203 lines
11 KiB
C++
203 lines
11 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
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#define ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
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#include "arm_compute/runtime/IFunction.h"
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#include "arm_compute/runtime/MemoryGroup.h"
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#include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h"
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#include "arm_compute/runtime/NEON/functions/NEFlattenLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
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#include "arm_compute/runtime/Tensor.h"
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namespace arm_compute
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{
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class NEFlattenLayerKernel;
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/** Basic function to reshape the weights of Fully Connected layer with NEON. This function calls the following kernels:
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*
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* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
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*/
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class NEFullyConnectedLayerReshapeWeights : public INESimpleFunctionNoBorder
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{
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public:
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/** Constructor */
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NEFullyConnectedLayerReshapeWeights() = default;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEFullyConnectedLayerReshapeWeights(const NEFullyConnectedLayerReshapeWeights &) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEFullyConnectedLayerReshapeWeights &operator=(const NEFullyConnectedLayerReshapeWeights &) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEFullyConnectedLayerReshapeWeights(NEFullyConnectedLayerReshapeWeights &&) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEFullyConnectedLayerReshapeWeights &operator=(NEFullyConnectedLayerReshapeWeights &&) = delete;
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/** Default destructor */
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~NEFullyConnectedLayerReshapeWeights() = default;
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/** Set the input and output tensors.
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*
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* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[out] output Destination tensor. Data type supported: Same as @p input.
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*/
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void configure(const ITensor *input, ITensor *output);
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/** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayerReshapeWeights
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*
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* @param[in] input Weights tensor info. The weights must be 2 dimensional. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] output Destination tensor info. Data type supported: Same as @p input.
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*
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* @return a status
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*/
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static Status validate(const ITensorInfo *input, const ITensorInfo *output);
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};
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namespace weights_transformations
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{
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/** Basic function to manage the reshape weights generated from @ref NEFullyConnectedLayerReshapeWeights */
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class NEFullyConnectedLayerReshapeWeightsManaged : public ITransformWeights
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{
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public:
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void run() override
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{
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_output.allocator()->allocate();
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_func.run();
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_reshape_run = true;
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}
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void release() override
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{
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_output.allocator()->free();
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}
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ITensor *get_weights() override
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{
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return &_output;
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}
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uint32_t uid() override
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{
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return _uid;
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}
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void configure(const ITensor *input)
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{
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_func.configure(input, &_output);
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}
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private:
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static constexpr uint32_t _uid = 0x0;
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Tensor _output{};
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NEFullyConnectedLayerReshapeWeights _func{};
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};
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} // namespace weights_transformations
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/** Basic function to compute a Fully Connected layer on NEON. This function calls the following NEON kernels:
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* -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer)
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* -# @ref NEFullyConnectedLayerReshapeWeights (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
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* -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
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* -# @ref NEGEMMMatrixAdditionKernel or @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is not equal to nullptr)
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*
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* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
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*/
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class NEFullyConnectedLayer : public IFunction
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{
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public:
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/** Constructor */
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NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains pointers) */
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NEFullyConnectedLayer(NEFullyConnectedLayer &&) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEFullyConnectedLayer &operator=(const NEFullyConnectedLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains pointers) */
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NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = delete;
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/** Default destructor */
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~NEFullyConnectedLayer();
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/** Set the input and output tensors.
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*
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* @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor. The weights must be 2 dimensional.
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* 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.
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* If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
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* Data type supported: Same as @p input.
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* @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
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* @param[out] output Destination tensor. Its shape should be equal to the output of a matrix multiplication between:
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* - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
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* - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
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* Data type supported: Same as @p input.
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* @param[in] fc_info (Optional) Fully connected layer additional info
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*/
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void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
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FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
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/** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayer
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*
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* @param[in] input Source tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor info. The weights must be 2 dimensional.
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* 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.
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* If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
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* Data type supported: Same as @p input.
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* @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
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* @param[in] output Destination tensor info. Its shape should be equal to the output of a matrix multiplication between:
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* - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
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* - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
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* Data type supported: Same as @p input.
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* @param[in] fc_info (Optional) Fully connected layer additional info
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*
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* @return a status
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*/
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static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
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FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
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//Inherited methods override
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void run() override;
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void prepare() override;
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private:
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void configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
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void configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
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void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
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MemoryGroup _memory_group;
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IWeightsManager *_weights_manager;
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std::unique_ptr<NEFlattenLayerKernel> _flatten_kernel;
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NEConvertFullyConnectedWeights _convert_weights;
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weights_transformations::NEConvertFullyConnectedWeightsManaged _convert_weights_managed;
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NEFullyConnectedLayerReshapeWeights _reshape_weights_function;
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weights_transformations::NEFullyConnectedLayerReshapeWeightsManaged _reshape_weights_managed_function;
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NEGEMM _mm_gemm;
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NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
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Tensor _flatten_output;
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Tensor _converted_weights_output;
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Tensor _reshape_weights_output;
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const ITensor *_original_weights;
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bool _are_weights_converted;
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bool _are_weights_reshaped;
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bool _is_fc_after_conv;
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bool _is_quantized_asymmetric;
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bool _is_prepared;
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};
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H */
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