// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "main.h" #include <Eigen/CXX11/Tensor> using Eigen::Tensor; using Eigen::DefaultDevice; template <int DataLayout> static void test_evals() { Tensor<float, 2, DataLayout> input(3, 3); Tensor<float, 1, DataLayout> kernel(2); input.setRandom(); kernel.setRandom(); Tensor<float, 2, DataLayout> result(2,3); result.setZero(); Eigen::array<Tensor<float, 2>::Index, 1> dims3{{0}}; typedef TensorEvaluator<decltype(input.convolve(kernel, dims3)), DefaultDevice> Evaluator; Evaluator eval(input.convolve(kernel, dims3), DefaultDevice()); eval.evalTo(result.data()); EIGEN_STATIC_ASSERT(Evaluator::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); VERIFY_IS_EQUAL(eval.dimensions()[0], 2); VERIFY_IS_EQUAL(eval.dimensions()[1], 3); VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0) + input(1,0)*kernel(1)); // index 0 VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0) + input(1,1)*kernel(1)); // index 2 VERIFY_IS_APPROX(result(0,2), input(0,2)*kernel(0) + input(1,2)*kernel(1)); // index 4 VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0) + input(2,0)*kernel(1)); // index 1 VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0) + input(2,1)*kernel(1)); // index 3 VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1)); // index 5 } template <int DataLayout> static void test_expr() { Tensor<float, 2, DataLayout> input(3, 3); Tensor<float, 2, DataLayout> kernel(2, 2); input.setRandom(); kernel.setRandom(); Tensor<float, 2, DataLayout> result(2,2); Eigen::array<ptrdiff_t, 2> dims; dims[0] = 0; dims[1] = 1; result = input.convolve(kernel, dims); VERIFY_IS_APPROX(result(0,0), input(0,0)*kernel(0,0) + input(0,1)*kernel(0,1) + input(1,0)*kernel(1,0) + input(1,1)*kernel(1,1)); VERIFY_IS_APPROX(result(0,1), input(0,1)*kernel(0,0) + input(0,2)*kernel(0,1) + input(1,1)*kernel(1,0) + input(1,2)*kernel(1,1)); VERIFY_IS_APPROX(result(1,0), input(1,0)*kernel(0,0) + input(1,1)*kernel(0,1) + input(2,0)*kernel(1,0) + input(2,1)*kernel(1,1)); VERIFY_IS_APPROX(result(1,1), input(1,1)*kernel(0,0) + input(1,2)*kernel(0,1) + input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1)); } template <int DataLayout> static void test_modes() { Tensor<float, 1, DataLayout> input(3); Tensor<float, 1, DataLayout> kernel(3); input(0) = 1.0f; input(1) = 2.0f; input(2) = 3.0f; kernel(0) = 0.5f; kernel(1) = 1.0f; kernel(2) = 0.0f; Eigen::array<ptrdiff_t, 1> dims; dims[0] = 0; Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, 1> padding; // Emulate VALID mode (as defined in // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). padding[0] = std::make_pair(0, 0); Tensor<float, 1, DataLayout> valid(1); valid = input.pad(padding).convolve(kernel, dims); VERIFY_IS_EQUAL(valid.dimension(0), 1); VERIFY_IS_APPROX(valid(0), 2.5f); // Emulate SAME mode (as defined in // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). padding[0] = std::make_pair(1, 1); Tensor<float, 1, DataLayout> same(3); same = input.pad(padding).convolve(kernel, dims); VERIFY_IS_EQUAL(same.dimension(0), 3); VERIFY_IS_APPROX(same(0), 1.0f); VERIFY_IS_APPROX(same(1), 2.5f); VERIFY_IS_APPROX(same(2), 4.0f); // Emulate FULL mode (as defined in // http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html). padding[0] = std::make_pair(2, 2); Tensor<float, 1, DataLayout> full(5); full = input.pad(padding).convolve(kernel, dims); VERIFY_IS_EQUAL(full.dimension(0), 5); VERIFY_IS_APPROX(full(0), 0.0f); VERIFY_IS_APPROX(full(1), 1.0f); VERIFY_IS_APPROX(full(2), 2.5f); VERIFY_IS_APPROX(full(3), 4.0f); VERIFY_IS_APPROX(full(4), 1.5f); } template <int DataLayout> static void test_strides() { Tensor<float, 1, DataLayout> input(13); Tensor<float, 1, DataLayout> kernel(3); input.setRandom(); kernel.setRandom(); Eigen::array<ptrdiff_t, 1> dims; dims[0] = 0; Eigen::array<ptrdiff_t, 1> stride_of_3; stride_of_3[0] = 3; Eigen::array<ptrdiff_t, 1> stride_of_2; stride_of_2[0] = 2; Tensor<float, 1, DataLayout> result; result = input.stride(stride_of_3).convolve(kernel, dims).stride(stride_of_2); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_APPROX(result(0), (input(0)*kernel(0) + input(3)*kernel(1) + input(6)*kernel(2))); VERIFY_IS_APPROX(result(1), (input(6)*kernel(0) + input(9)*kernel(1) + input(12)*kernel(2))); } void test_cxx11_tensor_convolution() { CALL_SUBTEST(test_evals<ColMajor>()); CALL_SUBTEST(test_evals<RowMajor>()); CALL_SUBTEST(test_expr<ColMajor>()); CALL_SUBTEST(test_expr<RowMajor>()); CALL_SUBTEST(test_modes<ColMajor>()); CALL_SUBTEST(test_modes<RowMajor>()); CALL_SUBTEST(test_strides<ColMajor>()); CALL_SUBTEST(test_strides<RowMajor>()); }