# 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 SparseReorder.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import sparse_ops from tensorflow.python.platform import test class SparseSplitOpTest(test.TestCase): def _SparseTensor_4x6(self): # [0 | |2 | |4 |5 ] # [ |11| |13|14| ] # [20| | |23| |25] # [30| |32|33| |35] ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3], [3, 5]]).astype(np.int64) val = np.array( [0, 2, 4, 5, 11, 13, 14, 20, 23, 25, 30, 32, 33, 35]).astype(np.int64) shape = np.array([4, 6]).astype(np.int64) return sparse_tensor.SparseTensor(ind, val, shape) def _SparseTensor_5x7(self): # [0 | |2 | |4 |5 | ] # [ |11| |13|14| |16] # [20| | |23| |25| ] # [30| |32|33| |35| ] # [ |41| | |44| |46] ind = np.array([[0, 0], [0, 2], [0, 4], [0, 5], [1, 1], [1, 3], [1, 4], [1, 6], [2, 0], [2, 3], [2, 5], [3, 0], [3, 2], [3, 3], [3, 5], [4, 1], [4, 4], [4, 6]]).astype(np.int64) val = np.array( [0, 2, 4, 5, 11, 13, 14, 16, 20, 23, 25, 30, 32, 33, 35, 41, 44, 46]).astype(np.int64) shape = np.array([5, 7]).astype(np.int64) return sparse_tensor.SparseTensor(ind, val, shape) def _SparseTensorValue_3x4x2(self): # slice(:,:, 0) # ['a0'| |'b0'| ] # [ |'c0'| |'d0'] # [ | |'e0'| ] # slice(:,:, 1) # ['a1'| |'b1'| ] # [ |'c1'| |'d1'] # [ | |'e1'| ] ind = np.array([[0, 0, 0], [0, 0, 1], [0, 2, 0], [0, 2, 1], [1, 1, 0], [1, 1, 1], [1, 3, 0], [1, 3, 1], [2, 2, 0], [2, 2, 1]]).astype(np.int64) val = np.array(['a0', 'a1', 'b0', 'b1', 'c0', 'c1', 'd0', 'd1', 'e0', 'e1']) shape = np.array([3, 4, 2]).astype(np.int64) return sparse_tensor.SparseTensorValue(ind, val, shape) def _SparseTensor_3x4x2(self): return sparse_tensor.SparseTensor.from_value(self._SparseTensorValue_3x4x2( )) 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 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): 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 testSplitAllRows(self): with self.test_session(use_gpu=False): sp_tensors = sparse_ops.sparse_split( sp_input=self._SparseTensor_4x6(), num_split=4, axis=0) self.assertAllEqual(len(sp_tensors), 4) self.assertAllEqual(sp_tensors[0].indices.eval(), [[0, 0], [0, 2], [0, 4], [0, 5]]) self.assertAllEqual(sp_tensors[0].values.eval(), [0, 2, 4, 5]) self.assertAllEqual(sp_tensors[0].dense_shape.eval(), [1, 6]) self.assertAllEqual(sp_tensors[1].indices.eval(), [[0, 1], [0, 3], [0, 4]]) self.assertAllEqual(sp_tensors[1].values.eval(), [11, 13, 14]) self.assertAllEqual(sp_tensors[1].dense_shape.eval(), [1, 6]) self.assertAllEqual(sp_tensors[2].indices.eval(), [[0, 0], [0, 3], [0, 5]]) self.assertAllEqual(sp_tensors[2].values.eval(), [20, 23, 25]) self.assertAllEqual(sp_tensors[2].dense_shape.eval(), [1, 6]) self.assertAllEqual(sp_tensors[3].indices.eval(), [[0, 0], [0, 2], [0, 3], [0, 5]]) self.assertAllEqual(sp_tensors[3].values.eval(), [30, 32, 33, 35]) self.assertAllEqual(sp_tensors[3].dense_shape.eval(), [1, 6]) 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]) 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 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 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) if __name__ == '__main__': test.main()