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231ba5f730
Auxiliary functions for extents and strides were using different functions. Additionally, many tags were used to distinguish between different tensor types. This patch simplifies interfaces of different core functions and unifies functions that can process different types of extent and stride types.
243 lines
6.5 KiB
C++
243 lines
6.5 KiB
C++
//
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// Copyright (c) 2018, Cem Bassoy, cem.bassoy@gmail.com
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// Copyright (c) 2019, Amit Singh, amitsingh19975@gmail.com
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//
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// Distributed under the Boost Software License, Version 1.0. (See
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// accompanying file LICENSE_1_0.txt or copy at
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// http://www.boost.org/LICENSE_1_0.txt)
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//
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// The authors gratefully acknowledge the support of
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// Google and Fraunhofer IOSB, Ettlingen, Germany
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//
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#include <boost/numeric/ublas/tensor.hpp>
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#include <boost/test/unit_test.hpp>
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#include <boost/multiprecision/cpp_bin_float.hpp>
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#include "utility.hpp"
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BOOST_AUTO_TEST_SUITE(test_tensor_static_arithmetic_operations)
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using double_extended = boost::multiprecision::cpp_bin_float_double_extended;
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using test_types = zip<int,float,double_extended>::with_t<boost::numeric::ublas::layout::first_order, boost::numeric::ublas::layout::last_order>;
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struct fixture
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{
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template<size_t... N>
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using extents_type = boost::numeric::ublas::extents<N...>;
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fixture() = default;
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std::tuple<
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extents_type<1,1>, // 1
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extents_type<2,3>, // 2
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extents_type<4,1,3>, // 3
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extents_type<4,2,3>, // 4
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extents_type<4,2,3,5> // 5
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> extents;
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};
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_binary_arithmetic_operations, value, test_types, fixture)
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{
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namespace ublas = boost::numeric::ublas;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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auto check = [](auto const& /*unused*/, auto& e)
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{
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using extents_type = std::decay_t<decltype(e)>;
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using tensor_type = ublas::tensor_static<value_type,extents_type,layout_type>;
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auto t = tensor_type ();
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auto t2 = tensor_type ();
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auto r = tensor_type ();
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auto v = value_type {};
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std::iota(t.begin(), t.end(), v);
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std::iota(t2.begin(), t2.end(), v+2);
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r = t + t + t + t2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 3*t(i) + t2(i) );
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r = t2 / (t+3) * (t+1) - t2; // r = ( t2/ ((t+3)*(t+1)) ) - t2
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), t2(i) / (t(i)+3)*(t(i)+1) - t2(i) );
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r = 3+t2 / (t+3) * (t+1) * t - t2; // r = 3+( t2/ ((t+3)*(t+1)*t) ) - t2
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 3+t2(i) / (t(i)+3)*(t(i)+1)*t(i) - t2(i) );
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r = t2 - t + t2 - t;
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for(auto i = 0ul; i < r.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 4 );
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r = tensor_type (1) + tensor_type (1);
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for(auto i = 0ul; i < r.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 2 );
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r = t * t * t * t2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), t(i)*t(i)*t(i)*t2(i) );
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r = (t2/t2) * (t2/t2);
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 1 );
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};
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for_each_in_tuple(extents,check);
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_unary_arithmetic_operations, value, test_types, fixture)
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{
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namespace ublas = boost::numeric::ublas;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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auto check = [](auto const& /*unused*/, auto& e)
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{
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using extents_type = std::decay_t<decltype(e)>;
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using tensor_type = ublas::tensor_static<value_type,extents_type,layout_type>;
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auto t = tensor_type ();
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auto t2 = tensor_type ();
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auto v = value_type {};
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std::iota(t.begin(), t.end(), v);
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std::iota(t2.begin(), t2.end(), v+2);
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tensor_type r1 = t + 2 + t + 2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r1(i), 2*t(i) + 4 );
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tensor_type r2 = 2 + t + 2 + t;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r2(i), 2*t(i) + 4 );
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tensor_type r3 = (t-2) + (t-2);
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r3(i), 2*t(i) - 4 );
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tensor_type r4 = (t*2) * (3*t);
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r4(i), 2*3*t(i)*t(i) );
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tensor_type r5 = (t2*2) / (2*t2) * t2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r5(i), (t2(i)*2) / (2*t2(i)) * t2(i) );
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tensor_type r6 = (t2/2+1) / (2/t2+1) / t2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r6(i), (t2(i)/2+1) / (2/t2(i)+1) / t2(i) );
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};
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for_each_in_tuple(extents,check);
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}
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BOOST_FIXTURE_TEST_CASE_TEMPLATE( test_tensor_assign_arithmetic_operations, value, test_types, fixture)
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{
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namespace ublas = boost::numeric::ublas;
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using value_type = typename value::first_type;
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using layout_type = typename value::second_type;
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auto check = [](auto const& /*unused*/, auto& e)
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{
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using extents_type = std::decay_t<decltype(e)>;
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using tensor_type = ublas::tensor_static<value_type,extents_type,layout_type>;
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auto t = tensor_type ();
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auto t2 = tensor_type ();
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auto r = tensor_type ();
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auto v = value_type {};
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std::iota(t.begin(), t.end(), v);
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std::iota(t2.begin(), t2.end(), v+2);
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r = t + 2;
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r += t;
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r += 2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 2*t(i) + 4 );
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r = 2 + t;
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r += t;
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r += 2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 2*t(i) + 4 );
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 2*t(i) + 4 );
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r = (t-2);
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r += t;
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r -= 2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 2*t(i) - 4 );
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r = (t*2);
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r *= 3;
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r *= t;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), 2*3*t(i)*t(i) );
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r = (t2*2);
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r /= 2;
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r /= t2;
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r *= t2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), (t2(i)*2) / (2*t2(i)) * t2(i) );
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r = (t2/2+1);
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r /= (2/t2+1);
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r /= t2;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( r(i), (t2(i)/2+1) / (2/t2(i)+1) / t2(i) );
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tensor_type q = -r;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( q(i), -r(i) );
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tensor_type p = +r;
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for(auto i = 0ul; i < t.size(); ++i)
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BOOST_CHECK_EQUAL ( p(i), r(i) );
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};
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for_each_in_tuple(extents,check);
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}
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BOOST_AUTO_TEST_SUITE_END()
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