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sparse_ops_test.py
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sparse_ops_test.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Python ops defined in sparse_ops."""
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import variables
import tensorflow.python.ops.sparse_grad # pylint: disable=unused-import
from tensorflow.python.platform import googletest
from tensorflow.python.platform import test
# TODO(zongheng): it'd be great to factor out this function and various random
# SparseTensor gen funcs.
def _sparsify(x, thresh=0.5, index_dtype=np.int64):
x[x < thresh] = 0
non_zero = np.where(x)
x_indices = np.vstack(non_zero).astype(index_dtype).T
x_values = x[non_zero]
x_shape = x.shape
return sparse_tensor.SparseTensor(
indices=x_indices, values=x_values, dense_shape=x_shape), len(x_values)
class SparseToIndicatorTest(test_util.TensorFlowTestCase):
def _SparseTensor_5x6(self, dtype):
ind = np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]])
val = np.array([0, 10, 13, 14, 32, 33])
shape = np.array([5, 6])
return sparse_tensor.SparseTensor(
constant_op.constant(ind, dtypes.int64),
constant_op.constant(val, dtype),
constant_op.constant(shape, dtypes.int64))
def _SparseTensor_2x3x4(self, dtype):
# Includes two entries with the form [1, 1, x] : 150.
ind = np.array([[0, 0, 1], [0, 1, 0], [0, 1, 2], [1, 0, 3], [1, 1, 0],
[1, 1, 1], [1, 1, 2], [1, 2, 2]])
val = np.array([1, 10, 12, 103, 150, 149, 150, 122])
shape = np.array([2, 3, 4])
return sparse_tensor.SparseTensor(
constant_op.constant(ind, dtypes.int64),
constant_op.constant(val, dtype),
constant_op.constant(shape, dtypes.int64))
def testInt32(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_5x6(dtypes.int32)
output = sparse_ops.sparse_to_indicator(sp_input, 50)
expected_output = np.zeros((5, 50), dtype=np.bool_)
expected_trues = ((0, 0), (1, 10), (1, 13), (1, 14), (3, 32), (3, 33))
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testInt64(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_5x6(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 50)
expected_output = np.zeros((5, 50), dtype=np.bool_)
expected_trues = [(0, 0), (1, 10), (1, 13), (1, 14), (3, 32), (3, 33)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testHigherRank(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x3x4(dtypes.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 200)
expected_output = np.zeros((2, 3, 200), dtype=np.bool_)
expected_trues = [(0, 0, 1), (0, 1, 10), (0, 1, 12), (1, 0, 103),
(1, 1, 149), (1, 1, 150), (1, 2, 122)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
class SparseMergeTest(test_util.TensorFlowTestCase):
def _SparseTensorValue_3x50(self, indices_dtype, values_dtype):
# NOTE: This input is intentionally not sorted to validate the
# already_sorted flag below.
ind = np.array([[0, 0], [1, 0], [1, 2], [2, 0], [2, 1], [1, 1]])
# NB: these are not sorted
indices = np.array([0, 13, 10, 33, 32, 14])
values = np.array([-3, 4, 1, 9, 5, 1])
shape = np.array([3, 3])
indices = sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(indices, indices_dtype), np.array(shape, np.int64))
values = sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(values, values_dtype), np.array(shape, np.int64))
return indices, values
def _SparseTensor_3x50(self, indices_dtype, values_dtype):
indices, values = self._SparseTensorValue_3x50(indices_dtype, values_dtype)
return (sparse_tensor.SparseTensor.from_value(indices),
sparse_tensor.SparseTensor.from_value(values))
def _AssertResultsSorted(self, output, vocab_size):
self.assertAllEqual(output.indices,
[[0, 0], [1, 10], [1, 13], [1, 14], [2, 32], [2, 33]])
self.assertAllEqual(output.values, [-3, 1, 4, 1, 5, 9])
self.assertAllEqual(output.dense_shape, [3, vocab_size])
def _AssertResultsNotSorted(self, output, vocab_size):
self.assertAllEqual(output.indices,
[[0, 0], [1, 13], [1, 10], [2, 33], [2, 32], [1, 14]])
self.assertAllEqual(output.values, [-3, 4, 1, 9, 5, 1])
self.assertAllEqual(output.dense_shape, [3, vocab_size])
