def run_once(global_step, target_cross_entropy_losses, target_cross_entropy_loss_weights, saver, summary_writer, summary_op):
"""Evaluates the latest model checkpoint.
Args:
model: Instance of ShowAndTellModel; the model to evaluate.
saver: Instance of tf.train.Saver for restoring model Variables.
summary_writer: Instance of SummaryWriter.
summary_op: Op for generating model summaries.
"""
# The lastest ckpt
model_path = tf.train.latest_checkpoint(checkpoint_dir)
# print(model_path) # /home/dsigpu4/Samba/im2txt/model/train_tl/model.ckpt-20000
# exit()
if not model_path:
tf.logging.info("Skipping evaluation. No checkpoint found in: %s",
checkpoint_dir)
return
with tf.Session() as sess:
# Load model from checkpoint.
tf.logging.info("Loading model from checkpoint: %s", model_path)
saver.restore(sess, model_path)
# global_step = tf.train.global_step(sess, model.global_step.name)
step = tf.train.global_step(sess, global_step.name)
tf.logging.info("Successfully loaded %s at global step = %d.",
# os.path.basename(model_path), global_step)
os.path.basename(model_path), step)
# if global_step < min_global_step:
if step < min_global_step:
# tf.logging.info("Skipping evaluation. Global step = %d < %d", global_step,
tf.logging.info("Skipping evaluation. Global step = %d < %d", step,
min_global_step)
return
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# Run evaluation on the latest checkpoint.
try:
evaluate_model(
sess=sess,
target_cross_entropy_losses=target_cross_entropy_losses,
target_cross_entropy_loss_weights=target_cross_entropy_loss_weights,
global_step=step,
summary_writer=summary_writer,
summary_op=summary_op)
except Exception, e: # pylint: disable=broad-except
tf.logging.error("Evaluation failed.")
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
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