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509 lines
16 KiB
C++
509 lines
16 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) 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 <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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template<typename>
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static void test_simple_reshape()
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{
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Tensor<float, 5> tensor1(2,3,1,7,1);
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tensor1.setRandom();
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Tensor<float, 3> tensor2(2,3,7);
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Tensor<float, 2> tensor3(6,7);
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Tensor<float, 2> tensor4(2,21);
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Tensor<float, 3>::Dimensions dim1(2,3,7);
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tensor2 = tensor1.reshape(dim1);
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Tensor<float, 2>::Dimensions dim2(6,7);
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tensor3 = tensor1.reshape(dim2);
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Tensor<float, 2>::Dimensions dim3(2,21);
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tensor4 = tensor1.reshape(dim1).reshape(dim3);
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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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for (int k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k));
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i+2*j,k));
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor4(i,j+3*k));
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}
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}
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}
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}
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template <typename>
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static void test_static_reshape() {
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#if defined(EIGEN_HAS_INDEX_LIST)
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using Eigen::type2index;
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Tensor<float, 5> tensor(2, 3, 1, 7, 1);
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tensor.setRandom();
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// New dimensions: [2, 3, 7]
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Eigen::IndexList<type2index<2>, type2index<3>, type2index<7>> dim;
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Tensor<float, 3> reshaped = tensor.reshape(dim);
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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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for (int k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor(i, j, 0, k, 0), reshaped(i, j, k));
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}
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}
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}
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#endif
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}
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template<typename>
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static void test_reshape_in_expr() {
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MatrixXf m1(2,3*5*7*11);
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MatrixXf m2(3*5*7*11,13);
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m1.setRandom();
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m2.setRandom();
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MatrixXf m3 = m1 * m2;
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TensorMap<Tensor<float, 5>> tensor1(m1.data(), 2,3,5,7,11);
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TensorMap<Tensor<float, 5>> tensor2(m2.data(), 3,5,7,11,13);
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Tensor<float, 2>::Dimensions newDims1(2,3*5*7*11);
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Tensor<float, 2>::Dimensions newDims2(3*5*7*11,13);
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typedef Tensor<float, 1>::DimensionPair DimPair;
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array<DimPair, 1> contract_along{{DimPair(1, 0)}};
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Tensor<float, 2> tensor3(2,13);
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tensor3 = tensor1.reshape(newDims1).contract(tensor2.reshape(newDims2), contract_along);
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Map<MatrixXf> res(tensor3.data(), 2, 13);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 13; ++j) {
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VERIFY_IS_APPROX(res(i,j), m3(i,j));
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}
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}
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}
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template<typename>
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static void test_reshape_as_lvalue()
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{
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Tensor<float, 3> tensor(2,3,7);
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tensor.setRandom();
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Tensor<float, 2> tensor2d(6,7);
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Tensor<float, 3>::Dimensions dim(2,3,7);
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tensor2d.reshape(dim) = tensor;
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float scratch[2*3*1*7*1];
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TensorMap<Tensor<float, 5>> tensor5d(scratch, 2,3,1,7,1);
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tensor5d.reshape(dim).device(Eigen::DefaultDevice()) = tensor;
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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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for (int k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor2d(i+2*j,k), tensor(i,j,k));
