def __init__(self, config, model_name, vocab_path,
ses_threads=2,
gpu_memory_fraction=1.0):
self.cu = CommonUtiler()
self.config = copy.deepcopy(config)
self.config.batch_size = 1
self.model_path = None
self.model_name = model_name
self.flag_load_model = False
self.vocab_path = vocab_path
self.vocab, self.rev_vocab = self.cu.load_vocabulary(vocab_path)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
self.session = session = tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=ses_threads,
gpu_options=gpu_options))
with tf.variable_scope("mRNNmodel", reuse=None):
self.model_init = mRNNModel(
is_training=False,
num_steps=1,
config=self.config,
model_name=self.model_name,
flag_with_saver=True)
with tf.variable_scope("mRNNmodel", reuse=True):
self.model_cont = mRNNModel(
is_training=False,
num_steps=1,
config=self.config,
model_name=self.model_name,
flag_with_saver=False,
flag_reset_state=True)
def __init__(self,
config,
model_name,
vocab_path,
ses_threads=2,
gpu_memory_fraction=1.0):
self.cu = CommonUtiler()
self.config = copy.deepcopy(config)
self.config.batch_size = 1
self.model_path = None
self.model_name = model_name
self.flag_load_model = False
self.vocab_path = vocab_path
self.vocab, self.rev_vocab = self.cu.load_vocabulary(vocab_path)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
self.session = session = tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=ses_threads, gpu_options=gpu_options))
with tf.variable_scope("mRNNmodel", reuse=None):
self.model_init = mRNNModel(is_training=False,
num_steps=1,
config=self.config,
model_name=self.model_name,
flag_with_saver=True)
with tf.variable_scope("mRNNmodel", reuse=True):
self.model_cont = mRNNModel(is_training=False,
num_steps=1,
config=self.config,
model_name=self.model_name,
flag_with_saver=False,
flag_reset_state=True)
def main(unused_args):
# Load model configuration
cu = CommonUtiler()
config_path = os.path.join('./model_conf', FLAGS.model_name + '.py')
config = cu.load_config(config_path)
# Start model training
with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=FLAGS.ses_threads)) as session:
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
assert len(config.buckets) >= 1
assert config.buckets[-1] == config.max_num_steps
models = []
with tf.variable_scope("mRNNmodel", reuse=None, initializer=initializer):
m = mRNNModel(is_training=True,
num_steps=config.buckets[0],
config=config,
model_name=FLAGS.model_name,
flag_with_saver=True,
model_root=FLAGS.model_root)
models.append(m)
with tf.variable_scope("mRNNmodel", reuse=True):
for bucket in config.buckets[1:]:
m = mRNNModel(is_training=True,
num_steps=bucket,
config=config,
model_name=FLAGS.model_name,
model_root=FLAGS.model_root)
models.append(m)
hdlr = logging.FileHandler(os.path.join(m.model_dir, 'log.txt'))
hdlr.setLevel(logging.INFO)
hdlr.setFormatter(logging.Formatter(formatter_log))
logger.addHandler(hdlr)
if FLAGS.pre_trained_model_path:
models[0].saver.restore(session, FLAGS.pre_trained_model_path)
logger.info('Continue to train from %s', FLAGS.pre_trained_model_path)
else:
tf.initialize_all_variables().run()
iters_done = 0
data_provider = mRNNCocoBucketDataProvider(FLAGS.anno_files_path.split(':'),
FLAGS.vocab_path, config.vocab_size, FLAGS.vf_dir, config.vf_size)
for i in range(config.num_epoch):
train_cost, iters_done = run_epoch(session, iters_done, config, models,
data_provider, verbose=True)
logger.info("Train cost for epoch %d is %.3f" % (i, train_cost))
# Save final copy of the model
models[0].saver.save(session, os.path.join(m.variable_dir,
'model_%d.ckpt' % iters_done))
def main(unused_args):
# Load model configuration
cu = CommonUtiler()
config_path = os.path.join('./model_conf', FLAGS.model_name + '.py')
config = cu.load_config(config_path)
# Start model training
with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=FLAGS.ses_threads)) as session:
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
assert len(config.buckets) >= 1
assert config.buckets[-1] == config.max_num_steps
models = []
with tf.variable_scope("mRNNmodel",
reuse=None,
initializer=initializer):
m = mRNNModel(is_training=True,
num_steps=config.buckets[0],
config=config,
model_name=FLAGS.model_name,
flag_with_saver=True,
model_root=FLAGS.model_root)
models.append(m)
with tf.variable_scope("mRNNmodel", reuse=True):
for bucket in config.buckets[1:]:
m = mRNNModel(is_training=True,
num_steps=bucket,
config=config,
model_name=FLAGS.model_name,
model_root=FLAGS.model_root)
models.append(m)
hdlr = logging.FileHandler(os.path.join(m.model_dir, 'log.txt'))
hdlr.setLevel(logging.INFO)
hdlr.setFormatter(logging.Formatter(formatter_log))
logger.addHandler(hdlr)
if FLAGS.pre_trained_model_path:
models[0].saver.restore(session, FLAGS.pre_trained_model_path)
logger.info('Continue to train from %s',
FLAGS.pre_trained_model_path)
else:
tf.initialize_all_variables().run()
iters_done = 0
data_provider = mRNNCocoBucketDataProvider(
FLAGS.anno_files_path.split(':'), FLAGS.vocab_path,
config.vocab_size, FLAGS.vf_dir, config.vf_size)
for i in range(config.num_epoch):
train_cost, iters_done = run_epoch(session,
iters_done,
config,
models,
data_provider,
verbose=True)
logger.info("Train cost for epoch %d is %.3f" % (i, train_cost))
# Save final copy of the model
models[0].saver.save(
session, os.path.join(m.variable_dir,
'model_%d.ckpt' % iters_done))
