553 lines
25 KiB
C
553 lines
25 KiB
C
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/*
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* Copyright (C) 2016-2019 C-SKY Limited. All rights reserved.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/* ----------------------------------------------------------------------
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* Title: csky_vdsp2_nnfunctions.h
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* Description: Public header file for CSI NN Library
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*
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* -------------------------------------------------------------------- */
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#ifndef _CSKY_VDSP2_NNFUNCTIONS_H
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#define _CSKY_VDSP2_NNFUNCTIONS_H
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#ifdef __cplusplus
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extern "C"
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{
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#endif
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#ifdef CSKY_VDSP2_MATH_DSP
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#include "csky_vdsp2_math.h"
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#include "csky_vdsp2_nnsupportfunctions.h"
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#endif
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/**
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* @brief Struct for specifying activation function types
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*
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*/
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typedef enum
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{
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CSKY_SIGMOID = 0, /**< Sigmoid activation function */
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CSKY_TANH = 1, /**< Tanh activation function */
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} csky_vdsp2_nn_activation_type;
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/**
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* @brief Basic Q7 convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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*/
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void csky_vdsp2_convolve_HWC_q7_basic(const q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA);
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/**
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* @brief Basic Q15 convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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*/
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void csky_vdsp2_convolve_HWC_q15_basic(const q15_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q15_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q15_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q15_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA);
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/**
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* @brief Fast Q7 convolution function (non-sqaure shape)
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in_x input tensor dimention x
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* @param[in] dim_im_in_y input tensor dimention y
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel_x filter kernel size x
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* @param[in] dim_kernel_y filter kernel size y
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* @param[in] padding_x padding size x
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* @param[in] padding_y padding size y
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* @param[in] stride_x convolution stride x
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* @param[in] stride_y convolution stride y
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out_x output tensor dimension x
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* @param[in] dim_im_out_y output tensor dimension y
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 4
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* ch_im_out is multiple of 2
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*/
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void csky_vdsp2_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
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const uint16_t dim_im_in_x,
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const uint16_t dim_im_in_y,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel_x,
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const uint16_t dim_kernel_y,
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const uint16_t padding_x,
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const uint16_t padding_y,
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const uint16_t stride_x,
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const uint16_t stride_y,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out_x,
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const uint16_t dim_im_out_y,
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q15_t * bufferA);
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/**
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* @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in_x input tensor dimention x
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* @param[in] dim_im_in_y input tensor dimention y
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel_x filter kernel size x
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* @param[in] dim_kernel_y filter kernel size y
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* @param[in] padding_x padding size x
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* @param[in] padding_y padding size y
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* @param[in] stride_x convolution stride x
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* @param[in] stride_y convolution stride y
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out_x output tensor dimension x
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* @param[in] dim_im_out_y output tensor dimension y
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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* This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
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* and dim_kernel_y=1). It can be used for
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* second half of MobileNets after depthwise separable convolution.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 4
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* ch_im_out is multiple of 2
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*/
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void csky_vdsp2_convolve_1x1_HWC_q7_fast(const q7_t * Im_in,
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const uint16_t dim_im_in_x,
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const uint16_t dim_im_in_y,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out_x,
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const uint16_t dim_im_out_y,
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q15_t * bufferA);
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/**
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* @brief Q7 version of convolution for RGB image
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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* This kernel is written exclusively for convolution with ch_im_in
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* equals 3. This applies on the first layer of CNNs which has input
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* image with RGB format.
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*/
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void csky_vdsp2_convolve_HWC_q7_RGB(const q7_t * Im_in,
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const uint16_t dim_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA);
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/**
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* @brief Q7 depthwise separable convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 2
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* ch_im_out is multiple of 2
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*/
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void csky_vdsp2_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA);
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/**
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* @brief Q7 depthwise separable convolution function (non-square shape)
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in_x input tensor dimention x
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* @param[in] dim_im_in_y input tensor dimention y
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel_x filter kernel size x
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* @param[in] dim_kernel_y filter kernel size y
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* @param[in] padding_x padding sizes x
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* @param[in] padding_y padding sizes y
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* @param[in] stride_x convolution stride x
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* @param[in] stride_y convolution stride y
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out_x output tensor dimension x
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* @param[in] dim_im_out_y output tensor dimension y
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* @param[in,out] bufferA pointer to buffer space for input
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* @return none.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 2
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* ch_im_out is multiple of 2
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*/
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void csky_vdsp2_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
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const uint16_t dim_im_in_x,
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const uint16_t dim_im_in_y,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel_x,
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const uint16_t dim_kernel_y,
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const uint16_t padding_x,
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const uint16_t padding_y,
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const uint16_t stride_x,
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const uint16_t stride_y,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out_x,
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const uint16_t dim_im_out_y,
|
||
|
|
q15_t * bufferA);
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q7 basic fully-connected layer function
|
||
|
|
* @param[in] pV pointer to input vector
|
||
|
|
* @param[in] pM pointer to matrix weights
|
||
|
|
* @param[in] dim_vec length of the vector
|
||
|
|
* @param[in] num_of_rows number of rows in weight matrix
|
||
|
|
* @param[in] bias_shift amount of left-shift for bias
|
||
|
|
* @param[in] out_shift amount of right-shift for output
|
||
|
|
* @param[in] bias pointer to bias
|
||
|
|
* @param[in,out] pOut pointer to output vector
|
||
|
|
* @return none.
