def main(argv):
config = Config()
config.input_file_pattern = FLAGS.input_file_pattern
config.optimizer = FLAGS.optimizer
config.attention_mechanism = FLAGS.attention
config.save_dir = FLAGS.train_dir
# Create training directory.
train_dir = config.save_dir
if not tf.gfile.IsDirectory(train_dir):
tf.logging.info("Creating training directory: %s", train_dir)
tf.gfile.MakeDirs(train_dir)
# Build the TensorFlow graph.
g = tf.Graph()
with g.as_default():
# Build the model.
model = CaptionGenerator(config, mode="train")
model.build()
# Set up the Saver for saving and restoring model checkpoints.
saver = tf.train.Saver(max_to_keep=config.max_checkpoints_to_keep)
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
# Run training.
tf.contrib.slim.learning.train(
model.opt_op,
train_dir,
log_every_n_steps=config.log_every_n_steps,
graph=g,
global_step=model.global_step,
number_of_steps=FLAGS.number_of_steps,
summary_op=model.summary,
save_summaries_secs=60,
save_interval_secs=600,
init_fn=None,
saver=saver,
session_config=sess_config)
def run():
"""Runs evaluation in a loop, and logs summaries to TensorBoard."""
# Create the evaluation directory if it doesn't exist.
eval_dir = FLAGS.eval_dir
if not tf.gfile.IsDirectory(eval_dir):
tf.logging.info("Creating eval directory: %s", eval_dir)
tf.gfile.MakeDirs(eval_dir)
# build vocabulary file
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
g = tf.Graph()
with g.as_default():
config = Config()
config.input_file_pattern = FLAGS.input_file_pattern
config.beam_size = FLAGS.beam_size
# Build the model for evaluation.
model = CaptionGenerator(config, mode="eval")
model.build()
# Create the Saver to restore model Variables.
saver = tf.train.Saver()
# Create the summary writer.
summary_writer = tf.summary.FileWriter(eval_dir)
g.finalize()
# Run a new evaluation run every eval_interval_secs.
while True:
start = time.time()
tf.logging.info("Starting evaluation at " + time.strftime(
"%Y-%m-%d-%H:%M:%S", time.localtime()))
run_once(model,vocab, saver, summary_writer)
time_to_next_eval = start + FLAGS.eval_interval_secs - time.time()
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
def export_graph(model_folder,model_name,config):
graph = tf.Graph()
with graph.as_default():
model = CaptionGenerator(config, mode="inference")
model.build()
# input tensor can't use tf.identity() to rename
# inputs = {}
outputs = {}
# # input
# inputs['contexts'] = tf.identity(model.contexts, name='contexts')
# inputs['last_word'] = tf.identity(model.last_word, name='last_word')
# inputs['last_memory'] = tf.identity(model.last_memory, name='last_memory')
# inputs['last_output'] = tf.identity(model.last_output, name='last_output')
# outputs
outputs['initial_memory'] = tf.identity(model.initial_memory, name='initial_memory')
outputs['initial_output'] = tf.identity(model.initial_output, name='initial_output')
# results
outputs['alpha'] = tf.identity(model.alpha, name='alpha')
outputs['memory'] = tf.identity(model.memory, name='memory')
outputs['output'] = tf.identity(model.output, name='output')
outputs['probs'] = tf.identity(model.probs, name='probs')
# logits = model.inference(input_image)
# y_conv = tf.nn.softmax(logits,name='outputdata')
restore_saver = tf.train.Saver()
with tf.Session(graph=graph) as sess:
sess.run(tf.global_variables_initializer())
latest_ckpt = tf.train.latest_checkpoint(model_folder)
restore_saver.restore(sess, latest_ckpt)
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph.as_graph_def(), list(outputs.keys()))
# tf.train.write_graph(output_graph_def, 'log', model_name, as_text=False)
with tf.gfile.GFile(model_name, "wb") as f:
f.write(output_graph_def.SerializeToString())
