def testSplitMatrixUnevenRows(self):
with self.session(use_gpu=False):
sp_tensors_2 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=2, axis=0)
self.assertAllEqual(sp_tensors_2[0].indices.eval(),
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
[1, 4], [1, 6], [2, 0], [2, 3], [2, 5]])
self.assertAllEqual(sp_tensors_2[0].values.eval(),
[0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25])
self.assertAllEqual(sp_tensors_2[0].dense_shape.eval(), [3, 7])
self.assertAllEqual(sp_tensors_2[1].indices.eval(),
[[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4],
[1, 6]])
self.assertAllEqual(sp_tensors_2[1].values.eval(),
[30, 32, 33, 35, 41, 44, 46])
self.assertAllEqual(sp_tensors_2[1].dense_shape.eval(), [2, 7])
self.assertAllEqual(len(sp_tensors_2), 2)
sp_tensors_3 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=3, axis=0)
self.assertAllEqual(len(sp_tensors_3), 3)
self.assertAllEqual(sp_tensors_3[0].indices.eval(),
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
[1, 4], [1, 6]])
self.assertAllEqual(sp_tensors_3[0].values.eval(),
[0, 2, 4, 5, 11, 13, 14, 16])
self.assertAllEqual(sp_tensors_3[0].dense_shape.eval(), [2, 7])
self.assertAllEqual(sp_tensors_3[1].values.eval(),
[20, 23, 25, 30, 32, 33, 35])
self.assertAllEqual(sp_tensors_3[1].dense_shape.eval(), [2, 7])
self.assertAllEqual(sp_tensors_3[2].indices.eval(), [[0, 1], [0, 4],
[0, 6]])
self.assertAllEqual(sp_tensors_3[2].values.eval(), [41, 44, 46])
self.assertAllEqual(sp_tensors_3[2].dense_shape.eval(), [1, 7])
return
def testSplitMatrixUnevenRows(self):
with self.test_session(use_gpu=False):
sp_tensors_2 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=2, axis=0)
self.assertAllEqual(sp_tensors_2[0].indices.eval(),
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
[1, 4], [1, 6], [2, 0], [2, 3], [2, 5]])
self.assertAllEqual(sp_tensors_2[0].values.eval(),
[0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25])
self.assertAllEqual(sp_tensors_2[0].dense_shape.eval(), [3, 7])
self.assertAllEqual(sp_tensors_2[1].indices.eval(),
[[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4],
[1, 6]])
self.assertAllEqual(sp_tensors_2[1].values.eval(),
[30, 32, 33, 35, 41, 44, 46])
self.assertAllEqual(sp_tensors_2[1].dense_shape.eval(), [2, 7])
self.assertAllEqual(len(sp_tensors_2), 2)
sp_tensors_3 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=3, axis=0)
self.assertAllEqual(len(sp_tensors_3), 3)
self.assertAllEqual(sp_tensors_3[0].indices.eval(),
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3],
[1, 4], [1, 6]])
self.assertAllEqual(sp_tensors_3[0].values.eval(),
[0, 2, 4, 5, 11, 13, 14, 16])
self.assertAllEqual(sp_tensors_3[0].dense_shape.eval(), [2, 7])
self.assertAllEqual(sp_tensors_3[1].values.eval(),
[20, 23, 25, 30, 32, 33, 35])
self.assertAllEqual(sp_tensors_3[1].dense_shape.eval(), [2, 7])
self.assertAllEqual(sp_tensors_3[2].indices.eval(), [[0, 1], [0, 4],
[0, 6]])
self.assertAllEqual(sp_tensors_3[2].values.eval(), [41, 44, 46])
self.assertAllEqual(sp_tensors_3[2].dense_shape.eval(), [1, 7])
return
def testSplitAllColumns(self):
for axis in (1, -1):
sparse_tensors = self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_4x6(),
num_split=6,
axis=axis))
self.assertAllEqual(len(sparse_tensors), 6)
self.assertAllEqual(sparse_tensors[0].indices,
[[0, 0], [2, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[0].values, [0, 20, 30])