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VERIFY_IS_EQUAL(tensor5d(i,j,0,k,0), tensor(i,j,k));
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}
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}
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}
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}
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template<int DataLayout>
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static void test_simple_slice()
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{
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Tensor<float, 5, DataLayout> tensor(2,3,5,7,11);
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tensor.setRandom();
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Tensor<float, 5, DataLayout> slice1(1,1,1,1,1);
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Eigen::DSizes<ptrdiff_t, 5> indices(1,2,3,4,5);
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Eigen::DSizes<ptrdiff_t, 5> sizes(1,1,1,1,1);
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slice1 = tensor.slice(indices, sizes);
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VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5));
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Tensor<float, 5, DataLayout> slice2(1,1,2,2,3);
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Eigen::DSizes<ptrdiff_t, 5> indices2(1,1,3,4,5);
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Eigen::DSizes<ptrdiff_t, 5> sizes2(1,1,2,2,3);
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slice2 = tensor.slice(indices2, sizes2);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 2; ++j) {
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for (int k = 0; k < 3; ++k) {
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VERIFY_IS_EQUAL(slice2(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k));
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}
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}
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}
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}
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template<typename=void>
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static void test_const_slice()
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{
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const float b[1] = {42};
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TensorMap<Tensor<const float, 1> > m(b, 1);
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DSizes<DenseIndex, 1> offsets;
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offsets[0] = 0;
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TensorRef<Tensor<const float, 1> > slice_ref(m.slice(offsets, m.dimensions()));
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VERIFY_IS_EQUAL(slice_ref(0), 42);
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}
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template<int DataLayout>
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static void test_slice_in_expr() {
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typedef Matrix<float, Dynamic, Dynamic, DataLayout> Mtx;
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Mtx m1(7,7);
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Mtx m2(3,3);
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m1.setRandom();
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m2.setRandom();
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Mtx m3 = m1.block(1, 2, 3, 3) * m2.block(0, 2, 3, 1);
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TensorMap<Tensor<float, 2, DataLayout>> tensor1(m1.data(), 7, 7);
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TensorMap<Tensor<float, 2, DataLayout>> tensor2(m2.data(), 3, 3);
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Tensor<float, 2, DataLayout> tensor3(3,1);
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typedef Tensor<float, 1>::DimensionPair DimPair;
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array<DimPair, 1> contract_along{{DimPair(1, 0)}};
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Eigen::DSizes<ptrdiff_t, 2> indices1(1,2);
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Eigen::DSizes<ptrdiff_t, 2> sizes1(3,3);
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Eigen::DSizes<ptrdiff_t, 2> indices2(0,2);
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Eigen::DSizes<ptrdiff_t, 2> sizes2(3,1);
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tensor3 = tensor1.slice(indices1, sizes1).contract(tensor2.slice(indices2, sizes2), contract_along);
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Map<Mtx> res(tensor3.data(), 3, 1);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 1; ++j) {
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VERIFY_IS_APPROX(res(i,j), m3(i,j));
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}
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}
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// Take an arbitrary slice of an arbitrarily sized tensor.
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TensorMap<Tensor<const float, 2, DataLayout>> tensor4(m1.data(), 7, 7);
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Tensor<float, 1, DataLayout> tensor6 = tensor4.reshape(DSizes<ptrdiff_t, 1>(7*7)).exp().slice(DSizes<ptrdiff_t, 1>(0), DSizes<ptrdiff_t, 1>(35));
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for (int i = 0; i < 35; ++i) {
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VERIFY_IS_APPROX(tensor6(i), expf(tensor4.data()[i]));
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}
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}
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template<int DataLayout>
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static void test_slice_as_lvalue()
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{
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Tensor<float, 3, DataLayout> tensor1(2,2,7);
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tensor1.setRandom();
