def testTrainingAndInferenceGraphsAreCompatible(self,
mock_provide_custom_data,
unused_mock_gan_train):
# Training and inference graphs can get out of sync if changes are made
# to one but not the other. This test will keep them in sync.
# Save the training graph
train_sess = tf.Session()
FLAGS.image_set_x_file_pattern = '/tmp/x/*.jpg'
FLAGS.image_set_y_file_pattern = '/tmp/y/*.jpg'
FLAGS.batch_size = 3
FLAGS.patch_size = 128
FLAGS.generator_lr = 0.02
FLAGS.discriminator_lr = 0.3
FLAGS.train_log_dir = self._export_dir
FLAGS.master = 'master'
FLAGS.task = 0
FLAGS.cycle_consistency_loss_weight = 2.0
FLAGS.max_number_of_steps = 1
mock_provide_custom_data.return_value = (tf.zeros([
3,
4,
4,
3,
]), tf.zeros([3, 4, 4, 3]))
train.main(None)
init_op = tf.global_variables_initializer()
train_sess.run(init_op)
train_saver = tf.train.Saver()
train_saver.save(train_sess, save_path=self._ckpt_path)
# Create inference graph
tf.reset_default_graph()
FLAGS.patch_dim = FLAGS.patch_size
logging.info('dir_path: %s', os.listdir(self._export_dir))
FLAGS.checkpoint_path = self._ckpt_path
FLAGS.image_set_x_glob = self._image_glob
FLAGS.image_set_y_glob = self._image_glob
FLAGS.generated_x_dir = self._genx_dir
FLAGS.generated_y_dir = self._geny_dir
inference_demo.main(None)
logging.info('gen x: %s', os.listdir(self._genx_dir))
# Check that the image names match
self.assertSetEqual(set(_basenames_from_glob(FLAGS.image_set_x_glob)),
set(os.listdir(FLAGS.generated_y_dir)))
self.assertSetEqual(set(_basenames_from_glob(FLAGS.image_set_y_glob)),
set(os.listdir(FLAGS.generated_x_dir)))
# Check that each image in the directory looks as expected
for directory in [FLAGS.generated_x_dir, FLAGS.generated_x_dir]:
for base_name in os.listdir(directory):
image_path = os.path.join(directory, base_name)
self.assertRealisticImage(image_path)
def testTrainingAndInferenceGraphsAreCompatible(
self, mock_provide_custom_data, unused_mock_gan_train):
# Training and inference graphs can get out of sync if changes are made
# to one but not the other. This test will keep them in sync.
# Save the training graph
train_sess = tf.Session()
FLAGS.image_set_x_file_pattern = '/tmp/x/*.jpg'
FLAGS.image_set_y_file_pattern = '/tmp/y/*.jpg'
FLAGS.batch_size = 3
FLAGS.patch_size = 128
FLAGS.generator_lr = 0.02
FLAGS.discriminator_lr = 0.3
FLAGS.train_log_dir = self._export_dir
FLAGS.master = 'master'
FLAGS.task = 0
FLAGS.cycle_consistency_loss_weight = 2.0
FLAGS.max_number_of_steps = 1
mock_provide_custom_data.return_value = (
tf.zeros([3, 4, 4, 3,]), tf.zeros([3, 4, 4, 3]))
train.main(None)
init_op = tf.global_variables_initializer()
train_sess.run(init_op)
train_saver = tf.train.Saver()
train_saver.save(train_sess, save_path=self._ckpt_path)
# Create inference graph
tf.reset_default_graph()
FLAGS.patch_dim = FLAGS.patch_size
logging.info('dir_path: %s', os.listdir(self._export_dir))
FLAGS.checkpoint_path = self._ckpt_path
FLAGS.image_set_x_glob = self._image_glob
FLAGS.image_set_y_glob = self._image_glob
FLAGS.generated_x_dir = self._genx_dir
FLAGS.generated_y_dir = self._geny_dir
inference_demo.main(None)
logging.info('gen x: %s', os.listdir(self._genx_dir))
# Check that the image names match
self.assertSetEqual(
set(_basenames_from_glob(FLAGS.image_set_x_glob)),
set(os.listdir(FLAGS.generated_y_dir)))
self.assertSetEqual(
set(_basenames_from_glob(FLAGS.image_set_y_glob)),
set(os.listdir(FLAGS.generated_x_dir)))
# Check that each image in the directory looks as expected
for directory in [FLAGS.generated_x_dir, FLAGS.generated_x_dir]:
for base_name in os.listdir(directory):
image_path = os.path.join(directory, base_name)
self.assertRealisticImage(image_path)
