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.
"""
with tf.compat.v1.name_scope('inputs'):
noise, one_hot_labels = _get_generator_inputs(
hparams.num_images_per_class, NUM_CLASSES, hparams.noise_dims)
# Generate images.
with tf.compat.v1.variable_scope(
'Generator'): # Same scope as in train job.
images = networks.conditional_generator((noise, one_hot_labels),
is_training=False)
# Visualize images.
reshaped_img = tfgan.eval.image_reshaper(
images, num_cols=hparams.num_images_per_class)
tf.compat.v1.summary.image('generated_images', reshaped_img, max_outputs=1)
# Calculate evaluation metrics.
tf.compat.v1.summary.scalar(
'MNIST_Classifier_score',
util.mnist_score(images, hparams.classifier_filename))
tf.compat.v1.summary.scalar(
'MNIST_Cross_entropy',
util.mnist_cross_entropy(images, one_hot_labels,
hparams.classifier_filename))
# Write images to disk.
image_write_ops = None
if hparams.write_to_disk:
image_write_ops = tf.io.write_file(
'%s/%s' % (hparams.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
evaluation.evaluate_repeatedly(
hparams.checkpoint_dir,
hooks=[
evaluation.SummaryAtEndHook(hparams.eval_dir),
evaluation.StopAfterNEvalsHook(1)
],
eval_ops=image_write_ops,
max_number_of_evaluations=hparams.max_number_of_evaluations)
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 real images.
with tf.compat.v1.name_scope('inputs'):
real_images, _ = data_provider.provide_data(
'train', hparams.num_images_generated, hparams.dataset_dir)
image_write_ops = None
if hparams.eval_real_images:
tf.compat.v1.summary.scalar(
'MNIST_Classifier_score',
util.mnist_score(real_images, hparams.classifier_filename))
else:
# In order for variables to load, use the same variable scope as in the
# train job.
with tf.compat.v1.variable_scope('Generator'):
images = networks.unconditional_generator(tf.random.normal(
[hparams.num_images_generated, hparams.noise_dims]),
is_training=False)
tf.compat.v1.summary.scalar(
'MNIST_Frechet_distance',
util.mnist_frechet_distance(real_images, images,
hparams.classifier_filename))
tf.compat.v1.summary.scalar(
'MNIST_Classifier_score',
util.mnist_score(images, hparams.classifier_filename))
if hparams.num_images_generated >= 100 and hparams.write_to_disk:
reshaped_images = tfgan.eval.image_reshaper(images[:100, ...],
num_cols=10)
uint8_images = data_provider.float_image_to_uint8(reshaped_images)
image_write_ops = tf.io.write_file(
'%s/%s' % (hparams.eval_dir, 'unconditional_gan.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,
hooks=[
evaluation.SummaryAtEndHook(hparams.eval_dir),
evaluation.StopAfterNEvalsHook(1)
],
eval_ops=image_write_ops,
max_number_of_evaluations=hparams.max_number_of_evaluations)
def _get_write_image_ops(eval_dir, filename, images):
"""Create Ops that write images to disk."""
return tf.io.write_file(
'%s/%s' % (eval_dir, filename),
tf.image.encode_png(data_provider.float_image_to_uint8(images)))