self.assertAllEqual(sparse_tensors[0].dense_shape, [4, 1])
self.assertAllEqual(sparse_tensors[1].indices, [[1, 0]])
self.assertAllEqual(sparse_tensors[1].values, [11])
self.assertAllEqual(sparse_tensors[1].dense_shape, [4, 1])
self.assertAllEqual(sparse_tensors[2].indices, [[0, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[2].values, [2, 32])
self.assertAllEqual(sparse_tensors[2].dense_shape, [4, 1])
self.assertAllEqual(sparse_tensors[3].indices,
[[1, 0], [2, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[3].dense_shape, [4, 1])
self.assertAllEqual(sparse_tensors[3].values, [13, 23, 33])
self.assertAllEqual(sparse_tensors[4].indices, [[0, 0], [1, 0]])
self.assertAllEqual(sparse_tensors[4].values, [4, 14])
self.assertAllEqual(sparse_tensors[4].dense_shape, [4, 1])
self.assertAllEqual(sparse_tensors[5].indices,
[[0, 0], [2, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[5].values, [5, 25, 35])
self.assertAllEqual(sparse_tensors[5].dense_shape, [4, 1])
def testSplitAllColumns(self):
with self.test_session(use_gpu=False):
sparse_tensors = sparse_ops.sparse_split(
sp_input=self._SparseTensor_4x6(), num_split=6, axis=1)
self.assertAllEqual(len(sparse_tensors), 6)
self.assertAllEqual(sparse_tensors[0].indices.eval(), [[0, 0], [2, 0],
[3, 0]])
self.assertAllEqual(sparse_tensors[0].values.eval(), [0, 20, 30])
self.assertAllEqual(sparse_tensors[0].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[1].indices.eval(), [[1, 0]])
self.assertAllEqual(sparse_tensors[1].values.eval(), [11])
self.assertAllEqual(sparse_tensors[1].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[2].indices.eval(), [[0, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[2].values.eval(), [2, 32])
self.assertAllEqual(sparse_tensors[2].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[3].indices.eval(), [[1, 0], [2, 0],
[3, 0]])
self.assertAllEqual(sparse_tensors[3].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[3].values.eval(), [13, 23, 33])
self.assertAllEqual(sparse_tensors[4].indices.eval(), [[0, 0], [1, 0]])
self.assertAllEqual(sparse_tensors[4].values.eval(), [4, 14])
self.assertAllEqual(sparse_tensors[4].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[5].indices.eval(), [[0, 0], [2, 0],
[3, 0]])
self.assertAllEqual(sparse_tensors[5].values.eval(), [5, 25, 35])
self.assertAllEqual(sparse_tensors[5].dense_shape.eval(), [4, 1])
def testSplitAllColumns(self):
with self.session(use_gpu=False):
sparse_tensors = sparse_ops.sparse_split(
sp_input=self._SparseTensor_4x6(), num_split=6, axis=1)
self.assertAllEqual(len(sparse_tensors), 6)
self.assertAllEqual(sparse_tensors[0].indices.eval(), [[0, 0], [2, 0],
[3, 0]])
self.assertAllEqual(sparse_tensors[0].values.eval(), [0, 20, 30])
self.assertAllEqual(sparse_tensors[0].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[1].indices.eval(), [[1, 0]])
self.assertAllEqual(sparse_tensors[1].values.eval(), [11])
self.assertAllEqual(sparse_tensors[1].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[2].indices.eval(), [[0, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[2].values.eval(), [2, 32])
self.assertAllEqual(sparse_tensors[2].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[3].indices.eval(), [[1, 0], [2, 0],
[3, 0]])
self.assertAllEqual(sparse_tensors[3].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[3].values.eval(), [13, 23, 33])
self.assertAllEqual(sparse_tensors[4].indices.eval(), [[0, 0], [1, 0]])
