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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_u8_basic_ver1.c
* Description: u8 depthwise convolution function
*
* $Date: 09. October 2020
* $Revision: V.1.1.1
*
* Target : Cortex-M CPUs
*
* -------------------------------------------------------------------- */
#include "arm_nnfunctions.h"
#include "arm_nnsupportfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
static void depthwise_conv_u8_mult_4(const uint8_t *input,
const int32_t input_x,
const int32_t input_y,
const int32_t input_ch,
const uint8_t *kernel,
const int32_t output_ch,
const int32_t ch_mult,
const int32_t kernel_x,
const int32_t kernel_y,
const int32_t pad_x,
const int32_t pad_y,
const int32_t stride_x,
const int32_t stride_y,
const int32_t *bias,
uint8_t *output,
const int32_t output_shift,
const int32_t output_mult,
const int32_t output_x,
const int32_t output_y,
const int32_t output_offset,
const int32_t input_offset,
const int32_t filter_offset,
const int32_t output_activation_min,
const int32_t output_activation_max)
{
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)
{
for (int32_t in_ch = 0, out_ch = 0, ker_w_start = MAX(0, -in_w); out_ch < output_ch;
++in_ch, out_ch += ch_mult)
{
for (int mult_tile = 0; mult_tile < ch_mult; mult_tile += 4)
{
int32_t out_buff[4];
out_buff[0] = 0;
out_buff[1] = 0;
out_buff[2] = 0;
out_buff[3] = 0;
for (int32_t ker_h = ker_h_start; ker_h < MIN(kernel_y, input_y - in_h); ++ker_h)
{
int32_t ker_idx = ker_h * (output_ch * kernel_x) + ker_w_start * output_ch + out_ch;
int32_t in_idx = (in_h + ker_h) * (input_ch * input_x) + in_w * input_ch + in_ch;
for (int32_t ker_w = ker_w_start; ker_w < MIN(kernel_x, input_x - in_w);
++ker_w, ker_idx += output_ch)
{
int32_t in_val = input[in_idx + ker_w * input_ch] + input_offset;
out_buff[0] += in_val * (kernel[ker_idx + 0 + mult_tile] + filter_offset);
out_buff[1] += in_val * (kernel[ker_idx + 1 + mult_tile] + filter_offset);
out_buff[2] += in_val * (kernel[ker_idx + 2 + mult_tile] + filter_offset);
out_buff[3] += in_val * (kernel[ker_idx + 3 + mult_tile] + filter_offset);
}
}
if (bias != NULL)
{
out_buff[0] += bias[out_ch + 0 + mult_tile];
out_buff[1] += bias[out_ch + 1 + mult_tile];
out_buff[2] += bias[out_ch + 2 + mult_tile];
out_buff[3] += bias[out_ch + 3 + mult_tile];
}
out_buff[0] = arm_nn_requantize(out_buff[0], output_mult, output_shift);
out_buff[1] = arm_nn_requantize(out_buff[1], output_mult, output_shift);
out_buff[2] = arm_nn_requantize(out_buff[2], output_mult, output_shift);
out_buff[3] = arm_nn_requantize(out_buff[3], output_mult, output_shift);
out_buff[0] += output_offset;
out_buff[1] += output_offset;
out_buff[2] += output_offset;
out_buff[3] += output_offset;
out_buff[0] = MIN(MAX(out_buff[0], output_activation_min), output_activation_max);
out_buff[1] = MIN(MAX(out_buff[1], output_activation_min), output_activation_max);
out_buff[2] = MIN(MAX(out_buff[2], output_activation_min), output_activation_max);
out_buff[3] = MIN(MAX(out_buff[3], output_activation_min), output_activation_max);
output[out_idx++] = (uint8_t)out_buff[0];
output[out_idx++] = (uint8_t)out_buff[1];
output[out_idx++] = (uint8_t)out_buff[2];
output[out_idx++] = (uint8_t)out_buff[3];
}
}
}
}
}
static void depthwise_conv_u8_generic(const uint8_t *input,
const int32_t input_x,
const int32_t input_y,
const int32_t input_ch,
const uint8_t *kernel,
const int32_t output_ch,
const int32_t ch_mult,
const int32_t kernel_x,
const int32_t kernel_y,
const int32_t pad_x,
const int32_t pad_y,
const int32_t stride_x,
const int32_t stride_y,
const int32_t *bias,
uint8_t *output,
const int32_t output_shift,
const int32_t output_mult,
const int32_t output_x,
const int32_t output_y,
const int32_t output_offset,
const int32_t input_offset,
const int32_t filter_offset,
const int32_t output_activation_min,
const int32_t output_activation_max)
{
(void)output_ch;
int i_out = 0;
for (int i_out_y = 0; i_out_y < output_y; i_out_y++)
{
const int16_t base_idx_y = (i_out_y * stride_y) - pad_y;
for (int i_out_x = 0; i_out_x < output_x; i_out_x++)
{
const int16_t base_idx_x = (i_out_x * stride_x) - pad_x;
for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++)
{
for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult++)
{
const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult;
int32_t acc_0;