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_fully_connected_q7(const q7_t * pV,
|
||
|
|
const q7_t * pM,
|
||
|
|
const uint16_t dim_vec,
|
||
|
|
const uint16_t num_of_rows,
|
||
|
|
const uint16_t bias_shift,
|
||
|
|
const uint16_t out_shift,
|
||
|
|
const q7_t * bias,
|
||
|
|
q7_t * pOut);
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q15 basic fully-connected layer function
|
||
|
|
* @param[in] pV pointer to input vector
|
||
|
|
* @param[in] pM pointer to matrix weights
|
||
|
|
* @param[in] dim_vec length of the vector
|
||
|
|
* @param[in] num_of_rows number of rows in weight matrix
|
||
|
|
* @param[in] bias_shift amount of left-shift for bias
|
||
|
|
* @param[in] out_shift amount of right-shift for output
|
||
|
|
* @param[in] bias pointer to bias
|
||
|
|
* @param[in,out] pOut pointer to output vector
|
||
|
|
* @return none.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_fully_connected_q15(const q15_t * pV,
|
||
|
|
const q15_t * pM,
|
||
|
|
const uint16_t dim_vec,
|
||
|
|
const uint16_t num_of_rows,
|
||
|
|
const uint16_t bias_shift,
|
||
|
|
const uint16_t out_shift,
|
||
|
|
const q15_t * bias,
|
||
|
|
q15_t * pOut);
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Mixed Q15-Q7 fully-connected layer function
|
||
|
|
* @param[in] pV pointer to input vector
|
||
|
|
* @param[in] pM pointer to matrix weights
|
||
|
|
* @param[in] dim_vec length of the vector
|
||
|
|
* @param[in] num_of_rows number of rows in weight matrix
|
||
|
|
* @param[in] bias_shift amount of left-shift for bias
|
||
|
|
* @param[in] out_shift amount of right-shift for output
|
||
|
|
* @param[in] bias pointer to bias
|
||
|
|
* @param[in,out] pOut pointer to output vector
|
||
|
|
* @return none.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_fully_connected_mat_q7_vec_q15(const q15_t * pV,
|
||
|
|
const q7_t * pM,
|
||
|
|
const uint16_t dim_vec,
|
||
|
|
const uint16_t num_of_rows,
|
||
|
|
const uint16_t bias_shift,
|
||
|
|
const uint16_t out_shift,
|
||
|
|
const q7_t * bias,
|
||
|
|
q15_t * pOut);
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q7 RELU function
|
||
|
|
* @param[in,out] data pointer to input
|
||
|
|
* @param[in] size number of elements
|
||
|
|
* @return none.
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_relu_q7(q7_t * data, uint16_t size);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q15 RELU function
|
||
|
|
* @param[in,out] data pointer to input
|
||
|
|
* @param[in] size number of elements
|
||
|
|
* @return none.
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_relu_q15(q15_t * data, uint16_t size);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q7 neural network activation function using direct table look-up
|
||
|
|
* @param[in,out] data pointer to input
|
||
|
|
* @param[in] size number of elements
|
||
|
|
* @param[in] int_width bit-width of the integer part, assume to be smaller than 3
|
||
|
|
* @param[in] type type of activation functions
|
||
|
|
* @return none.
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_nn_activations_direct_q7(q7_t * data, uint16_t size,
|
||
|
|
uint16_t int_width,
|
||
|
|
csky_vdsp2_nn_activation_type type);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q15 neural network activation function using direct table look-up
|
||
|
|
* @param[in,out] data pointer to input
|
||
|
|
* @param[in] size number of elements
|
||
|
|
* @param[in] int_width bit-width of the integer part, assume to be smaller than 3
|
||
|
|
* @param[in] type type of activation functions
|
||
|
|
* @return none.
