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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;
struct InsertZeros {
DSizes<DenseIndex, 2> dimensions(const Tensor<float, 2>& input) const {
DSizes<DenseIndex, 2> result;
result[0] = input.dimension(0) * 2;
result[1] = input.dimension(1) * 2;
return result;
}
template <typename Output, typename Device>
void eval(const Tensor<float, 2>& input, Output& output, const Device& device) const
{
array<DenseIndex, 2> strides;
strides[0] = 2;
strides[1] = 2;
output.stride(strides).device(device) = input;
Eigen::DSizes<DenseIndex, 2> offsets(1,1);
Eigen::DSizes<DenseIndex, 2> extents(output.dimension(0)-1, output.dimension(1)-1);
output.slice(offsets, extents).stride(strides).device(device) = input.constant(0.0f);
}
};
static void test_custom_unary_op()
{
Tensor<float, 2> tensor(3,5);
tensor.setRandom();
Tensor<float, 2> result = tensor.customOp(InsertZeros());
VERIFY_IS_EQUAL(result.dimension(0), 6);
VERIFY_IS_EQUAL(result.dimension(1), 10);
for (int i = 0; i < 6; i+=2) {
for (int j = 0; j < 10; j+=2) {
VERIFY_IS_EQUAL(result(i, j), tensor(i/2, j/2));
}
}
for (int i = 1; i < 6; i+=2) {
for (int j = 1; j < 10; j+=2) {
VERIFY_IS_EQUAL(result(i, j), 0);
}
}
}
struct BatchMatMul {
DSizes<DenseIndex, 3> dimensions(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2) const {
DSizes<DenseIndex, 3> result;
result[0] = input1.dimension(0);
result[1] = input2.dimension(1);
result[2] = input2.dimension(2);
return result;
}
template <typename Output, typename Device>
void eval(const Tensor<float, 3>& input1, const Tensor<float, 3>& input2,
Output& output, const Device& device) const
{
typedef Tensor<float, 3>::DimensionPair DimPair;
array<DimPair, 1> dims;
dims[0] = DimPair(1, 0);
for (int i = 0; i < output.dimension(2); ++i) {
output.template chip<2>(i).device(device) = input1.chip<2>(i).contract(input2.chip<2>(i), dims);
}
}
};
static void test_custom_binary_op()
{
Tensor<float, 3> tensor1(2,3,5);
tensor1.setRandom();
Tensor<float, 3> tensor2(3,7,5);
tensor2.setRandom();
Tensor<float, 3> result = tensor1.customOp(tensor2, BatchMatMul());
for (int i = 0; i < 5; ++i) {
typedef Tensor<float, 3>::DimensionPair DimPair;
array<DimPair, 1> dims;
dims[0] = DimPair(1, 0);
Tensor<float, 2> reference = tensor1.chip<2>(i).contract(tensor2.chip<2>(i), dims);
TensorRef<Tensor<float, 2> > val = result.chip<2>(i);
for (int j = 0; j < 2; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(val(j, k), reference(j, k));
}
}
}
}
void test_cxx11_tensor_custom_op()
{
CALL_SUBTEST(test_custom_unary_op());
CALL_SUBTEST(test_custom_binary_op());
}
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