/*
* 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_depthwise_separable_conv_HWC_q7_nonsquare.c
* Description: Q7 depthwise separable convolution function (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 Q7 depthwise separable convolution function (non-square 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 sizes x
* @param[in] padding_y padding sizes 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 the version with full list of optimization tricks, but with
* some contraints:
* ch_im_in is multiple of 2
* ch_im_out is multiple of 2
*/
arm_status arm_depthwise_separable_conv_HWC_q7_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 */
/*
* Implementation:
* There are 3 nested loop here:
* Inner loop: calculate each output value with MAC instruction over an accumulator
* Mid loop: loop over different output channel
* Outer loop: loop over different output (x, y)
*
*/
int16_t i_out_y, i_out_x;
int16_t i_ker_y, i_ker_x;
q7_t *colBuffer = (q7_t *) bufferA;
q7_t *pBuffer = colBuffer;
const q7_t *pBias = bias;
q7_t *pOut = Im_out;
uint16_t rowCnt;
uint16_t row_shift;
/* do some checking here, basically ch_im_in == ch_im_out */
if (ch_im_in != ch_im_out)
{
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++)
{
/* we first do im2col here */
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_q7(0, pBuffer, ch_im_in); */
} else
{
/* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
memcpy(pBuffer
, (q7_t
*) Im_in
+ (i_ker_y
* dim_im_in_x
+ i_ker_x
) * ch_im_in
, ch_im_in
); }
pBuffer += ch_im_in;
}
}
/* we will do the computation here for each channel */
rowCnt = ch_im_out >> 2;
row_shift = 0;
pBias = bias;
while (rowCnt)
{
q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
uint16_t colCnt = (dim_kernel_x * dim_kernel_y) >> 1;
q7_t *pB = colBuffer + row_shift;
const q7_t *pA = wt + row_shift;
row_shift += 4;
#ifdef USE_INTRINSIC
#ifndef ARM_MATH_BIG_ENDIAN
while (colCnt)
{
q31_t inA1, inA2, inB1, inB2, opA, opB;
inB1 = *__SIMD32(pB);
pB += ch_im_in;
opB = *__SIMD32(pB);
pB += ch_im_in;
inB2 = __PKHTB(opB, inB1, 16);
inB1 = __PKHBT(inB1, opB, 16);
inA1 = *__SIMD32(pA);
pA += ch_im_in;
opB = *__SIMD32(pA);
pA += ch_im_in;
inA2 = __PKHTB(opB, inA1, 16);
inA1 = __PKHBT(inA1, opB, 16);
opA = __SXTB16(inA1);
opB = __SXTB16(inB1);
sum = __SMLAD(opA, opB, sum);
opA = __SXTB16(__ROR(inA1, 8));
opB = __SXTB16(__ROR(inB1, 8));
sum2 = __SMLAD(opA, opB, sum2);
opA = __SXTB16(inA2);
opB = __SXTB16(inB2);
sum3 = __SMLAD(opA, opB, sum3);
opA = __SXTB16(__ROR(inA2, 8));
opB = __SXTB16(__ROR(inB2, 8));
sum4 = __SMLAD(opA, opB, sum4);
colCnt--;
}
#else
while (colCnt)
{
q31_t inA1, inA2, inB1, inB2, opA, opB;
inB1 = *__SIMD32(pB);
pB += ch_im_in;
opB = *__SIMD32(pB);
pB += ch_im_in;
inB2 = __PKHBT(opB, inB1, 16);
inB1 = __PKHTB(inB1, opB, 16);
inA1 = *__SIMD32(pA);
pA += ch_im_in;
opB = *__SIMD32(pA);
pA += ch_im_in;
inA2 = __PKHBT(opB, inA1, 16);
inA1 = __PKHTB(inA1, opB, 16);
opA = __SXTB16(inA1);
opB = __SXTB16(inB1);
sum2 = __SMLAD(opA, opB, sum2);
opA = __SXTB16(__ROR(inA1, 8));
opB = __SXTB16(__ROR(inB1, 8));
sum = __SMLAD(opA, opB, sum);
opA = __SXTB16(inA2);
opB = __SXTB16(inB2);
sum4 = __SMLAD(opA, opB, sum4);
opA = __SXTB16(__ROR(inA2, 8));
opB = __SXTB16(__ROR(inB2, 8));
sum3 = __SMLAD(opA, opB, sum3);
colCnt--;
}
#endif /* ARM_MATH_BIG_ENDIAN */
#else
#ifndef ARM_MATH_BIG_ENDIAN
// r0 r1 r2 r3 r4 r5
// inA1, inA2, inB1, inB2, opA, opB
asm volatile ("COL_LOOP:\n"
"ldr.w r2, [%[pB], #0]\n"
"add.w %[pB], %[pB], %[ch_im_in]\n"
"ldr.w r5, [%[pB], #0]\n"
"add.w %[pB], %[pB], %[ch_im_in]\n"
"pkhtb r3, r5, r2, ASR #16\n"
"pkhbt r2, r2, r5, LSL #16\n"
"ldr.w r0, [%[pA], #0]\n"
"add.w %[pA], %[pA], %[ch_im_in]\n"
