/* * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "Accumulate.h" #include "arm_compute/core/Types.h" #include "tests/validation/Helpers.h" namespace arm_compute { namespace test { namespace validation { namespace reference { template SimpleTensor accumulate(const SimpleTensor &src, DataType output_data_type) { SimpleTensor dst{ src.shape(), output_data_type }; library->fill_tensor_uniform(dst, 1, static_cast(0), static_cast(std::numeric_limits::max())); using intermediate_type = typename common_promoted_signed_type::intermediate_type; for(int i = 0; i < src.num_elements(); ++i) { intermediate_type val = static_cast(src[i]) + static_cast(dst[i]); dst[i] = saturate_cast(val); } return dst; } template SimpleTensor accumulate_weighted(const SimpleTensor &src, float alpha, DataType output_data_type) { ARM_COMPUTE_ERROR_ON_MSG(alpha < 0.f || alpha > 1.f, "Weight (alpha) specified in accumulate_weighted must be within the range [0, 1]"); SimpleTensor dst{ src.shape(), output_data_type }; library->fill_tensor_uniform(dst, 1, static_cast(0), static_cast(std::numeric_limits::max())); using intermediate_type = typename common_promoted_signed_type::intermediate_type; for(int i = 0; i < src.num_elements(); ++i) { double val = (1. - static_cast(alpha)) * static_cast(dst[i]) + static_cast(alpha) * static_cast(src[i]); dst[i] = static_cast(val); } return dst; } template SimpleTensor accumulate_squared(const SimpleTensor &src, uint32_t shift, DataType output_data_type) { ARM_COMPUTE_ERROR_ON_MSG(shift > 15, "Shift in accumulate_squared must be within the range [0, 15]"); SimpleTensor dst{ src.shape(), output_data_type }; library->fill_tensor_uniform(dst, 1, static_cast(0), static_cast(std::numeric_limits::max())); using intermediate_type = typename common_promoted_signed_type::intermediate_type; intermediate_type denom = 1 << shift; for(int i = 0; i < src.num_elements(); ++i) { intermediate_type val = static_cast(dst[i]) + (static_cast(src[i]) * static_cast(src[i]) / denom); dst[i] = saturate_cast(val); } return dst; } template SimpleTensor accumulate(const SimpleTensor &src, DataType output_data_type); template SimpleTensor accumulate_weighted(const SimpleTensor &src, float alpha, DataType output_data_type); template SimpleTensor accumulate_squared(const SimpleTensor &src, uint32_t shift, DataType output_data_type); } // namespace reference } // namespace validation } // namespace test } // namespace arm_compute