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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (c) 2017-2018 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_NEFULLYCONNECTEDLAYEREX_H__
#define __ARM_COMPUTE_NEFULLYCONNECTEDLAYEREX_H__
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/NEON/kernels/NETransposeKernel.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
#include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/runtime/Tensor.h"
namespace arm_compute
{
/** Basic function to compute a Fully Connected layer on NEON. This function calls the following
* NEON kernels:
* -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref NEFullyConnectedLayerReshapeWeights (if @p are_weights_reshaped is set to false and
* transpose_weights is set to true ) (called once)
* -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized
* asymmetric)
* -# @ref NEGEMMMatrixAccumulateBiasesKernel or @ref
* NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is
* not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
* @note The difference from NEFullyConnectedLayer is that this class supports weights as input
* with performance loss.
*/
class NEFullyConnectedLayerEx : public IFunction
{
public:
/** Constructor */
NEFullyConnectedLayerEx(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayerEx(const NEFullyConnectedLayerEx &) = delete;
/** Default move constructor */
NEFullyConnectedLayerEx(NEFullyConnectedLayerEx &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayerEx &operator=(const NEFullyConnectedLayerEx &) = delete;
/** Default move assignment operator */
NEFullyConnectedLayerEx &operator=(NEFullyConnectedLayerEx &&) = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32.
* @param[in] weights Weights tensor. The weights must be 2 dimensional.
* If this function is called after a Convolution Layer, the (transposed)
* weights will have as many rows as the product of the first 3 input's dimensions.
* If it is called after another FullyConnected Layer, the (transposed)
* weights will have as many rows as the input's first dimension.
* Data type supported: Same as @p input.
* @param[in] biases Bias tensor. Can be nullptr. Data type supported:Same as @p input.
* @param[out] output Destination tensor. Its shape should be equal to the output of a matrix
* multiplication between:
* - The output of im2col on the input and the (transposed) 2D weights, if the
* function is called after a Convolution Layer
* - The input tensor and the (transposed) 2D weights, if the function is
* called after another FullyConnected Layer.
* Data type supported: Same as @p input.
* @param[in] fc_info (Optional) Fully connected layer additional info
*/
void configure(const ITensor *input, const ITensor *weights, const ITensor *biases,
ITensor *output, FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref
* NEFullyConnectedLayerEx
*
* @param[in] input Source tensor info. Data type supported: QASYMM8/F16/F32.
* @param[in] weights Weights tensor info. The weights must be 2 dimensional.
* If this function is called after a Convolution Layer, the (transposed)
* weights will have as many rows as the product of the first 3 input's dimensions.
* If it is called after another FullyConnected Layer, the (transposed)
* weights will have as many rows as the input's first dimension.
* Data type supported: Same as @p input.
* @param[in] biases Bias tensor info. Can be nullptr. Data type supported:Same as @p input.
* @param[out] output Destination tensor info. Its shape should be equal to the output of a
* matrix multiplication between:
* - The output of im2col on the input and the (transposed) 2D weights, if the
* function is called after a Convolution Layer
* - The input tensor and the (transposed) 2D weights, if the function is
* called after another FullyConnected Layer.
* Data type supported: Same as @p input.
* @param[in] fc_info (Optional) Fully connected layer additional info
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights,
const ITensorInfo *biases, const ITensorInfo *output,
FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
// Inherited methods override
void run() override;
void prepare() override;
private:
void configure_fc_fc(const ITensor *input, const ITensor *weights, ITensor *output);
void configure_conv_fc(const ITensor *input, const ITensor *weights, ITensor *output);
void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output);
MemoryGroup _memory_group;
NEFlattenLayerKernel _flatten_kernel;
NEConvertFullyConnectedWeights _convert_weights;
NEFullyConnectedLayerReshapeWeights _reshape_weights_function;
NEGEMM _mm_gemm;
NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
NEGEMMMatrixAccumulateBiasesKernel _accumulate_biases_kernel;
Tensor _flatten_output;
Tensor _gemmlowp_output;
Tensor _converted_weights_output;
Tensor _reshape_weights_output;
const ITensor *_original_weights;
bool _are_weights_converted;
bool _are_weights_reshaped;
bool _is_fc_after_conv;
bool _accumulate_biases;
bool _is_quantized;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NEFULLYCONNECTEDLAYEREX_H__ */
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