/* Condition for kernel start dimension: (base_idx_<x,y> + ker_<x,y>_start) >= 0 */
const int ker_y_start = MAX(0, -base_idx_y);
const int ker_x_start = MAX(0, -base_idx_x);
/* Condition for kernel end dimension: (base_idx_<x,y> + ker_<x,y>_end) < input_<x,y> */
const int ker_y_end = MIN(kernel_y, input_y - base_idx_y);
const int ker_x_end = MIN(kernel_x, input_x - base_idx_x);
acc_0 = 0;
for (int i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++)
{
const int32_t idx_y = base_idx_y + i_ker_y;
for (int i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++)
{
const int32_t idx_x = base_idx_x + i_ker_x;
int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch;
int32_t ker_idx_0 = (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch;
acc_0 += (input[idx_0] + input_offset) * (kernel[ker_idx_0] + filter_offset);
}
}
if (bias != NULL)
{
acc_0 += bias[idx_out_ch];
}
/* Requantize and clamp output to provided range */
acc_0 = arm_nn_requantize(acc_0, output_mult, output_shift);
acc_0 += output_offset;
acc_0 = MAX(acc_0, output_activation_min);
acc_0 = MIN(acc_0, output_activation_max);
output[i_out++] = acc_0;
}
}
}
}
}
/**
* @brief uint8 depthwise convolution function with asymmetric quantization
*
* @param[in] input Pointer to input tensor
* @param[in] input_x Width of input tensor
* @param[in] input_y Height of input tensor
* @param[in] input_ch Channels in input tensor
* @param[in] kernel Pointer to kernel weights
* @param[in] kernel_x Width of kernel
* @param[in] kernel_y Height of kernel
* @param[in] ch_mult Number of channel multiplier
* @param[in] pad_x Padding sizes x
* @param[in] pad_y Padding sizes y
* @param[in] stride_x Convolution stride along the width
* @param[in] stride_y Convolution stride along the height
* @param[in] dilation_x Dilation along width. Not used and intended for future enhancement.
* @param[in] dilation_y Dilation along height. Not used and intended for future enhancement.
* @param[in] bias Pointer to optional bias values. If no bias is
* available, NULL is expected
* @param[in] input_offset Input tensor zero offset
* @param[in] filter_offset Kernel tensor zero offset
* @param[in] output_offset Output tensor zero offset
* @param[in,out] output Pointer to output tensor
* @param[in] output_x Width of output tensor
* @param[in] output_y Height of output tensor
* @param[in] output_activation_min Minimum value to clamp the output to. Range : {0, 255}
* @param[in] output_activation_max Minimum value to clamp the output to. Range : {0, 255}
* @param[in] output_shift Amount of right-shift for output
* @param[in] output_mult Output multiplier for requantization
* @return The function returns one of the following
* <code>ARM_MATH_SIZE_MISMATCH</code> - Not supported dimension of tensors
* <code>ARM_MATH_SUCCESS</code> - Successful operation
* <code>ARM_MATH_ARGUMENT_ERROR</code> - Implementation not available
*
*
*/
arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input,
const uint16_t input_x,
const uint16_t input_y,
const uint16_t input_ch,
const uint8_t *kernel,
const uint16_t kernel_x,
const uint16_t kernel_y,
const int16_t ch_mult,
const int16_t pad_x,
const int16_t pad_y,
const int16_t stride_x,
const int16_t stride_y,
const int16_t dilation_x,
const int16_t dilation_y,
const int32_t *bias,
const int32_t input_offset,
const int32_t filter_offset,
const int32_t output_offset,
uint8_t *output,
const uint16_t output_x,
const uint16_t output_y,
const int32_t output_activation_min,
const int32_t output_activation_max,
const int32_t output_shift,
const int32_t output_mult)
{
(void)dilation_x;
(void)dilation_y;
if (ch_mult % 4 == 0)
{
depthwise_conv_u8_mult_4(input,
input_x,
input_y,
input_ch,
kernel,
ch_mult * input_ch,
ch_mult,
kernel_x,
kernel_y,
pad_x,
pad_y,
stride_x,
stride_y,
bias,
output,
output_shift,
output_mult,
output_x,
output_y,
output_offset,
input_offset,
filter_offset,
output_activation_min,
output_activation_max);
}
else
{
depthwise_conv_u8_generic(input,
input_x,
input_y,
input_ch,
kernel,
ch_mult * input_ch,
ch_mult,
kernel_x,
kernel_y,
pad_x,
pad_y,
stride_x,
stride_y,
bias,
output,
output_shift,
output_mult,
output_x,
output_y,
output_offset,
input_offset,
filter_offset,
output_activation_min,
output_activation_max);
}
/* Return to application */
return ARM_MATH_SUCCESS;
}
/**
* @} end of NNConv group
*/
|
