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123 lines
3.2 KiB
C++
123 lines
3.2 KiB
C++
// 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) 2016 Igor Babuschkin <igor@babuschk.in>
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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 <numeric>
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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template <int DataLayout, typename Type=float>
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static void test_1d_scan()
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{
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int size = 50;
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Tensor<Type, 1, DataLayout> tensor(size);
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tensor.setRandom();
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Tensor<Type, 1, DataLayout> result = tensor.cumsum(0);
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VERIFY_IS_EQUAL(tensor.dimension(0), result.dimension(0));
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float accum = 0;
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for (int i = 0; i < size; i++) {
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accum += tensor(i);
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VERIFY_IS_EQUAL(result(i), accum);
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}
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accum = 1;
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result = tensor.cumprod(0);
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for (int i = 0; i < size; i++) {
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accum *= tensor(i);
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VERIFY_IS_EQUAL(result(i), accum);
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}
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}
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template <int DataLayout, typename Type=float>
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static void test_1d_inclusive_scan()
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{
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int size = 50;
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Tensor<Type, 1, DataLayout> tensor(size);
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tensor.setRandom();
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Tensor<Type, 1, DataLayout> result = tensor.cumsum(0, true);
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VERIFY_IS_EQUAL(tensor.dimension(0), result.dimension(0));
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float accum = 0;
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for (int i = 0; i < size; i++) {
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VERIFY_IS_EQUAL(result(i), accum);
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accum += tensor(i);
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}
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accum = 1;
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result = tensor.cumprod(0, true);
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for (int i = 0; i < size; i++) {
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VERIFY_IS_EQUAL(result(i), accum);
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accum *= tensor(i);
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}
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}
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template <int DataLayout, typename Type=float>
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static void test_4d_scan()
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{
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int size = 5;
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Tensor<Type, 4, DataLayout> tensor(size, size, size, size);
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tensor.setRandom();
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Tensor<Type, 4, DataLayout> result(size, size, size, size);
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result = tensor.cumsum(0);
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float accum = 0;
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for (int i = 0; i < size; i++) {
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accum += tensor(i, 0, 0, 0);
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VERIFY_IS_EQUAL(result(i, 0, 0, 0), accum);
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}
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result = tensor.cumsum(1);
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accum = 0;
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for (int i = 0; i < size; i++) {
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accum += tensor(0, i, 0, 0);
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VERIFY_IS_EQUAL(result(0, i, 0, 0), accum);
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}
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result = tensor.cumsum(2);
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accum = 0;
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for (int i = 0; i < size; i++) {
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accum += tensor(0, 0, i, 0);
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VERIFY_IS_EQUAL(result(0, 0, i, 0), accum);
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}
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result = tensor.cumsum(3);
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accum = 0;
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for (int i = 0; i < size; i++) {
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accum += tensor(0, 0, 0, i);
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VERIFY_IS_EQUAL(result(0, 0, 0, i), accum);
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}
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}
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template <int DataLayout>
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static void test_tensor_maps() {
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int inputs[20];
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TensorMap<Tensor<int, 1, DataLayout> > tensor_map(inputs, 20);
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tensor_map.setRandom();
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Tensor<int, 1, DataLayout> result = tensor_map.cumsum(0);
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int accum = 0;
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for (int i = 0; i < 20; ++i) {
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accum += tensor_map(i);
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VERIFY_IS_EQUAL(result(i), accum);
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}
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}
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void test_cxx11_tensor_scan() {
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CALL_SUBTEST(test_1d_scan<ColMajor>());
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CALL_SUBTEST(test_1d_scan<RowMajor>());
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CALL_SUBTEST(test_4d_scan<ColMajor>());
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CALL_SUBTEST(test_4d_scan<RowMajor>());
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CALL_SUBTEST(test_tensor_maps<ColMajor>());
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CALL_SUBTEST(test_tensor_maps<RowMajor>());
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}
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