157 lines
6.1 KiB
C
157 lines
6.1 KiB
C
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/*
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* Copyright (C) 2010-2022 Arm Limited or its affiliates.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/* ----------------------------------------------------------------------
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* Project: CMSIS NN Library
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* Title: arm_convolve_s16.c
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* Description: s16 version of convolution using symmetric quantization.
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*
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* $Date: January 13, 2022
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* $Revision: V.1.1.0
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*
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* Target Processor: Cortex-M cores
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*
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* -------------------------------------------------------------------- */
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#include "arm_nnfunctions.h"
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#include "arm_nnsupportfunctions.h"
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/**
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* @ingroup groupNN
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*/
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/**
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* @addtogroup NNConv
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* @{
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*/
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/*
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* Basic s16 convolution function.
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*
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* Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels
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* are multiples of 4 or atleast greater than 4.
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*
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*/
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arm_status arm_convolve_s16(const cmsis_nn_context *ctx,
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const cmsis_nn_conv_params *conv_params,
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const cmsis_nn_per_channel_quant_params *quant_params,
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const cmsis_nn_dims *input_dims,
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const q15_t *input_data,
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const cmsis_nn_dims *filter_dims,
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const q7_t *filter_data,
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const cmsis_nn_dims *bias_dims,
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const int64_t *bias_data,
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const cmsis_nn_dims *output_dims,
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q15_t *output_data)
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{
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(void)bias_dims;
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(void)ctx;
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const int32_t input_batches = input_dims->n;
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const int32_t input_x = input_dims->w;
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const int32_t input_y = input_dims->h;
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const int32_t input_ch = input_dims->c;
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const int32_t kernel_x = filter_dims->w;
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const int32_t kernel_y = filter_dims->h;
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const int32_t output_x = output_dims->w;
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const int32_t output_y = output_dims->h;
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const int32_t output_ch = output_dims->c;
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const int32_t pad_x = conv_params->padding.w;
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const int32_t pad_y = conv_params->padding.h;
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const int32_t stride_x = conv_params->stride.w;
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const int32_t stride_y = conv_params->stride.h;
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const int32_t dilation_x = conv_params->dilation.w;
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const int32_t dilation_y = conv_params->dilation.h;
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const int32_t out_activation_min = conv_params->activation.min;
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const int32_t out_activation_max = conv_params->activation.max;
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int32_t *output_mult = quant_params->multiplier;
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int32_t *output_shift = quant_params->shift;
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for (int i_batch = 0; i_batch < input_batches; i_batch++)
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{
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/* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
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for (int32_t i_out_ch = 0; i_out_ch < output_ch; i_out_ch++)
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{
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const q31_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i_out_ch]);
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for (int32_t base_idx_y = -pad_y, i_out_y = 0; i_out_y < output_y; base_idx_y += stride_y, i_out_y++)
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{
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for (int32_t base_idx_x = -pad_x, i_out_x = 0; i_out_x < output_x; base_idx_x += stride_x, i_out_x++)
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{
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int64_t conv_out_acc = 0;
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const int32_t start_y_max = (-base_idx_y + dilation_y - 1) / dilation_y;
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const int32_t ker_y_start = MAX(0, start_y_max);
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const int32_t start_x_max = (-base_idx_x + dilation_x - 1) / dilation_x;
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const int32_t ker_x_start = MAX(0, start_x_max);
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const int32_t end_min_y = (input_y - base_idx_y + dilation_y - 1) / dilation_y;
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const int32_t ker_y_end = MIN(kernel_y, end_min_y);
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const int32_t end_min_x = (input_x - base_idx_x + dilation_x - 1) / dilation_x;
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const int32_t ker_x_end = MIN(kernel_x, end_min_x);
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for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++)
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{
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for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++)
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{
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const int32_t in_row = base_idx_y + dilation_y * i_ker_y;
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const int32_t in_col = base_idx_x + dilation_x * i_ker_x;
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for (int32_t i_input_ch = 0; i_input_ch < input_ch; i_input_ch++)
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{
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conv_out_acc += input_data[(in_row * input_x + in_col) * input_ch + i_input_ch] *
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filter_data[i_out_ch * input_ch * kernel_y * kernel_x +
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(i_ker_y * kernel_x + i_ker_x) * input_ch + i_input_ch];
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}
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}
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}
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if (bias_data)
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{
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conv_out_acc += bias_data[i_out_ch];
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}
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int32_t conv_out = arm_nn_requantize_s64(conv_out_acc, reduced_multiplier, output_shift[i_out_ch]);
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conv_out = MAX(conv_out, out_activation_min);
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conv_out = MIN(conv_out, out_activation_max);
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output_data[i_out_ch + (i_out_y * output_x + i_out_x) * output_ch] = (int16_t)conv_out;
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}
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}
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}
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/* Advance to the next batch */
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input_data += (input_x * input_y * input_ch);
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output_data += (output_x * output_y * output_ch);
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}
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/* Return to application */
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return ARM_MATH_SUCCESS;
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}
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int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
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{
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(void)input_dims;
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(void)filter_dims;
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return 0;
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}
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/**
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* @} end of NNConv group
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*/
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