self.assertAllEqual(sparse_tensors[4].values.eval(), [4, 14])
self.assertAllEqual(sparse_tensors[4].dense_shape.eval(), [4, 1])
self.assertAllEqual(sparse_tensors[5].indices.eval(), [[0, 0], [2, 0],
[3, 0]])
self.assertAllEqual(sparse_tensors[5].values.eval(), [5, 25, 35])
self.assertAllEqual(sparse_tensors[5].dense_shape.eval(), [4, 1])
def testArgumentErrors(self):
with self.assertRaisesRegex(ValueError, 'Keyword arguments are required'):
sparse_ops.sparse_split(3, 2, 1)
with self.assertRaisesRegex(ValueError, 'sp_input is required'):
sparse_ops.sparse_split()
with self.assertRaisesRegex(ValueError, 'num_split is required'):
sparse_ops.sparse_split(sp_input=1)
with self.assertRaisesRegex(ValueError, 'axis is required'):
sparse_ops.sparse_split(num_split=2, sp_input=1)
def testArgumentErrors(self):
with self.assertRaisesRegexp(ValueError, 'Keyword arguments are required'):
sparse_ops.sparse_split(3, 2, 1)
with self.assertRaisesRegexp(ValueError, 'sp_input is required'):
sparse_ops.sparse_split()
with self.assertRaisesRegexp(ValueError, 'num_split is required'):
sparse_ops.sparse_split(sp_input=1)
with self.assertRaisesRegexp(ValueError, 'axis is required'):
sparse_ops.sparse_split(num_split=2, sp_input=1)
def testInvalidAxis(self):
for axis in (-3, 2):
with self.assertRaisesRegexp(errors.InvalidArgumentError,
r'axis should be in range \[-2, 2\)'):
self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_4x6(),
num_split=3,
axis=axis))
def testSplitMatrixUnevenCols(self):
with self.test_session(use_gpu=False):
sp_tensors_3 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=3, axis=1)
self.assertAllEqual(len(sp_tensors_3), 3)
self.assertAllEqual(
sp_tensors_3[0].indices.eval(),
[[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2], [4, 1]])
self.assertAllEqual(sp_tensors_3[0].values.eval(),
[0, 2, 11, 20, 30, 32, 41])
self.assertAllEqual(sp_tensors_3[0].dense_shape.eval(), [5, 3])
self.assertAllEqual(
sp_tensors_3[1].indices.eval(),
[[0, 1], [1, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_3[1].values.eval(),
[4, 13, 14, 23, 33, 44])
self.assertAllEqual(sp_tensors_3[1].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_3[2].indices.eval(),
[[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_3[2].values.eval(),
[5, 16, 25, 35, 46])
self.assertAllEqual(sp_tensors_3[2].dense_shape.eval(), [5, 2])
sp_tensors_4 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=4, axis=1)
self.assertAllEqual(len(sp_tensors_4), 4)
self.assertAllEqual(sp_tensors_4[0].indices.eval(),
[[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_4[0].values.eval(),
[0, 11, 20, 30, 41])
self.assertAllEqual(sp_tensors_4[0].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_4[1].indices.eval(),
[[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
self.assertAllEqual(sp_tensors_4[1].values.eval(),
[2, 13, 23, 32, 33])
self.assertAllEqual(sp_tensors_4[1].dense_shape.eval(), [5, 2])
self.assertAllEqual(
sp_tensors_4[2].indices.eval(),
[[0, 0], [0, 1], [1, 0], [2, 1], [3, 1], [4, 0]])
self.assertAllEqual(sp_tensors_4[2].values.eval(),
[4, 5, 14, 25, 35, 44])
self.assertAllEqual(sp_tensors_4[2].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_4[3].indices.eval(),
[[1, 0], [4, 0]])
self.assertAllEqual(sp_tensors_4[3].values.eval(), [16, 46])
self.assertAllEqual(sp_tensors_4[3].dense_shape.eval(), [5, 1])
def testInvalidArgumentError(self):