def testInt32AndFloat32(self):
vocab_size = 50
indices_v, values_v = self._SparseTensorValue_3x50(np.int32, np.float32)
with test_util.force_cpu():
for indices in (indices_v,
sparse_tensor.SparseTensor.from_value(indices_v)):
for values in (values_v,
sparse_tensor.SparseTensor.from_value(values_v)):
sp_output = sparse_ops.sparse_merge(indices, values, vocab_size)
output = self.evaluate(sp_output)
self._AssertResultsSorted(output, vocab_size)
def testInt64AndFloat32(self):
vocab_size = 50
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float32)
sp_output = sparse_ops.sparse_merge(indices, values, vocab_size)
output = self.evaluate(sp_output)
self._AssertResultsSorted(output, vocab_size)
def testInt64AndFloat64(self):
vocab_size = 50
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float64)
sp_output = sparse_ops.sparse_merge(indices, values, vocab_size)
output = self.evaluate(sp_output)
self._AssertResultsSorted(output, vocab_size)
def testInt32AndFloat32NonCanonicalOrder(self):
vocab_size = 50
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int32, np.float32)
sp_output = sparse_ops.sparse_merge(
indices, values, vocab_size, already_sorted=True)
output = self.evaluate(sp_output)
self._AssertResultsNotSorted(output, vocab_size)
def testInt64AndFloat32NonCanonicalOrder(self):
vocab_size = 50
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float32)
sp_output = sparse_ops.sparse_merge(
indices, values, vocab_size, already_sorted=True)
output = self.evaluate(sp_output)
self._AssertResultsNotSorted(output, vocab_size)
def testInt64AndFloat64NonCanonicalOrder(self):
vocab_size = 50
vocab_size_tensor = constant_op.constant(vocab_size, dtypes.int64)
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float64)
sp_output = sparse_ops.sparse_merge(
indices, values, vocab_size_tensor, already_sorted=True)
output = self.evaluate(sp_output)
self._AssertResultsNotSorted(output, vocab_size)
def testShouldSetLastDimensionInDynamicShape(self):
with ops.Graph().as_default():
shape = constant_op.constant([2, 2], dtype=dtypes.int64)
dynamic_shape = array_ops.placeholder_with_default(shape, shape=[2])
ids = sparse_tensor.SparseTensor(
indices=[[0, 0], [0, 1]],
values=[1, 3],
dense_shape=dynamic_shape)
values = sparse_tensor.SparseTensor(
indices=[[0, 0], [0, 1]],
values=[0.4, 0.7],
dense_shape=dynamic_shape)
merged = sparse_ops.sparse_merge(
sp_ids=ids, sp_values=values, vocab_size=5)
self.assertEqual(5, merged.get_shape()[1])
class SparseMergeHighDimTest(test_util.TensorFlowTestCase):
def _SparseTensor_3x50(self, indices_dtype, values_dtype):
# NOTE: This input is intentionally not sorted to validate the
# already_sorted flag below.
ind = np.array([[0, 0], [1, 0], [1, 2], [2, 0], [2, 1], [1, 1]])
# NB: these are not sorted
indices0 = np.array([0, 13, 10, 33, 32, 14])
indices1 = np.array([12, 4, 0, 0, 1, 30])
values = np.array([-3, 4, 1, 9, 5, 1])
shape = np.array([3, 3])
indices0 = sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(indices0, indices_dtype), np.array(shape, np.int64))
indices1 = sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(indices1, indices_dtype), np.array(shape, np.int64))
values = sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(values, values_dtype), np.array(shape, np.int64))
return ([sparse_tensor.SparseTensor.from_value(indices0),
sparse_tensor.SparseTensor.from_value(indices1)],
sparse_tensor.SparseTensor.from_value(values))
def _AssertResultsSorted(self, output, vocab_size):
self.assertAllEqual(
output.indices,
[[0, 0, 12], [1, 10, 0], [1, 13, 4], [1, 14, 30], [2, 32, 1],
[2, 33, 0]])
self.assertAllEqual(output.values, [-3, 1, 4, 1, 5, 9])
self.assertAllEqual(output.dense_shape, [3] + vocab_size)
def testInt64AndFloat32(self):
vocab_size = [50, 31]
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float32)
sp_output = sparse_ops.sparse_merge(indices, values, vocab_size)
output = self.evaluate(sp_output)
self._AssertResultsSorted(output, vocab_size)
def testInt64AndFloat64(self):
vocab_size = [50, 31]
