def test4DRaggedTensorWithTwoRaggedDimensions(self):
rt = ragged.constant([[[[1, 2], [3, 4]], [[5, 6], [7, 8], [9, 10]]],
[[[11, 12]], [], [[13, 14]]], []],
ragged_rank=2)
with self.test_session():
st = ragged.to_sparse(rt).eval()
self.assertAllEqual(
st.indices,
[
[0, 0, 0, 0], # index for value=1
[0, 0, 0, 1], # index for value=2
[0, 0, 1, 0], # index for value=3
[0, 0, 1, 1], # index for value=4
[0, 1, 0, 0], # index for value=5
[0, 1, 0, 1], # index for value=6
[0, 1, 1, 0], # index for value=7
[0, 1, 1, 1], # index for value=8
[0, 1, 2, 0], # index for value=9
[0, 1, 2, 1], # index for value=10
[1, 0, 0, 0], # index for value=11
[1, 0, 0, 1], # index for value=12
[1, 2, 0, 0], # index for value=13
[1, 2, 0, 1], # index for value=14
])
self.assertAllEqual(
st.values, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
self.assertAllEqual(st.dense_shape, [3, 3, 3, 2])
def test4DRaggedTensorWithTwoRaggedDimensions(self):
rt = ragged.constant([[[[1, 2], [3, 4]], [[5, 6], [7, 8], [9, 10]]],
[[[11, 12]], [], [[13, 14]]], []],
ragged_rank=2)
with self.test_session():
st = ragged.to_sparse(rt).eval()
self.assertAllEqual(
st.indices,
[
[0, 0, 0, 0], # index for value=1
[0, 0, 0, 1], # index for value=2
[0, 0, 1, 0], # index for value=3
[0, 0, 1, 1], # index for value=4
[0, 1, 0, 0], # index for value=5
[0, 1, 0, 1], # index for value=6
[0, 1, 1, 0], # index for value=7
[0, 1, 1, 1], # index for value=8
[0, 1, 2, 0], # index for value=9
[0, 1, 2, 1], # index for value=10
[1, 0, 0, 0], # index for value=11
[1, 0, 0, 1], # index for value=12
[1, 2, 0, 0], # index for value=13
[1, 2, 0, 1], # index for value=14
])
self.assertAllEqual(st.values,
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
self.assertAllEqual(st.dense_shape, [3, 3, 3, 2])
def test2DRaggedTensorWithOneRaggedDimension(self):
rt = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']])
with self.test_session():
st = ragged.to_sparse(rt).eval()
self.assertAllEqual(
st.indices, [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [2, 0], [4, 0]])
self.assertAllEqual(st.values, b'a b c d e f g'.split())
self.assertAllEqual(st.dense_shape, [5, 3])
def test2DRaggedTensorWithOneRaggedDimension(self):
rt = ragged.constant([['a', 'b'], ['c', 'd', 'e'], ['f'], [], ['g']])
with self.test_session():
st = ragged.to_sparse(rt).eval()
self.assertAllEqual(
st.indices,
[[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [2, 0], [4, 0]])
self.assertAllEqual(st.values, b'a b c d e f g'.split())
self.assertAllEqual(st.dense_shape, [5, 3])
def testDocStringExample(self):
rt = ragged.constant([[1, 2, 3], [4], [], [5, 6]])
st = ragged.to_sparse(rt)
expected = ('SparseTensorValue(indices='
'array([[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]]), '
'values=array([1, 2, 3, 4, 5, 6], dtype=int32), '
'dense_shape=array([4, 3]))')
with self.test_session():
self.assertEqual(' '.join(repr(st.eval()).split()), expected)
def testShape(self):
rt = ragged.constant([[1, 2], [3, 4, 5], [6], [], [7]])
st = ragged.to_sparse(rt)
self.assertEqual(st.indices.shape.as_list(), [7, 2])
self.assertEqual(st.values.shape.as_list(), [7])
self.assertEqual(st.dense_shape.shape.as_list(), [2])
rt = ragged.constant([[[1, 2]], [], [[3, 4]], []], ragged_rank=1)
st = ragged.to_sparse(rt)
self.assertEqual(st.indices.shape.as_list(), [4, 3])
self.assertEqual(st.values.shape.as_list(), [4])
self.assertEqual(st.dense_shape.shape.as_list(), [3])
rt = ragged.constant([[[1], [2, 3, 4, 5, 6, 7]], [[]]])
st = ragged.to_sparse(rt)
self.assertEqual(st.indices.shape.as_list(), [7, 3])
self.assertEqual(st.values.shape.as_list(), [7])
self.assertEqual(st.dense_shape.shape.as_list(), [3])
def testDocStringExample(self):
rt = ragged.constant([[1, 2, 3], [4], [], [5, 6]])
