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>
test_evals()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>
test_expr()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>
test_modes()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>
test_strides()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
EIGEN_DECLARE_TEST(cxx11_tensor_convolution)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 }
151