示例#1
0
文件: util_test.py 项目: yyht/gan
 def test_get_inception_scores(self, mock_inception_score):
     mock_inception_score.return_value = 1.0
     batch_size = 100
     util.get_inception_scores(tf.zeros([batch_size, 28, 28, 3],
                                        dtype=tf.float32),
                               batch_size=batch_size,
                               num_inception_images=10)
示例#2
0
def evaluate(hparams, run_eval_loop=True):
    """Runs an evaluation loop.

  Args:
    hparams: An HParams instance containing the eval hyperparameters.
    run_eval_loop: Whether to run the full eval loop. Set to False for testing.
  """
    # Fetch and generate images to run through Inception.
    with tf.name_scope('inputs'):
        real_data, _ = data_provider.provide_data('test',
                                                  hparams.num_images_generated,
                                                  shuffle=False)
        generated_data = _get_generated_data(hparams.num_images_generated)

    # Compute Frechet Inception Distance.
    if hparams.eval_frechet_inception_distance:
        fid = util.get_frechet_inception_distance(real_data, generated_data,
                                                  hparams.num_images_generated,
                                                  hparams.num_inception_images)
        tf.summary.scalar('frechet_inception_distance', fid)

    # Compute normal Inception scores.
    if hparams.eval_real_images:
        inc_score = util.get_inception_scores(real_data,
                                              hparams.num_images_generated,
                                              hparams.num_inception_images)
    else:
        inc_score = util.get_inception_scores(generated_data,
                                              hparams.num_images_generated,
                                              hparams.num_inception_images)
    tf.summary.scalar('inception_score', inc_score)

    # Create ops that write images to disk.
    image_write_ops = None
    if hparams.num_images_generated >= 100 and hparams.write_to_disk:
        reshaped_imgs = tfgan.eval.image_reshaper(generated_data[:100],
                                                  num_cols=10)
        uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
        image_write_ops = tf.io.write_file(
            '%s/%s' % (hparams.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
    evaluation.evaluate_repeatedly(
        hparams.checkpoint_dir,
        master=hparams.master,
        hooks=[
            evaluation.SummaryAtEndHook(hparams.eval_dir),
            evaluation.StopAfterNEvalsHook(1)
        ],
        eval_ops=image_write_ops,
        max_number_of_evaluations=hparams.max_number_of_evaluations)