fer2013_eval_bc.py

"""Evaluation for FER2013."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import math
import time

import numpy as np
import tensorflow as tf

import fer2013

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('eval_dir', '/tmp/fer2013_eval',
                           """Directory where to write event logs.""")
tf.app.flags.DEFINE_string('eval_data', 'test',
                           """Either 'test' or 'train_eval'.""")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/fer2013_train',
                           """Directory where to read model checkpoints.""")
tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,
                            """How often to run the eval.""")
tf.app.flags.DEFINE_integer('num_examples', 3589,
                            """Number of examples to run.""")
tf.app.flags.DEFINE_boolean('run_once', True,
                         """Whether to run eval only once.""")


def eval_once(saver, summary_writer, logits, labels, top_k_op, summary_op):
  # print("Called eval_once ...")
  """Run Eval once.

  Args:
    saver: Saver.
    summary_writer: Summary writer.
    top_k_op: Top K op.
    summary_op: Summary op.
  """
  with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
    if ckpt and ckpt.model_checkpoint_path:
      # Restores from checkpoint
      saver.restore(sess, ckpt.model_checkpoint_path)
      print("Checkpoint file path:", ckpt.model_checkpoint_path)
      # Assuming model_checkpoint_path looks something like:
      #   /my-favorite-path/fer2013_train/model.ckpt-0,
      # extract global_step from it.
      global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
    else:
      print('No checkpoint file found')
      return

    # Start the queue runners.
    coord = tf.train.Coordinator()
    try:
      threads = []
      for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
        threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
                                         start=True))

      num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_input_size))
      true_count = 0  # Counts the number of correct predictions.
      total_sample_count = num_iter * FLAGS.batch_input_size
      step = 0
      time.sleep(1)

      # print("step = %d, num_iter = %d  " % (step, num_iter))

      emotion_dict = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Sad'}

      while step < num_iter and not coord.should_stop():
        # print("Inside while ...")
        result1, result2  = sess.run([logits, labels])
        #label = sess.run(labels)
        # print('Step:', step, 'result',result1, 'Label:', result2)
        print("-----------------------------------------------------")
        print('LABEL FOR INPUT IMAGE:', result2[0], '->', emotion_dict[result2[0]])
        print("-----------------------------------------------------")
        step += 1
        break

      # print("Exited while! Next...")

      # Compute precision @ 1.
      precision = true_count / step
      # print('Summary -- Step:', step, 'Accurcy:',true_count * 100.0 / step * 1.0, )
      # print('%s: total:%d true:%d precision @ 1 = %.3f' % (datetime.now(), total_sample_count, true_count, precision))

    except Exception as e:  # pylint: disable=broad-except
      coord.request_stop(e)

    coord.request_stop()
    coord.join(threads, stop_grace_period_secs=10)


def evaluate():
  """Eval FER2013 for a number of steps."""
  with tf.Graph().as_default():
    # Get images and labels for FER2013.
    eval_data = FLAGS.eval_data == 'test'
    images, labels = fer2013.inputs(eval_data=eval_data)

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = fer2013.inference(images)

    # Calculate predictions.
    top_k_op = tf.nn.in_top_k(logits, labels, 1)

    # Restore the moving average version of the learned variables for eval.
    variable_averages = tf.train.ExponentialMovingAverage(
        fer2013.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)

    # Build the summary operation based on the TF collection of Summaries.
    summary_op = tf.merge_all_summaries()

    graph_def = tf.get_default_graph().as_graph_def()
    summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
                                            graph_def=graph_def)

    while True:
      eval_once(saver, summary_writer, logits, labels, top_k_op, summary_op)

      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs)


def main(argv=None):
  if tf.gfile.Exists(FLAGS.eval_dir):
    tf.gfile.DeleteRecursively(FLAGS.eval_dir)
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  evaluate()


if __name__ == '__main__':
  tf.app.run()