def main(_, run_eval_loop=True): # Fetch and generate images to run through Inception. with tf.name_scope('inputs'): real_data, num_classes = _get_real_data( FLAGS.num_images_generated, FLAGS.dataset_dir) generated_data = _get_generated_data( FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes) # Compute Frechet Inception Distance. if FLAGS.eval_frechet_inception_distance: fid = util.get_frechet_inception_distance( real_data, generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images) tf.summary.scalar('frechet_inception_distance', fid) # Compute normal Inception scores. if FLAGS.eval_real_images: inc_score = util.get_inception_scores( real_data, FLAGS.num_images_generated, FLAGS.num_inception_images) else: inc_score = util.get_inception_scores( generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images) tf.summary.scalar('inception_score', inc_score) # If conditional, display an image grid of difference classes. if FLAGS.conditional_eval and not FLAGS.eval_real_images: reshaped_imgs = util.get_image_grid( generated_data, FLAGS.num_images_generated, num_classes, FLAGS.num_images_per_class) tf.summary.image('generated_data', reshaped_imgs, max_outputs=1) # Create ops that write images to disk. image_write_ops = None if FLAGS.conditional_eval and FLAGS.write_to_disk: reshaped_imgs = util.get_image_grid( generated_data, FLAGS.num_images_generated, num_classes, FLAGS.num_images_per_class) uint8_images = data_provider.float_image_to_uint8(reshaped_imgs) image_write_ops = tf.write_file( '%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'), tf.image.encode_png(uint8_images[0])) else: if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk: reshaped_imgs = tfgan.eval.image_reshaper( generated_data[:100], num_cols=FLAGS.num_images_per_class) uint8_images = data_provider.float_image_to_uint8(reshaped_imgs) image_write_ops = tf.write_file( '%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'), tf.image.encode_png(uint8_images[0])) # For unit testing, use `run_eval_loop=False`. if not run_eval_loop: return tf.contrib.training.evaluate_repeatedly( FLAGS.checkpoint_dir, master=FLAGS.master, 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 main(_, run_eval_loop=True): # Fetch and generate images to run through Inception. with tf.name_scope('inputs'): real_data, num_classes = _get_real_data( FLAGS.num_images_generated, FLAGS.dataset_dir) generated_data = _get_generated_data( FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes) # Compute Frechet Inception Distance. if FLAGS.eval_frechet_inception_distance: fid = util.get_frechet_inception_distance( real_data, generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images) tf.summary.scalar('frechet_inception_distance', fid) # Compute normal Inception scores. if FLAGS.eval_real_images: inc_score = util.get_inception_scores( real_data, FLAGS.num_images_generated, FLAGS.num_inception_images) else: inc_score = util.get_inception_scores( generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images) tf.summary.scalar('inception_score', inc_score) # If conditional, display an image grid of difference classes. if FLAGS.conditional_eval and not FLAGS.eval_real_images: reshaped_imgs = util.get_image_grid( generated_data, FLAGS.num_images_generated, num_classes, FLAGS.num_images_per_class) tf.summary.image('generated_data', reshaped_imgs, max_outputs=1) # Create ops that write images to disk. image_write_ops = None if FLAGS.conditional_eval and FLAGS.write_to_disk: reshaped_imgs = util.get_image_grid( generated_data, FLAGS.num_images_generated, num_classes, FLAGS.num_images_per_class) uint8_images = data_provider.float_image_to_uint8(reshaped_imgs) image_write_ops = tf.write_file( '%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'), tf.image.encode_png(uint8_images[0])) else: if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk: reshaped_imgs = tfgan.eval.image_reshaper( generated_data[:100], num_cols=FLAGS.num_images_per_class) uint8_images = data_provider.float_image_to_uint8(reshaped_imgs) image_write_ops = tf.write_file( '%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'), tf.image.encode_png(uint8_images[0])) # For unit testing, use `run_eval_loop=False`. if not run_eval_loop: return tf.contrib.training.evaluate_repeatedly( FLAGS.checkpoint_dir, master=FLAGS.master, 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 test_get_inception_scores(self): # Mock `inception_score` which is expensive. with mock.patch.object(util.tfgan.eval, 'inception_score') as mock_inception_score: mock_inception_score.return_value = 1.0 util.get_inception_scores(tf.placeholder(tf.float32, shape=[None, 28, 28, 3]), batch_size=100, num_inception_images=10)
def test_get_inception_scores(self, mock_inception_score): mock_inception_score.return_value = 1.0 util.get_inception_scores(tf.placeholder(tf.float32, shape=[None, 28, 28, 3]), batch_size=100, num_inception_images=10)
def main(_, run_eval_loop=True): # Fetch and generate images to run through Inception. with tf.name_scope('inputs'): real_data, num_classes = _get_real_data( # FLAGS.bs, FLAGS.dataset_dir) FLAGS.bs, FLAGS.dl) generated_data = _get_generated_data(FLAGS.bs, FLAGS.conditional_eval, num_classes) # Compute Frechet Inception Distance. if FLAGS.eval_frechet_inception_distance: fid = util.get_frechet_inception_distance(real_data, generated_data, FLAGS.bs, FLAGS.num_inception_images) tf.summary.scalar('frechet_inception_distance', fid) # Compute normal Inception scores. if FLAGS.eval_real_images: inc_score = util.get_inception_scores(real_data, FLAGS.bs, FLAGS.num_inception_images) else: inc_score = util.get_inception_scores(generated_data, FLAGS.bs, FLAGS.num_inception_images) tf.summary.scalar('inception_score', inc_score) # If conditional, display an image grid of difference classes. if FLAGS.conditional_eval and not FLAGS.eval_real_images: reshaped_imgs = util.get_image_grid(generated_data, FLAGS.bs, num_classes, FLAGS.num_images_per_class) tf.summary.image('generated_data', reshaped_imgs, max_outputs=1) # Create ops that write images to disk. image_write_ops = None if FLAGS.conditional_eval and FLAGS.write_to_disk: reshaped_imgs = util.get_image_grid(generated_data, FLAGS.bs, num_classes, FLAGS.num_images_per_class) uint8_images = data_provider.float_image_to_uint8(reshaped_imgs) image_write_ops = tf.write_file( '%s/%s' % (FLAGS.eval_dir, 'conditional_cifar10.png'), tf.image.encode_png(uint8_images[0])) else: if FLAGS.bs >= 100 and FLAGS.write_to_disk: reshaped_imgs = tfgan.eval.image_reshaper( generated_data[:100], num_cols=FLAGS.num_images_per_class) uint8_images = data_provider.float_image_to_uint8(reshaped_imgs) image_write_ops = tf.write_file( '%s/%s' % (FLAGS.eval_dir, 'unconditional_cifar10.png'), tf.image.encode_png(uint8_images[0])) # For unit testing, use `run_eval_loop=False`. if not run_eval_loop: return checkpoint_path = tf_saver.latest_checkpoint(FLAGS.ckpt) if (checkpoint_path is None): print("error, no checkpoint path") # set mkl env mkl_setup() sess_config = create_config_proto() session_creator = monitored_session.ChiefSessionCreator( scaffold=None, checkpoint_filename_with_path=checkpoint_path, master=FLAGS.master, config=sess_config) with monitored_session.MonitoredSession(session_creator=session_creator, hooks=None) as session: eval_ops = image_write_ops for warmup_i in range(FLAGS.nw): session.run(eval_ops, feed_dict=None) start = time.time() for batch_i in range(FLAGS.nb): session.run(eval_ops, feed_dict=None) end = time.time() inference_time = end - start print("Batch size:", FLAGS.bs, "\nBatches number:", FLAGS.nb) print("Time spent per BATCH: %.4f ms" % (inference_time * 1000 / (FLAGS.nb))) print("Total samples/sec: %.4f samples/s" % (FLAGS.nb * FLAGS.bs / inference_time))