unsupported/test/tensor_ref.cpp - mirror - Git at Google

 // 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/.
 // SPDX-License-Identifier: MPL-2.0

 #include "main.h"

 #include <Eigen/Tensor>

 using Eigen::RowMajor;
 using Eigen::Tensor;

 static void test_simple_lvalue_ref() {
   Tensor<int, 1> input(6);
   input.setRandom();

   TensorRef<Tensor<int, 1>> ref3(input);
   TensorRef<Tensor<int, 1>> ref4 = input;

   VERIFY_IS_EQUAL(ref3.data(), input.data());
   VERIFY_IS_EQUAL(ref4.data(), input.data());

   for (int i = 0; i < 6; ++i) {
     VERIFY_IS_EQUAL(ref3(i), input(i));
     VERIFY_IS_EQUAL(ref4(i), input(i));
   }

   for (int i = 0; i < 6; ++i) {
     ref3.coeffRef(i) = i;
   }
   for (int i = 0; i < 6; ++i) {
     VERIFY_IS_EQUAL(input(i), i);
   }
   for (int i = 0; i < 6; ++i) {
     ref4.coeffRef(i) = -i * 2;
   }
   for (int i = 0; i < 6; ++i) {
     VERIFY_IS_EQUAL(input(i), -i * 2);
   }
 }

 static void test_simple_rvalue_ref() {
   Tensor<int, 1> input1(6);
   setRandomDataInRange(input1, -1000, 1000);
   Tensor<int, 1> input2(6);
   setRandomDataInRange(input2, -1000, 1000);

   TensorRef<const Tensor<int, 1>> ref3(input1 + input2);
   TensorRef<const Tensor<int, 1>> ref4 = input1 + input2;

   VERIFY_IS_NOT_EQUAL(ref3.data(), input1.data());
   VERIFY_IS_NOT_EQUAL(ref4.data(), input1.data());
   VERIFY_IS_NOT_EQUAL(ref3.data(), input2.data());
   VERIFY_IS_NOT_EQUAL(ref4.data(), input2.data());

   for (int i = 0; i < 6; ++i) {
     VERIFY_IS_EQUAL(ref3(i), input1(i) + input2(i));
     VERIFY_IS_EQUAL(ref4(i), input1(i) + input2(i));
   }
 }

 static void test_multiple_dims() {
   Tensor<float, 3> input(3, 5, 7);
   input.setRandom();

   TensorRef<Tensor<float, 3>> ref(input);
   VERIFY_IS_EQUAL(ref.data(), input.data());
   VERIFY_IS_EQUAL(ref.dimension(0), 3);
   VERIFY_IS_EQUAL(ref.dimension(1), 5);
   VERIFY_IS_EQUAL(ref.dimension(2), 7);

   for (int i = 0; i < 3; ++i) {
     for (int j = 0; j < 5; ++j) {
       for (int k = 0; k < 7; ++k) {
         VERIFY_IS_EQUAL(ref(i, j, k), input(i, j, k));
       }
     }
   }
 }

 static void test_slice() {
   Tensor<float, 5> tensor(2, 3, 5, 7, 11);
   tensor.setRandom();

   Eigen::DSizes<ptrdiff_t, 5> indices(1, 2, 3, 4, 5);
   Eigen::DSizes<ptrdiff_t, 5> sizes(1, 1, 1, 1, 1);
   TensorRef<Tensor<float, 5>> slice = tensor.slice(indices, sizes);
   VERIFY_IS_EQUAL(slice(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));

   Eigen::DSizes<ptrdiff_t, 5> indices2(1, 1, 3, 4, 5);
   Eigen::DSizes<ptrdiff_t, 5> sizes2(1, 1, 2, 2, 3);
   slice = tensor.slice(indices2, sizes2);
   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, 3 + i, 4 + j, 5 + k));
       }
     }
   }

