/* * 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_NEFULLYCONNECTEDLAYER_H__ #define __ARM_COMPUTE_NEFULLYCONNECTEDLAYER_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/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 reshape the weights of Fully Connected layer with NEON. This function calls the following kernels: * * -# @ref NETransposeKernel * * @note The fully connected layer accepts "weights" tensors only with 2 dimensions. */ class NEFullyConnectedLayerReshapeWeights : public INESimpleFunctionNoBorder { public: /** Set the input and output tensors. * * @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported: QASYMM8/F16/F32. * @param[out] output Destination tensor. Data type supported: Same as @p input. */ void configure(const ITensor *input, ITensor *output); /** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayerReshapeWeights * * @param[in] input Weights tensor info. The weights must be 2 dimensional. Data types supported: QASYMM8/F16/F32. * @param[in] output Destination tensor info. Data type supported: Same as @p input. * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *output); }; /** 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. */ class NEFullyConnectedLayer : public IFunction { public: /** Constructor */ NEFullyConnectedLayer(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copied (As this class contains pointers) */ NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete; /** Default move constructor */ NEFullyConnectedLayer(NEFullyConnectedLayer &&) = default; /** Prevent instances of this class from being copied (As this class contains pointers) */ NEFullyConnectedLayer &operator=(const NEFullyConnectedLayer &) = delete; /** Default move assignment operator */ NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = 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 NEFullyConnectedLayer * * @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_NEFULLYCONNECTEDLAYER_H__ */