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float64)
sp_output = sparse_ops.sparse_merge(indices, values, vocab_size)
output = self.evaluate(sp_output)
self._AssertResultsSorted(output, vocab_size)
def testInt64AndFloat64Shape(self):
vocab_size = [50, 30]
with test_util.force_cpu():
indices, values = self._SparseTensor_3x50(np.int64, np.float64)
sp_output = sparse_ops.sparse_merge(indices, values, vocab_size)
output = self.evaluate(sp_output)
self._AssertResultsSorted(output, vocab_size)
class SparseRetainTest(test_util.TensorFlowTestCase):
def _SparseTensorValue_5x6(self):
ind = np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]])
val = np.array([0, 10, 13, 14, 32, 33])
shape = np.array([5, 6])
return sparse_tensor.SparseTensorValue(
np.array(ind, np.int64),
np.array(val, np.int32), np.array(shape, np.int64))
def _SparseTensor_5x6(self):
return sparse_tensor.SparseTensor.from_value(self._SparseTensorValue_5x6())
def testBasic(self):
with test_util.force_cpu():
for sp_input in (self._SparseTensorValue_5x6(), self._SparseTensor_5x6()):
to_retain = np.array([1, 0, 0, 1, 1, 0], dtype=np.bool_)
sp_output = sparse_ops.sparse_retain(sp_input, to_retain)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0], [1, 4], [3, 2]])
self.assertAllEqual(output.values, [0, 14, 32])
self.assertAllEqual(output.dense_shape, [5, 6])
def testRetainNone(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_5x6()
to_retain = np.zeros((6,), dtype=np.bool_)
sp_output = sparse_ops.sparse_retain(sp_input, to_retain)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, np.array([]).reshape((0, 2)))
self.assertAllEqual(output.values, [])
self.assertAllEqual(output.dense_shape, [5, 6])
def testMismatchedRetainShape(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_5x6()
to_retain = np.array([1, 0, 0, 1, 0], dtype=np.bool_)
with self.assertRaises(ValueError):
sparse_ops.sparse_retain(sp_input, to_retain)
class SparseResetShapeTest(test_util.TensorFlowTestCase):
_IND_2_5_6 = np.array(
[[0, 0, 0], [0, 1, 0], [0, 1, 3], [1, 1, 4], [1, 3, 2], [1, 3, 3]],
dtype=np.int64)
_VAL_2_5_6 = np.array([0, 10, 13, 14, 32, 33], dtype=np.int32)
_SHP_2_5_6 = np.array([2, 5, 6], dtype=np.int64)
def _SparseTensor_2x5x6(self):
return sparse_tensor.SparseTensor(
constant_op.constant(self._IND_2_5_6, dtypes.int64),
constant_op.constant(self._VAL_2_5_6, dtypes.int32),
constant_op.constant(self._SHP_2_5_6, dtypes.int64))
def _SparseTensor_2x5x6_Empty(self):
return sparse_tensor.SparseTensor(
constant_op.constant(
np.empty(shape=[0, 3], dtype=np.int64), dtypes.int64),
constant_op.constant(np.empty(shape=[0], dtype=np.int32), dtypes.int32),
constant_op.constant(self._SHP_2_5_6, dtypes.int64))
def _SparseTensorValue_2x5x6(self):
return sparse_tensor.SparseTensorValue(self._IND_2_5_6, self._VAL_2_5_6,
self._SHP_2_5_6)
def testStaticShapeInfoPreservedWhenNewShapeIsProvidedAndStatic(self):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
self.assertAllEqual([3, 6, 7], sp_output.get_shape())
def testBasic(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
def testInputUnavailableInGraphConstructionOk(self):
with test_util.force_cpu():
sp_input = self._SparseTensorValue_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
@test_util.run_deprecated_v1
def testFeedInputUnavailableInGraphConstructionOk(self):
with self.session(use_gpu=False) as sess:
sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = sess.run(sp_output,
feed_dict={sp_input: self._SparseTensorValue_2x5x6()})
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
def testTightBoundingBox(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [2, 4, 5])
def testTightBoundingBoxEmpty(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6_Empty()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices.shape, [0, 3])
self.assertAllEqual(output.values.shape, [0])
self.assertAllEqual(output.dense_shape, [0, 0, 0])
def testInvalidRank(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7], dtype=np.int64)
with self.assertRaises(ValueError):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
