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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (c) 2016-2018 ARM Limited.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef __ARM_COMPUTE_NENORMALIZATIONLAYEREXKERNEL_H__
#define __ARM_COMPUTE_NENORMALIZATIONLAYEREXKERNEL_H__
#include "arm_compute/core/NEON/INEKernel.h"
namespace arm_compute
{
class ITensor;
/** Interface for the normalization layer kernel.
*/
class NENormalizationLayerExKernel : public INEKernel
{
public:
const char *name() const override { return "NENormalizationLayerKernel"; }
/** Default constructor */
NENormalizationLayerExKernel();
/** Prevent instances of this class from being copied (As this class contains pointers) */
NENormalizationLayerExKernel(const NENormalizationLayerExKernel &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NENormalizationLayerExKernel &operator=(const NENormalizationLayerExKernel &) = delete;
/** Default Move Constructor. */
NENormalizationLayerExKernel(NENormalizationLayerExKernel &&) = default;
/** Default move assignment operator */
NENormalizationLayerExKernel &operator=(NENormalizationLayerExKernel &&) = default;
/** Default destructor */
~NENormalizationLayerExKernel() = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. 3 lower dims represent a single input with dimensions
* [width, height, IFM],
* and an optional 4th dimension for batch of inputs. Data types
* supported: FP16/F32.
* @param[in] input_squared Source with each element has been squared. 3 lower dims represent a
* single input with dimensions [width, height, IFM],
* Data type supported: same as @p input
* @param[out] output Destination tensor. Output will have the same number of dimensions as
* input. Data type supported: same as @p input
* @param[in] norm_info Normalization layer information like the normalization type,
* normalization size and other parameters.
*/
void configure(const ITensor *input, const ITensor *input_squared, ITensor *output,
NormalizationLayerInfo norm_info);
/** Static function to check if given info will lead to a valid configuration of @ref
* NENormalizationLayerKernel
*
* @param[in] input Source tensor. 3 lower dims represent a single input with dimensions
* [width, height, IFM],
* and an optional 4th dimension for batch of inputs. Data types
* supported: FP16/F32.
* @param[in] input_squared Source with each element has been squared. 3 lower dims represent a
* single input with dimensions [width, height, IFM],
* Data type supported: same as @p input
* @param[in] output Destination tensor. Output will have the same number of dimensions as
* input. Data type supported: same as @p input
* @param[in] norm_info Normalization layer information like the normalization type,
* normalization size and other parameters.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *input_squared,
const ITensorInfo *output, NormalizationLayerInfo norm_info);
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
BorderSize border_size() const override;
private:
/** Function to perform normalization depending on the given template
* dimension. The second template parameter specifies whether the
* normalization has to be 1D or 2D.
*
* @note Only supported normalizations are:
* - 1D over X or Z
* - 2D over X and Y
*
* @param[in] window Region on which to execute the kernel.
*/
template <DataType dt, unsigned int dim, bool do_2D_norm>
void normalize_float(const Window &window);
/** Common signature for all the specialised normalization functions
*
* @param[in] window Region on which to execute the kernel.
*/
using NormalizationFunctionEx = void (NENormalizationLayerExKernel::*)(const Window &window);
private:
NormalizationFunctionEx _func;
const ITensor *_input;
const ITensor *_input_squared;
ITensor *_output;
NormalizationLayerInfo _norm_info;
BorderSize _border_size;
};
} // namespace arm_compute
#endif /*__ARM_COMPUTE_NENORMALIZATIONLAYEREXKERNEL_H__ */
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