# Test case for GitHub issue 53660.
axis = [1, 2]
with self.assertRaisesRegexp(errors.InvalidArgumentError,
r'axis should be a scalar'):
self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_4x6(),
num_split=3,
axis=axis))
def testSliceConcat(self):
for sp_input in (self._SparseTensorValue_3x4x2(),
self._SparseTensor_3x4x2()):
with self.test_session(use_gpu=False):
sparse_tensors = sparse_ops.sparse_split(
sp_input=sp_input, num_split=2, axis=1)
concat_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
expected_output = self._SparseTensor_3x4x2()
self.assertAllEqual(concat_tensor.indices.eval(),
expected_output.indices.eval())
def testSliceConcat(self):
for sp_input in (self._SparseTensorValue_3x4x2(),
self._SparseTensor_3x4x2()):
with self.cached_session(use_gpu=False):
sparse_tensors = sparse_ops.sparse_split(
sp_input=sp_input, num_split=2, axis=1)
concat_tensor = sparse_ops.sparse_concat(1, sparse_tensors)
expected_output = self._SparseTensor_3x4x2()
self.assertAllEqual(concat_tensor.indices.eval(),
expected_output.indices.eval())
def testSplitMatrixUnevenCols(self):
for axis in (1, -1):
sp_tensors_3 = self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_5x7(),
num_split=3,
axis=axis))
self.assertAllEqual(len(sp_tensors_3), 3)
self.assertAllEqual(
sp_tensors_3[0].indices,
[[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2], [4, 1]])
self.assertAllEqual(sp_tensors_3[0].values,
[0, 2, 11, 20, 30, 32, 41])
self.assertAllEqual(sp_tensors_3[0].dense_shape, [5, 3])
self.assertAllEqual(
sp_tensors_3[1].indices,
[[0, 1], [1, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_3[1].values,
[4, 13, 14, 23, 33, 44])
self.assertAllEqual(sp_tensors_3[1].dense_shape, [5, 2])
self.assertAllEqual(sp_tensors_3[2].indices,
[[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_3[2].values, [5, 16, 25, 35, 46])
self.assertAllEqual(sp_tensors_3[2].dense_shape, [5, 2])
sp_tensors_4 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=4, axis=axis)
self.assertAllEqual(len(sp_tensors_4), 4)
self.assertAllEqual(sp_tensors_4[0].indices,
[[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_4[0].values, [0, 11, 20, 30, 41])
self.assertAllEqual(sp_tensors_4[0].dense_shape, [5, 2])
self.assertAllEqual(sp_tensors_4[1].indices,
[[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
self.assertAllEqual(sp_tensors_4[1].values, [2, 13, 23, 32, 33])
self.assertAllEqual(sp_tensors_4[1].dense_shape, [5, 2])
self.assertAllEqual(
sp_tensors_4[2].indices,
[[0, 0], [0, 1], [1, 0], [2, 1], [3, 1], [4, 0]])
self.assertAllEqual(sp_tensors_4[2].values, [4, 5, 14, 25, 35, 44])
self.assertAllEqual(sp_tensors_4[2].dense_shape, [5, 2])
self.assertAllEqual(sp_tensors_4[3].indices, [[1, 0], [4, 0]])
self.assertAllEqual(sp_tensors_4[3].values, [16, 46])
self.assertAllEqual(sp_tensors_4[3].dense_shape, [5, 1])
def testSplitEmpty(self):
sp_empty = self._SparseTensor_4x6_empty()
sparse_splits0 = sparse_ops.sparse_split(sp_input=sp_empty,
num_split=2,
axis=0)
sparse_splits1 = sparse_ops.sparse_split(sp_input=sp_empty,
num_split=2,
axis=1)
empty_inds = np.empty(shape=(0, 2), dtype=np.int64)
self.assertAllEqual(sparse_splits0[0].indices, empty_inds)
self.assertAllEqual(sparse_splits0[0].values, [])
self.assertAllEqual(sparse_splits0[0].dense_shape, [2, 6])
self.assertAllEqual(sparse_splits0[1].indices, empty_inds)
self.assertAllEqual(sparse_splits0[1].values, [])
self.assertAllEqual(sparse_splits0[1].dense_shape, [2, 6])
self.assertAllEqual(sparse_splits1[0].indices, empty_inds)
self.assertAllEqual(sparse_splits1[0].values, [])
self.assertAllEqual(sparse_splits1[0].dense_shape, [4, 3])
self.assertAllEqual(sparse_splits1[1].indices, empty_inds)
self.assertAllEqual(sparse_splits1[1].values, [])
self.assertAllEqual(sparse_splits1[1].dense_shape, [4, 3])
def testSliceConcat(self):
for sp_input in (self._SparseTensorValue_3x4x2(),
self._SparseTensor_3x4x2()):
for axis in (1, -2):
sparse_tensors = sparse_ops.sparse_split(sp_input=sp_input,
num_split=2,
axis=axis)
concat_tensor = self.evaluate(
sparse_ops.sparse_concat(1, sparse_tensors))
expected_output = self._SparseTensor_3x4x2()
self.assertAllEqual(concat_tensor.indices,
expected_output.indices)
def split_dictionary(dictionary):
"""Split a dictionary into shards."""