@test_util.run_deprecated_v1
def testInvalidRankNewShapeUnavailableInGraphConstruction(self):
with self.session(use_gpu=False) as sess:
new_shape = array_ops.placeholder(dtype=dtypes.int64)
sp_input = self._SparseTensor_2x5x6()
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x == y did not hold element-wise"):
sess.run(out, feed_dict={new_shape: np.array([3, 7], dtype=np.int64)})
def testInvalidDimensionSizeStatic(self):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7, 5], dtype=np.int64)
with self.assertRaisesRegex(ValueError, "should have dimension sizes"):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
@test_util.run_deprecated_v1
def testInvalidDimensionSizeDynamic(self):
with self.session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = array_ops.placeholder(dtype=dtypes.int32)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out, feed_dict={new_shape: [3, 7, 5]})
@test_util.run_deprecated_v1
def testInvalidDimensionSizeInputUnavailableInGraphConstruction(self):
sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
with self.session(use_gpu=False) as sess:
new_shape = np.array([3, 7, 5], dtype=np.int64)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out, feed_dict={sp_input: self._SparseTensorValue_2x5x6()})
class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase):
def _SparseTensorValue_5x6(self, dtype=np.int32):
ind = np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]])
val = np.array([0, 10, 13, 14, 32, 33])
shape = np.array([5, 6])
return sparse_tensor.SparseTensorValue(
np.array(ind, np.int64), np.array(val, dtype), np.array(
shape, np.int64))
def _SparseTensor_5x6(self):
return sparse_tensor.SparseTensor.from_value(self._SparseTensorValue_5x6())
def _SparseTensor_String5x6(self):
ind = np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]])
val = np.array(["a", "b", "c", "d", "e", "f"])
shape = np.array([5, 6])
return sparse_tensor.SparseTensor(
constant_op.constant(ind, dtypes.int64),
constant_op.constant(val, dtypes.string),
constant_op.constant(shape, dtypes.int64))
def _SparseTensor_2x6(self):
ind = np.array([[0, 0], [1, 0], [1, 3], [1, 4]])
val = np.array([0, 10, 13, 14])
shape = np.array([2, 6])
return sparse_tensor.SparseTensor(
constant_op.constant(ind, dtypes.int64),
constant_op.constant(val, dtypes.int32),
constant_op.constant(shape, dtypes.int64))
def testFillNumber(self):
with test_util.use_gpu():
for sp_input in (self._SparseTensorValue_5x6(), self._SparseTensor_5x6()):
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(
output.indices,
[[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
self.assertAllEqual(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
self.assertAllEqual(output.dense_shape, [5, 6])
self.assertAllEqual(empty_row_indicator_out,
np.array([0, 0, 1, 0, 1]).astype(np.bool_))
@test_util.run_deprecated_v1
def testFillFloat(self):
with self.session():
values = constant_op.constant(
[0.0, 10.0, 13.0, 14.0, 32.0, 33.0], dtype=dtypes.float64)
default_value = constant_op.constant(-1.0, dtype=dtypes.float64)
sp_input = sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]]),
values=values,
dense_shape=np.array([5, 6]))
sp_output, empty_row_indicator = (sparse_ops.sparse_fill_empty_rows(
sp_input, default_value))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4],
[2, 0], [3, 2], [3, 3], [4, 0]])
self.assertAllClose(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
self.assertAllEqual(output.dense_shape, [5, 6])
self.assertAllEqual(empty_row_indicator_out,
np.array([0, 0, 1, 0, 1]).astype(np.bool_))
values_grad_err = gradient_checker.compute_gradient_error(
values, values.shape.as_list(), sp_output.values, [8], delta=1e-8)
self.assertGreater(values_grad_err, 0)
self.assertLess(values_grad_err, 1e-8)
default_value_grad_err = gradient_checker.compute_gradient_error(
default_value,
default_value.shape.as_list(),
sp_output.values, [8],
delta=1e-8)
self.assertGreater(default_value_grad_err, 0)
self.assertLess(default_value_grad_err, 1e-8)
