134 lines
5.2 KiB
C
134 lines
5.2 KiB
C
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/*
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* Copyright (C) 2010-2021 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_wrapper_s8.c
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* Description: s8 convolution layer wrapper function with the main purpose to call the optimal kernel available in
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* cmsis-nn to perform the convolution.
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*
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* $Date: 02. December 2021
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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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/**
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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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* Convolution layer
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*
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* Refer header file for details.
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*
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*/
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arm_status arm_convolve_wrapper_s8(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 q7_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 int32_t *bias_data,
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const cmsis_nn_dims *output_dims,
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q7_t *output_data)
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{
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if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (input_dims->c % 4 == 0) &&
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(conv_params->stride.w == 1) && (conv_params->stride.h == 1) && (filter_dims->w == 1) &&
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(filter_dims->h == 1) && (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
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{
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return arm_convolve_1x1_s8_fast(ctx,
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conv_params,
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quant_params,
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input_dims,
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input_data,
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filter_dims,
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filter_data,
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bias_dims,
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bias_data,
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output_dims,
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output_data);
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}
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else if ((output_dims->h == 1) && (input_dims->h == 1) && (filter_dims->h == 1) && (output_dims->w % 4 == 0) &&
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(input_dims->n == 1) && (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
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{
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return arm_convolve_1_x_n_s8(ctx,
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conv_params,
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quant_params,
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input_dims,
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input_data,
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filter_dims,
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filter_data,
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bias_dims,
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bias_data,
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output_dims,
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output_data);
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}
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else
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{
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return arm_convolve_s8(ctx,
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conv_params,
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quant_params,
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input_dims,
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input_data,
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filter_dims,
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filter_data,
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bias_dims,
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bias_data,
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output_dims,
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output_data);
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}
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}
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int32_t arm_convolve_wrapper_s8_get_buffer_size(const cmsis_nn_conv_params *conv_params,
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const cmsis_nn_dims *input_dims,
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const cmsis_nn_dims *filter_dims,
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const cmsis_nn_dims *output_dims)
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{
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if ((conv_params->padding.w == 0) && (conv_params->padding.h == 0) && (input_dims->c % 4 == 0) &&
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(conv_params->stride.w == 1) && (conv_params->stride.h == 1) && (filter_dims->w == 1) &&
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(filter_dims->h == 1) && (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
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{
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return arm_convolve_1x1_s8_fast_get_buffer_size(input_dims);
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}
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else if ((output_dims->h == 1) && (input_dims->h == 1) && (filter_dims->h == 1) && (output_dims->w % 4 == 0) &&
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(input_dims->n == 1) && (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
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{
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return arm_convolve_1_x_n_s8_get_buffer_size(input_dims, filter_dims);
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}
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else
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{
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return arm_convolve_s8_get_buffer_size(input_dims, filter_dims);
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}
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}
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/**
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* @} end of NNConv group
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*/
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