conditional_eval.py

# Copyright 2017 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.
# ==============================================================================
"""Evaluates a conditional TFGAN trained MNIST model."""

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


import tensorflow as tf

import data_provider
import networks
import util

flags = tf.flags
tfgan = tf.contrib.gan


flags.DEFINE_string('checkpoint_dir', '/tmp/mnist/',
                    'Directory where the model was written to.')

flags.DEFINE_string('eval_dir', '/tmp/mnist/',
                    'Directory where the results are saved to.')

flags.DEFINE_integer('num_images_per_class', 10,
                     'Number of images to generate per class.')

flags.DEFINE_integer('noise_dims', 64,
                     'Dimensions of the generator noise vector')

flags.DEFINE_string('classifier_filename', None,
                    'Location of the pretrained classifier. If `None`, use '
                    'default.')

flags.DEFINE_integer('max_number_of_evaluations', None,
                     'Number of times to run evaluation. If `None`, run '
                     'forever.')

FLAGS = flags.FLAGS
NUM_CLASSES = 10


def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise, one_hot_labels = _get_generator_inputs(
        FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)

  # Generate images.
  with tf.variable_scope('Generator'):  # Same scope as in train job.
    images = networks.conditional_generator((noise, one_hot_labels))

  # Visualize images.
  reshaped_img = tfgan.eval.image_reshaper(
      images, num_cols=FLAGS.num_images_per_class)
  tf.summary.image('generated_images', reshaped_img, max_outputs=1)

  # Calculate evaluation metrics.
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(images, FLAGS.classifier_filename))
  tf.summary.scalar('MNIST_Cross_entropy',
                    util.mnist_cross_entropy(
                        images, one_hot_labels, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = tf.write_file(
      '%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
      tf.image.encode_png(data_provider.float_image_to_uint8(reshaped_img[0])))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)


def _get_generator_inputs(num_images_per_class, num_classes, noise_dims):
  # Since we want a grid of numbers for the conditional generator, manually
  # construct the desired class labels.
  num_images_generated = num_images_per_class * num_classes
  noise = tf.random_normal([num_images_generated, noise_dims])
  labels = [lbl for lbl in range(num_classes) for _
            in range(num_images_per_class)]
  one_hot_labels = tf.one_hot(tf.constant(labels), num_classes)
  return noise, one_hot_labels


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