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Tensor<float, 3, DataLayout> tensor2(2,2,7);
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tensor2.setRandom();
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Tensor<float, 3, DataLayout> tensor3(4,3,5);
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tensor3.setRandom();
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Tensor<float, 3, DataLayout> tensor4(4,3,2);
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tensor4.setRandom();
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Tensor<float, 3, DataLayout> tensor5(10,13,12);
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tensor5.setRandom();
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Tensor<float, 3, DataLayout> result(4,5,7);
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Eigen::DSizes<ptrdiff_t, 3> sizes12(2,2,7);
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Eigen::DSizes<ptrdiff_t, 3> first_slice(0,0,0);
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result.slice(first_slice, sizes12) = tensor1;
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Eigen::DSizes<ptrdiff_t, 3> second_slice(2,0,0);
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result.slice(second_slice, sizes12).device(Eigen::DefaultDevice()) = tensor2;
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Eigen::DSizes<ptrdiff_t, 3> sizes3(4,3,5);
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Eigen::DSizes<ptrdiff_t, 3> third_slice(0,2,0);
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result.slice(third_slice, sizes3) = tensor3;
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Eigen::DSizes<ptrdiff_t, 3> sizes4(4,3,2);
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Eigen::DSizes<ptrdiff_t, 3> fourth_slice(0,2,5);
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result.slice(fourth_slice, sizes4) = tensor4;
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for (int j = 0; j < 2; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int i = 0; i < 2; ++i) {
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VERIFY_IS_EQUAL(result(i,j,k), tensor1(i,j,k));
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VERIFY_IS_EQUAL(result(i+2,j,k), tensor2(i,j,k));
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}
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}
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}
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for (int i = 0; i < 4; ++i) {
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for (int j = 2; j < 5; ++j) {
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for (int k = 0; k < 5; ++k) {
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VERIFY_IS_EQUAL(result(i,j,k), tensor3(i,j-2,k));
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}
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for (int k = 5; k < 7; ++k) {
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VERIFY_IS_EQUAL(result(i,j,k), tensor4(i,j-2,k-5));
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}
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}
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}
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Eigen::DSizes<ptrdiff_t, 3> sizes5(4,5,7);
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Eigen::DSizes<ptrdiff_t, 3> fifth_slice(0,0,0);
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result.slice(fifth_slice, sizes5) = tensor5.slice(fifth_slice, sizes5);
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for (int i = 0; i < 4; ++i) {
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for (int j = 2; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(result(i,j,k), tensor5(i,j,k));
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}
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}
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}
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}
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template<int DataLayout>
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static void test_slice_raw_data()
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{
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Tensor<float, 4, DataLayout> tensor(3,5,7,11);
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tensor.setRandom();
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Eigen::DSizes<ptrdiff_t, 4> offsets(1,2,3,4);
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Eigen::DSizes<ptrdiff_t, 4> extents(1,1,1,1);
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typedef TensorEvaluator<decltype(tensor.slice(offsets, extents)), DefaultDevice> SliceEvaluator;
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auto slice1 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice1.dimensions().TotalSize(), 1);
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VERIFY_IS_EQUAL(slice1.data()[0], tensor(1,2,3,4));
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if (DataLayout == ColMajor) {
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extents = Eigen::DSizes<ptrdiff_t, 4>(2,1,1,1);
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auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2);
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VERIFY_IS_EQUAL(slice2.data()[0], tensor(1,2,3,4));
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VERIFY_IS_EQUAL(slice2.data()[1], tensor(2,2,3,4));
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} else {
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extents = Eigen::DSizes<ptrdiff_t, 4>(1,1,1,2);
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auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2);
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VERIFY_IS_EQUAL(slice2.data()[0], tensor(1,2,3,4));
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VERIFY_IS_EQUAL(slice2.data()[1], tensor(1,2,3,5));
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}
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extents = Eigen::DSizes<ptrdiff_t, 4>(1,2,1,1);
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auto slice3 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice3.dimensions().TotalSize(), 2);
