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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_H__
#define __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_H__
#include "arm_compute/runtime/Array.h"
#include "tests/RawTensor.h"
#include "tests/Types.h"
#include <map>
#include <vector>
namespace arm_compute
{
namespace test
{
namespace validation
{
/** Interface for reference implementations. */
class Reference
{
public:
/** Compute reference Harris corners.
*
* @param[in] shape Shape of input tensor
* @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[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.
*
* @return Computed corners' keypoints.
*/
static KeyPointArray compute_reference_harris_corners(const TensorShape &shape, float threshold, float min_dist, float sensitivity,
int32_t gradient_size, int32_t block_size, BorderMode border_mode, uint8_t constant_border_value);
/** Compute reference absolute difference.
*
* @param[in] shape Shape of the input and output tensors.
* @param[in] dt_in0 Data type of first input tensor.
* @param[in] dt_in1 Data type of second input tensor.
* @param[in] dt_out Data type of the output tensor.
*
* @return Computed raw tensor.
*/
static RawTensor compute_reference_absolute_difference(const TensorShape &shape, DataType dt_in0, DataType dt_in1, DataType dt_out);
/** Compute reference accumulate.
*
* @param[in] shape Shape of the input and output tensors.
*
* @return Computed raw tensor.
*/
static RawTensor compute_reference_accumulate(const TensorShape &shape);
/** Compute reference accumulate.
*
* @param[in] shape Shape of the input and output tensors.
* @param[in] shift A uint32_t value within the range of [0, 15]
*
* @return Computed raw tensor.
*/
static RawTensor compute_reference_accumulate_squared(const TensorShape &shape, uint32_t shift);
/** Compute reference accumulate.
*
* @param[in] shape Shape of the input and output tensors.
* @param[in] alpha A float value within the range of [0, 1]
*
* @return Computed raw tensor.
*/
static RawTensor compute_reference_accumulate_weighted(const TensorShape &shape, float alpha);
/** Compute reference pixel-wise multiplication
*
* @param[in] shape Shape of the input and output tensors.
* @param[in] dt_in0 Data type of first input tensor.
* @param[in] dt_in1 Data type of second input tensor.
* @param[in] dt_out Data type of the output tensor.
* @param[in] scale Non-negative scale.
* @param[in] convert_policy Overflow policy of the operation.
* @param[in] rounding_policy Rounding policy of the operation.
*
* @return Computed raw tensor.
*/
static RawTensor compute_reference_pixel_wise_multiplication(const TensorShape &shape, DataType dt_in0, DataType dt_in1, DataType dt_out, float scale, ConvertPolicy convert_policy,
RoundingPolicy rounding_policy);
/** Compute reference pixel-wise multiplication.
*
* @param[in] shape Shape of the input and output tensors.
* @param[in] dt_in0 Data type of first input tensor.
* @param[in] dt_in1 Data type of second input tensor.
* @param[in] dt_out Data type of the output tensor.
* @param[in] scale Scale to apply after multiplication. Must be positive.
* @param[in] fixed_point_position Fixed point position that expresses the number of bits for the fractional part of the number.
* @param[in] convert_policy Overflow policy of the operation.
* @param[in] rounding_policy Rounding policy of the operation.
*
* @return Computed raw tensor.
*/
static RawTensor compute_reference_fixed_point_pixel_wise_multiplication(const TensorShape &shape, DataType dt_in0, DataType dt_in1, DataType dt_out, float scale, int fixed_point_position,
ConvertPolicy convert_policy, RoundingPolicy rounding_policy);
/** Compute reference roi pooling layer.
*
* @param[in] shape Shape of the input tensor.
* @param[in] dt Data type of input and output tensors.
* @param[in] rois Region of interest vector.
* @param[in] pool_info ROI Pooling Layer information.
*/
static RawTensor compute_reference_roi_pooling_layer(const TensorShape &shape, DataType dt, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info);
protected:
Reference() = default;
~Reference() = default;
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_H__ */
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