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14f537c296
template<typename OtherDerived> TensorStridingOp& operator = (const OtherDerived& other) provides a valid assignment operator for the striding operation, and therefore refuses to compile code like: result.stride(foo) = source.stride(bar); Added the explicit TensorStridingOp& operator = (const TensorStridingOp& other) as a workaround to get the code to compile, and did the same in all the operations that can be used as lvalues.
188 lines
5.0 KiB
C++
188 lines
5.0 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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using Eigen::array;
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template <int DataLayout>
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static void test_simple_shuffling()
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{
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Tensor<float, 4, DataLayout> tensor(2,3,5,7);
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tensor.setRandom();
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array<ptrdiff_t, 4> shuffles;
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shuffles[0] = 0;
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shuffles[1] = 1;
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shuffles[2] = 2;
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shuffles[3] = 3;
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Tensor<float, 4, DataLayout> no_shuffle;
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no_shuffle = tensor.shuffle(shuffles);
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VERIFY_IS_EQUAL(no_shuffle.dimension(0), 2);
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VERIFY_IS_EQUAL(no_shuffle.dimension(1), 3);
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VERIFY_IS_EQUAL(no_shuffle.dimension(2), 5);
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VERIFY_IS_EQUAL(no_shuffle.dimension(3), 7);
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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 < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), no_shuffle(i,j,k,l));
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}
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}
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}
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}
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shuffles[0] = 2;
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shuffles[1] = 3;
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shuffles[2] = 1;
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shuffles[3] = 0;
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Tensor<float, 4, DataLayout> shuffle;
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shuffle = tensor.shuffle(shuffles);
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VERIFY_IS_EQUAL(shuffle.dimension(0), 5);
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VERIFY_IS_EQUAL(shuffle.dimension(1), 7);
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VERIFY_IS_EQUAL(shuffle.dimension(2), 3);
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VERIFY_IS_EQUAL(shuffle.dimension(3), 2);
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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 < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i));
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}
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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_expr_shuffling()
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{
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Tensor<float, 4, DataLayout> tensor(2,3,5,7);
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tensor.setRandom();
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array<ptrdiff_t, 4> shuffles;
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shuffles[0] = 2;
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shuffles[1] = 3;
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shuffles[2] = 1;
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shuffles[3] = 0;
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Tensor<float, 4, DataLayout> expected;
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expected = tensor.shuffle(shuffles);
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Tensor<float, 4, DataLayout> result(5,7,3,2);
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array<int, 4> src_slice_dim{{2,3,1,7}};
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array<int, 4> src_slice_start{{0,0,0,0}};
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array<int, 4> dst_slice_dim{{1,7,3,2}};
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array<int, 4> dst_slice_start{{0,0,0,0}};
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for (int i = 0; i < 5; ++i) {
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result.slice(dst_slice_start, dst_slice_dim) =
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tensor.slice(src_slice_start, src_slice_dim).shuffle(shuffles);
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src_slice_start[2] += 1;
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dst_slice_start[0] += 1;
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}
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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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VERIFY_IS_EQUAL(result.dimension(2), 3);
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VERIFY_IS_EQUAL(result.dimension(3), 2);
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for (int i = 0; i < expected.dimension(0); ++i) {
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for (int j = 0; j < expected.dimension(1); ++j) {
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for (int k = 0; k < expected.dimension(2); ++k) {
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for (int l = 0; l < expected.dimension(3); ++l) {
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VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
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}
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}
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}
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}
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dst_slice_start[0] = 0;
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result.setRandom();
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for (int i = 0; i < 5; ++i) {
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result.slice(dst_slice_start, dst_slice_dim) =
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tensor.shuffle(shuffles).slice(dst_slice_start, dst_slice_dim);
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dst_slice_start[0] += 1;
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}
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for (int i = 0; i < expected.dimension(0); ++i) {
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for (int j = 0; j < expected.dimension(1); ++j) {
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for (int k = 0; k < expected.dimension(2); ++k) {
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for (int l = 0; l < expected.dimension(3); ++l) {
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VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,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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template <int DataLayout>
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static void test_shuffling_as_value()
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{
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Tensor<float, 4, DataLayout> tensor(2,3,5,7);
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tensor.setRandom();
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array<ptrdiff_t, 4> shuffles;
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shuffles[2] = 0;
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shuffles[3] = 1;
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shuffles[1] = 2;
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shuffles[0] = 3;
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Tensor<float, 4, DataLayout> shuffle(5,7,3,2);
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shuffle.shuffle(shuffles) = tensor;
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VERIFY_IS_EQUAL(shuffle.dimension(0), 5);
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VERIFY_IS_EQUAL(shuffle.dimension(1), 7);
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VERIFY_IS_EQUAL(shuffle.dimension(2), 3);
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VERIFY_IS_EQUAL(shuffle.dimension(3), 2);
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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 < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i));
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}
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}
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}
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}
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array<ptrdiff_t, 4> no_shuffle;
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no_shuffle[0] = 0;
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no_shuffle[1] = 1;
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no_shuffle[2] = 2;
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no_shuffle[3] = 3;
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Tensor<float, 4, DataLayout> shuffle2(5,7,3,2);
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shuffle2.shuffle(shuffles) = tensor.shuffle(no_shuffle);
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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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for (int k = 0; k < 3; ++k) {
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for (int l = 0; l < 2; ++l) {
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VERIFY_IS_EQUAL(shuffle2(i,j,k,l), shuffle(i,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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void test_cxx11_tensor_shuffling()
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{
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CALL_SUBTEST(test_simple_shuffling<ColMajor>());
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CALL_SUBTEST(test_simple_shuffling<RowMajor>());
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CALL_SUBTEST(test_expr_shuffling<ColMajor>());
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CALL_SUBTEST(test_expr_shuffling<RowMajor>());
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CALL_SUBTEST(test_shuffling_as_value<ColMajor>());
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CALL_SUBTEST(test_shuffling_as_value<RowMajor>());
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}
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