def testCreateVariables_NCHW(self):
height, width, groups = 3, 3, 4
images = random_ops.random_uniform((5, 2 * groups, height, width),
seed=1)
normalization.group_norm(images,
groups=4,
channels_axis=-3,
reduction_axes=(-2, -1),
center=True,
scale=True)
beta = contrib_variables.get_variables_by_name('beta')[0]
gamma = contrib_variables.get_variables_by_name('gamma')[0]
self.assertEqual('GroupNorm/beta', beta.op.name)
self.assertEqual('GroupNorm/gamma', gamma.op.name)
def testCreateOpFloat64(self):
height, width, groups = 3, 3, 5
images = random_ops.random_uniform((5, height, width, 4 * groups),
dtype=dtypes.float64,
seed=1)
output = normalization.group_norm(images, groups=groups)
self.assertEqual(dtypes.float64, output.dtype)
self.assertListEqual([5, height, width, 4 * groups],
output.shape.as_list())
def testParamsShapeNotFullyDefinedBatchAxis(self):
height, width, groups = 3, 3, 4
inputs = array_ops.placeholder(dtypes.float32,
shape=(None, height, width, 2 * groups))
output = normalization.group_norm(inputs,
channels_axis=-1,
reduction_axes=[-3, -2],
groups=groups)
self.assertListEqual([None, height, width, 2 * groups],
output.shape.as_list())
def testCreateOp(self):
height, width, groups = 3, 3, 4
images = random_ops.random_uniform((5, height, width, 2 * groups),
seed=1)
output = normalization.group_norm(images,
groups=groups,
channels_axis=-1,
reduction_axes=[-3, -2])
print('name: ', output.op.name)
self.assertListEqual([5, height, width, 2 * groups],
output.shape.as_list())
def testCreateOpNoScaleCenter(self):
height, width, groups = 3, 3, 7
images = random_ops.random_uniform((5, height, width, 3 * groups),
dtype=dtypes.float32,
seed=1)
output = normalization.group_norm(images,
groups=groups,
center=False,
scale=False)
self.assertListEqual([5, height, width, 3 * groups],
output.shape.as_list())
self.assertEqual(0,
len(contrib_variables.get_variables_by_name('beta')))
self.assertEqual(0,
len(contrib_variables.get_variables_by_name('gamma')))
def testNotMutuallyExclusiveAxis(self):
inputs = array_ops.placeholder(dtypes.float32, shape=(10, 32, 32, 32))
# Specify axis with negative values.
with self.assertRaisesRegexp(ValueError, 'mutually exclusive'):
normalization.group_norm(inputs,
channels_axis=-2,
reduction_axes=[-2])
# Specify axis with positive values.
with self.assertRaisesRegexp(ValueError, 'mutually exclusive'):
normalization.group_norm(inputs,
channels_axis=1,
reduction_axes=[1, 3])
# Specify axis with mixed positive and negative values.
with self.assertRaisesRegexp(ValueError, 'mutually exclusive'):
normalization.group_norm(inputs,
channels_axis=-2,
reduction_axes=[2])
def doOutputTest(self,
input_shape,
channels_axis=None,
reduction_axes=None,
mean_close_to_zero=False,
groups=2,
tol=1e-2):
# Select the axis for the channel and the dimensions along which statistics
# are accumulated.
if channels_axis < 0:
channels_axis += len(input_shape)
reduced_axes = [channels_axis + 1]
for a in reduction_axes:
if a < 0:
a += len(input_shape)
if a < channels_axis:
reduced_axes.append(a)
else:
reduced_axes.append(a + 1)
reduced_axes = tuple(reduced_axes)
# Calculate the final shape for the output Tensor.
axes_before_channels = input_shape[:channels_axis]
axes_after_channels = input_shape[channels_axis + 1:]
channels = input_shape[channels_axis]
outputs_shape = (axes_before_channels + [groups, channels // groups] +
axes_after_channels)
# Calculate the final shape for the output statistics.
reduced_shape = []
for i, a in enumerate(outputs_shape):
if i not in reduced_axes:
reduced_shape.append(a)
if mean_close_to_zero:
mu_tuple = (1e-4, 1e-2, 1.0)
sigma_tuple = (1e-2, 0.1, 1.0)
else:
mu_tuple = (1.0, 1e2)
sigma_tuple = (1.0, 0.1)
for mu in mu_tuple:
for sigma in sigma_tuple:
# Determine shape of Tensor after normalization.
expected_mean = np.zeros(reduced_shape)
expected_var = np.ones(reduced_shape)
inputs = random_ops.random_normal(input_shape,
seed=0) * sigma + mu
output_op = normalization.group_norm(
inputs,
groups=groups,
center=False,
scale=False,
channels_axis=channels_axis,
reduction_axes=reduction_axes,
mean_close_to_zero=mean_close_to_zero)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
outputs = sess.run(output_op)
# Make sure that there are no NaNs
self.assertFalse(np.isnan(outputs).any())
outputs = np.reshape(outputs, outputs_shape)
mean = np.mean(outputs, axis=reduced_axes)
var = np.var(outputs, axis=reduced_axes)
# The mean and variance of each example should be close to 0 and 1
# respectively.
self.assertAllClose(expected_mean,
mean,
rtol=tol,
atol=tol)
self.assertAllClose(expected_var, var, rtol=tol, atol=tol)
def testParamsShapeNotFullyDefinedReductionAxes(self):
inputs = array_ops.placeholder(dtypes.float32, shape=(1, 32, None, 4))
with self.assertRaisesRegexp(ValueError, 'undefined dimensions'):
normalization.group_norm(inputs)
def testUnknownShape(self):
inputs = array_ops.placeholder(dtypes.float32)
with self.assertRaisesRegexp(ValueError, 'undefined rank'):
normalization.group_norm(inputs)
def testAxisIsBad(self):
inputs = array_ops.placeholder(dtypes.float32, shape=(1, 2, 4, 5))
with self.assertRaisesRegexp(ValueError, 'Axis is out of bounds.'):
normalization.group_norm(inputs, channels_axis=5)
with self.assertRaisesRegexp(ValueError, 'Axis is out of bounds.'):
normalization.group_norm(inputs, reduction_axes=[1, 5])