1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 """Tests for tensorflow.kernels.sparse_op.""" 16 17 from __future__ import absolute_import 18 from __future__ import division 19 from __future__ import print_function 20 21 import numpy as np 22 23 from tensorflow.python.framework import dtypes 24 from tensorflow.python.ops import array_ops 25 from tensorflow.python.ops import sparse_ops 26 from tensorflow.python.platform import test 27 28 29 def _SparseToDense(sparse_indices, 30 output_size, 31 sparse_values, 32 default_value, 33 validate_indices=True): 34 return sparse_ops.sparse_to_dense( 35 sparse_indices, 36 output_size, 37 sparse_values, 38 default_value=default_value, 39 validate_indices=validate_indices) 40 41 42 class SparseToDenseTest(test.TestCase): 43 44 def testInt(self): 45 with self.test_session(use_gpu=False): 46 tf_ans = _SparseToDense([1, 3], [5], 1, 0).eval() 47 np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32) 48 self.assertAllClose(np_ans, tf_ans) 49 50 def testFloat(self): 51 with self.test_session(use_gpu=False): 52 tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0).eval() 53 np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32) 54 self.assertAllClose(np_ans, tf_ans) 55 56 def testString(self): 57 with self.test_session(use_gpu=False): 58 tf_ans = _SparseToDense([1, 3], [5], "a", "b").eval() 59 np_ans = np.array(["b", "a", "b", "a", "b"]).astype(np.string_) 60 self.assertAllEqual(np_ans, tf_ans) 61 62 def testSetValue(self): 63 with self.test_session(use_gpu=False): 64 tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1).eval() 65 np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32) 66 self.assertAllClose(np_ans, tf_ans) 67 68 def testSetSingleValue(self): 69 with self.test_session(use_gpu=False): 70 tf_ans = _SparseToDense([1, 3], [5], 1, -1).eval() 71 np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32) 72 self.assertAllClose(np_ans, tf_ans) 73 74 def test2d(self): 75 # pylint: disable=bad-whitespace 76 with self.test_session(use_gpu=False): 77 tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1).eval() 78 np_ans = np.array([[-1, -1, -1, -1], 79 [-1, -1, -1, 1], 80 [ 1, -1, -1, -1]]).astype(np.int32) 81 self.assertAllClose(np_ans, tf_ans) 82 83 def testZeroDefault(self): 84 with self.test_session(): 85 x = sparse_ops.sparse_to_dense(2, [4], 7).eval() 86 self.assertAllEqual(x, [0, 0, 7, 0]) 87 88 def test3d(self): 89 with self.test_session(use_gpu=False): 90 tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1).eval() 91 np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1 92 np_ans[1, 3, 0] = 1 93 np_ans[2, 0, 1] = 1 94 self.assertAllClose(np_ans, tf_ans) 95 96 def testBadShape(self): 97 with self.test_session(): 98 with self.assertRaisesWithPredicateMatch(ValueError, "must be rank 1"): 99 _SparseToDense([1, 3], [[5], [3]], 1, -1) 100 101 def testBadValue(self): 102 with self.test_session(): 103 dense = _SparseToDense([1, 3], [5], [[5], [3]], -1) 104 with self.assertRaisesOpError( 105 r"sparse_values has incorrect shape \[2,1\], " 106 r"should be \[\] or \[2\]"): 107 dense.eval() 108 109 def testBadNumValues(self): 110 with self.test_session(): 111 dense = _SparseToDense([1, 3], [5], [1, 2, 3], -1) 112 with self.assertRaisesOpError( 113 r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"): 114 dense.eval() 115 116 def testBadDefault(self): 117 with self.test_session(): 118 dense = _SparseToDense([1, 3], [5], [1, 2], [0]) 119 with self.assertRaisesOpError("default_value should be a scalar"): 120 dense.eval() 121 122 def testOutOfBoundsIndicesWithWithoutValidation(self): 123 with self.test_session(): 124 dense = _SparseToDense( 125 sparse_indices=[[1], [10]], 126 output_size=[5], 127 sparse_values=[-1.0, 1.0], 128 default_value=0.0) 129 with self.assertRaisesOpError( 130 r"indices\[1\] = \[10\] is out of bounds: need 0 <= index < \[5\]"): 131 dense.eval() 132 # Disable checks, the allocation should still fail. 133 with self.assertRaisesOpError("out of bounds"): 134 dense_without_validation = _SparseToDense( 135 sparse_indices=[[1], [10]], 136 output_size=[5], 137 sparse_values=[-1.0, 1.0], 138 default_value=0.0, 139 validate_indices=False) 140 dense_without_validation.eval() 141 142 def testRepeatingIndicesWithWithoutValidation(self): 143 with self.test_session(): 144 dense = _SparseToDense( 145 sparse_indices=[[1], [1]], 146 output_size=[5], 147 sparse_values=[-1.0, 1.0], 148 default_value=0.0) 149 with self.assertRaisesOpError(r"indices\[1\] = \[1\] is repeated"): 150 dense.eval() 151 # Disable checks 152 dense_without_validation = _SparseToDense( 153 sparse_indices=[[1], [1]], 154 output_size=[5], 155 sparse_values=[-1.0, 1.0], 156 default_value=0.0, 157 validate_indices=False) 158 dense_without_validation.eval() 159 160 def testUnsortedIndicesWithWithoutValidation(self): 161 with self.test_session(): 162 dense = _SparseToDense( 163 sparse_indices=[[2], [1]], 164 output_size=[5], 165 sparse_values=[-1.0, 1.0], 166 default_value=0.0) 167 with self.assertRaisesOpError(r"indices\[1\] = \[1\] is out of order"): 168 dense.eval() 169 # Disable checks 170 dense_without_validation = _SparseToDense( 171 sparse_indices=[[2], [1]], 172 output_size=[5], 173 sparse_values=[-1.0, 1.0], 174 default_value=0.0, 175 validate_indices=False) 176 dense_without_validation.eval() 177 178 def testShapeInferenceKnownShape(self): 179 with self.test_session(use_gpu=False): 180 indices = array_ops.placeholder(dtypes.int64) 181 182 shape = [4, 5, 6] 183 output = sparse_ops.sparse_to_dense(indices, shape, 1, 0) 184 self.assertEqual(output.get_shape(), [4, 5, 6]) 185 186 shape = array_ops.placeholder(dtypes.int64, shape=(3,)) 187 output = sparse_ops.sparse_to_dense(indices, shape, 1, 0) 188 self.assertEqual(output.get_shape().as_list(), [None, None, None]) 189 190 def testShapeInferenceUnknownShape(self): 191 with self.test_session(use_gpu=False): 192 indices = array_ops.placeholder(dtypes.int64) 193 shape = array_ops.placeholder(dtypes.int64) 194 output = sparse_ops.sparse_to_dense(indices, shape, 1, 0) 195 self.assertEqual(output.get_shape().ndims, None) 196 197 198 if __name__ == "__main__": 199 test.main() 200
