2014-10-02 11:38:22 +08:00
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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#include <limits>
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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static void test_simple_reductions()
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{
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Tensor<float, 4> tensor(2,3,5,7);
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tensor.setRandom();
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array<ptrdiff_t, 2> reduction_axis;
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reduction_axis[0] = 1;
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reduction_axis[1] = 3;
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Tensor<float, 2> result = tensor.sum(reduction_axis);
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VERIFY_IS_EQUAL(result.dimension(0), 2);
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VERIFY_IS_EQUAL(result.dimension(1), 5);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 5; ++j) {
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float sum = 0.0f;
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for (int k = 0; k < 3; ++k) {
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for (int l = 0; l < 7; ++l) {
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sum += tensor(i, k, j, l);
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}
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}
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VERIFY_IS_APPROX(result(i, j), sum);
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}
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}
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reduction_axis[0] = 0;
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reduction_axis[1] = 2;
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result = tensor.maximum(reduction_axis);
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VERIFY_IS_EQUAL(result.dimension(0), 3);
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VERIFY_IS_EQUAL(result.dimension(1), 7);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 7; ++j) {
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float max_val = std::numeric_limits<float>::lowest();
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for (int k = 0; k < 2; ++k) {
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for (int l = 0; l < 5; ++l) {
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max_val = (std::max)(max_val, tensor(k, i, l, j));
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}
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}
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VERIFY_IS_APPROX(result(i, j), max_val);
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}
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}
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reduction_axis[0] = 0;
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reduction_axis[1] = 1;
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result = tensor.minimum(reduction_axis);
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VERIFY_IS_EQUAL(result.dimension(0), 5);
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VERIFY_IS_EQUAL(result.dimension(1), 7);
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for (int i = 0; i < 5; ++i) {
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for (int j = 0; j < 7; ++j) {
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float min_val = (std::numeric_limits<float>::max)();
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for (int k = 0; k < 2; ++k) {
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for (int l = 0; l < 3; ++l) {
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min_val = (std::min)(min_val, tensor(k, l, i, j));
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}
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}
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VERIFY_IS_APPROX(result(i, j), min_val);
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}
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}
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}
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static void test_full_reductions()
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{
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Tensor<float, 2> tensor(2,3);
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tensor.setRandom();
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array<ptrdiff_t, 2> reduction_axis;
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reduction_axis[0] = 0;
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reduction_axis[1] = 1;
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Tensor<float, 1> result = tensor.sum(reduction_axis);
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VERIFY_IS_EQUAL(result.dimension(0), 1);
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float sum = 0.0f;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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sum += tensor(i, j);
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}
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}
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VERIFY_IS_APPROX(result(0), sum);
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result = tensor.square().sum(reduction_axis).sqrt();
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VERIFY_IS_EQUAL(result.dimension(0), 1);
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sum = 0.0f;
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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sum += tensor(i, j) * tensor(i, j);
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}
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}
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VERIFY_IS_APPROX(result(0), sqrtf(sum));
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}
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struct UserReducer {
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UserReducer(float offset) : offset_(offset), sum_(0.0f) {}
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void reduce(const float val) {
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sum_ += val * val;
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}
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float finalize() const {
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return 1.0f / (sum_ + offset_);
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}
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private:
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float offset_;
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float sum_;
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};
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static void test_user_defined_reductions()
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{
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Tensor<float, 2> tensor(5,7);
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tensor.setRandom();
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array<ptrdiff_t, 1> reduction_axis;
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reduction_axis[0] = 1;
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UserReducer reducer(10.0f);
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Tensor<float, 1> result = tensor.reduce(reduction_axis, reducer);
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VERIFY_IS_EQUAL(result.dimension(0), 5);
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for (int i = 0; i < 5; ++i) {
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float expected = 10.0f;
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for (int j = 0; j < 7; ++j) {
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expected += tensor(i, j) * tensor(i, j);
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}
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expected = 1.0f / expected;
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VERIFY_IS_APPROX(result(i), expected);
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}
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}
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2014-10-10 06:36:23 +08:00
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static void test_tensor_maps()
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{
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int inputs[2*3*5*7];
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TensorMap<Tensor<int, 4> > tensor_map(inputs, 2,3,5,7);
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TensorMap<Tensor<const int, 4> > tensor_map_const(inputs, 2,3,5,7);
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const TensorMap<Tensor<const int, 4> > tensor_map_const_const(inputs, 2,3,5,7);
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tensor_map.setRandom();
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array<ptrdiff_t, 2> reduction_axis;
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reduction_axis[0] = 1;
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reduction_axis[1] = 3;
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Tensor<int, 2> result = tensor_map.sum(reduction_axis);
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Tensor<int, 2> result2 = tensor_map_const.sum(reduction_axis);
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Tensor<int, 2> result3 = tensor_map_const_const.sum(reduction_axis);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 5; ++j) {
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int sum = 0;
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for (int k = 0; k < 3; ++k) {
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for (int l = 0; l < 7; ++l) {
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sum += tensor_map(i, k, j, l);
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}
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}
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VERIFY_IS_EQUAL(result(i, j), sum);
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VERIFY_IS_EQUAL(result2(i, j), sum);
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VERIFY_IS_EQUAL(result3(i, j), sum);
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}
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}
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}
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2014-10-02 11:38:22 +08:00
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void test_cxx11_tensor_reduction()
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{
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CALL_SUBTEST(test_simple_reductions());
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CALL_SUBTEST(test_full_reductions());
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CALL_SUBTEST(test_user_defined_reductions());
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2014-10-10 06:36:23 +08:00
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CALL_SUBTEST(test_tensor_maps());
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2014-10-02 11:38:22 +08:00
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}
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