def testEmptyUpdateOps(self):
with tf.Graph().as_default():
tf.compat.v1.random.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
tf_predictions = batchnorm_classifier(tf_inputs)
self.assertNotEmpty(
tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS))
loss = tf.compat.v1.losses.log_loss(tf_labels, tf_predictions)
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=1.0)
train_op = contrib_utils.create_train_op(loss, optimizer, update_ops=[])
moving_mean = contrib_utils.get_variables_by_name('moving_mean')[0]
moving_variance = contrib_utils.get_variables_by_name(
'moving_variance')[0]
with self.cached_session() as sess:
# Initialize all variables
sess.run(tf.compat.v1.global_variables_initializer())
mean, variance = sess.run([moving_mean, moving_variance])
# After initialization moving_mean == 0 and moving_variance == 1.
self.assertAllClose(mean, [0] * 4)
self.assertAllClose(variance, [1] * 4)
for _ in range(10):
sess.run(train_op)
mean = sess.run(moving_mean)
variance = sess.run(moving_variance)
# Since we skip update_ops the moving_vars are not updated.
self.assertAllClose(mean, [0] * 4)
self.assertAllClose(variance, [1] * 4)
def testCreateVariables(self):
if tf.executing_eagerly():
# Collections don't work with eager.
return
height, width = 3, 3
images = tf.random.uniform((5, height, width, 3), seed=1)
norm.instance_norm(images, center=True, scale=True)
self.assertLen(contrib_utils.get_variables_by_name('beta'), 1)
self.assertLen(contrib_utils.get_variables_by_name('gamma'), 1)
def testReuseVariables(self):
if tf.executing_eagerly():
# Variable reuse doesn't work with eager.
return
height, width = 3, 3
images = tf.random.uniform((5, height, width, 3), seed=1)
norm.instance_norm(images, scale=True, scope='IN')
norm.instance_norm(images, scale=True, scope='IN', reuse=True)
self.assertLen(contrib_utils.get_variables_by_name('beta'), 1)
self.assertLen(contrib_utils.get_variables_by_name('gamma'), 1)
def testCreateOpNoScaleCenter(self):
if tf.executing_eagerly():
# Collections don't work with eager.
return
height, width = 3, 3
images = tf.random.uniform((5, height, width, 3), dtype=tf.float64, seed=1)
output = norm.instance_norm(images, center=False, scale=False)
self.assertListEqual([5, height, width, 3], output.shape.as_list())
self.assertEmpty(contrib_utils.get_variables_by_name('beta'))
self.assertEmpty(contrib_utils.get_variables_by_name('gamma'))
def testCreateVariables_NCHW(self):
if tf.executing_eagerly():
# Collections don't work with eager.
return
height, width, groups = 3, 3, 4
images = tf.random.uniform((5, 2 * groups, height, width), seed=1)
norm.group_norm(images,
groups=4,
channels_axis=-3,
reduction_axes=(-2, -1),
center=True,
scale=True)
self.assertLen(contrib_utils.get_variables_by_name('beta'), 1)
self.assertLen(contrib_utils.get_variables_by_name('gamma'), 1)