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_nn_activations_direct_q15(q15_t * data, uint16_t size,
|
||
|
|
uint16_t int_width,
|
||
|
|
csky_vdsp2_nn_activation_type type);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q7 max pooling function
|
||
|
|
* @param[in] Im_in pointer to input tensor
|
||
|
|
* @param[in] dim_im_in input tensor dimention
|
||
|
|
* @param[in] ch_im_in number of input tensor channels
|
||
|
|
* @param[in] dim_kernel filter kernel size
|
||
|
|
* @param[in] padding padding sizes
|
||
|
|
* @param[in] stride convolution stride
|
||
|
|
* @param[in] dim_im_out output tensor dimension
|
||
|
|
* @param[in,out] bufferA pointer to buffer space for input
|
||
|
|
* @param[in,out] Im_out pointer to output tensor
|
||
|
|
* @return none.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_maxpool_q7_HWC(q7_t * Im_in,
|
||
|
|
const uint16_t dim_im_in,
|
||
|
|
const uint16_t ch_im_in,
|
||
|
|
const uint16_t dim_kernel,
|
||
|
|
const uint16_t padding,
|
||
|
|
const uint16_t stride,
|
||
|
|
const uint16_t dim_im_out,
|
||
|
|
q7_t * bufferA,
|
||
|
|
q7_t * Im_out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q7 average pooling function
|
||
|
|
* @param[in] Im_in pointer to input tensor
|
||
|
|
* @param[in] dim_im_in input tensor dimention
|
||
|
|
* @param[in] ch_im_in number of input tensor channels
|
||
|
|
* @param[in] dim_kernel filter kernel size
|
||
|
|
* @param[in] padding padding sizes
|
||
|
|
* @param[in] stride convolution stride
|
||
|
|
* @param[in] dim_im_out output tensor dimension
|
||
|
|
* @param[in,out] bufferA pointer to buffer space for input
|
||
|
|
* @param[in,out] Im_out pointer to output tensor
|
||
|
|
* @return none.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_avepool_q7_HWC(q7_t * Im_in,
|
||
|
|
const uint16_t dim_im_in,
|
||
|
|
const uint16_t ch_im_in,
|
||
|
|
const uint16_t dim_kernel,
|
||
|
|
const uint16_t padding,
|
||
|
|
const uint16_t stride,
|
||
|
|
const uint16_t dim_im_out,
|
||
|
|
q7_t * bufferA,
|
||
|
|
q7_t * Im_out);
|
||
|
|
|
||
|
|
|
||
|
|
void csky_vdsp2_avepool_q7_HWC_nonsquare(
|
||
|
|
q7_t *Im_in, // input image
|
||
|
|
const uint16_t dim_im_in_x, // input image dimension
|
||
|
|
const uint16_t dim_im_in_y, // input image dimension
|
||
|
|
const uint16_t ch_im_in, // number of input image channels
|
||
|
|
const uint16_t dim_kernel_x, // window kernel size
|
||
|
|
const uint16_t dim_kernel_y, // window kernel size
|
||
|
|
const uint16_t padding_x, // padding sizes
|
||
|
|
const uint16_t padding_y, // padding sizes
|
||
|
|
const uint16_t stride_x, // stride
|
||
|
|
const uint16_t stride_y, // stride
|
||
|
|
const uint16_t dim_im_out_x, // output image dimension
|
||
|
|
const uint16_t dim_im_out_y, // output image dimension
|
||
|
|
q7_t *bufferA, // a buffer for local storage
|
||
|
|
q7_t *Im_out, // output feature
|
||
|
|
const uint16_t out_lshift); // output left shift (scaling)
|
||
|
|
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q7 softmax function
|
||
|
|
* @param[in] vec_in pointer to input vector
|
||
|
|
* @param[in] dim_vec input vector dimention
|
||
|
|
* @param[out] p_out pointer to output vector
|
||
|
|
* @return none.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_softmax_q7(const q7_t *vec_in, const uint16_t dim_vec, q7_t *p_out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Q15 softmax function
|
||
|
|
* @param[in] vec_in pointer to input vector
|
||
|
|
* @param[in] dim_vec input vector dimention
|
||
|
|
* @param[out] p_out pointer to output vector
|
||
|
|
* @return none.
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
|
||
|
|
void csky_vdsp2_softmax_q15(const q15_t *vec_in, const uint16_t dim_vec,
|
||
|
|
q15_t *p_out);
|
||
|
|
|
||
|
|
#ifdef __cplusplus
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
|
||
|
|
#endif
|