Merge remote-tracking branch 'tensorflow/master'

research/gan/image_compression/train_test.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.
+# ==============================================================================
+"""Tests for image_compression.train."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import numpy as np
+import tensorflow as tf
+import train
+
+FLAGS = tf.flags.FLAGS
+mock = tf.test.mock
+
+
+class TrainTest(tf.test.TestCase):
+
+  def _test_build_graph_helper(self, weight_factor):
+    FLAGS.max_number_of_steps = 0
+    FLAGS.weight_factor = weight_factor
+
+    batch_size = 3
+    patch_size = 16
+
+    FLAGS.batch_size = batch_size
+    FLAGS.patch_size = patch_size
+    mock_imgs = np.zeros([batch_size, patch_size, patch_size, 3],
+                         dtype=np.float32)
+
+    with mock.patch.object(train, 'data_provider') as mock_data_provider:
+      mock_data_provider.provide_data.return_value = mock_imgs
+      train.main(None)
+
+  def test_build_graph_noadversarialloss(self):
+    self._test_build_graph_helper(0.0)
+
+  def test_build_graph_adversarialloss(self):
+    self._test_build_graph_helper(1.0)
+
+
+if __name__ == '__main__':
+  tf.test.main()
+

research/gan/mnist/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()