   Eigen::DSizes<ptrdiff_t, 5> indices3(0, 0, 0, 0, 0);
   Eigen::DSizes<ptrdiff_t, 5> sizes3(2, 3, 1, 1, 1);
   slice = tensor.slice(indices3, sizes3);
   VERIFY_IS_EQUAL(slice.data(), tensor.data());
 }

 static void test_ref_of_trace() {
   Tensor<int, 2> input(6, 6);
   setRandomDataInRange(input, -1000, 1000);
   int trace = 0;
   for (int i = 0; i < 6; ++i) {
     trace += input(i, i);
   }
   TensorRef<const Tensor<int, 0>> ref(input.trace());
   VERIFY_IS_EQUAL(ref.coeff(0), trace);
 }

 static void test_fixed_size_scalar_ref() {
   Tensor<Vector2d, 2> input(3, 3);
   TensorRef<Tensor<Vector2d, 2>> ref(input);

   VERIFY_IS_EQUAL(ref.data(), input.data());
   VERIFY_IS_EQUAL(ref.dimension(0), 3);
   VERIFY_IS_EQUAL(ref.dimension(1), 3);

   ref.coeffRef(0) = Vector2d(1.0, 2.0);
   VERIFY_IS_APPROX(input.coeff(0), Vector2d(1.0, 2.0));
 }

 static void test_ref_of_ref() {
   Tensor<float, 3> input(3, 5, 7);
   input.setRandom();

   TensorRef<Tensor<float, 3>> ref(input);
   TensorRef<Tensor<float, 3>> ref_of_ref(ref);
   TensorRef<Tensor<float, 3>> ref_of_ref2;
   ref_of_ref2 = ref;

   VERIFY_IS_EQUAL(ref_of_ref.data(), input.data());
   VERIFY_IS_EQUAL(ref_of_ref.dimension(0), 3);
   VERIFY_IS_EQUAL(ref_of_ref.dimension(1), 5);
   VERIFY_IS_EQUAL(ref_of_ref.dimension(2), 7);

   VERIFY_IS_EQUAL(ref_of_ref2.data(), input.data());
   VERIFY_IS_EQUAL(ref_of_ref2.dimension(0), 3);
   VERIFY_IS_EQUAL(ref_of_ref2.dimension(1), 5);
   VERIFY_IS_EQUAL(ref_of_ref2.dimension(2), 7);

   for (int i = 0; i < 3; ++i) {
     for (int j = 0; j < 5; ++j) {
       for (int k = 0; k < 7; ++k) {
         VERIFY_IS_EQUAL(ref_of_ref(i, j, k), input(i, j, k));
         VERIFY_IS_EQUAL(ref_of_ref2(i, j, k), input(i, j, k));
       }
     }
   }
 }

 static void test_ref_in_expr() {
   Tensor<float, 3> input(3, 5, 7);
   input.setRandom();
   TensorRef<Tensor<float, 3>> input_ref(input);

   Tensor<float, 3> result(3, 5, 7);
   result.setRandom();
   TensorRef<const Tensor<float, 3>> result_ref(result);

   Tensor<float, 3> bias(3, 5, 7);
   bias.setRandom();

   result_ref = input_ref + bias;
   for (int i = 0; i < 3; ++i) {
     for (int j = 0; j < 5; ++j) {
       for (int k = 0; k < 7; ++k) {
         VERIFY_IS_EQUAL(result_ref(i, j, k), input(i, j, k) + bias(i, j, k));
         VERIFY_IS_NOT_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
       }
     }
   }

   result = result_ref;
   for (int i = 0; i < 3; ++i) {
     for (int j = 0; j < 5; ++j) {
       for (int k = 0; k < 7; ++k) {
         VERIFY_IS_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
       }
     }
   }
 }

 static void test_coeff_ref() {
   Tensor<float, 5> tensor(2, 3, 5, 7, 11);
   tensor.setRandom();
   Tensor<float, 5> original = tensor;

   TensorRef<Tensor<float, 4>> slice = tensor.chip(7, 4);
   slice.coeffRef(0, 0, 0, 0) = 1.0f;
   slice.coeffRef(1, 0, 0, 0) += 2.0f;

