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/*
* Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_convolve_HWC_q15_basic.c
* Description: Q15 version of convolution
*
* $Date: 17. January 2018
* $Revision: V.1.0.0
*
* Target Processor: Cortex-M cores
*
* -------------------------------------------------------------------- */
#include "arm_math.h"
#include "arm_nnfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
/**
* @brief Basic Q15 convolution 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] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns <code>ARM_MATH_SUCCESS</code>
*
* @details
*
* <b>Buffer size:</b>
*
* bufferA size: ch_im_in*dim_kernel*dim_kernel
*
* bufferB size: 0
*
* This basic version is designed to work for any input tensor and weight
* dimension.
*/
arm_status
arm_convolve_HWC_q15_basic(const q15_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q15_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q15_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q15_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB)
{
#if defined (ARM_MATH_DSP)
/* Run the following code for Cortex-M4 and Cortex-M7 */
int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
uint16_t im2col_out_pixel_index = 0;
q15_t *pBuffer = bufferA;
q15_t *pOut = Im_out;
q15_t *im_buffer = bufferA;
const q15_t *pA;
int i;
/* This part implements the im2col function */
for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
{
for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
{
for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
{
for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
{
if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
{
/* Filling 0 for out-of-bound paddings */
/* arm_fill_q15(0, pBuffer, ch_im_in); */
memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
} else
{
/* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
}
pBuffer += ch_im_in;
}
}
pA = wt;
for (i = 0; i < ch_im_out; i++)
{
q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
q15_t *pB = im_buffer;
uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
while (colCnt)
{
q31_t inA1 = *__SIMD32(pA)++;
q31_t inB1 = *__SIMD32(pB)++;
q31_t inA2 = *__SIMD32(pA)++;
q31_t inB2 = *__SIMD32(pB)++;
sum = __SMLAD(inA1, inB1, sum);
sum = __SMLAD(inA2, inB2, sum);
colCnt--;
}
colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
while (colCnt)
{
q15_t inA1 = *pA++;
q15_t inB1 = *pB++;
sum += inA1 * inB1;
colCnt--;
}
*pOut = (q15_t) __SSAT((sum >> out_shift), 16);
pOut++;
}
/* counter reset */
pBuffer = im_buffer;
im2col_out_pixel_index++;
}
}
#else
/* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
uint16_t i, j, k, l, m, n;
int conv_out;
signed char in_row, in_col;
for (i = 0; i < ch_im_out; i++)
{
for (j = 0; j < dim_im_out; j++)
{
for (k = 0; k < dim_im_out; k++)
{
conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
for (m = 0; m < dim_kernel; m++)
{
for (n = 0; n < dim_kernel; n++)
{
in_row = stride * j + m - padding;
in_col = stride * k + n - padding;
if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
{
for (l = 0; l < ch_im_in; l++)
{
conv_out +=
Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
n) * ch_im_in + l];
}
}
}
}
Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
}
}
}
#endif /* ARM_MATH_DSP */
/* Return to application */
return ARM_MATH_SUCCESS;
}
/**
* @} end of NNConv group
*/
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