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{{0}};
29
30 typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator;
31 Evaluator eval(input.convolve(kernel, dims3), DefaultDevice());
32 eval.evalTo(result.data());
33 EIGEN_STATIC_ASSERT(Evaluator::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE);
34 VERIFY_IS_EQUAL(eval.dimensions()[0], 2);
35 VERIFY_IS_EQUAL(eval.dimensions()[1], 3);
36
37 VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0) + input(1,0)*kernel(1)); // index 0
38 VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0) + input(1,1)*kernel(1)); // index 2
39 VERIFY_IS_APPROX(result(0,2), input(0,2)*kernel(0) + input(1,2)*kernel(1)); // index 4
40 VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0) + input(2,0)*kernel(1)); // index 1
41 VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0) + input(2,1)*kernel(1)); // index 3
42 VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1)); // index 5
43 }
44
45 template <int DataLayout>
test_expr()46 static void test_expr()
47 {
48 Tensor<float, 2, DataLayout> input(3, 3);
49 Tensor<float, 2, DataLayout> kernel(2, 2);
50 input.setRandom();
51 kernel.setRandom();
52
53 Tensor<float, 2, DataLayout> result(2,2);
54 Eigen::array<ptrdiff_t, 2> dims;
55 dims[0] = 0;
56 dims[1] = 1;
57 result = input.convolve(kernel, dims);
58
59 VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0,0) + input(0,1)*kernel(0,1) +
60 input(1,0)*kernel(1,0) + input(1,1)*kernel(1,1));
61 VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0,0) + input(0,2)*kernel(0,1) +
62 input(1,1)*kernel(1,0) + input(1,2)*kernel(1,1));
63 VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0,0) + input(1,1)*kernel(0,1) +
64 input(2,0)*kernel(1,0) + input(2,1)*kernel(1,1));
65 VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0,0) + input(1,2)*kernel(0,1) +
66 input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1));
67 }
68
69 template <int DataLayout>
test_modes()70 static void test_modes() {
71 Tensor<float, 1, DataLayout> input(3);
72 Tensor<float, 1, DataLayout> kernel(3);
73 input(0) = 1.0f;
74 input(1) = 2.0f;
75 input(2) = 3.0f;
76 kernel(0) = 0.5f;
77 kernel(1) = 1.0f;
78 kernel(2) = 0.0f;
79
80 Eigen::array<ptrdiff_t, 1> dims;
81 dims[0] = 0;
82 Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, 1> padding;
83
84 // Emulate VALID mode (as defined in
85 // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
86 padding[0] = std::make_pair(0, 0);
87 Tensor<float, 1, DataLayout> valid(1);
88 valid = input.pad(padding).convolve(kernel, dims);
89 VERIFY_IS_EQUAL(valid.dimension(0), 1);
90 VERIFY_IS_APPROX(valid(0), 2.5f);
91
92 // Emulate SAME mode (as defined in
93 // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
94 padding[0] = std::make_pair(1, 1);
95 Tensor<float, 1, DataLayout> same(3);
96 same = input.pad(padding).convolve(kernel, dims);
97 VERIFY_IS_EQUAL(same.dimension(0), 3);
98 VERIFY_IS_APPROX(same(0), 1.0f);
99 VERIFY_IS_APPROX(same(1), 2.5f);
100 VERIFY_IS_APPROX(same(2), 4.0f);
101
102 // Emulate FULL mode (as defined in
103 // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
104 padding[0] = std::make_pair(2, 2);
105 Tensor<float, 1, DataLayout> full(5);
106 full = input.pad(padding).convolve(kernel, dims);
107 VERIFY_IS_EQUAL(full.dimension(0), 5);
108 VERIFY_IS_APPROX(full(0), 0.0f);
109 VERIFY_IS_APPROX(full(1), 1.0f);
110 VERIFY_IS_APPROX(full(2), 2.5f);
111 VERIFY_IS_APPROX(full(3), 4.0f);
112 VERIFY_IS_APPROX(full(4), 1.5f);
113 }
114
115 template <int DataLayout>
test_strides()116 static void test_strides() {
117 Tensor<float, 1, DataLayout> input(13);
118 Tensor<float, 1, DataLayout> kernel(3);
119 input.setRandom();
120 kernel.setRandom();
121
122 Eigen::array<ptrdiff_t, 1> dims;
123 dims[0] = 0;
124 Eigen::array<ptrdiff_t, 1> stride_of_3;
125 stride_of_3[0] = 3;
126 Eigen::array<ptrdiff_t, 1> stride_of_2;
127 stride_of_2[0] = 2;
128
129 Tensor<float, 1, DataLayout> result;
130 result = input.stride(stride_of_3).convolve(kernel, dims).stride(stride_of_2);
131
132 VERIFY_IS_EQUAL(result.dimension(0), 2);
133 VERIFY_IS_APPROX(result(0), (input(0)*kernel(0) + input(3)*kernel(1) +
134 input(6)*kernel(2)));
135 VERIFY_IS_APPROX(result(1), (input(6)*kernel(0) + input(9)*kernel(1) +
136 input(12)*kernel(2)));
137 }
138
test_cxx11_tensor_convolution()139 void test_cxx11_tensor_convolution()
140 {
141 CALL_SUBTEST(test_evals<ColMajor>());
142 CALL_SUBTEST(test_evals<RowMajor>());
143 CALL_SUBTEST(test_expr<ColMajor>());
144 CALL_SUBTEST(test_expr<RowMajor>());
145 CALL_SUBTEST(test_modes<ColMajor>());
146 CALL_SUBTEST(test_modes<RowMajor>());
147 CALL_SUBTEST(test_strides<ColMajor>());
148 CALL_SUBTEST(test_strides<RowMajor>());
149 }
150