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/*
* 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 */
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