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// 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>());
}
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