Tukukkk

I am using the following function to calculate extra metrics for my training. I create a host_call with: host_call = (host_call_fn, metric_args) and pass it to host_call argument of estimator. However, calling this leads to memory leak and I can not figure out what is the problem. Using heap, it seems large dictionaries are being made somehow and they are not released.

p_temp = tf.reshape(policy_loss, [1], name='policy_loss_reshape')

v_temp = tf.reshape(value_loss, [1], name='value_loss_reshape')

e_temp = tf.reshape(entropy_loss, [1], name='entropy_loss_reshape')

t_temp = tf.reshape(total_loss, [1], name='total_loss_reshape')

g_temp = tf.reshape(global_step, [1], name='global_step_reshape')

#

metric_args = [p_temp, v_temp, e_temp, t_temp, g_temp]



host_call_fn = functools.partial(

  eval_metrics_host_call_fn, est_mode=tf.estimator.ModeKeys.TRAIN)

host_call = (host_call_fn, metric_args)

The following function calculates the extra evaluation metrics and writes it to summary directory for Tensorboard.

def eval_metrics_host_call_fn(p_temp,

                            v_temp,

                            e_temp,

                            t_temp,

                            step,

                            est_mode=tf.estimator.ModeKeys.TRAIN):

#

with tf.variable_scope('metrics'):

  metric_ops = {

      'policy_loss': tf.metrics.mean(p_temp, name='policy_loss_metric'),

      'value_loss': tf.metrics.mean(v_temp, name='value_loss_metric'),

      'entropy_loss': tf.metrics.mean(e_temp, name='entropy_loss_metric'),

      'total_loss': tf.metrics.mean(t_temp, name='total_loss_metric')

  }

if est_mode == tf.estimator.ModeKeys.EVAL:

  return metric_ops

eval_step = tf.reduce_min(step)

# Create summary ops so that they show up in SUMMARIES collection

# That way, they get logged automatically during training

summary_writer = summary.create_file_writer(FLAGS.summary_dir)

with summary_writer.as_default(

), summary.record_summaries_every_n_global_steps(FLAGS.summary_steps,

                                                 eval_step):

  for metric_name, metric_op in metric_ops.items():

    summary.scalar(metric_name, metric_op[1], step=eval_step)

# Reset metrics occasionally so that they are mean of recent batches.

reset_op = tf.variables_initializer(tf.local_variables('metrics'))

cond_reset_op = tf.cond(

    tf.equal(eval_step % FLAGS.summary_steps, tf.to_int64(1)),

    lambda: reset_op, lambda: tf.no_op())



return summary.all_summary_ops() + [cond_reset_op]