def testFillString(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_String5x6()
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, ""))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(
output.indices,
[[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
self.assertAllEqual(output.values,
[b"a", b"b", b"c", b"d", b"", b"e", b"f", b""])
self.assertAllEqual(output.dense_shape, [5, 6])
self.assertAllEqual(empty_row_indicator_out,
np.array([0, 0, 1, 0, 1]).astype(np.bool_))
def testNoEmptyRows(self):
with test_util.use_gpu():
sp_input = self._SparseTensor_2x6()
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4]])
self.assertAllEqual(output.values, [0, 10, 13, 14])
self.assertAllEqual(output.dense_shape, [2, 6])
self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool_))
def testNoEmptyRowsAndUnordered(self):
with test_util.use_gpu():
sp_input = sparse_tensor.SparseTensor(
indices=np.array([[1, 2], [1, 3], [0, 1], [0, 3]]),
values=np.array([1, 3, 2, 4]),
dense_shape=np.array([2, 5]))
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, [[0, 1], [0, 3], [1, 2], [1, 3]])
self.assertAllEqual(output.values, [2, 4, 1, 3])
self.assertAllEqual(output.dense_shape, [2, 5])
self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool_))
def testUnordered(self):
with test_util.use_gpu():
sp_input = sparse_tensor.SparseTensor(
indices=np.array([[2, 3], [2, 2], [0, 1], [0, 3]]),
values=np.array([1, 3, 2, 4]),
dense_shape=np.array([3, 5]))
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices,
[[0, 1], [0, 3], [1, 0], [2, 3], [2, 2]])
self.assertAllEqual(output.values, [2, 4, -1, 1, 3])
self.assertAllEqual(output.dense_shape, [3, 5])
self.assertAllEqual(empty_row_indicator_out, [False, True, False])
def testEmptyIndicesTensor(self):
with test_util.use_gpu():
sp_input = sparse_tensor.SparseTensor(
indices=np.ones([0, 2]),
values=np.ones([0]),
dense_shape=np.array([2, 5]))
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, [[0, 0], [1, 0]])
self.assertAllEqual(output.values, [-1, -1])
self.assertAllEqual(output.dense_shape, [2, 5])
self.assertAllEqual(empty_row_indicator_out, np.ones(2).astype(np.bool_))
def testEmptyOutput(self):
with test_util.use_gpu():
sp_input = sparse_tensor.SparseTensor(
indices=np.ones([0, 2]),
values=np.ones([0]),
dense_shape=np.array([0, 3]))
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = self.evaluate(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, np.ones([0, 2]))
self.assertAllEqual(output.values, np.ones([0]))
self.assertAllEqual(output.dense_shape, [0, 3])
self.assertAllEqual(empty_row_indicator_out, [])
def testInvalidIndices(self):
with test_util.use_gpu():
sp_input = sparse_tensor.SparseTensor(
indices=np.array([[1, 2], [1, 3], [99, 1], [99, 3]]),
values=np.array([1, 3, 2, 4]),
dense_shape=np.array([2, 5]))
with self.assertRaisesRegex(errors.InvalidArgumentError,
r"indices\(2, 0\) is invalid"):
self.evaluate(sparse_ops.sparse_fill_empty_rows(sp_input, -1))
class SparseAddTest(test_util.TensorFlowTestCase):
def testValuesInVariable(self):
indices = constant_op.constant([[0]], dtype=dtypes.int64)
values = variables.Variable([1], trainable=False, dtype=dtypes.float32)
shape = constant_op.constant([1], dtype=dtypes.int64)
sp_input = sparse_tensor.SparseTensor(indices, values, shape)
sp_output = sparse_ops.sparse_add(sp_input, sp_input)
with test_util.force_cpu():
self.evaluate(variables.global_variables_initializer())
output = self.evaluate(sp_output)
self.assertAllEqual(output.values, [2])
class SparseReduceTest(test_util.TensorFlowTestCase):
# [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64)
vals = np.array([1, 1, 1]).astype(np.int32)
dense_shape = np.array([2, 3]).astype(np.int64)
def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
densified = self.evaluate(sparse_ops.sparse_tensor_to_dense(sp_t))
np_ans = densified
if reduction_axes is None:
if do_sum:
np_ans = np.sum(np_ans, keepdims=keep_dims)
else:
np_ans = np.max(np_ans, keepdims=keep_dims)
else:
if not isinstance(reduction_axes, list): # Single scalar.
