cifar10_runsingle.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""Evaluation for CIFAR-10.

Accuracy:
cifar10_train.py achieves 83.0% accuracy after 100K steps (256 epochs
of data) as judged by cifar10_eval.py.

Speed:
On a single Tesla K40, cifar10_train.py processes a single batch of 128 images
in 0.25-0.35 sec (i.e. 350 - 600 images /sec). The model reaches ~86%
accuracy after 100K steps in 8 hours of training time.

Usage:
Please see the tutorial and website for how to download the CIFAR-10
data set, compile the program and train the model.

http://tensorflow.org/tutorials/deep_cnn/
"""
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 cifar10
import sys


if len(sys.argv)==1:
  SinglePicPath = "/tmp/9da80aad-cd2b-4d47-b204-7e9741432495.JPEG"
else:
  SinglePicPath =sys.argv[1]

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval',
                           """Directory where to write event logs.""")
tf.app.flags.DEFINE_string('eval_data', 'single',
                           """Either 'test' or 'train_eval'.""")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_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', 1000,
                            """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, top_k_op, summary_op):
  """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)
      # Assuming model_checkpoint_path looks something like:
      #   /my-favorite-path/cifar10_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_size))
      true_count = 0  # Counts the number of correct predictions.
      step = 0
      while step < 1 and not coord.should_stop():
        predictions = sess.run([top_k_op])
        print(predictions)
        true_count += np.sum(predictions)
        step += 1

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

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

def loadSingleImage(imgsrc):
  from PIL import Image
  import numpy as np

  im = Image.open(imgsrc)
  im = im.resize((32, 32))
  im = (np.array(im))

  r = im[:, :, 0].flatten()
  g = im[:, :, 1].flatten()
  b = im[:, :, 2].flatten()
  label = [3]

  out = np.array(list(label) + list(r) + list(g) + list(b), np.uint8)
  out.tofile(cifar10.getdataDir() + "/single_batch.bin")
  return cifar10.getdataDir() + "/single_batch.bin"
def evaluate():
  """Eval CIFAR-10 for a number of steps."""
  with tf.Graph().as_default() as g:
    # Get images and labels for CIFAR-10.
    print(SinglePicPath)
    loadSingleImage(SinglePicPath)
    images, labels = cifar10.inputs(True,True)

    logits = cifar10.inference(images)
    top_k_op= tf.argmax(logits,1)
    variable_averages = tf.train.ExponentialMovingAverage(
        cifar10.MOVING_AVERAGE_DECAY)
    variables_to_restore = variable_averages.variables_to_restore()
    saver = tf.train.Saver(variables_to_restore)
    summary_op = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
    while True:
      eval_once(saver, summary_writer, top_k_op, summary_op)
      if FLAGS.run_once:
        break
      time.sleep(FLAGS.eval_interval_secs)

def main(argv=None):  # pylint: disable=unused-argument
  # cifar10.maybe_download_and_extract()
  # 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()