   VERIFY_IS_EQUAL(tensor(0, 0, 0, 0, 7), 1.0f);
   VERIFY_IS_EQUAL(tensor(1, 0, 0, 0, 7), original(1, 0, 0, 0, 7) + 2.0f);
 }

 static void test_nested_ops_with_ref() {
   Tensor<float, 4> t(2, 3, 5, 7);
   t.setRandom();
   TensorMap<Tensor<const float, 4>> m(t.data(), 2, 3, 5, 7);
   array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings;
   paddings[0] = std::make_pair(0, 0);
   paddings[1] = std::make_pair(2, 1);
   paddings[2] = std::make_pair(3, 4);
   paddings[3] = std::make_pair(0, 0);
   DSizes<Eigen::DenseIndex, 4> shuffle_dims(0, 1, 2, 3);
   TensorRef<const Tensor<const float, 4>> ref(m.pad(paddings));
   array<std::pair<ptrdiff_t, ptrdiff_t>, 4> trivial;
   trivial[0] = std::make_pair(0, 0);
   trivial[1] = std::make_pair(0, 0);
   trivial[2] = std::make_pair(0, 0);
   trivial[3] = std::make_pair(0, 0);
   Tensor<float, 4> padded = ref.shuffle(shuffle_dims).pad(trivial);
   VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
   VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
   VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
   VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);

   for (int i = 0; i < 2; ++i) {
     for (int j = 0; j < 6; ++j) {
       for (int k = 0; k < 12; ++k) {
         for (int l = 0; l < 7; ++l) {
           if (j >= 2 && j < 5 && k >= 3 && k < 8) {
             VERIFY_IS_EQUAL(padded(i, j, k, l), t(i, j - 2, k - 3, l));
           } else {
             VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
           }
         }
       }
     }
   }
 }

 // Regression test for issue #1616: unsigned index types must not trigger
 // narrowing errors in the variadic accessors.
 static void test_unsigned_indices() {
   Tensor<float, 3> tensor(2, 3, 7);
   tensor.setRandom();

   TensorRef<Tensor<float, 3>> ref(tensor);
   VERIFY_IS_EQUAL(ref(1u, std::size_t(2), 6ul), tensor(1, 2, 6));
   ref.coeffRef(1u, 2ul, std::size_t(6)) = 5.0f;
   VERIFY_IS_EQUAL(tensor(1, 2, 6), 5.0f);
 }

 // TensorRef's accessors must take the plain object's index type. Unqualified Index in the class body does not find
 // the base's typedef, so coeffRef used to take Eigen::Index while operator() took the plain object's index type.
 static void test_index_type() {
   using RefType = Eigen::TensorRef<Tensor<float, 3, Eigen::ColMajor, int>>;
   VERIFY((std::is_same<RefType::Index, int>::value));

   Tensor<float, 3, Eigen::ColMajor, int> tensor(2, 3, 7);
   tensor.setZero();
   RefType ref(tensor);

   float& (RefType::*coeff_ref)(RefType::Index) = &RefType::coeffRef;
   (ref.*coeff_ref)(3) = 5.0f;
   VERIFY_IS_EQUAL(tensor.coeff(3), 5.0f);
 }

 // An index the ref's index type cannot represent must assert rather than silently truncate. size_t(-1) is
 // representable by no signed index type, so this holds whichever index type the accessor takes.
 static void test_narrowing_indices() {
   Tensor<float, 3> tensor(2, 3, 7);
   tensor.setRandom();

   TensorRef<Tensor<float, 3>> ref(tensor);
   VERIFY_RAISES_ASSERT(ref(0, 0, std::size_t(-1)));
   VERIFY_RAISES_ASSERT(ref.coeffRef(0, 0, std::size_t(-1)) = 1.0f);
 }

 EIGEN_DECLARE_TEST(tensor_ref) {
   CALL_SUBTEST(test_simple_lvalue_ref());
   CALL_SUBTEST(test_simple_rvalue_ref());
   CALL_SUBTEST(test_multiple_dims());
   CALL_SUBTEST(test_slice());
   CALL_SUBTEST(test_ref_of_trace());
   CALL_SUBTEST(test_fixed_size_scalar_ref());
   CALL_SUBTEST(test_ref_of_ref());
   CALL_SUBTEST(test_ref_in_expr());
   CALL_SUBTEST(test_coeff_ref());
   CALL_SUBTEST(test_nested_ops_with_ref());
   CALL_SUBTEST(test_unsigned_indices());
   CALL_SUBTEST(test_index_type());
   CALL_SUBTEST(test_narrowing_indices());
 }
	// 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/.
	// SPDX-License-Identifier: MPL-2.0

