cxx11_tensor_convolution.cpp (5381B)
1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> 5 // 6 // This Source Code Form is subject to the terms of the Mozilla 7 // Public License v. 2.0. If a copy of the MPL was not distributed 8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 10 #include "main.h" 11 12 #include <Eigen/CXX11/Tensor> 13 14 using Eigen::Tensor; 15 using Eigen::DefaultDevice; 16 17 template <int DataLayout> 18 static void test_evals() 19 { 20 Tensor<float, 2, DataLayout> input(3, 3); 21 Tensor<float, 1, DataLayout> kernel(2); 22 23 input.setRandom(); 24 kernel.setRandom(); 25 26 Tensor<float, 2, DataLayout> result(2,3); 27 result.setZero(); 28 Eigen::array<Tensor<float, 2>::Index, 1> dims3; 29 dims3[0] = 0; 30 31 typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator; 32 Evaluator eval(input.convolve(kernel, dims3), DefaultDevice()); 33 eval.evalTo(result.data()); 34 EIGEN_STATIC_ASSERT(Evaluator::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); 35 VERIFY_IS_EQUAL(eval.dimensions()[0], 2); 36 VERIFY_IS_EQUAL(eval.dimensions()[1], 3); 37 38 VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0) + input(1,0)*kernel(1)); // index 0 39 VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0) + input(1,1)*kernel(1)); // index 2 40 VERIFY_IS_APPROX(result(0,2), input(0,2)*kernel(0) + input(1,2)*kernel(1)); // index 4 41 VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0) + input(2,0)*kernel(1)); // index 1 42 VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0) + input(2,1)*kernel(1)); // index 3 43 VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1)); // index 5 44 } 45 46 template <int DataLayout> 47 static void test_expr() 48 { 49 Tensor<float, 2, DataLayout> input(3, 3); 50 Tensor<float, 2, DataLayout> kernel(2, 2); 51 input.setRandom(); 52 kernel.setRandom(); 53 54 Tensor<float, 2, DataLayout> result(2,2); 55 Eigen::array<ptrdiff_t, 2> dims; 56 dims[0] = 0; 57 dims[1] = 1; 58 result = input.convolve(kernel, dims); 59 60 VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0,0) + input(0,1)*kernel(0,1) + 61 input(1,0)*kernel(1,0) + input(1,1)*kernel(1,1)); 62 VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0,0) + input(0,2)*kernel(0,1) + 63 input(1,1)*kernel(1,0) + input(1,2)*kernel(1,1)); 64 VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0,0) + input(1,1)*kernel(0,1) + 65 input(2,0)*kernel(1,0) + input(2,1)*kernel(1,1)); 66 VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0,0) + input(1,2)*kernel(0,1) + 67 input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1)); 68 } 69 70 template <int DataLayout> 71 static void test_modes() { 72 Tensor<float, 1, DataLayout> input(3); 73 Tensor<float, 1, DataLayout> kernel(3); 74 input(0) = 1.0f; 75 input(1) = 2.0f; 76 input(2) = 3.0f; 77 kernel(0) = 0.5f; 78 kernel(1) = 1.0f; 79 kernel(2) = 0.0f; 80 81 Eigen::array<ptrdiff_t, 1> dims; 82 dims[0] = 0; 83 Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, 1> padding; 84 85 // Emulate VALID mode (as defined in 86 // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). 87 padding[0] = std::make_pair(0, 0); 88 Tensor<float, 1, DataLayout> valid(1); 89 valid = input.pad(padding).convolve(kernel, dims); 90 VERIFY_IS_EQUAL(valid.dimension(0), 1); 91 VERIFY_IS_APPROX(valid(0), 2.5f); 92 93 // Emulate SAME mode (as defined in 94 // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). 95 padding[0] = std::make_pair(1, 1); 96 Tensor<float, 1, DataLayout> same(3); 97 same = input.pad(padding).convolve(kernel, dims); 98 VERIFY_IS_EQUAL(same.dimension(0), 3); 99 VERIFY_IS_APPROX(same(0), 1.0f); 100 VERIFY_IS_APPROX(same(1), 2.5f); 101 VERIFY_IS_APPROX(same(2), 4.0f); 102 103 // Emulate FULL mode (as defined in 104 // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). 105 padding[0] = std::make_pair(2, 2); 106 Tensor<float, 1, DataLayout> full(5); 107 full = input.pad(padding).convolve(kernel, dims); 108 VERIFY_IS_EQUAL(full.dimension(0), 5); 109 VERIFY_IS_APPROX(full(0), 0.0f); 110 VERIFY_IS_APPROX(full(1), 1.0f); 111 VERIFY_IS_APPROX(full(2), 2.5f); 112 VERIFY_IS_APPROX(full(3), 4.0f); 113 VERIFY_IS_APPROX(full(4), 1.5f); 114 } 115 116 template <int DataLayout> 117 static void test_strides() { 118 Tensor<float, 1, DataLayout> input(13); 119 Tensor<float, 1, DataLayout> kernel(3); 120 input.setRandom(); 121 kernel.setRandom(); 122 123 Eigen::array<ptrdiff_t, 1> dims; 124 dims[0] = 0; 125 Eigen::array<ptrdiff_t, 1> stride_of_3; 126 stride_of_3[0] = 3; 127 Eigen::array<ptrdiff_t, 1> stride_of_2; 128 stride_of_2[0] = 2; 129 130 Tensor<float, 1, DataLayout> result; 131 result = input.stride(stride_of_3).convolve(kernel, dims).stride(stride_of_2); 132 133 VERIFY_IS_EQUAL(result.dimension(0), 2); 134 VERIFY_IS_APPROX(result(0), (input(0)*kernel(0) + input(3)*kernel(1) + 135 input(6)*kernel(2))); 136 VERIFY_IS_APPROX(result(1), (input(6)*kernel(0) + input(9)*kernel(1) + 137 input(12)*kernel(2))); 138 } 139 140 EIGEN_DECLARE_TEST(cxx11_tensor_convolution) 141 { 142 CALL_SUBTEST(test_evals<ColMajor>()); 143 CALL_SUBTEST(test_evals<RowMajor>()); 144 CALL_SUBTEST(test_expr<ColMajor>()); 145 CALL_SUBTEST(test_expr<RowMajor>()); 146 CALL_SUBTEST(test_modes<ColMajor>()); 147 CALL_SUBTEST(test_modes<RowMajor>()); 148 CALL_SUBTEST(test_strides<ColMajor>()); 149 CALL_SUBTEST(test_strides<RowMajor>()); 150 }