reduction_axes = [reduction_axes]
reduction_axes = np.array(reduction_axes).astype(np.int32)
# Handles negative axes.
reduction_axes = (reduction_axes + ndims) % ndims
# Loop below depends on sorted.
reduction_axes.sort()
for ra in reduction_axes.ravel()[::-1]:
if do_sum:
np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
else:
np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
with self.cached_session():
if do_sum:
tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
keep_dims)
else:
tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
keep_dims)
out_dense = self.evaluate(tf_dense_ans)
if do_sum:
tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t,
reduction_axes,
keep_dims)
else:
tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t,
reduction_axes,
keep_dims)
# Convert to dense for comparison purposes.
out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans)
self.assertAllClose(np_ans, out_dense)
self.assertAllClose(np_ans, out_sparse)
def _compare_all(self, sp_t, reduction_axes, ndims):
self._compare(sp_t, reduction_axes, ndims, False, False)
self._compare(sp_t, reduction_axes, ndims, False, True)
self._compare(sp_t, reduction_axes, ndims, True, False)
self._compare(sp_t, reduction_axes, ndims, True, True)
# (TODO:b/133851381): Re-enable this test.
def disabledtestSimpleAndRandomInputs(self):
if np.__version__ == "1.13.0":
self.skipTest("numpy 1.13.0 bug")
sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_shape)
with test_util.force_cpu():
self._compare_all(sp_t, None, ndims=2)
self._compare_all(sp_t, 0, ndims=2)
self._compare_all(sp_t, [1], ndims=2)
self._compare_all(sp_t, [0, 1], ndims=2)
self._compare_all(sp_t, [1, 0], ndims=2)
self._compare_all(sp_t, [-1], ndims=2)
self._compare_all(sp_t, [1, -2], ndims=2)
np.random.seed(1618)
test_dims = [(1618, 1, 11, 7, 1), (1,), (1, 1, 1)]
with test_util.force_cpu():
for dims in test_dims:
sp_t, unused_nnz = _sparsify(np.random.randn(*dims))
# reduce all using None
self._compare_all(sp_t, None, ndims=len(dims))
# reduce random axes from 1D to N-D
for d in range(1, len(dims) + 1):
axes = np.random.choice(len(dims), size=d, replace=False).tolist()
self._compare_all(sp_t, axes, ndims=len(dims))
def testInvalidAxes(self):
sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_shape)
with test_util.force_cpu():
with self.assertRaisesOpError("Invalid reduction dimension -3"):
self.evaluate(sparse_ops.sparse_reduce_sum(sp_t, -3))
with self.assertRaisesOpError("Invalid reduction dimension 2"):
self.evaluate(sparse_ops.sparse_reduce_sum(sp_t, 2))
with self.assertRaisesOpError("Invalid reduction dimension -3"):
self.evaluate(sparse_ops.sparse_reduce_max(sp_t, -3))
with self.assertRaisesOpError("Invalid reduction dimension 2"):
self.evaluate(sparse_ops.sparse_reduce_max(sp_t, 2))
@test_util.run_deprecated_v1
def testGradient(self):
np.random.seed(8161)
test_dims = [(11, 1, 5, 7, 1), (2, 2)]
with self.session(use_gpu=False):
for dims in test_dims:
sp_t, nnz = _sparsify(np.random.randn(*dims))
# reduce random axes from 1D to N-D
for d in range(1, len(dims) + 1):
axes = np.random.choice(len(dims), size=d, replace=False).tolist()
reduced = sparse_ops.sparse_reduce_sum(sp_t, axes)
err = gradient_checker.compute_gradient_error(
sp_t.values, (nnz,), reduced,
self.evaluate(reduced).shape)
self.assertLess(err, 1e-3)
# Tests for negative axes.
reduced = sparse_ops.sparse_reduce_sum(sp_t, -1)
err = gradient_checker.compute_gradient_error(
sp_t.values, (nnz,), reduced,
self.evaluate(reduced).shape)
self.assertLess(err, 1e-3)
def _testSparseReduceShape(self, sp_t, reduction_axes, ndims, keep_dims,
do_sum):
densified = self.evaluate(sparse_ops.sparse_tensor_to_dense(sp_t))
np_op = np.sum
tf_op = sparse_ops.sparse_reduce_sum
if not do_sum:
np_op = np.max
tf_op = sparse_ops.sparse_reduce_max
np_ans = densified
if reduction_axes is None:
np_ans = np_op(np_ans, keepdims=keep_dims)
else:
if not isinstance(reduction_axes, list): # Single scalar.
