111 lines
4.0 KiB
C++
111 lines
4.0 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_SCALE_FIXTURE
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#define ARM_COMPUTE_TEST_SCALE_FIXTURE
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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#include "tests/Globals.h"
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#include "tests/Utils.h"
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#include "tests/framework/Fixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace benchmark
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{
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template <typename TensorType, typename Function, typename Accessor>
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class ScaleFixture : public framework::Fixture
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{
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public:
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template <typename...>
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void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy)
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{
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constexpr float max_width = 8192.0f;
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constexpr float max_height = 6384.0f;
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// Change shape in case of NHWC.
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if(data_layout == DataLayout::NHWC)
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{
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permute(shape, PermutationVector(2U, 0U, 1U));
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}
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std::mt19937 generator(library->seed());
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std::uniform_real_distribution<float> distribution_float(0.25f, 3.0f);
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float scale_x = distribution_float(generator);
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float scale_y = distribution_float(generator);
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scale_x = ((shape.x() * scale_x) > max_width) ? (max_width / shape.x()) : scale_x;
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scale_y = ((shape.y() * scale_y) > max_height) ? (max_height / shape.y()) : scale_y;
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std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
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uint8_t constant_border_value = static_cast<uint8_t>(distribution_u8(generator));
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const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
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const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
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TensorShape shape_scaled(shape);
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shape_scaled.set(idx_width, shape[idx_width] * scale_x);
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shape_scaled.set(idx_height, shape[idx_height] * scale_y);
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// Create tensors
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src = create_tensor<TensorType>(shape, data_type);
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dst = create_tensor<TensorType>(shape_scaled, data_type);
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// Create and configure function
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scale_func.configure(&src, &dst, ScaleKernelInfo{ policy, border_mode, constant_border_value, sampling_policy, false });
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// Allocate tensors
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src.allocator()->allocate();
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dst.allocator()->allocate();
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}
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void run()
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{
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scale_func.run();
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}
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void sync()
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{
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sync_if_necessary<TensorType>();
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sync_tensor_if_necessary<TensorType>(dst);
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}
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void teardown()
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{
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src.allocator()->free();
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dst.allocator()->free();
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}
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private:
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TensorType src{};
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TensorType dst{};
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Function scale_func{};
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};
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} // namespace benchmark
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_SCALE_FIXTURE */
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