st = ragged.to_sparse(rt)
expected = ('SparseTensorValue(indices='
'array([[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]]), '
'values=array([1, 2, 3, 4, 5, 6], dtype=int32), '
'dense_shape=array([4, 3]))')
with self.test_session():
self.assertEqual(' '.join(repr(st.eval()).split()), expected)
def testShape(self):
rt = ragged.constant([[1, 2], [3, 4, 5], [6], [], [7]])
st = ragged.to_sparse(rt)
self.assertEqual(st.indices.shape.as_list(), [7, 2])
self.assertEqual(st.values.shape.as_list(), [7])
self.assertEqual(st.dense_shape.shape.as_list(), [2])
rt = ragged.constant([[[1, 2]], [], [[3, 4]], []], ragged_rank=1)
st = ragged.to_sparse(rt)
self.assertEqual(st.indices.shape.as_list(), [4, 3])
self.assertEqual(st.values.shape.as_list(), [4])
self.assertEqual(st.dense_shape.shape.as_list(), [3])
rt = ragged.constant([[[1], [2, 3, 4, 5, 6, 7]], [[]]])
st = ragged.to_sparse(rt)
self.assertEqual(st.indices.shape.as_list(), [7, 3])
self.assertEqual(st.values.shape.as_list(), [7])
self.assertEqual(st.dense_shape.shape.as_list(), [3])
def testKernelErrors(self):
# An empty vector, defined using a placeholder to ensure that we can't
# determine that it's invalid at graph-construction time.
empty_vector = array_ops.placeholder_with_default(array_ops.zeros(
[0], dtypes.int64),
shape=None)
bad_rt1 = ragged.from_row_splits(row_splits=[2, 3], values=[1, 2, 3])
with self.test_session():
bad_split0_error = r'First value of ragged splits must be 0.*'
self.assertRaisesRegexp(errors.InvalidArgumentError,
bad_split0_error,
ragged.to_sparse(bad_rt1).eval)
bad_rt2 = ragged.from_row_splits(row_splits=[0, 5],
values=empty_vector)
bad_rt3 = ragged.from_row_splits(row_splits=[0, 1],
values=ragged.from_row_splits(
row_splits=[0, 5],
values=empty_vector))
with self.test_session():
split_mismatch1_error = r'Final value of ragged splits must match.*'
for rt in [bad_rt2, bad_rt3]:
self.assertRaisesRegexp(errors.InvalidArgumentError,
split_mismatch1_error,
ragged.to_sparse(rt).eval)
bad_rt4 = ragged.from_row_splits(row_splits=[0, 5],
values=ragged.from_row_splits(
row_splits=[0],
values=empty_vector))
with self.test_session():
split_mismatch2_error = r'Final value of ragged splits must match.*'
self.assertRaisesRegexp(errors.InvalidArgumentError,
split_mismatch2_error,
ragged.to_sparse(bad_rt4).eval)
bad_rt5 = ragged.from_row_splits(row_splits=empty_vector, values=[])
with self.test_session():
empty_splits_error = (r'ragged splits may not be empty.*')
self.assertRaisesRegexp(errors.InvalidArgumentError,
empty_splits_error,
ragged.to_sparse(bad_rt5).eval)
def test3DRaggedTensorWithOneRaggedDimension(self):
rt = ragged.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8], [9, 10]],
[[11, 12]], [], [[13, 14]]],
ragged_rank=1)
with self.test_session():
st = ragged.to_sparse(rt).eval()
self.assertAllEqual(
st.indices, [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0],
[1, 0, 1], [1, 1, 0], [1, 1, 1], [1, 2, 0], [1, 2, 1],
[2, 0, 0], [2, 0, 1], [4, 0, 0], [4, 0, 1]])
self.assertAllEqual(st.values,
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
self.assertAllEqual(st.dense_shape, [5, 3, 2])
def test3DRaggedTensorWithOneRaggedDimension(self):
rt = ragged.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8], [9, 10]],
[[11, 12]], [], [[13, 14]]],
ragged_rank=1)
with self.test_session():
st = ragged.to_sparse(rt).eval()
self.assertAllEqual(
st.indices,
[[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0],
[1, 0, 1], [1, 1, 0], [1, 1, 1], [1, 2, 0], [1, 2, 1],
[2, 0, 0], [2, 0, 1], [4, 0, 0], [4, 0, 1]])
self.assertAllEqual(
st.values, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
self.assertAllEqual(st.dense_shape, [5, 3, 2])
def testKernelErrors(self):