reduction_axes = [reduction_axes]
reduction_axes = np.array(reduction_axes).astype(np.int32)
# Handles negative axes.
reduction_axes = (reduction_axes + ndims) % ndims
# Loop below depends on sorted.
reduction_axes.sort()
for ra in reduction_axes.ravel()[::-1]:
np_ans = np_op(np_ans, axis=ra, keepdims=keep_dims)
tf_ans = tf_op(sp_t, reduction_axes, keep_dims)
self.assertAllEqual(np_ans.shape, tf_ans.get_shape().as_list())
# (TODO:b/133851381): Re-enable this test
def disabledtestSparseReduceSumOrMaxShape(self):
sp_t = sparse_tensor.SparseTensor(self.ind, self.vals, self.dense_shape)
with test_util.force_cpu():
for do_sum in [True, False]:
for keep_dims in [True, False]:
self._testSparseReduceShape(sp_t, None, 2, keep_dims, do_sum)
self._testSparseReduceShape(sp_t, 0, 2, keep_dims, do_sum)
self._testSparseReduceShape(sp_t, [1], 2, keep_dims, do_sum)
self._testSparseReduceShape(sp_t, [0, 1], 2, keep_dims, do_sum)
self._testSparseReduceShape(sp_t, [1, 0], 2, keep_dims, do_sum)
self._testSparseReduceShape(sp_t, [-1], 2, keep_dims, do_sum)
self._testSparseReduceShape(sp_t, [1, -2], 2, keep_dims, do_sum)
def testIntegerOverflow(self):
with self.cached_session(use_gpu=False):
with self.assertRaises(errors.InvalidArgumentError):
res = sparse_ops.gen_sparse_ops.sparse_reduce_max(
input_indices=[[1, 2], [3, 4]],
input_shape=[2**32, 2**31],
input_values=[1, 3],
reduction_axes=[0],
keep_dims=False,
name=None)
self.evaluate(res)
with self.assertRaises(errors.InvalidArgumentError):
res = sparse_ops.gen_sparse_ops.sparse_reduce_max_sparse(
input_indices=[[1, 2], [3, 4]],
input_shape=[2**32, 2**31],
input_values=[1, 3],
reduction_axes=[0],
keep_dims=False,
name=None)
self.evaluate(res)
with self.assertRaises(errors.InvalidArgumentError):
res = sparse_ops.gen_sparse_ops.sparse_reduce_sum(
input_indices=[[1, 2], [3, 4]],
input_shape=[2**32, 2**31],
input_values=[1, 3],
reduction_axes=[0],
keep_dims=False,
name=None)
self.evaluate(res)
class SparseMathOpsTest(test_util.TensorFlowTestCase):
def _check(self, result_tensor, result_np, input_sp_t):
self.assertTrue(isinstance(result_tensor, sparse_tensor.SparseTensor))
self.assertTrue(isinstance(input_sp_t, sparse_tensor.SparseTensor))
self.assertAllCloseAccordingToType(input_sp_t.indices,
result_tensor.indices)
self.assertAllCloseAccordingToType(input_sp_t.dense_shape,
result_tensor.dense_shape)
res_densified = sparse_ops.sparse_to_dense(
result_tensor.indices, result_tensor.dense_shape, result_tensor.values)
self.assertAllCloseAccordingToType(result_np, res_densified)
@test_util.run_deprecated_v1
def testCwiseShapeValidation(self):