shards = [{} for _ in range(number_of_shards)]
for name, tensor in six.iteritems(dictionary):
if isinstance(tensor, sparse_tensor.SparseTensor):
for i, shard in enumerate(
sparse_ops.sparse_split(
sp_input=tensor, num_split=number_of_shards, axis=0)):
shards[i][name] = shard
else:
for i, shard in enumerate(array_ops.split(tensor, number_of_shards)):
shards[i][name] = shard
return shards
def testSplitMatrixUnevenCols(self):
with self.test_session(use_gpu=False):
sp_tensors_3 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=3, axis=1)
self.assertAllEqual(len(sp_tensors_3), 3)
self.assertAllEqual(sp_tensors_3[0].indices.eval(),
[[0, 0], [0, 2], [1, 1], [2, 0], [3, 0], [3, 2],
[4, 1]])
self.assertAllEqual(sp_tensors_3[0].values.eval(),
[0, 2, 11, 20, 30, 32, 41])
self.assertAllEqual(sp_tensors_3[0].dense_shape.eval(), [5, 3])
self.assertAllEqual(sp_tensors_3[1].indices.eval(),
[[0, 1], [1, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_3[1].values.eval(),
[4, 13, 14, 23, 33, 44])
self.assertAllEqual(sp_tensors_3[1].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_3[2].indices.eval(),
[[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_3[2].values.eval(), [5, 16, 25, 35, 46])
self.assertAllEqual(sp_tensors_3[2].dense_shape.eval(), [5, 2])
sp_tensors_4 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=4, axis=1)
self.assertAllEqual(len(sp_tensors_4), 4)
self.assertAllEqual(sp_tensors_4[0].indices.eval(),
[[0, 0], [1, 1], [2, 0], [3, 0], [4, 1]])
self.assertAllEqual(sp_tensors_4[0].values.eval(), [0, 11, 20, 30, 41])
self.assertAllEqual(sp_tensors_4[0].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_4[1].indices.eval(),
[[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
self.assertAllEqual(sp_tensors_4[1].values.eval(), [2, 13, 23, 32, 33])
self.assertAllEqual(sp_tensors_4[1].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_4[2].indices.eval(),
[[0, 0], [0, 1], [1, 0], [2, 1], [3, 1], [4, 0]])
self.assertAllEqual(sp_tensors_4[2].values.eval(), [4, 5, 14, 25, 35, 44])
self.assertAllEqual(sp_tensors_4[2].dense_shape.eval(), [5, 2])
self.assertAllEqual(sp_tensors_4[3].indices.eval(), [[1, 0], [4, 0]])
self.assertAllEqual(sp_tensors_4[3].values.eval(), [16, 46])
self.assertAllEqual(sp_tensors_4[3].dense_shape.eval(), [5, 1])
def testSplitMatrixUnevenRows(self):
for axis in (0, -2):
sp_tensors_2 = self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_5x7(),
num_split=2,
axis=axis))
self.assertAllEqual(
sp_tensors_2[0].indices,
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4],
[1, 6], [2, 0], [2, 3], [2, 5]])
self.assertAllEqual(sp_tensors_2[0].values,
[0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25])
self.assertAllEqual(sp_tensors_2[0].dense_shape, [3, 7])
self.assertAllEqual(
sp_tensors_2[1].indices,