# An empty vector, defined using a placeholder to ensure that we can't
# determine that it's invalid at graph-construction time.
empty_vector = array_ops.placeholder_with_default(
array_ops.zeros([0], dtypes.int64), shape=None)
bad_rt1 = ragged.from_row_splits(row_splits=[2, 3], values=[1, 2, 3])
with self.test_session():
bad_split0_error = r'First value of ragged splits must be 0.*'
self.assertRaisesRegexp(errors.InvalidArgumentError, bad_split0_error,
ragged.to_sparse(bad_rt1).eval)
bad_rt2 = ragged.from_row_splits(row_splits=[0, 5], values=empty_vector)
bad_rt3 = ragged.from_row_splits(
row_splits=[0, 1],
values=ragged.from_row_splits(row_splits=[0, 5], values=empty_vector))
with self.test_session():
split_mismatch1_error = r'Final value of ragged splits must match.*'
for rt in [bad_rt2, bad_rt3]:
self.assertRaisesRegexp(errors.InvalidArgumentError,
split_mismatch1_error,
ragged.to_sparse(rt).eval)
bad_rt4 = ragged.from_row_splits(
row_splits=[0, 5],
values=ragged.from_row_splits(row_splits=[0], values=empty_vector))
with self.test_session():
split_mismatch2_error = r'Final value of ragged splits must match.*'
self.assertRaisesRegexp(errors.InvalidArgumentError,
split_mismatch2_error,
ragged.to_sparse(bad_rt4).eval)
bad_rt5 = ragged.from_row_splits(row_splits=empty_vector, values=[])
with self.test_session():
empty_splits_error = (r'ragged splits may not be empty.*')
self.assertRaisesRegexp(errors.InvalidArgumentError, empty_splits_error,
ragged.to_sparse(bad_rt5).eval)
def testGradient(self):
# rt1.shape == rt2.shape == [2, (D2), (D3), 2].
rt1 = ragged.constant([[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0]]]],
ragged_rank=2)
rt2 = ragged.constant([[[[9.0, 8.0], [7.0, 6.0]], [[5.0, 4.0]]]],
ragged_rank=2)
rt = ragged.map_inner_values(math_ops.add, rt1, rt2 * 2.0)
st = ragged.to_sparse(rt)
g1, g2 = gradients_impl.gradients(st.values, [rt1.inner_values,
rt2.inner_values])
print(g1, g2)
with self.test_session():
self.assertEqual(g1.eval().tolist(), [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])
self.assertEqual(g2.eval().tolist(), [[2.0, 2.0], [2.0, 2.0], [2.0, 2.0]])
def testGradient(self):
# rt1.shape == rt2.shape == [2, (D2), (D3), 2].
rt1 = ragged.constant([[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0]]]],
ragged_rank=2)
rt2 = ragged.constant([[[[9.0, 8.0], [7.0, 6.0]], [[5.0, 4.0]]]],
ragged_rank=2)
rt = rt1 + rt2 * 2.0
st = ragged.to_sparse(rt)
g1, g2 = gradients_impl.gradients(st.values,
[rt1.inner_values, rt2.inner_values])
print(g1, g2)
with self.test_session():
self.assertEqual(g1.eval().tolist(),
[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])
self.assertEqual(g2.eval().tolist(),
[[2.0, 2.0], [2.0, 2.0], [2.0, 2.0]])