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VERIFY_IS_EQUAL(slice3.data(), static_cast<float*>(0));
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if (DataLayout == ColMajor) {
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offsets = Eigen::DSizes<ptrdiff_t, 4>(0,2,3,4);
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extents = Eigen::DSizes<ptrdiff_t, 4>(3,2,1,1);
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auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 6);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 2; ++j) {
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VERIFY_IS_EQUAL(slice4.data()[i+3*j], tensor(i,2+j,3,4));
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}
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}
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} else {
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offsets = Eigen::DSizes<ptrdiff_t, 4>(1,2,3,0);
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extents = Eigen::DSizes<ptrdiff_t, 4>(1,1,2,11);
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auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 22);
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for (int l = 0; l < 11; ++l) {
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for (int k = 0; k < 2; ++k) {
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VERIFY_IS_EQUAL(slice4.data()[l+11*k], tensor(1,2,3+k,l));
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}
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}
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}
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if (DataLayout == ColMajor) {
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offsets = Eigen::DSizes<ptrdiff_t, 4>(0,0,0,4);
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extents = Eigen::DSizes<ptrdiff_t, 4>(3,5,7,2);
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auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 210);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 2; ++l) {
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int slice_index = i + 3 * (j + 5 * (k + 7 * l));
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VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i,j,k,l+4));
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}
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}
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}
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}
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} else {
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offsets = Eigen::DSizes<ptrdiff_t, 4>(1,0,0,0);
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extents = Eigen::DSizes<ptrdiff_t, 4>(2,5,7,11);
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auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 770);
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for (int l = 0; l < 11; ++l) {
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for (int k = 0; k < 7; ++k) {
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for (int j = 0; j < 5; ++j) {
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for (int i = 0; i < 2; ++i) {
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int slice_index = l + 11 * (k + 7 * (j + 5 * i));
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VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i+1,j,k,l));
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}
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}
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}
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}
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}
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offsets = Eigen::DSizes<ptrdiff_t, 4>(0,0,0,0);
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extents = Eigen::DSizes<ptrdiff_t, 4>(3,5,7,11);
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auto slice6 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
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VERIFY_IS_EQUAL(slice6.dimensions().TotalSize(), 3*5*7*11);
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VERIFY_IS_EQUAL(slice6.data(), tensor.data());
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}
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template<int DataLayout>
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static void test_strided_slice()
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{
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typedef Tensor<float, 5, DataLayout> Tensor5f;
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typedef Eigen::DSizes<Eigen::DenseIndex, 5> Index5;
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typedef Tensor<float, 2, DataLayout> Tensor2f;
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typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2;
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Tensor<float, 5, DataLayout> tensor(2,3,5,7,11);
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Tensor<float, 2, DataLayout> tensor2(7,11);
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tensor.setRandom();
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tensor2.setRandom();
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if (true) {
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Tensor2f slice(2,3);
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Index2 strides(-2,-1);
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Index2 indicesStart(5,7);
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Index2 indicesStop(0,4);
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slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
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for (int j = 0; j < 2; ++j) {
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for (int k = 0; k < 3; ++k) {
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VERIFY_IS_EQUAL(slice(j,k), tensor2(5-2*j,7-k));
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}
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}
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}
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if(true) {
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Tensor2f slice(0,1);
