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Diffstat (limited to 'tests/validation/fixtures/ROIPoolingLayerFixture.h')
| -rw-r--r-- | tests/validation/fixtures/ROIPoolingLayerFixture.h | 199 |
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diff --git a/tests/validation/fixtures/ROIPoolingLayerFixture.h b/tests/validation/fixtures/ROIPoolingLayerFixture.h new file mode 100644 index 0000000000..4b46a6176d --- /dev/null +++ b/tests/validation/fixtures/ROIPoolingLayerFixture.h @@ -0,0 +1,199 @@ +/* + * Copyright (c) 2021, 2023 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: + 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: + 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: + 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 */
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