def discriminator(images_and_lbls, unused_conditioning, mode):
"""TF-GAN compatible discriminator."""
del unused_conditioning, mode
images, labels = images_and_lbls['images'], images_and_lbls['labels']
if hparams.debug_params.fake_nets:
# Need discriminator variables and to depend on the generator.
logits = tf.zeros(
[tf.shape(input=images)[0], 20]) * tf.compat.v1.get_variable(
'dummy_d',
initializer=2.0) * tf.reduce_mean(input_tensor=images)
discriminator_vars = ()
else:
num_trainable_variables = len(tf.compat.v1.trainable_variables())
logits, discriminator_vars = dis_module.discriminator(
images, labels, hparams.df_dim, hparams.num_classes)
if num_trainable_variables != len(
tf.compat.v1.trainable_variables()):
# Log the generated variables only in the first time the function is
# called and new variables are generated (it is called twice: once for
# the generated data and once for the real data).
eval_lib.log_and_summarize_variables(
discriminator_vars, 'dvars',
hparams.tpu_params.use_tpu_estimator)
logits.shape.assert_is_compatible_with([None, None])
return logits
def test_generator_shapes_and_ranges(self):
"""Tests the discriminator.
Make sure the image shapes and output value ranges are as expected.
"""
if tf.executing_eagerly():
# `compute_spectral_norm` doesn't work when executing eagerly.
return
batch_size = 10
num_classes = 1000
gen_class_logits = tf.zeros((batch_size, num_classes))
gen_class_ints = tf.multinomial(gen_class_logits, 1)
gen_sparse_class = tf.squeeze(gen_class_ints)
images = tf.random.normal([10, 32, 32, 3])
d_out, var_list = discriminator.discriminator(images, gen_sparse_class,
16, 1000)
sess = tf.train.MonitoredTrainingSession()
images_np = sess.run(d_out)
self.assertEqual((batch_size, 1), images_np.shape)
self.assertAllInRange(images_np, -1.0, 1.0)
self.assertIsInstance(var_list, list)