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Index2 strides(1,1);
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Index2 indicesStart(5,4);
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Index2 indicesStop(5,5);
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slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
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}
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if(true) { // test clamped degenerate interavls
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Tensor2f slice(7,11);
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Index2 strides(1,-1);
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Index2 indicesStart(-3,20); // should become 0,10
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Index2 indicesStop(20,-11); // should become 11, -1
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slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
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for (int j = 0; j < 7; ++j) {
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for (int k = 0; k < 11; ++k) {
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VERIFY_IS_EQUAL(slice(j,k), tensor2(j,10-k));
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}
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}
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}
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if(true) {
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Tensor5f slice1(1,1,1,1,1);
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Eigen::DSizes<Eigen::DenseIndex, 5> indicesStart(1, 2, 3, 4, 5);
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Eigen::DSizes<Eigen::DenseIndex, 5> indicesStop(2, 3, 4, 5, 6);
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Eigen::DSizes<Eigen::DenseIndex, 5> strides(1, 1, 1, 1, 1);
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slice1 = tensor.stridedSlice(indicesStart, indicesStop, strides);
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VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5));
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}
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if(true) {
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Tensor5f slice(1,1,2,2,3);
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Index5 start(1, 1, 3, 4, 5);
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Index5 stop(2, 2, 5, 6, 8);
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Index5 strides(1, 1, 1, 1, 1);
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slice = tensor.stridedSlice(start, stop, strides);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 2; ++j) {
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for (int k = 0; k < 3; ++k) {
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VERIFY_IS_EQUAL(slice(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k));
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}
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}
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|
}
|
|
}
|
|
|
|
if(true) {
|
|
Tensor5f slice(1,1,2,2,3);
|
|
Index5 strides3(1, 1, -2, 1, -1);
|
|
Index5 indices3Start(1, 1, 4, 4, 7);
|
|
Index5 indices3Stop(2, 2, 0, 6, 4);
|
|
slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3);
|
|
for (int i = 0; i < 2; ++i) {
|
|
for (int j = 0; j < 2; ++j) {
|
|
for (int k = 0; k < 3; ++k) {
|
|
VERIFY_IS_EQUAL(slice(0,0,i,j,k), tensor(1,1,4-2*i,4+j,7-k));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if(false) { // tests degenerate interval
|
|
Tensor5f slice(1,1,2,2,3);
|
|
Index5 strides3(1, 1, 2, 1, 1);
|
|
Index5 indices3Start(1, 1, 4, 4, 7);
|
|
Index5 indices3Stop(2, 2, 0, 6, 4);
|
|
slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3);
|
|
}
|
|
}
|
|
|
|
template<int DataLayout>
|
|
static void test_strided_slice_write()
|
|
{
|
|
typedef Tensor<float, 2, DataLayout> Tensor2f;
|
|
typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2;
|
|
|
|
Tensor<float, 2, DataLayout> tensor(7,11),tensor2(7,11);
|
|
tensor.setRandom();
|
|
tensor2=tensor;
|
|
Tensor2f slice(2,3);
|
|
|
|
slice.setRandom();
|
|
|
|
Index2 strides(1,1);
|
|
Index2 indicesStart(3,4);
|
|
Index2 indicesStop(5,7);
|
|
Index2 lengths(2,3);
|
|
|
|
tensor.slice(indicesStart,lengths)=slice;
|
|
tensor2.stridedSlice(indicesStart,indicesStop,strides)=slice;
|
|
|
|
for(int i=0;i<7;i++) for(int j=0;j<11;j++){
|
|
VERIFY_IS_EQUAL(tensor(i,j), tensor2(i,j));
|
|
}
|
|
}
|
|
|
|
|
|
template<int DataLayout>
|
|
static void test_composition()
|
|
{
|
|
Eigen::Tensor<float, 2, DataLayout> matrix(7, 11);
|
|
matrix.setRandom();
|
|
|
|
const DSizes<ptrdiff_t, 3> newDims(1, 1, 11);
|
|
Eigen::Tensor<float, 3, DataLayout> tensor =
|
|
matrix.slice(DSizes<ptrdiff_t, 2>(2, 0), DSizes<ptrdiff_t, 2>(1, 11)).reshape(newDims);
|
|
|
|
VERIFY_IS_EQUAL(tensor.dimensions().TotalSize(), 11);
|
|
VERIFY_IS_EQUAL(tensor.dimension(0), 1);
|
|
VERIFY_IS_EQUAL(tensor.dimension(1), 1);
|
|
VERIFY_IS_EQUAL(tensor.dimension(2), 11);
|
|
for (int i = 0; i < 11; ++i) {
|
|
VERIFY_IS_EQUAL(tensor(0,0,i), matrix(2,i));
|
|
}
|
|
}
|
|
|
|
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_morphing)
|
|
{
|
|
CALL_SUBTEST_1(test_simple_reshape<void>());
|
|
CALL_SUBTEST_1(test_static_reshape<void>());
|
|
CALL_SUBTEST_1(test_reshape_in_expr<void>());
|
|
CALL_SUBTEST_1(test_reshape_as_lvalue<void>());
|
|
|
|
CALL_SUBTEST_1(test_simple_slice<ColMajor>());
|
|
CALL_SUBTEST_1(test_simple_slice<RowMajor>());
|
|
CALL_SUBTEST_1(test_const_slice());
|
|
CALL_SUBTEST_2(test_slice_in_expr<ColMajor>());
|
|
CALL_SUBTEST_3(test_slice_in_expr<RowMajor>());
|
|
CALL_SUBTEST_4(test_slice_as_lvalue<ColMajor>());
|
|
CALL_SUBTEST_4(test_slice_as_lvalue<RowMajor>());
|
|
CALL_SUBTEST_5(test_slice_raw_data<ColMajor>());
|
|
CALL_SUBTEST_5(test_slice_raw_data<RowMajor>());
|
|
|
|
CALL_SUBTEST_6(test_strided_slice_write<ColMajor>());
|
|
CALL_SUBTEST_6(test_strided_slice<ColMajor>());
|
|
CALL_SUBTEST_6(test_strided_slice_write<RowMajor>());
|
|
CALL_SUBTEST_6(test_strided_slice<RowMajor>());
|
|
|
|
CALL_SUBTEST_7(test_composition<ColMajor>());
|
|
CALL_SUBTEST_7(test_composition<RowMajor>());
|
|
}
|