def test_get_frechet_inception_distance(self, mock_fid):
mock_fid.return_value = 1.0
batch_size = 100
util.get_frechet_inception_distance(tf.zeros([batch_size, 28, 28, 3],
dtype=tf.float32),
tf.zeros([batch_size, 28, 28, 3],
dtype=tf.float32),
batch_size=batch_size,
num_inception_images=10)
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)