	#include "main.h"

	#include <Eigen/Tensor>

	using Eigen::RowMajor;
	using Eigen::Tensor;

	static void test_simple_lvalue_ref() {
	Tensor<int, 1> input(6);
	input.setRandom();

	TensorRef<Tensor<int, 1>> ref3(input);
	TensorRef<Tensor<int, 1>> ref4 = input;

	VERIFY_IS_EQUAL(ref3.data(), input.data());
	VERIFY_IS_EQUAL(ref4.data(), input.data());

	for (int i = 0; i < 6; ++i) {
	VERIFY_IS_EQUAL(ref3(i), input(i));
	VERIFY_IS_EQUAL(ref4(i), input(i));
	}

	for (int i = 0; i < 6; ++i) {
	ref3.coeffRef(i) = i;
	}
	for (int i = 0; i < 6; ++i) {
	VERIFY_IS_EQUAL(input(i), i);
	}
	for (int i = 0; i < 6; ++i) {
	ref4.coeffRef(i) = -i * 2;
	}
	for (int i = 0; i < 6; ++i) {
	VERIFY_IS_EQUAL(input(i), -i * 2);
	}
	}

	static void test_simple_rvalue_ref() {
	Tensor<int, 1> input1(6);
	setRandomDataInRange(input1, -1000, 1000);
	Tensor<int, 1> input2(6);
	setRandomDataInRange(input2, -1000, 1000);

	TensorRef<const Tensor<int, 1>> ref3(input1 + input2);
	TensorRef<const Tensor<int, 1>> ref4 = input1 + input2;

	VERIFY_IS_NOT_EQUAL(ref3.data(), input1.data());
	VERIFY_IS_NOT_EQUAL(ref4.data(), input1.data());
	VERIFY_IS_NOT_EQUAL(ref3.data(), input2.data());
	VERIFY_IS_NOT_EQUAL(ref4.data(), input2.data());

	for (int i = 0; i < 6; ++i) {
	VERIFY_IS_EQUAL(ref3(i), input1(i) + input2(i));
	VERIFY_IS_EQUAL(ref4(i), input1(i) + input2(i));
	}
	}

	static void test_multiple_dims() {
	Tensor<float, 3> input(3, 5, 7);
	input.setRandom();

	TensorRef<Tensor<float, 3>> ref(input);
	VERIFY_IS_EQUAL(ref.data(), input.data());
	VERIFY_IS_EQUAL(ref.dimension(0), 3);
	VERIFY_IS_EQUAL(ref.dimension(1), 5);
	VERIFY_IS_EQUAL(ref.dimension(2), 7);

	for (int i = 0; i < 3; ++i) {
	for (int j = 0; j < 5; ++j) {
	for (int k = 0; k < 7; ++k) {
	VERIFY_IS_EQUAL(ref(i, j, k), input(i, j, k));
	}
	}
	}
	}

	static void test_slice() {
	Tensor<float, 5> tensor(2, 3, 5, 7, 11);
	tensor.setRandom();

	Eigen::DSizes<ptrdiff_t, 5> indices(1, 2, 3, 4, 5);
	Eigen::DSizes<ptrdiff_t, 5> sizes(1, 1, 1, 1, 1);
	TensorRef<Tensor<float, 5>> slice = tensor.slice(indices, sizes);
	VERIFY_IS_EQUAL(slice(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));

	Eigen::DSizes<ptrdiff_t, 5> indices2(1, 1, 3, 4, 5);
	Eigen::DSizes<ptrdiff_t, 5> sizes2(1, 1, 2, 2, 3);
	slice = tensor.slice(indices2, sizes2);
	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, 3 + i, 4 + j, 5 + k));
	}
	}
	}