[[0, 0], [0, 2], [0, 3], [0, 5], [1, 1], [1, 4], [1, 6]])
self.assertAllEqual(sp_tensors_2[1].values,
[30, 32, 33, 35, 41, 44, 46])
self.assertAllEqual(sp_tensors_2[1].dense_shape, [2, 7])
self.assertAllEqual(len(sp_tensors_2), 2)
sp_tensors_3 = sparse_ops.sparse_split(
sp_input=self._SparseTensor_5x7(), num_split=3, axis=axis)
self.assertAllEqual(len(sp_tensors_3), 3)
self.assertAllEqual(sp_tensors_3[0].indices,
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1],
[1, 3], [1, 4], [1, 6]])
self.assertAllEqual(sp_tensors_3[0].values,
[0, 2, 4, 5, 11, 13, 14, 16])
self.assertAllEqual(sp_tensors_3[0].dense_shape, [2, 7])
self.assertAllEqual(sp_tensors_3[1].values,
[20, 23, 25, 30, 32, 33, 35])
self.assertAllEqual(sp_tensors_3[1].dense_shape, [2, 7])
self.assertAllEqual(sp_tensors_3[2].indices,
[[0, 1], [0, 4], [0, 6]])
self.assertAllEqual(sp_tensors_3[2].values, [41, 44, 46])
self.assertAllEqual(sp_tensors_3[2].dense_shape, [1, 7])
def testSplitMatrixRows(self):
with self.test_session(use_gpu=False):
sp_tensors = sparse_ops.sparse_split(
sp_input=self._SparseTensor_4x6(), num_split=2, axis=0)
self.assertAllEqual(len(sp_tensors), 2)
self.assertAllEqual(sp_tensors[0].indices.eval(), [[0, 0], [0, 2], [0, 4],
[0, 5], [1, 1], [1, 3],
[1, 4]])
self.assertAllEqual(sp_tensors[0].values.eval(), [0, 2, 4, 5, 11, 13, 14])
self.assertAllEqual(sp_tensors[0].dense_shape.eval(), [2, 6])
self.assertAllEqual(sp_tensors[1].indices.eval(), [[0, 0], [0, 3], [0, 5],
[1, 0], [1, 2], [1, 3],
[1, 5]])
self.assertAllEqual(sp_tensors[1].values.eval(),
[20, 23, 25, 30, 32, 33, 35])
self.assertAllEqual(sp_tensors[1].dense_shape.eval(), [2, 6])
def testSplitMatrixRows(self):
with self.session(use_gpu=False):
sp_tensors = sparse_ops.sparse_split(
sp_input=self._SparseTensor_4x6(), num_split=2, axis=0)
self.assertAllEqual(len(sp_tensors), 2)
self.assertAllEqual(sp_tensors[0].indices.eval(), [[0, 0], [0, 2], [0, 4],
[0, 5], [1, 1], [1, 3],
[1, 4]])
self.assertAllEqual(sp_tensors[0].values.eval(), [0, 2, 4, 5, 11, 13, 14])
self.assertAllEqual(sp_tensors[0].dense_shape.eval(), [2, 6])
self.assertAllEqual(sp_tensors[1].indices.eval(), [[0, 0], [0, 3], [0, 5],
[1, 0], [1, 2], [1, 3],
[1, 5]])
self.assertAllEqual(sp_tensors[1].values.eval(),
[20, 23, 25, 30, 32, 33, 35])
self.assertAllEqual(sp_tensors[1].dense_shape.eval(), [2, 6])
def testSplitColumns(self):
with self.session(use_gpu=False):
sparse_tensors = sparse_ops.sparse_split(
sp_input=self._SparseTensor_4x6(), num_split=3, axis=1)
self.assertAllEqual(len(sparse_tensors), 3)
self.assertAllEqual(sparse_tensors[0].indices.eval(), [[0, 0], [1, 1],
[2, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[0].values.eval(), [0, 11, 20, 30])
self.assertAllEqual(sparse_tensors[0].dense_shape.eval(), [4, 2])
self.assertAllEqual(sparse_tensors[1].indices.eval(),
