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/*
* Copyright (c) 2017-2021 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_NEFULLYCONNECTEDLAYER_H
#define ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/NEON/functions/NETranspose.h"
#include "arm_compute/runtime/Tensor.h"
#include <memory>
namespace arm_compute
{
namespace weights_transformations
{
/** Basic function to manage the reshape weights generated from @ref NETranspose */
class NEFullyConnectedLayerReshapeWeightsManaged : public ITransformWeights
{
public:
void run() override
{
_output.allocator()->allocate();
_func.run();
_reshape_run = true;
}
void release() override
{
_output.allocator()->free();
}
ITensor *get_weights() override
{
return &_output;
}
uint32_t uid() override
{
return _uid;
}
void configure(const ITensor *input)
{
_func.configure(input, &_output);
}
private:
static constexpr uint32_t _uid = 0x0;
Tensor _output{};
NETranspose _func{};
};
} // namespace weights_transformations
/** Basic function to compute a Fully Connected layer. This function calls the following kernels:
* -# @ref cpu::kernels::CpuIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref NETranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
* -# @ref NEGEMM or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
* -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel or @ref NEGEMMLowpOutputStage (if quantized asymmetric) (if @p biases is not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class NEFullyConnectedLayer : public IFunction
{
public:
/** Constructor */
NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains pointers) */
NEFullyConnectedLayer(NEFullyConnectedLayer &&) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer &operator=(const NEFullyConnectedLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains pointers) */
NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = delete;
/** Default destructor */
~NEFullyConnectedLayer();
/** Set the input and output tensors.
*
* Valid data layouts:
* - NHWC
* - NCHW
*
* Valid data type configurations:
* |src0 |src1 |src2 |dst |
* |:--------------|:------------------|:------|:--------------|
* |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |
* |QASYMM8 |QASYMM8 |S32 |QASYMM8 |
* |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED |
*
* @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/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 weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
* @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 NEFullyConnectedLayer
*
* Similar to @ref NEFullyConnectedLayer
*
* @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:
struct Impl;
std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H */