def test_generator_grad_norm_progress(self):
if tf.executing_eagerly():
# tf.placeholder() is not compatible with eager execution.
return
stable_stage_num_images = 2
transition_stage_num_images = 3
current_image_id_ph = tf.compat.v1.placeholder(tf.int32, [])
progress = networks.compute_progress(
current_image_id_ph,
stable_stage_num_images,
transition_stage_num_images,
num_blocks=3)
z = tf.random.normal([2, 10], dtype=tf.float32)
x, _ = networks.generator(
z, progress, _num_filters_stub,
networks.ResolutionSchedule(
start_resolutions=(4, 4), scale_base=2, num_resolutions=3))
fake_loss = tf.reduce_sum(input_tensor=tf.square(x))
grad_norms = [
_get_grad_norm(
fake_loss,
tf.compat.v1.trainable_variables('.*/progressive_gan_block_1/.*')),
_get_grad_norm(
fake_loss,
tf.compat.v1.trainable_variables('.*/progressive_gan_block_2/.*')),
_get_grad_norm(
fake_loss,
tf.compat.v1.trainable_variables('.*/progressive_gan_block_3/.*'))
]
grad_norms_output = None
with self.cached_session(use_gpu=True) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
x1_np = sess.run(x, feed_dict={current_image_id_ph: 0.12})
x2_np = sess.run(x, feed_dict={current_image_id_ph: 1.8})
grad_norms_output = np.array([
sess.run(grad_norms, feed_dict={current_image_id_ph: i})
for i in range(15) # total num of images
])
self.assertEqual((2, 16, 16, 3), x1_np.shape)
self.assertEqual((2, 16, 16, 3), x2_np.shape)
# The gradient of block_1 is always on.
self.assertEqual(
np.argmax(grad_norms_output[:, 0] > 0), 0,
'gradient norms {} for block 1 is not always on'.format(
grad_norms_output[:, 0]))
# The gradient of block_2 is on after 1 stable stage.
self.assertEqual(
np.argmax(grad_norms_output[:, 1] > 0), 3,
'gradient norms {} for block 2 is not on at step 3'.format(
grad_norms_output[:, 1]))
# The gradient of block_3 is on after 2 stable stage + 1 transition stage.
self.assertEqual(
np.argmax(grad_norms_output[:, 2] > 0), 8,
'gradient norms {} for block 3 is not on at step 8'.format(
grad_norms_output[:, 2]))
def test_discriminator_grad_norm_progress(self):
stable_stage_num_images = 2
transition_stage_num_images = 3
current_image_id_ph = tf.compat.v1.placeholder(tf.int32, [])
progress = networks.compute_progress(current_image_id_ph,
stable_stage_num_images,
transition_stage_num_images,
num_blocks=3)
x = tf.random.normal([2, 16, 16, 3])
logits, _ = networks.discriminator(
x, progress, _num_filters_stub,
networks.ResolutionSchedule(start_resolutions=(4, 4),
scale_base=2,
num_resolutions=3))
fake_loss = tf.reduce_sum(input_tensor=tf.square(logits))
grad_norms = [
_get_grad_norm(
fake_loss,
tf.compat.v1.trainable_variables(
'.*/progressive_gan_block_1/.*')),
_get_grad_norm(
fake_loss,
tf.compat.v1.trainable_variables(
'.*/progressive_gan_block_2/.*')),
_get_grad_norm(
fake_loss,
tf.compat.v1.trainable_variables(
'.*/progressive_gan_block_3/.*'))
]
grad_norms_output = None
with self.cached_session(use_gpu=True) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
grad_norms_output = np.array([
sess.run(grad_norms, feed_dict={current_image_id_ph: i})
for i in range(15) # total num of images
])
# The gradient of block_1 is always on.
self.assertEqual(
np.argmax(grad_norms_output[:, 0] > 0), 0,
'gradient norms {} for block 1 is not always on'.format(
grad_norms_output[:, 0]))
# The gradient of block_2 is on after 1 stable stage.
self.assertEqual(
np.argmax(grad_norms_output[:, 1] > 0), 3,
'gradient norms {} for block 2 is not on at step 3'.format(
grad_norms_output[:, 1]))
# The gradient of block_3 is on after 2 stable stage + 1 transition stage.
self.assertEqual(
np.argmax(grad_norms_output[:, 2] > 0), 8,
'gradient norms {} for block 3 is not on at step 8'.format(
grad_norms_output[:, 2]))
def test_blend_images_in_stable_stage(self):
x_np = np.random.normal(size=[2, 8, 8, 3])
x = tf.constant(x_np, tf.float32)
x_blend = networks.blend_images(
x,
progress=tf.constant(0.0),
resolution_schedule=networks.ResolutionSchedule(
scale_base=2, num_resolutions=2),
num_blocks=2)
with self.cached_session(use_gpu=True) as sess:
x_blend_np = sess.run(x_blend)
x_blend_expected_np = sess.run(layers.upscale(layers.downscale(x, 2), 2))
self.assertNDArrayNear(x_blend_np, x_blend_expected_np, 1.0e-6)
def test_resolution_schedule_correct(self):
rs = networks.ResolutionSchedule(
start_resolutions=[5, 3], scale_base=2, num_resolutions=3)
self.assertEqual(rs.start_resolutions, (5, 3))
self.assertEqual(rs.scale_base, 2)
self.assertEqual(rs.num_resolutions, 3)
self.assertEqual(rs.final_resolutions, (20, 12))
self.assertEqual(rs.scale_factor(1), 4)
self.assertEqual(rs.scale_factor(2), 2)
self.assertEqual(rs.scale_factor(3), 1)
with self.assertRaises(ValueError):
rs.scale_factor(0)
with self.assertRaises(ValueError):
rs.scale_factor(4)
def make_resolution_schedule(**kwargs):
"""Returns an object of `ResolutionSchedule`."""
return networks.ResolutionSchedule(
start_resolutions=(kwargs['start_height'], kwargs['start_width']),
scale_base=kwargs['scale_base'],
num_resolutions=kwargs['num_resolutions'])