[[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
self.assertAllEqual(sparse_tensors[1].values.eval(), [2, 13, 23, 32, 33])
self.assertAllEqual(sparse_tensors[1].dense_shape.eval(), [4, 2])
self.assertAllEqual(sparse_tensors[2].indices.eval(),
[[0, 0], [0, 1], [1, 0], [2, 1], [3, 1]])
self.assertAllEqual(sparse_tensors[2].values.eval(), [4, 5, 14, 25, 35])
self.assertAllEqual(sparse_tensors[2].dense_shape.eval(), [4, 2])
def testSplitMatrixRows(self):
for axis in (0, -2):
sp_tensors = self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_4x6(),
num_split=2,
axis=axis))
self.assertAllEqual(len(sp_tensors), 2)
self.assertAllEqual(
sp_tensors[0].indices,
[[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4]])
self.assertAllEqual(sp_tensors[0].values, [0, 2, 4, 5, 11, 13, 14])
self.assertAllEqual(sp_tensors[0].dense_shape, [2, 6])
self.assertAllEqual(
sp_tensors[1].indices,
[[0, 0], [0, 3], [0, 5], [1, 0], [1, 2], [1, 3], [1, 5]])
self.assertAllEqual(sp_tensors[1].values,
[20, 23, 25, 30, 32, 33, 35])
self.assertAllEqual(sp_tensors[1].dense_shape, [2, 6])
def testSplitColumns(self):
for axis in (1, -1):
sparse_tensors = self.evaluate(
sparse_ops.sparse_split(sp_input=self._SparseTensor_4x6(),
num_split=3,
axis=axis))
self.assertAllEqual(len(sparse_tensors), 3)
self.assertAllEqual(sparse_tensors[0].indices,
[[0, 0], [1, 1], [2, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[0].values, [0, 11, 20, 30])
self.assertAllEqual(sparse_tensors[0].dense_shape, [4, 2])
self.assertAllEqual(sparse_tensors[1].indices,
[[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
self.assertAllEqual(sparse_tensors[1].values, [2, 13, 23, 32, 33])
self.assertAllEqual(sparse_tensors[1].dense_shape, [4, 2])
self.assertAllEqual(sparse_tensors[2].indices,
[[0, 0], [0, 1], [1, 0], [2, 1], [3, 1]])
self.assertAllEqual(sparse_tensors[2].values, [4, 5, 14, 25, 35])
self.assertAllEqual(sparse_tensors[2].dense_shape, [4, 2])
def testSplitColumns(self):
with self.test_session(use_gpu=False):
sparse_tensors = sparse_ops.sparse_split(
sp_input=self._SparseTensor_4x6(), num_split=3, axis=1)
self.assertAllEqual(len(sparse_tensors), 3)
self.assertAllEqual(sparse_tensors[0].indices.eval(),
[[0, 0], [1, 1], [2, 0], [3, 0]])
self.assertAllEqual(sparse_tensors[0].values.eval(),
[0, 11, 20, 30])
self.assertAllEqual(sparse_tensors[0].dense_shape.eval(), [4, 2])
self.assertAllEqual(sparse_tensors[1].indices.eval(),
[[0, 0], [1, 1], [2, 1], [3, 0], [3, 1]])
self.assertAllEqual(sparse_tensors[1].values.eval(),
[2, 13, 23, 32, 33])
self.assertAllEqual(sparse_tensors[1].dense_shape.eval(), [4, 2])
self.assertAllEqual(sparse_tensors[2].indices.eval(),
[[0, 0], [0, 1], [1, 0], [2, 1], [3, 1]])
self.assertAllEqual(sparse_tensors[2].values.eval(),
[4, 5, 14, 25, 35])
self.assertAllEqual(sparse_tensors[2].dense_shape.eval(), [4, 2])
