# 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 tensorflow.kernels.sparse_op.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test def _SparseToDense(sparse_indices, output_size, sparse_values, default_value, validate_indices=True): return sparse_ops.sparse_to_dense( sparse_indices, output_size, sparse_values, default_value=default_value, validate_indices=validate_indices) class SparseToDenseTest(test.TestCase): def testInt(self): with self.test_session(use_gpu=False): tf_ans = _SparseToDense([1, 3], [5], 1, 0).eval() np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def testFloat(self): with self.test_session(use_gpu=False): tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0).eval() np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32) self.assertAllClose(np_ans, tf_ans) def testString(self): with self.test_session(use_gpu=False): tf_ans = _SparseToDense([1, 3], [5], "a", "b").eval() np_ans = np.array(["b", "a", "b", "a", "b"]).astype(np.string_) self.assertAllEqual(np_ans, tf_ans) def testSetValue(self): with self.test_session(use_gpu=False): tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1).eval() np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def testSetSingleValue(self): with self.test_session(use_gpu=False): tf_ans = _SparseToDense([1, 3], [5], 1, -1).eval() np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def test2d(self): # pylint: disable=bad-whitespace with self.test_session(use_gpu=False): tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1).eval() np_ans = np.array([[-1, -1, -1, -1], [-1, -1, -1, 1], [ 1, -1, -1, -1]]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def testZeroDefault(self): with self.cached_session(): x = sparse_ops.sparse_to_dense(2, [4], 7).eval() self.assertAllEqual(x, [0, 0, 7, 0]) def test3d(self): with self.test_session(use_gpu=False): tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1).eval() np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1 np_ans[1, 3, 0] = 1 np_ans[2, 0, 1] = 1 self.assertAllClose(np_ans, tf_ans) def testBadShape(self): with self.cached_session(): with self.assertRaisesWithPredicateMatch(ValueError, "must be rank 1"): _SparseToDense([1, 3], [[5], [3]], 1, -1) def testBadValue(self): with self.cached_session(): dense = _SparseToDense([1, 3], [5], [[5], [3]], -1) with self.assertRaisesOpError( r"sparse_values has incorrect shape \[2,1\], " r"should be \[\] or \[2\]"): dense.eval() def testBadNumValues(self): with self.cached_session(): dense = _SparseToDense([1, 3], [5], [1, 2, 3], -1) with self.assertRaisesOpError( r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"): dense.eval() def testBadDefault(self): with self.cached_session(): dense = _SparseToDense([1, 3], [5], [1, 2], [0]) with self.assertRaisesOpError("default_value should be a scalar"): dense.eval() def testOutOfBoundsIndicesWithWithoutValidation(self): with self.cached_session(): dense = _SparseToDense( sparse_indices=[[1], [10]], output_size=[5], sparse_values=[-1.0, 1.0], default_value=0.0) with self.assertRaisesOpError( r"indices\[1\] = \[10\] is out of bounds: need 0 <= index < \[5\]"): dense.eval() # Disable checks, the allocation should still fail. with self.assertRaisesOpError("out of bounds"): dense_without_validation = _SparseToDense( sparse_indices=[[1], [10]], output_size=[5], sparse_values=[-1.0, 1.0], default_value=0.0, validate_indices=False) dense_without_validation.eval() def testRepeatingIndicesWithWithoutValidation(self): with self.cached_session(): dense = _SparseToDense( sparse_indices=[[1], [1]], output_size=[5], sparse_values=[-1.0, 1.0], default_value=0.0) with self.assertRaisesOpError(r"indices\[1\] = \[1\] is repeated"): dense.eval() # Disable checks dense_without_validation = _SparseToDense( sparse_indices=[[1], [1]], output_size=[5], sparse_values=[-1.0, 1.0], default_value=0.0, validate_indices=False) dense_without_validation.eval() def testUnsortedIndicesWithWithoutValidation(self): with self.cached_session(): dense = _SparseToDense( sparse_indices=[[2], [1]], output_size=[5], sparse_values=[-1.0, 1.0], default_value=0.0) with self.assertRaisesOpError(r"indices\[1\] = \[1\] is out of order"): dense.eval() # Disable checks dense_without_validation = _SparseToDense( sparse_indices=[[2], [1]], output_size=[5], sparse_values=[-1.0, 1.0], default_value=0.0, validate_indices=False) dense_without_validation.eval() def testShapeInferenceKnownShape(self): with self.test_session(use_gpu=False): indices = array_ops.placeholder(dtypes.int64) shape = [4, 5, 6] output = sparse_ops.sparse_to_dense(indices, shape, 1, 0) self.assertEqual(output.get_shape(), [4, 5, 6]) shape = array_ops.placeholder(dtypes.int64, shape=(3,)) output = sparse_ops.sparse_to_dense(indices, shape, 1, 0) self.assertEqual(output.get_shape().as_list(), [None, None, None]) def testShapeInferenceUnknownShape(self): with self.test_session(use_gpu=False): indices = array_ops.placeholder(dtypes.int64) shape = array_ops.placeholder(dtypes.int64) output = sparse_ops.sparse_to_dense(indices, shape, 1, 0) self.assertEqual(output.get_shape().ndims, None) if __name__ == "__main__": test.main()