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/*
* Copyright (c) 2017 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_REFERENCE_REFERENCE_CPP_H__
#define __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_CPP_H__
#include "RawTensor.h"
#include "Reference.h"
#include <map>
#include <memory>
#include <ostream>
#include <vector>
namespace arm_compute
{
class Tensor;
namespace test
{
namespace validation
{
/** C++ reference implementation. */
class ReferenceCPP final : public Reference
{
public:
/** Function to compute reference Harris corners.
*
* @param[in] src Input tensor
* @param[in] Gx Tensor used to compute Sobel along the x axis
* @param[in] Gy Tensor used to compute Sobel along the y axis
* @param[in] candidates Tensor used to store candidate corners
* @param[in] non_maxima Tensor used to store non_maxima suppressed candidate corners
* @param[in] threshold Minimum threshold with which to eliminate Harris Corner scores (computed using the normalized Sobel kernel).
* @param[in] min_dist Radial Euclidean distance for the euclidean distance stage
* @param[in] sensitivity Sensitivity threshold k from the Harris-Stephens equation
* @param[in] gradient_size The gradient window size to use on the input. The implementation supports 3, 5, and 7
* @param[in] block_size The block window size used to compute the Harris Corner score. The implementation supports 3, 5, and 7.
* @param[out] corners Array of keypoints to store the results.
* @param[in] border_mode Border mode to use
* @param[in] constant_border_value Constant value to use for borders if border_mode is set to CONSTANT.
*
*/
static void harris_corners(RawTensor &src, RawTensor &Gx, RawTensor &Gy, const RawTensor &candidates, const RawTensor &non_maxima, float threshold, float min_dist, float sensitivity,
int32_t gradient_size, int32_t block_size, KeyPointArray &corners, BorderMode border_mode, uint8_t constant_border_value);
/** Function to compute the integral image of a tensor.
*
* @param[in] src Input tensor.
* @param[out] dst Result tensor.
*/
static void integral_image(const RawTensor &src, RawTensor &dst);
/** Function to compute the absolute difference between two tensors.
*
* @param[in] src1 First tensor.
* @param[in] src2 Second tensor.
* @param[out] dst Result tensor.
*/
static void absolute_difference(const RawTensor &src1, const RawTensor &src2, RawTensor &dst);
/** Function to accumulate an input tensor into an output tensor.
*
* @param[in] src Input tensor.
* @param[in, out] dst Result tensor.
*/
static void accumulate(const RawTensor &src, RawTensor &dst);
/** Function to accumulate a squared value from an input tensor to an output tensor.
*
* @param[in] src Input tensor.
* @param[in, out] dst Result tensor.
* @param[in] shift A uint32_t value within the range of [0, 15]
*/
static void accumulate_squared(const RawTensor &src, RawTensor &dst, uint32_t shift);
/** Function to accumulate a weighted value from an input tensor to an output tensor.
*
* @param[in] src Input tensor.
* @param[in, out] dst Result tensor.
* @param[in] alpha A float value within the range of [0, 1]
*/
static void accumulate_weighted(const RawTensor &src, RawTensor &dst, float alpha);
/** Compute non linear filter function.
*
* @param[in] src First input tensor
* @param[out] dst Output tensor
* @param[in] function Non linear function to perform
* @param[in] mask_size Mask size. Supported sizes: 3, 5
* @param[in] pattern Matrix pattern
* @param[in] mask The given mask.
* @param[in] border_mode Strategy to use for borders.
* @param[in] constant_border_value (Optional) Constant value to use for borders if border_mode is set to CONSTANT.
*/
static void non_linear_filter(const RawTensor &src, RawTensor &dst, NonLinearFilterFunction function, unsigned int mask_size,
MatrixPattern pattern, const uint8_t *mask, BorderMode border_mode, uint8_t constant_border_value = 0);
/** Element-wise multiplication of @p src1, @p src2 and @p scale
*
* @param[in] src1 First tensor.
* @param[in] src2 Second tensor.
* @param[out] dst Result tensor.
* @param[in] scale A non-negative float multiplied to each product.
* @param[in] convert_policy Overflow policy.
* @param[in] rounding_policy Rounding policy.
*/
static void pixel_wise_multiplication(const RawTensor &src1, const RawTensor &src2, RawTensor &dst, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy);
/** Fixed-point Pixel-wise multiplication of @p src1 by @p src2
*
* @param[in] src1 First tensor.
* @param[in] src2 Second tensor.
* @param[out] dst Result tensor.
* @param[in] scale A non-negative float multiplied to each product.
* @param[in] convert_policy Overflow policy.
* @param[in] rounding_policy Rounding policy.
*/
static void fixed_point_pixel_wise_multiplication(const RawTensor &src1, const RawTensor &src2, RawTensor &dst, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy);
/** Threshold of@p src to @p dst
*
* @param[in] src Input tensor.
* @param[out] dst Result tensor.
* @param[in] threshold Threshold. When the threhold type is RANGE, this is used as the lower threshold.
* @param[in] false_value value to set when the condition is not respected.
* @param[in] true_value value to set when the condition is respected.
* @param[in] type Thresholding type. Either RANGE or BINARY.
* @param[in] upper Upper threshold. Only used when the thresholding type is RANGE.
*/
static void threshold(const RawTensor &src, RawTensor &dst, uint8_t threshold, uint8_t false_value, uint8_t true_value, ThresholdType type, uint8_t upper);
/** ROI Pooling layer of @p src based on the information from @p pool_info and @p rois.
*
* @param[in] src Input tensor.
* @param[out] dst Result tensor.
* @param[in] rois Region of Interest points.
* @param[in] pool_info ROI Pooling Layer information.
*/
static void roi_pooling_layer(const RawTensor &src, RawTensor &dst, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info);
/** Fixed point operations of @p src
*
* @param[in] src Input tensor.
* @param[out] dst Result tensor.
* @param[in] op Fixed point operation to perform.
*/
static void fixed_point_operation(const RawTensor &src, RawTensor &dst, FixedPointOp op);
private:
ReferenceCPP() = delete;
~ReferenceCPP() = delete;
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_CPP_H__ */
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