| /* |
| * Copyright (c) 2021 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_ROIPOOLINGLAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/ROIPoolingLayer.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ROIPoolingLayerGenericFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo) |
| { |
| _target = compute_target(input_shape, data_type, data_layout, pool_info, rois_shape, qinfo, output_qinfo); |
| _reference = compute_reference(input_shape, data_type, pool_info, rois_shape, qinfo, output_qinfo); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor) |
| { |
| library->fill_tensor_uniform(tensor, 0); |
| } |
| |
| template <typename U> |
| void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape, DataLayout data_layout = DataLayout::NCHW) |
| { |
| 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<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)] / roi_scale) / pool_width); |
| const auto scaled_height = static_cast<float>((shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)] / roi_scale) / pool_height); |
| const auto min_width = static_cast<float>(pool_width / roi_scale); |
| const auto min_height = static_cast<float>(pool_height / roi_scale); |
| |
| // Create distributions |
| std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1); |
| std::uniform_int_distribution<> dist_x1(0, scaled_width); |
| std::uniform_int_distribution<> dist_y1(0, scaled_height); |
| std::uniform_int_distribution<> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width)); |
| std::uniform_int_distribution<> 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] = static_cast<uint16_t>(x1); |
| rois_ptr[values_per_roi * pw + 2] = static_cast<uint16_t>(y1); |
| rois_ptr[values_per_roi * pw + 3] = static_cast<uint16_t>(x2); |
| rois_ptr[values_per_roi * pw + 4] = static_cast<uint16_t>(y2); |
| } |
| } |
| |
| TensorType compute_target(TensorShape input_shape, |
| DataType data_type, |
| DataLayout data_layout, |
| const ROIPoolingLayerInfo &pool_info, |
| const TensorShape rois_shape, |
| const QuantizationInfo &qinfo, |
| const QuantizationInfo &output_qinfo) |
| { |
| const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo(); |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, qinfo, data_layout); |
| TensorType rois_tensor = create_tensor<TensorType>(rois_shape, _rois_data_type, 1, rois_qinfo); |
| |
| // Initialise shape and declare output tensor dst |
| const TensorShape dst_shape; |
| TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout); |
| |
| // Create and configure function |
| FunctionType roi_pool_layer; |
| roi_pool_layer.configure(&src, &rois_tensor, &dst, pool_info); |
| |
| ARM_COMPUTE_ASSERT(src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(rois_tensor.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| rois_tensor.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!rois_tensor.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); |
| |
| // Fill tensors |
| fill(AccessorType(src)); |
| generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape, data_layout); |
| |
| // Compute function |
| roi_pool_layer.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &input_shape, |
| DataType data_type, |
| const ROIPoolingLayerInfo &pool_info, |
| const TensorShape rois_shape, |
| const QuantizationInfo &qinfo, |
| const QuantizationInfo &output_qinfo) |
| { |
| // Create reference tensor |
| SimpleTensor<T> src{ input_shape, data_type, 1, qinfo }; |
| const QuantizationInfo rois_qinfo = is_data_type_quantized(data_type) ? QuantizationInfo(0.125f, 0) : QuantizationInfo(); |
| SimpleTensor<uint16_t> rois_tensor{ rois_shape, _rois_data_type, 1, rois_qinfo }; |
| |
| // Fill reference tensor |
| fill(src); |
| generate_rois(rois_tensor, input_shape, pool_info, rois_shape); |
| |
| return reference::roi_pool_layer(src, rois_tensor, pool_info, output_qinfo); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| const DataType _rois_data_type{ DataType::U16 }; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ROIPoolingLayerQuantizedFixture : public ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, |
| DataLayout data_layout, QuantizationInfo qinfo, QuantizationInfo output_qinfo) |
| { |
| ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape, |
| data_type, data_layout, qinfo, output_qinfo); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class ROIPoolingLayerFixture : public ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type, DataLayout data_layout) |
| { |
| ROIPoolingLayerGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, pool_info, rois_shape, data_type, data_layout, |
| QuantizationInfo(), QuantizationInfo()); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| |
| #endif /* ARM_COMPUTE_TEST_ROIPOOLINGLAYER_FIXTURE */ |