/*
|
* 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_fast.c
|
* Description: Fast Q15 version of convolution
|
*
|
* $Date: 24. May 2018
|
* $Revision: V.1.0.0
|
*
|
* Target Processor: Cortex-M cores
|
*
|
* -------------------------------------------------------------------- */
|
|
#include "arm_math.h"
|
#include "arm_nnfunctions.h"
|
|
/**
|
* @ingroup groupNN
|
*/
|
|
/**
|
* @addtogroup NNConv
|
* @{
|
*/
|
|
/**
|
* @brief Fast Q15 convolution function (non-sqaure shape)
|
* @param[in] Im_in pointer to input tensor
|
* @param[in] dim_im_in_x input tensor dimention x
|
* @param[in] dim_im_in_y input tensor dimention y
|
* @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_x filter kernel size x
|
* @param[in] dim_kernel_y filter kernel size y
|
* @param[in] padding_x padding size x
|
* @param[in] padding_y padding size y
|
* @param[in] stride_x convolution stride x
|
* @param[in] stride_y convolution stride y
|
* @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_x output tensor dimension x
|
* @param[in] dim_im_out_y output tensor dimension y
|
* @param[in,out] bufferA pointer to buffer space for input
|
* @param[in,out] bufferB pointer to buffer space for output
|
* @return The function returns either
|
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
*
|
* @details
|
*
|
* <b>Buffer size:</b>
|
*
|
* bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
|
*
|
* bufferB size: 0
|
*
|
* <b>Input dimension constraints:</b>
|
*
|
* ch_im_in is multiple of 2
|
*
|
* ch_im_out is multipe of 2
|
*
|
*/
|
|
arm_status
|
arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
|
const uint16_t dim_im_in_x,
|
const uint16_t dim_im_in_y,
|
const uint16_t ch_im_in,
|
const q15_t * wt,
|
const uint16_t ch_im_out,
|
const uint16_t dim_kernel_x,
|
const uint16_t dim_kernel_y,
|
const uint16_t padding_x,
|
const uint16_t padding_y,
|
const uint16_t stride_x,
|
const uint16_t stride_y,
|
const q15_t * bias,
|
const uint16_t bias_shift,
|
const uint16_t out_shift,
|
q15_t * Im_out,
|
const uint16_t dim_im_out_x,
|
const uint16_t dim_im_out_y,
|
q15_t * bufferA,
|
q7_t * bufferB)
|
{
|
|
#if defined (ARM_MATH_DSP)
|
int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
|
|
q15_t *pBuffer = bufferA;
|
q15_t *im_buffer = bufferA;
|
q15_t *pOut = Im_out;
|
|
if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
|
{
|
/* check if the input dimension meets the constraints */
|
return ARM_MATH_SIZE_MISMATCH;
|
}
|
|
/* Run the following code for Cortex-M4 and Cortex-M7 */
|
|
/* This part implements the im2col function */
|
for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
|
{
|
for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
|
{
|
for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
|
{
|
for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
|
{
|
if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
|
{
|
/* 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_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
|
memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
|
}
|
pBuffer += ch_im_in;
|
}
|
}
|
|
if (i_out_x & 0x1)
|
{
|
int i;
|
/* initialize the matrix pointers for A */
|
const q15_t *pA = wt;
|
|
/* set up the second output pointers */
|
q15_t *pOut2 = pOut + ch_im_out;
|
|
/* this loop over rows in A */
|
for (i = 0; i < ch_im_out; i += 2)
|
{
|
/* setup pointers for B */
|
q15_t *pB = im_buffer;
|
const q15_t *pB2 = pB + ch_im_in * dim_kernel_y * dim_kernel_x;
|
|
/* aling the second pointer for A */
|
const q15_t *pA2 = pA + ch_im_in * dim_kernel_y * dim_kernel_x;
|
|
/* init the sum with bias */
|
q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
|
q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
|
q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
|
q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
|
|
uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 1;
|
/* accumulate over the vector */
|
while (colCnt)
|
{
|
q31_t inA1 = *__SIMD32(pA)++;
|
q31_t inB1 = *__SIMD32(pB)++;
|
q31_t inA2 = *__SIMD32(pA2)++;
|
q31_t inB2 = *__SIMD32(pB2)++;
|
|
sum = __SMLAD(inA1, inB1, sum);
|
sum2 = __SMLAD(inA1, inB2, sum2);
|
sum3 = __SMLAD(inA2, inB1, sum3);
|
sum4 = __SMLAD(inA2, inB2, sum4);
|
|
colCnt--;
|
} /* while over colCnt */
|
colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x1;
|
while (colCnt)
|
{
|
q15_t inA1 = *pA++;
|
q15_t inB1 = *pB++;
|
q15_t inA2 = *pA2++;
|
q15_t inB2 = *pB2++;
|
|
sum += inA1 * inB1;
|
sum2 += inA1 * inB2;
|
sum3 += inA2 * inB1;
|
sum4 += inA2 * inB2;
|
colCnt--;
|
} /* while over colCnt */
|
*pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
|
*pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
|
*pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
|
*pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
|
|
/* skip the row computed with A2 */
|
pA += ch_im_in * dim_kernel_y * dim_kernel_x;
|
} /* for over ch_im_out */
|
|
pOut += ch_im_out;
|
/* counter reset */
|
pBuffer = im_buffer;
|
}
|
}
|
}
|
|
#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;
|
|
if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
|
{
|
/* check if the input dimension meets the constraints */
|
return ARM_MATH_SIZE_MISMATCH;
|
}
|
|
for (i = 0; i < ch_im_out; i++)
|
{
|
for (j = 0; j < dim_im_out_y; j++)
|
{
|
for (k = 0; k < dim_im_out_x; k++)
|
{
|
conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
|
for (m = 0; m < dim_kernel_y; m++)
|
{
|
for (n = 0; n < dim_kernel_x; n++)
|
{
|
in_row = stride_y * j + m - padding_y;
|
in_col = stride_x * k + n - padding_x;
|
if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
|
{
|
for (l = 0; l < ch_im_in; l++)
|
{
|
conv_out +=
|
Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in +
|
l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x +
|
n) * ch_im_in + l];
|
}
|
}
|
}
|
}
|
Im_out[i + (j * dim_im_out_x + 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
|
*/
|
