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/*
* Copyright (C) 2010-2020 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_conv_3x3_s8.c
* Description: Optimized s8 depthwise convolution function for channel
* multiplier of 1 and 3x3 kernel size.
*
* $Date: 09. October 2020
* $Revision: V.2.0.1
*
* Target Processor: Cortex-M CPUs
*
* -------------------------------------------------------------------- */
#include "arm_nnfunctions.h"
#include "arm_nnsupportfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
/*
* Optimized s8 depthwise convolution function with constraint that
* in_channel == out_channel and kernel_x == kernel_y == 3 with pads at most 1
*
* Refer prototype header file for details.
*
*/
arm_status arm_depthwise_conv_3x3_s8(const cmsis_nn_context *ctx,
const cmsis_nn_dw_conv_params *dw_conv_params,
const cmsis_nn_per_channel_quant_params *quant_params,
const cmsis_nn_dims *input_dims,
const q7_t *input,
const cmsis_nn_dims *filter_dims,
const q7_t *kernel,
const cmsis_nn_dims *bias_dims,
const int32_t *bias,
const cmsis_nn_dims *output_dims,
q7_t *output)
{
(void)ctx;
(void)bias_dims;
const int32_t input_x = input_dims->w;
const int32_t input_y = input_dims->h;
const int32_t input_ch = input_dims->c;
const int32_t output_ch = output_dims->c;
const int32_t pad_x = dw_conv_params->padding.w;
const int32_t pad_y = dw_conv_params->padding.h;
const int32_t stride_x = dw_conv_params->stride.w;
const int32_t stride_y = dw_conv_params->stride.h;
const int32_t *output_shift = quant_params->shift;
const int32_t *output_mult = quant_params->multiplier;
const int32_t output_x = output_dims->w;
const int32_t output_y = output_dims->h;
const int32_t output_offset = dw_conv_params->output_offset;
const int32_t input_offset = dw_conv_params->input_offset;
const int32_t output_activation_min = dw_conv_params->activation.min;
const int32_t output_activation_max = dw_conv_params->activation.max;
/* Check input constraints input_ch == output_ch */
if (input_ch != output_ch)
{
return ARM_MATH_SIZE_MISMATCH;
}
/* Check input constraints pad_x <= 1 */
if (pad_x > 1 || filter_dims->w != 3 || filter_dims->h != 3)
{
return ARM_MATH_ARGUMENT_ERROR;
}
for (int32_t in_h = -pad_y, out_h = 0, out_idx = 0; out_h < output_y; in_h += stride_y, ++out_h)
{
for (int32_t in_w = -pad_x, out_w = 0, ker_h_start = MAX(0, -in_h); out_w < output_x; in_w += stride_x, ++out_w)
{
int32_t in_ch = 0;
int32_t ker_w_start = MAX(0, -in_w);
for (; in_ch <= (input_ch - 4); in_ch += 4)
{
int32_t out_buff0 = bias[in_ch + 0];
int32_t out_buff1 = bias[in_ch + 1];
int32_t out_buff2 = bias[in_ch + 2];
int32_t out_buff3 = bias[in_ch + 3];
const int8_t *input_ptr = input + (in_h + ker_h_start) * (input_ch * input_x) + in_w * input_ch + in_ch;
const int8_t *kernel_ptr = kernel + ker_h_start * (input_ch * 3) + in_ch;
for (int32_t ker_h = ker_h_start; ker_h < MIN(3, input_y - in_h); ++ker_h)
{
int32_t in_val = 0;
int32_t ker_val = 0;
if (ker_w_start == 0)
{
in_val = arm_nn_read_q7x4(input_ptr);
ker_val = arm_nn_read_q7x4(kernel_ptr);
out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val;
out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8);
out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16);
out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24);
}
in_val = arm_nn_read_q7x4(input_ptr + input_ch);
ker_val = arm_nn_read_q7x4(kernel_ptr + input_ch);
out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val;
out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8);
out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16);
out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24);
if ((input_x - in_w) >= 3)
{
in_val = arm_nn_read_q7x4(input_ptr + (input_ch << 1));
ker_val = arm_nn_read_q7x4(kernel_ptr + (input_ch << 1));
out_buff0 += ((int8_t)in_val + input_offset) * (int8_t)ker_val;
out_buff1 += ((int8_t)(in_val >> 8) + input_offset) * (int8_t)(ker_val >> 8);
out_buff2 += ((int8_t)(in_val >> 16) + input_offset) * (int8_t)(ker_val >> 16);
out_buff3 += ((int8_t)(in_val >> 24) + input_offset) * (int8_t)(ker_val >> 24);
}
input_ptr += (input_ch * input_x);
kernel_ptr += (input_ch * 3);
}
out_buff0 = arm_nn_requantize(out_buff0, output_mult[in_ch + 0], output_shift[in_ch + 0]);
out_buff1 = arm_nn_requantize(out_buff1, output_mult[in_ch + 1], output_shift[in_ch + 1]);
out_buff2 = arm_nn_requantize(out_buff2, output_mult[in_ch + 2], output_shift[in_ch + 2]);
out_buff3 = arm_nn_requantize(out_buff3, output_mult[in_ch + 3], output_shift[in_ch + 3]);
out_buff0 += output_offset;
out_buff1 += output_offset;
out_buff2 += output_offset;
out_buff3 += output_offset;
out_buff0 = MIN(MAX(out_buff0, output_activation_min), output_activation_max);
out_buff1 = MIN(MAX(out_buff1, output_activation_min), output_activation_max);
out_buff2 = MIN(MAX(out_buff2, output_activation_min), output_activation_max);
out_buff3 = MIN(MAX(out_buff3, output_activation_min), output_activation_max);
output[out_idx++] = (int8_t)out_buff0;
output[out_idx++] = (int8_t)out_buff1;
output[out_idx++] = (int8_t)out_buff2;
output[out_idx++] = (int8_t)out_buff3;
}
// Leftover
for (; in_ch < input_ch; ++in_ch)
{
int32_t out_buff = bias[in_ch];
const int8_t *input_ptr = input + (in_h + ker_h_start) * (input_ch * input_x) + in_w * input_ch + in_ch;
const int8_t *kernel_ptr = kernel + ker_h_start * (input_ch * 3) + in_ch;
for (int32_t ker_h = ker_h_start; ker_h < MIN(3, input_y - in_h); ++ker_h)
{
if (ker_w_start == 0)
{
out_buff += (*(input_ptr) + input_offset) * *(kernel_ptr);
}
out_buff += (*(input_ptr + input_ch) + input_offset) * *(kernel_ptr + input_ch);
if ((input_x - in_w) >= 3)
{
out_buff += (*(input_ptr + (input_ch << 1)) + input_offset) * *(kernel_ptr + (input_ch << 1));
}
input_ptr += (input_ch * input_x);
kernel_ptr += (input_ch * 3);
}
out_buff = arm_nn_requantize(out_buff, output_mult[in_ch], output_shift[in_ch]);
out_buff += output_offset;
out_buff = MIN(MAX(out_buff, output_activation_min), output_activation_max);
output[out_idx++] = (int8_t)out_buff;
}
}
}
/* Return to application */
return ARM_MATH_SUCCESS;
}
/**
* @} end of NNConv group
*/
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