def test_conv3d(self):
with tf.Graph().as_default():
x = 10.0 * tf.random_uniform(shape=(16, 4, 5, 5, 32))
with arg_scope([glow_ops.actnorm], init=True):
conv3d = glow_ops.conv(
"conv3d", x, output_channels=64, apply_actnorm=True)
conv3d_zeros = glow_ops.conv(
"conv3d_zeros", x, output_channels=64, apply_actnorm=False,
conv_init="zeros")
with tf.Session() as session:
session.run(tf.global_variables_initializer())
# test if apply_actnorm is set to True, the first minibatch has
# zero mean and unit variance.
conv3d_np, conv3d_zeros_np = session.run([conv3d, conv3d_zeros])
self.assertEqual(conv3d_np.shape, (16, 4, 5, 5, 64))
for i in range(4):
curr_step = conv3d_np[:, i, :, :, :]
mean = np.mean(curr_step, axis=(0, 1, 2))
var = np.var(curr_step, axis=(0, 1, 2))
self.assertTrue(np.allclose(mean, 0.0, atol=1e-5))
self.assertTrue(np.allclose(var, 1.0, atol=1e-5))
# test shape in case apply_actnorm is set to False,
self.assertTrue(np.allclose(conv3d_zeros_np, 0.0))
def test_conv2d(self):
with tf.Graph().as_default():
x = 10.0 * tf.random_uniform(shape=(16, 5, 5, 32))
with arg_scope([glow_ops.actnorm], init=True):
actnorm_conv2d = glow_ops.conv("actnorm_conv2d",
x,
output_channels=64,
apply_actnorm=True)
actnorm_zeros2d = glow_ops.conv("actnorm_zeros2d",
x,
output_channels=64,
apply_actnorm=False)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
# test if apply_actnorm is set to True, the first minibatch has
# zero mean and unit variance.
actnorm_np, zeros_np = session.run(
[actnorm_conv2d, actnorm_zeros2d])
self.assertEqual(actnorm_np.shape, (16, 5, 5, 64))
mean = np.mean(actnorm_np, axis=(0, 1, 2))
var = np.var(actnorm_np, axis=(0, 1, 2))
self.assertTrue(np.allclose(mean, 0.0, atol=1e-5))
self.assertTrue(np.allclose(var, 1.0, atol=1e-5))
# test shape in case apply_actnorm is set to False,
self.assertEqual(zeros_np.shape, (16, 5, 5, 64))
def test_conv3d(self):
with tf.Graph().as_default():
x = 10.0 * tf.random_uniform(shape=(16, 4, 5, 5, 32))
with arg_scope([glow_ops.actnorm], init=True):
conv3d = glow_ops.conv(
"conv3d", x, output_channels=64, apply_actnorm=True)
conv3d_zeros = glow_ops.conv(
"conv3d_zeros", x, output_channels=64, apply_actnorm=False,
conv_init="zeros")
with tf.Session() as session:
session.run(tf.global_variables_initializer())
# test if apply_actnorm is set to True, the first minibatch has
# zero mean and unit variance.
conv3d_np, conv3d_zeros_np = session.run([conv3d, conv3d_zeros])
self.assertEqual(conv3d_np.shape, (16, 4, 5, 5, 64))
for i in range(4):
curr_step = conv3d_np[:, i, :, :, :]
mean = np.mean(curr_step, axis=(0, 1, 2))
var = np.var(curr_step, axis=(0, 1, 2))
self.assertTrue(np.allclose(mean, 0.0, atol=1e-5))
self.assertTrue(np.allclose(var, 1.0, atol=1e-5))
# test shape in case apply_actnorm is set to False,
self.assertTrue(np.allclose(conv3d_zeros_np, 0.0))
def get_squeeze_prior(self):
"""Model the prior over z_{t} as a function of X_{t-1}.
Returns:
objective: float, log-likelihood.
dist: instance of tfp.distributions.Normal.
Raises:
ValueError: If input_height is not equal to input_width, not even
or if the image width is smaller than the latent width.
"""
_, prior_height, _, prior_channels = self.z_top_shape
_, input_height, input_width, _ = common_layers.shape_list(self.input_frame)
if input_height != input_width:
raise ValueError("input height should be equal to input width")
if input_height % 2 != 0:
raise ValueError("input height should be even")
if input_height < prior_height:
raise ValueError("input should be larger than the prior.")
# mean, log_std = NN(X_0)
# Reduce the spatial dimension by a factor of "squeeze_factor".
# and convolve with a stride of 2
squeeze_factor = input_height // (2 * prior_height)
x = glow_ops.squeeze(
"prior_squeeze", self.input_frame, factor=squeeze_factor, reverse=False)
mean_and_log_std = glow_ops.conv(
"prior_conv", x, 2*prior_channels, stride=[2, 2], apply_actnorm=False,
conv_init="zeros")
mean, log_scale = tf.split(mean_and_log_std, num_or_size_splits=2, axis=-1)
return tfp.distributions.Normal(mean, tf.exp(log_scale))
def test_conv2d(self):
with tf.Graph().as_default():
x = 10.0 * tf.random_uniform(shape=(16, 5, 5, 32))
with arg_scope([glow_ops.actnorm], init=True):
actnorm_conv2d = glow_ops.conv(
"actnorm_conv2d", x, output_channels=64, apply_actnorm=True)
actnorm_zeros2d = glow_ops.conv(
"actnorm_zeros2d", x, output_channels=64, apply_actnorm=False)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
# test if apply_actnorm is set to True, the first minibatch has
# zero mean and unit variance.
actnorm_np, zeros_np = session.run([actnorm_conv2d, actnorm_zeros2d])
self.assertEqual(actnorm_np.shape, (16, 5, 5, 64))
mean = np.mean(actnorm_np, axis=(0, 1, 2))
var = np.var(actnorm_np, axis=(0, 1, 2))
self.assertTrue(np.allclose(mean, 0.0, atol=1e-5))
self.assertTrue(np.allclose(var, 1.0, atol=1e-5))
# test shape in case apply_actnorm is set to False,
self.assertEqual(zeros_np.shape, (16, 5, 5, 64))
