/*
* 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_1x1_HWC_q7_fast_nonsquare.c
* Description: Fast Q7 version of 1x1 convolution (non-square shape)
*
* $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 Fast Q7 version of 1x1 convolution (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.
*
* This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1
* and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise
* separable convolution.
*
* This function is the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 4
* ch_im_out is multiple of 2
*
* [1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
* https://arxiv.org/abs/1704.04861
*/
arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
const uint16_t dim_im_in_x,
const uint16_t dim_im_in_y,
const uint16_t ch_im_in,
const q7_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 q7_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q7_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)
/* Run the following code for Cortex-M4 and Cortex-M7 */
int16_t i_out_y, i_out_x;
int16_t i_ch_out;
/* -----------------------
* Here we use bufferA as q15_t internally as computation are done with q15_t level
* im2col are done to output in q15_t format from q7_t input
*/
q15_t *pBuffer = bufferA;
q7_t *pOut = Im_out;
if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
|| padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
{
/* check if the input dimension meets the constraints */
return ARM_MATH_SIZE_MISMATCH;
}
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++)
{
/* This part implements the im2col function */
arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_out_y * dim_im_in_x + i_out_x) * ch_im_in, pBuffer,
ch_im_in);
pBuffer += ch_im_in;
if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
{
pOut =
arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in, bias_shift, out_shift, bias, pOut);
/* counter reset */
pBuffer = bufferA;
}
}
}
/* check if there is left-over for compute */
if (pBuffer != bufferA)
{
const q7_t *pA = wt;
for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
{
q31_t sum = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
q15_t *pB = bufferA;
/* basically each time it process 4 entries */
uint16_t colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
while (colCnt)
{
q31_t inA1, inA2;
q31_t inB1, inB2;
pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
inB1 = *__SIMD32(pB)++;
sum = __SMLAD(inA1, inB1, sum);
inB2 = *__SIMD32(pB)++;
sum = __SMLAD(inA2, inB2, sum);
colCnt--;
}
colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
while (colCnt)
{
q7_t inA1 = *pA++;
q15_t inB1 = *pB++;
sum += inA1 * inB1;
colCnt--;
}
*pOut = (q7_t) __SSAT((sum >> out_shift), 8);
pOut++;
}
}
#else
/* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
int i, j, k, l, m, n;
int conv_out;
int in_row, in_col;
if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
|| padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
{
/* 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++)
{
// if-for implementation
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_y * dim_kernel_x + (m * dim_kernel_y + n) * ch_im_in + l];
}
}
}
}
Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
}
}
}
#endif /* ARM_MATH_DSP */
/* Return to application */
return ARM_MATH_SUCCESS;
}
/**
* @} end of NNConv group
*/