	Eigen::DSizes<ptrdiff_t, 5> indices3(0, 0, 0, 0, 0);
	Eigen::DSizes<ptrdiff_t, 5> sizes3(2, 3, 1, 1, 1);
	slice = tensor.slice(indices3, sizes3);
	VERIFY_IS_EQUAL(slice.data(), tensor.data());
	}

	static void test_ref_of_trace() {
	Tensor<int, 2> input(6, 6);
	setRandomDataInRange(input, -1000, 1000);
	int trace = 0;
	for (int i = 0; i < 6; ++i) {
	trace += input(i, i);
	}
	TensorRef<const Tensor<int, 0>> ref(input.trace());
	VERIFY_IS_EQUAL(ref.coeff(0), trace);
	}

	static void test_fixed_size_scalar_ref() {
	Tensor<Vector2d, 2> input(3, 3);
	TensorRef<Tensor<Vector2d, 2>> ref(input);

	VERIFY_IS_EQUAL(ref.data(), input.data());
	VERIFY_IS_EQUAL(ref.dimension(0), 3);
	VERIFY_IS_EQUAL(ref.dimension(1), 3);

	ref.coeffRef(0) = Vector2d(1.0, 2.0);
	VERIFY_IS_APPROX(input.coeff(0), Vector2d(1.0, 2.0));
	}

	static void test_ref_of_ref() {
	Tensor<float, 3> input(3, 5, 7);
	input.setRandom();

	TensorRef<Tensor<float, 3>> ref(input);
	TensorRef<Tensor<float, 3>> ref_of_ref(ref);
	TensorRef<Tensor<float, 3>> ref_of_ref2;
	ref_of_ref2 = ref;

	VERIFY_IS_EQUAL(ref_of_ref.data(), input.data());
	VERIFY_IS_EQUAL(ref_of_ref.dimension(0), 3);
	VERIFY_IS_EQUAL(ref_of_ref.dimension(1), 5);
	VERIFY_IS_EQUAL(ref_of_ref.dimension(2), 7);

	VERIFY_IS_EQUAL(ref_of_ref2.data(), input.data());
	VERIFY_IS_EQUAL(ref_of_ref2.dimension(0), 3);
	VERIFY_IS_EQUAL(ref_of_ref2.dimension(1), 5);
	VERIFY_IS_EQUAL(ref_of_ref2.dimension(2), 7);

	for (int i = 0; i < 3; ++i) {
	for (int j = 0; j < 5; ++j) {
	for (int k = 0; k < 7; ++k) {
	VERIFY_IS_EQUAL(ref_of_ref(i, j, k), input(i, j, k));
	VERIFY_IS_EQUAL(ref_of_ref2(i, j, k), input(i, j, k));
	}
	}
	}
	}

	static void test_ref_in_expr() {
	Tensor<float, 3> input(3, 5, 7);
	input.setRandom();
	TensorRef<Tensor<float, 3>> input_ref(input);

	Tensor<float, 3> result(3, 5, 7);
	result.setRandom();
	TensorRef<const Tensor<float, 3>> result_ref(result);

	Tensor<float, 3> bias(3, 5, 7);
	bias.setRandom();

	result_ref = input_ref + bias;
	for (int i = 0; i < 3; ++i) {
	for (int j = 0; j < 5; ++j) {
	for (int k = 0; k < 7; ++k) {
	VERIFY_IS_EQUAL(result_ref(i, j, k), input(i, j, k) + bias(i, j, k));
	VERIFY_IS_NOT_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
	}
	}
	}

	result = result_ref;
	for (int i = 0; i < 3; ++i) {
	for (int j = 0; j < 5; ++j) {
	for (int k = 0; k < 7; ++k) {
	VERIFY_IS_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
	}
	}
	}
	}

	static void test_coeff_ref() {
	Tensor<float, 5> tensor(2, 3, 5, 7, 11);
	tensor.setRandom();
	Tensor<float, 5> original = tensor;

	TensorRef<Tensor<float, 4>> slice = tensor.chip(7, 4);
	slice.coeffRef(0, 0, 0, 0) = 1.0f;
	slice.coeffRef(1, 0, 0, 0) += 2.0f;

