CMSIS-NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c

/*
 * 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
 *                           availble, 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
 */