# Test case for GitHub 24072.
with test_util.force_cpu():
a = array_ops.ones([3, 4, 1], dtype=dtypes.int32)
b = sparse_tensor.SparseTensor([[0, 0, 1, 0], [0, 0, 3, 0]], [10, 20],
[1, 1, 4, 2])
c = a * b
with self.assertRaisesRegex(
errors.InvalidArgumentError,
"broadcasts dense to sparse only; got incompatible shapes"):
self.evaluate(c)
def testCwiseDivAndMul(self):
np.random.seed(1618)
sp_shapes = [(10, 10, 10), (5, 5), (1618,), (3, 3, 7)]
dense_shapes = [(10, 10, 1), (5, 5), (1,), (1, 7)]
with test_util.force_cpu():
for dtype in [np.float32, np.float64, np.int32, np.int64]:
for sp_shape, dense_shape in zip(sp_shapes, dense_shapes):
sp_vals_np = np.random.rand(*sp_shape).astype(dtype) + 1
dense_vals_np = np.random.rand(*dense_shape).astype(dtype) + 1
sp_t, unused_nnz = _sparsify(sp_vals_np, thresh=1.5)
sp_t_densified = sparse_ops.sparse_tensor_to_dense(sp_t)
dense_t = constant_op.constant(dense_vals_np)
self._check(sp_t / dense_t, sp_t_densified / dense_vals_np, sp_t)
# Check commutative.
self._check(sp_t * dense_t, sp_t_densified * dense_vals_np, sp_t)
self._check(dense_t * sp_t, sp_t_densified * dense_vals_np, sp_t)
if dtype in [np.int32, np.int64]:
res = sp_t / dense_t # should invoke "__truediv__"
self.assertEqual(res.values.dtype, np.float64)
def testCwiseAdd(self):
with test_util.force_cpu():
# Identity(2) + AllOnes(2,2). Should be equal to 2 * Identity(2).
indices = [[0, 0], [1, 1]]
vals = [1, 1]
shape = (2, 2)
sp_t = sparse_tensor.SparseTensor(indices, vals, shape)
dense_t = array_ops.ones(shape, dtype=dtypes.int32)
self._check(
sparse_ops.sparse_dense_cwise_add(sp_t, dense_t),
np.identity(2) * 2, sp_t)
# Variant of above, but broadcasts the dense side.
dense_t = array_ops.ones([1], dtype=dtypes.int32)
self._check(
sparse_ops.sparse_dense_cwise_add(sp_t, dense_t),
np.identity(2) * 2, sp_t)
@test_util.run_deprecated_v1
def testGradients(self):
np.random.seed(1618)
sp_shapes = [(10, 10, 10), (5, 5), (1618,), (3, 3, 7)]
dense_shapes = [(10, 10, 1), (5, 5), (1,), (1, 7)]
with self.session(use_gpu=False):
for dtype in [np.float32, np.float64]:
for sp_shape, dense_shape in zip(sp_shapes, dense_shapes):
sp_vals_np = np.random.rand(*sp_shape).astype(dtype) + 1
dense_vals_np = np.random.rand(*dense_shape).astype(dtype) + 1
sp_t, nnz = _sparsify(sp_vals_np, thresh=1.5)
dense_t = constant_op.constant(dense_vals_np)
cmul = sp_t * dense_t
err = gradient_checker.compute_gradient_error([sp_t.values, dense_t],
[(nnz,), dense_shape],
cmul.values, (nnz,))
self.assertLess(err, 1e-4)
cdiv = sp_t / dense_t
err = gradient_checker.compute_gradient_error(sp_t.values, (nnz,),
cdiv.values, (nnz,))
self.assertLess(err, 1e-4)
err = gradient_checker.compute_gradient_error(
dense_t,
dense_shape,
cdiv.values, (nnz,),
x_init_value=dense_vals_np)
self.assertLess(err, 2e-4)
class SparseSoftmaxTest(test_util.TensorFlowTestCase):
@test_util.run_deprecated_v1
def testEquivalentToDensified(self):
np.random.seed(1618)
n, m = np.random.choice(20, size=2)
for dtype in [np.float32, np.float64]:
sp_vals_np = np.random.rand(n, m).astype(dtype)
batched_sp_t, unused_nnz1 = _sparsify(
sp_vals_np.reshape((1, n, m)), thresh=0.) # No masking.
with test_util.force_cpu():
densified = constant_op.constant(sp_vals_np)
sp_result = self.evaluate(
sparse_ops.sparse_softmax(batched_sp_t)).values.reshape((n, m))