def test3DConvolution(self):
with tf_ops.Graph().as_default():
batch_size = 1
width = 3
in_channels = 3**3
out_channels = 4
factor = ff.ConvInputKroneckerFactor(
inputs=(random_ops.random_uniform(
(batch_size, width, width, width, in_channels), seed=0), ),
filter_shape=(width, width, width, in_channels, out_channels),
padding='SAME',
strides=(2, 2, 2),
extract_patches_fn='extract_convolution_patches',
has_bias=False)
factor.instantiate_cov_variables()
# Ensure shape of covariance matches input size of filter.
input_size = in_channels * (width**3)
self.assertEqual([input_size, input_size],
factor.get_cov().shape.as_list())
# Ensure cov_update_op doesn't crash.
with self.cached_session() as sess:
sess.run(tf_variables.global_variables_initializer())
sess.run(factor.make_covariance_update_op(0.0))
cov = sess.run(factor.get_cov())
# Cov should be rank-8, as the filter will be applied at each corner of
# the 4-D cube.
self.assertMatrixRank(8, cov)
def testDilationRate(self):
with tf_ops.Graph().as_default():
batch_size = 1
width = 3
in_channels = 2
out_channels = 4
factor = ff.ConvInputKroneckerFactor(
inputs=(random_ops.random_uniform(
(batch_size, width, width, in_channels), seed=0), ),
filter_shape=(3, 3, in_channels, out_channels),
padding='SAME',
extract_patches_fn='extract_image_patches',
strides=(1, 1, 1, 1),
dilation_rate=(1, width, width, 1),
has_bias=False)
factor.instantiate_cov_variables()
with self.test_session() as sess:
sess.run(tf_variables.global_variables_initializer())
sess.run(factor.make_covariance_update_op(0.0))
cov = sess.run(factor.get_cov_var())
# Cov should be rank = in_channels, as only the center of the filter
# receives non-zero input for each input channel.
self.assertMatrixRank(in_channels, cov)
def testPointwiseConv2d(self):
with tf_ops.Graph().as_default():
batch_size = 1
width = 3
in_channels = 3**2
out_channels = 4
factor = ff.ConvInputKroneckerFactor(
inputs=(random_ops.random_uniform(
(batch_size, width, width, in_channels), seed=0), ),
filter_shape=(1, 1, in_channels, out_channels),
padding='SAME',
strides=(1, 1, 1, 1),
extract_patches_fn='extract_pointwise_conv2d_patches',
has_bias=False)
factor.instantiate_cov_variables()
# Ensure shape of covariance matches input size of filter.
self.assertEqual([in_channels, in_channels],
factor.get_cov_var().shape.as_list())
# Ensure cov_update_op doesn't crash.
with self.test_session() as sess:
sess.run(tf_variables.global_variables_initializer())
sess.run(factor.make_covariance_update_op(0.0))
cov = sess.run(factor.get_cov_var())
# Cov should be rank-9, as the filter will be applied at each location.
self.assertMatrixRank(9, cov)
def testConvInputKroneckerFactorInit(self):
with tf_ops.Graph().as_default():
random_seed.set_random_seed(200)
tensor = array_ops.ones((2, 3), name='a/b/c')
factor = ff.ConvInputKroneckerFactor(
tensor, (1, 2, 3, 4), 3, 2, has_bias=True)
self.assertEqual([1 * 2 * 3 + 1, 1 * 2 * 3 + 1],
factor.get_cov().get_shape().as_list())
def testConvInputKroneckerFactorInit(self):
with tf_ops.Graph().as_default():
tensor = array_ops.ones((64, 1, 2, 3), name='a/b/c')
factor = ff.ConvInputKroneckerFactor(
(tensor,), filter_shape=(1, 2, 3, 4), padding='SAME', has_bias=True)
factor.instantiate_cov_variables()
self.assertEqual([1 * 2 * 3 + 1, 1 * 2 * 3 + 1],
factor.get_cov().get_shape().as_list())
def testConvInputKroneckerFactorInitNoBias(self):
with tf_ops.Graph().as_default():
random_seed.set_random_seed(200)
tensor = array_ops.ones((2, 3), name='a/b/c')
factor = ff.ConvInputKroneckerFactor(tensor, (1, 2, 3, 4),
3,
2,
has_bias=False)
factor.instantiate_cov_variables()
self.assertEqual([1 * 2 * 3, 1 * 2 * 3],
factor.get_cov().get_shape().as_list())
def testMakeCovarianceUpdateOpNoBias(self):
with tf_ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
tensor = array_ops.constant(
np.arange(1., 17.).reshape(2, 2, 2, 2), dtype=dtypes.float32)
factor = ff.ConvInputKroneckerFactor(tensor, (1, 2, 1, 1), [1, 1, 1, 1],
'SAME')
sess.run(tf_variables.global_variables_initializer())
new_cov = sess.run(factor.make_covariance_update_op(.5))
self.assertAllClose([[34.375, 37], [37, 41]], new_cov)
def testConvInputKroneckerFactorInitFloat64(self):
with tf_ops.Graph().as_default():
dtype = dtypes.float64_ref
random_seed.set_random_seed(200)
tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c')
factor = ff.ConvInputKroneckerFactor(
tensor, (1, 2, 3, 4), 3, 2, has_bias=True)
cov = factor.get_cov()
self.assertEqual(cov.dtype, dtype)
self.assertEqual([1 * 2 * 3 + 1, 1 * 2 * 3 + 1],
cov.get_shape().as_list())
def testMakeCovarianceUpdateOpNoBias(self):
with tf_ops.Graph().as_default(), self.test_session() as sess:
input_shape = (2, 1, 1, 1)
tensor = array_ops.constant(
np.arange(1, 1 + np.prod(input_shape)).reshape(input_shape).astype(
np.float32))
factor = ff.ConvInputKroneckerFactor(
(tensor,), filter_shape=(1, 1, 1, 1), padding='SAME')
factor.instantiate_cov_variables()
sess.run(tf_variables.global_variables_initializer())
new_cov = sess.run(factor.make_covariance_update_op(0.))
self.assertAllClose([[(1. + 4.) / 2.]], new_cov)
def testConvInputKroneckerFactorInitFloat64(self):
with tf_ops.Graph().as_default():
dtype = dtypes.float64_ref
tensor = array_ops.ones((64, 1, 2, 3),
name='a/b/c',
dtype=dtypes.float64)
factor = ff.ConvInputKroneckerFactor(tensor,
filter_shape=(1, 2, 3, 4),
padding='SAME',
has_bias=True)
factor.instantiate_cov_variables()
cov = factor.get_cov()
self.assertEqual(cov.dtype, dtype)
self.assertEqual([1 * 2 * 3 + 1, 1 * 2 * 3 + 1],
cov.get_shape().as_list())
def testStrides(self):
with tf_ops.Graph().as_default():
batch_size = 1
width = 3
in_channels = 3**2
out_channels = 4
factor = ff.ConvInputKroneckerFactor(
inputs=(random_ops.random_uniform(
(batch_size, width, width, in_channels), seed=0), ),
filter_shape=(1, 1, in_channels, out_channels),
padding='SAME',
strides=(1, 2, 1, 1),
extract_patches_fn='extract_image_patches',
has_bias=False)
factor.instantiate_cov_variables()
with self.test_session() as sess:
sess.run(tf_variables.global_variables_initializer())
sess.run(factor.make_covariance_update_op(0.0))
cov = sess.run(factor.get_cov_var())
# Cov should be the sum of 3 * 2 = 6 outer products.
self.assertMatrixRank(6, cov)