	VERIFY_IS_EQUAL(tensor(0, 0, 0, 0, 7), 1.0f);
	VERIFY_IS_EQUAL(tensor(1, 0, 0, 0, 7), original(1, 0, 0, 0, 7) + 2.0f);
	}

	static void test_nested_ops_with_ref() {
	Tensor<float, 4> t(2, 3, 5, 7);
	t.setRandom();
	TensorMap<Tensor<const float, 4>> m(t.data(), 2, 3, 5, 7);
	array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings;
	paddings[0] = std::make_pair(0, 0);
	paddings[1] = std::make_pair(2, 1);
	paddings[2] = std::make_pair(3, 4);
	paddings[3] = std::make_pair(0, 0);
	DSizes<Eigen::DenseIndex, 4> shuffle_dims(0, 1, 2, 3);
	TensorRef<const Tensor<const float, 4>> ref(m.pad(paddings));
	array<std::pair<ptrdiff_t, ptrdiff_t>, 4> trivial;
	trivial[0] = std::make_pair(0, 0);
	trivial[1] = std::make_pair(0, 0);
	trivial[2] = std::make_pair(0, 0);
	trivial[3] = std::make_pair(0, 0);
	Tensor<float, 4> padded = ref.shuffle(shuffle_dims).pad(trivial);
	VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
	VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
	VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
	VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);

	for (int i = 0; i < 2; ++i) {
	for (int j = 0; j < 6; ++j) {
	for (int k = 0; k < 12; ++k) {
	for (int l = 0; l < 7; ++l) {
	if (j >= 2 && j < 5 && k >= 3 && k < 8) {
	VERIFY_IS_EQUAL(padded(i, j, k, l), t(i, j - 2, k - 3, l));
	} else {
	VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
	}
	}
	}
	}
	}
	}

	// Regression test for issue #1616: unsigned index types must not trigger
	// narrowing errors in the variadic accessors.
	static void test_unsigned_indices() {
	Tensor<float, 3> tensor(2, 3, 7);
	tensor.setRandom();

	TensorRef<Tensor<float, 3>> ref(tensor);
	VERIFY_IS_EQUAL(ref(1u, std::size_t(2), 6ul), tensor(1, 2, 6));
	ref.coeffRef(1u, 2ul, std::size_t(6)) = 5.0f;
	VERIFY_IS_EQUAL(tensor(1, 2, 6), 5.0f);
	}

	// TensorRef's accessors must take the plain object's index type. Unqualified Index in the class body does not find
	// the base's typedef, so coeffRef used to take Eigen::Index while operator() took the plain object's index type.
	static void test_index_type() {
	using RefType = Eigen::TensorRef<Tensor<float, 3, Eigen::ColMajor, int>>;
	VERIFY((std::is_same<RefType::Index, int>::value));

	Tensor<float, 3, Eigen::ColMajor, int> tensor(2, 3, 7);
	tensor.setZero();
	RefType ref(tensor);

	float& (RefType::*coeff_ref)(RefType::Index) = &RefType::coeffRef;
	(ref.*coeff_ref)(3) = 5.0f;
	VERIFY_IS_EQUAL(tensor.coeff(3), 5.0f);
	}

	// An index the ref's index type cannot represent must assert rather than silently truncate. size_t(-1) is
	// representable by no signed index type, so this holds whichever index type the accessor takes.
	static void test_narrowing_indices() {
	Tensor<float, 3> tensor(2, 3, 7);
	tensor.setRandom();

	TensorRef<Tensor<float, 3>> ref(tensor);
	VERIFY_RAISES_ASSERT(ref(0, 0, std::size_t(-1)));
	VERIFY_RAISES_ASSERT(ref.coeffRef(0, 0, std::size_t(-1)) = 1.0f);
	}

	EIGEN_DECLARE_TEST(tensor_ref) {
	CALL_SUBTEST(test_simple_lvalue_ref());
	CALL_SUBTEST(test_simple_rvalue_ref());
	CALL_SUBTEST(test_multiple_dims());
	CALL_SUBTEST(test_slice());
	CALL_SUBTEST(test_ref_of_trace());
	CALL_SUBTEST(test_fixed_size_scalar_ref());
	CALL_SUBTEST(test_ref_of_ref());
	CALL_SUBTEST(test_ref_in_expr());
	CALL_SUBTEST(test_coeff_ref());
	CALL_SUBTEST(test_nested_ops_with_ref());
	CALL_SUBTEST(test_unsigned_indices());
	CALL_SUBTEST(test_index_type());
	CALL_SUBTEST(test_narrowing_indices());
	}