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/*
* Copyright (c) 2018-2021, 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 ACL_SRC_CORE_NEON_KERNELS_NESTACKLAYERKERNEL_H
#define ACL_SRC_CORE_NEON_KERNELS_NESTACKLAYERKERNEL_H
#include "arm_compute/core/Types.h"
#include "src/core/NEON/INEKernel.h"
#include <cstdint>
#include <functional>
namespace arm_compute
{
class ITensor;
/** Basic kernel to stack a rank-R tensor into one with rank-(R+1) along the axis dimension. */
class NEStackLayerKernel : public INEKernel
{
public:
const char *name() const override
{
return "NEStackLayerKernel";
}
/** Default constructor */
NEStackLayerKernel();
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEStackLayerKernel(const NEStackLayerKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEStackLayerKernel &operator=(const NEStackLayerKernel &) = delete;
/** Allow instances of this class to be moved */
NEStackLayerKernel(NEStackLayerKernel &&) = default;
/** Allow instances of this class to be moved */
NEStackLayerKernel &operator=(NEStackLayerKernel &&) = default;
/** Default destructor */
~NEStackLayerKernel() = default;
/** Initialise the kernel's inputs and output
*
* @note Supported input tensor rank: up to 4
*
* @param[in] input Input tensors. Data types supported: All
* @param[in] axis The dimension to stack the tensors along. It must be smaller than the number of input dimensions.
* @param[out] output Output tensor. Data types supported: Same as @p input.
*
*/
void configure(const std::vector<ITensor *> &input, uint32_t axis, ITensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref NEStackLayerKernel
*
* @note Supported input tensor rank: up to 4
*
* @param[in] input Input tensor infos. Data types supported: All
* @param[in] axis The dimension to stack the tensors along. It must be smaller than the number of input dimensions.
* @param[in] output Output tensor info. Data types supported: Same as @p input.
*
* @return a status
*/
static Status validate(const std::vector<ITensorInfo *> &input, uint32_t axis, const ITensorInfo *output);
/** Prepare the reshape kernel for execution (Only executed once) for
* choosing the window and the algorithm.
*/
void prepare();
// Inherited methods overridden
void run(const Window &window, const ThreadInfo &info) override;
/** Get the dimension to split the kernel workload
*
* @return the split dimension
*/
uint32_t get_split_dimension() const
{
return _split_dimension;
}
private:
std::vector<ITensor *> _input;
ITensor *_output;
uint32_t _axis;
uint32_t _split_dimension;
std::function<void(const std::vector<ITensor *> &, ITensor *, uint32_t, const Window &)> _stack_fn{};
};
} // namespace arm_compute
#endif // ACL_SRC_CORE_NEON_KERNELS_NESTACKLAYERKERNEL_H
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