"ldr.w r5, [%[pA], #0]\n"
"add.w %[pA], %[pA], %[ch_im_in]\n"
"pkhtb r1, r5, r0, ASR #16\n"
"pkhbt r0, r0, r5, LSL #16\n"
"sxtb16 r4, r0\n"
"sxtb16 r5, r2\n"
"smlad %[sum], r4, r5, %[sum]\n"
"mov.w r4, r0, ror #8\n"
"mov.w r5, r2, ror #8\n"
"sxtb16 r4, r4\n"
"sxtb16 r5, r5\n"
"smlad %[sum2], r4, r5, %[sum2]\n"
"sxtb16 r4, r1\n"
"sxtb16 r5, r3\n"
"smlad %[sum3], r4, r5, %[sum3]\n"
"mov.w r4, r1, ror #8\n"
"mov.w r5, r3, ror #8\n"
"sxtb16 r4, r4\n"
"sxtb16 r5, r5\n"
"smlad %[sum4], r4, r5, %[sum4]\n"
"subs %[colCnt], #1\n"
"bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
[sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
#else
// r0 r1 r2 r3 r4 r5
// inA1, inA2, inB1, inB2, opA, opB
asm volatile ("COL_LOOP:\n"
"ldr.w r2, [%[pB], #0]\n"
"add.w %[pB], %[pB], %[ch_im_in]\n"
"ldr.w r5, [%[pB], #0]\n"
"add.w %[pB], %[pB], %[ch_im_in]\n"
"pkhbt r3, r5, r2, LSL #16\n"
"pkhtb r2, r2, r5, ASR #16\n"
"ldr.w r0, [%[pA], #0]\n"
"add.w %[pA], %[pA], %[ch_im_in]\n"
"ldr.w r5, [%[pA], #0]\n"
"add.w %[pA], %[pA], %[ch_im_in]\n"
"pkhbt r1, r5, r0, LSL #16\n"
"pkhtb r0, r0, r5, ASR #16\n"
"sxtb16 r4, r0\n"
"sxtb16 r5, r2\n"
"smlad %[sum2], r4, r5, %[sum2]\n"
"mov.w r4, r0, ror #8\n"
"mov.w r5, r2, ror #8\n"
"sxtb16 r4, r4\n"
"sxtb16 r5, r5\n"
"smlad %[sum], r4, r5, %[sum]\n"
"sxtb16 r4, r1\n"
"sxtb16 r5, r3\n"
"smlad %[sum4], r4, r5, %[sum4]\n"
"mov.w r4, r1, ror #8\n"
"mov.w r5, r3, ror #8\n"
"sxtb16 r4, r4\n"
"sxtb16 r5, r5\n"
"smlad %[sum3], r4, r5, %[sum3]\n"
"subs %[colCnt], #1\n"
"bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
[sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
#endif /*ARM_MATH_BIG_ENDIAN */
#endif /* USE_INTRINSIC */
colCnt = (dim_kernel_x * dim_kernel_y) & 0x1;
while (colCnt)
{
union arm_nnword inA, inB;
inA.word = *__SIMD32(pA);
pA += ch_im_in;
inB.word = *__SIMD32(pB);
pB += ch_im_in;
sum += inA.bytes[0] * inB.bytes[0];
sum2 += inA.bytes[1] * inB.bytes[1];
sum3 += inA.bytes[2] * inB.bytes[2];
sum4 += inA.bytes[3] * inB.bytes[3];
colCnt--;
}
*pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
*pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
*pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
*pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
rowCnt--;
}
rowCnt = ch_im_out & 0x3;
while (rowCnt)
{
q7_t *pB = colBuffer + row_shift;
const q7_t *pA = wt + row_shift;
q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
uint16_t colCnt = (dim_kernel_x * dim_kernel_y);
row_shift += 1;
while (colCnt)
{
q7_t A1 = *pA;
q7_t B1 = *pB;
pA += ch_im_in;
pB += ch_im_in;
sum += A1 * B1;
colCnt--;
}
*pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
rowCnt--;
}
// clear counter and pointers
pBuffer = colBuffer;
}
}
#else
/* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
int i_out_y, i_out_x, i_ch_out;
int i_ker_y, i_ker_x;
/* do some checking here, basically ch_im_in == ch_im_out */
if (ch_im_in != ch_im_out)
{
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++)
{
for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
{
// for each output
int conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++)
{
for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++)
{
int in_row = stride_y * i_out_y + i_ker_y - padding_y;
int in_col = stride_x * i_out_x + i_ker_x - padding_x;
if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
{
conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] *
wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out];
}
}
}
Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] =
(q7_t) __SSAT((conv_out >> out_shift), 8);
}
}
}
#endif /* ARM_MATH_DSP */
/* Return to application */
return ARM_MATH_SUCCESS;
}
/**
* @} end of NNConv group
*/