| /* |
| * Copyright (c) 2017-2019 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. |
| */ |
| #ifndef ARM_COMPUTE_TEST_ROIPOOLINGLAYERFIXTURE |
| #define ARM_COMPUTE_TEST_ROIPOOLINGLAYERFIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "tests/Globals.h" |
| #include "tests/Utils.h" |
| #include "tests/framework/Fixture.h" |
| |
| #include <vector> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace benchmark |
| { |
| /** Fixture that can be used for NEON and CL */ |
| template <typename TensorType, typename Function, typename AccessorType, typename T> |
| class ROIPoolingLayerFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, int batches) |
| { |
| // Set batched in source and destination shapes |
| |
| TensorShape shape_dst; |
| rois_tensor = create_tensor<TensorType>(rois_shape, DataType::U16); |
| |
| input_shape.set(input_shape.num_dimensions(), batches); |
| shape_dst.set(0, pool_info.pooled_width()); |
| shape_dst.set(1, pool_info.pooled_height()); |
| shape_dst.set(2, input_shape.z()); |
| shape_dst.set(3, rois_shape[1]); |
| |
| // Create tensors |
| src = create_tensor<TensorType>(input_shape, data_type, 1); |
| dst = create_tensor<TensorType>(shape_dst, data_type, 1); |
| |
| // Create and configure function |
| roi_pool.configure(&src, &rois_tensor, &dst, pool_info); |
| |
| // Allocate tensors |
| rois_tensor.allocator()->allocate(); |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| // Create random ROIs |
| generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape); |
| } |
| |
| void run() |
| { |
| roi_pool.run(); |
| } |
| |
| void sync() |
| { |
| sync_if_necessary<TensorType>(); |
| sync_tensor_if_necessary<TensorType>(dst); |
| } |
| |
| void teardown() |
| { |
| src.allocator()->free(); |
| dst.allocator()->free(); |
| } |
| |
| protected: |
| template <typename U> |
| void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape) |
| { |
| const size_t values_per_roi = rois_shape.x(); |
| const size_t num_rois = rois_shape.y(); |
| |
| std::mt19937 gen(library->seed()); |
| uint16_t *rois_ptr = static_cast<uint16_t *>(rois.data()); |
| |
| const float pool_width = pool_info.pooled_width(); |
| const float pool_height = pool_info.pooled_height(); |
| const float roi_scale = pool_info.spatial_scale(); |
| |
| // Calculate distribution bounds |
| const auto scaled_width = static_cast<uint16_t>((shape.x() / roi_scale) / pool_width); |
| const auto scaled_height = static_cast<uint16_t>((shape.y() / roi_scale) / pool_height); |
| const auto min_width = static_cast<uint16_t>(pool_width / roi_scale); |
| const auto min_height = static_cast<uint16_t>(pool_height / roi_scale); |
| |
| // Create distributions |
| std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1); |
| std::uniform_int_distribution<uint16_t> dist_x1(0, scaled_width); |
| std::uniform_int_distribution<uint16_t> dist_y1(0, scaled_height); |
| std::uniform_int_distribution<uint16_t> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width)); |
| std::uniform_int_distribution<uint16_t> dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height)); |
| |
| for(unsigned int pw = 0; pw < num_rois; ++pw) |
| { |
| const auto batch_idx = dist_batch(gen); |
| const auto x1 = dist_x1(gen); |
| const auto y1 = dist_y1(gen); |
| const auto x2 = x1 + dist_w(gen); |
| const auto y2 = y1 + dist_h(gen); |
| |
| rois_ptr[values_per_roi * pw] = batch_idx; |
| rois_ptr[values_per_roi * pw + 1] = x1; |
| rois_ptr[values_per_roi * pw + 2] = y1; |
| rois_ptr[values_per_roi * pw + 3] = x2; |
| rois_ptr[values_per_roi * pw + 4] = y2; |
| } |
| } |
| |
| private: |
| TensorType src{}; |
| TensorType dst{}; |
| TensorType rois_tensor{}; |
| Function roi_pool{}; |
| }; |
| } // namespace benchmark |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_ROIPOOLINGLAYERFIXTURE */ |