaries(model, **self._config)
train.train(model, **self._config)
def main(_):
if not tf.gfile.Exists(FLAGS.train_root_dir):
tf.gfile.MakeDirs(FLAGS.train_root_dir)
config = _make_config_from_flags()
LOGGING.info('\n'.join(['{}={}'.format(k, v) for k, v in config.iteritems()]))
for stage_id in train.get_stage_ids(**config):
batch_size = train.get_batch_size(stage_id, **config)
tf.reset_default_graph()
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
real_images = None
with tf.device('/cpu:0'), tf.name_scope('inputs'):
real_images = _provide_real_images(batch_size, **config)
model = train.build_model(stage_id, batch_size, real_images, **config)
train.add_model_summaries(model, **config)
train.train(model, **config)
def main(_):
if not tf.gfile.Exists(FLAGS.train_root_dir):
tf.gfile.MakeDirs(FLAGS.train_root_dir)
config = _make_config_from_flags()
logging.info('\n'.join(['{}={}'.format(k, v) for k, v in config.iteritems()]))
for stage_id in train.get_stage_ids(**config):
batch_size = train.get_batch_size(stage_id, **config)
tf.reset_default_graph()
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
real_images = None
with tf.device('/cpu:0'), tf.name_scope('inputs'):
real_images = _provide_real_images(batch_size, **config)
model = train.build_model(stage_id, batch_size, real_images, **config)
train.add_model_summaries(model, **config)
train.train(model, **config)
def main(_):
logging.info("Setting up directory")
if not tf.gfile.Exists(FLAGS.train_root_dir):
tf.gfile.MakeDirs(FLAGS.train_root_dir)
logging.info("Set up logging")
config = _make_config_from_flags()
logging.info('\n'.join(['{}={}'.format(k, v) for k, v in config.items()]))
for stage_id in train.get_stage_ids(**config):#change stage_id to restore from latest model ###IMPORTANT
logging.info("Get batch_size")
batch_size = train.get_batch_size(stage_id, **config)
tf.reset_default_graph()
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
real_images = None
logging.info("Setup data")
with tf.device('/cpu:0'), tf.name_scope('inputs'):
real_images = _provide_real_images(batch_size, **config)
logging.info("Building model")
model = train.build_model(stage_id, batch_size, real_images, **config)
logging.info("Adding Summaries")
train.add_model_summaries(model, **config)
train.train(model, **config)
def test_get_batch_size(self):
config = {'num_resolutions': 5, 'batch_size_schedule': [8, 4, 2]}
# batch_size_schedule is expanded to [8, 8, 8, 4, 2]
# At stage level it is [8, 8, 8, 8, 8, 4, 4, 2, 2]
for i, expected_batch_size in enumerate([8, 8, 8, 8, 8, 4, 4, 2, 2]):
self.assertEqual(train.get_batch_size(i, **config), expected_batch_size)