def prepare_metric_arguments(generator_inputs, generated_data, real_data,
discriminator_real_outputs,
discriminator_gen_outputs):
"""Prepares the arguments needed for get_metrics.
When training on TPUs, this function should be executed on TPU.
Args:
generator_inputs: Inputs to the generator fn.
generated_data: Output from the generator.
real_data: A sample of real data.
discriminator_real_outputs: Discriminator output on real data.
discriminator_gen_outputs: Discriminator output on generated data.
Returns:
A metric dictionary.
"""
del generator_inputs, discriminator_real_outputs, discriminator_gen_outputs
real_images = (real_data['images'] if isinstance(real_data, dict) else
real_data)
gen_images = (generated_data['images'] if isinstance(generated_data, dict)
else generated_data)
# Get logits and pools for real and generated images.
real_logits, real_pools = eval_lib.get_activations(
lambda: real_images, num_batches=1, get_logits=True)
fake_logits, fake_pools = eval_lib.get_activations(
lambda: gen_images, num_batches=1, get_logits=True)
return {
'real_logits': real_logits,
'real_pools': real_pools,
'fake_logits': fake_logits,
'fake_pools': fake_pools
}
def get_metrics(generator_inputs, generated_data, real_data,
discriminator_real_outputs, discriminator_gen_outputs,
hparams):
"""Return metrics for SAGAN experiment on TPU, CPU, or GPU.
Args:
generator_inputs: Inputs to the generator fn.
generated_data: Output from the generator.
real_data: A sample of real data.
discriminator_real_outputs: Discriminator output on real data.
discriminator_gen_outputs: Discriminator output on generated data.
hparams: An hparams object.
Returns:
A metric dictionary.
"""
del generator_inputs, discriminator_real_outputs, discriminator_gen_outputs
real_images = real_data['images']
gen_images = generated_data['images']
# Get logits and pools for real and generated images.
real_logits, real_pools = eval_lib.get_activations(lambda: real_images,
num_batches=1,
get_logits=True)
fake_logits, fake_pools = eval_lib.get_activations(lambda: gen_images,
num_batches=1,
get_logits=True)
if hparams.eval_on_tpu:
# TODO(dyoel): Rewrite once b/135664219 is resolved.
real_iscore = tfgan.eval.classifier_score_from_logits(real_logits)
generated_iscore = tfgan.eval.classifier_score_from_logits(fake_logits)
fid = tfgan.eval.frechet_classifier_distance_from_activations(
real_pools, fake_pools)
# Tile metrics because TPU requires metric outputs to be [batch_size, ...].
batch_size = tf.shape(input=gen_images)[0]
real_iscore_tiled = tf.tile([real_iscore], [batch_size])
generated_iscore_tiled = tf.tile([generated_iscore], [batch_size])
frechet_distance_tiled = tf.tile([fid], [batch_size])
return {
'eval/real_incscore': real_iscore_tiled,
'eval/incscore': generated_iscore_tiled,
'eval/fid': frechet_distance_tiled,
}
else:
metric_dict = {
'eval/real_incscore':
tfgan.eval.classifier_score_from_logits_streaming(real_logits),
'eval/incscore':
tfgan.eval.classifier_score_from_logits_streaming(fake_logits),
'eval/fid':
tfgan.eval.frechet_classifier_distance_from_activations_streaming(
real_pools, fake_pools),
}
metric_dict.update(_generator_summary_ops(gen_images, real_images))
return metric_dict