Hey-Edge / edge-impulse-sdk /CMSIS /NN /Source /ConvolutionFunctions /arm_depthwise_conv_wrapper_s8.c
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#include "edge-impulse-sdk/classifier/ei_classifier_config.h"
#if EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES
/*
* Copyright (C) 2010-2022 Arm Limited or its affiliates.
*
* 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_wrapper_s8.c
* Description: Wrapper API to select appropriate depthwise conv API based
* on dimensions.
*
* $Date: 19 April 2022
* $Revision: V.2.0.0
*
* Target Processor: Cortex-M CPUs
*
* -------------------------------------------------------------------- */
#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
/*
* s8 Depthwise conv wrapper function
*
* Refer header file for details.
*
*/
arm_cmsis_nn_status arm_depthwise_conv_wrapper_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 *filter,
const cmsis_nn_dims *bias_dims,
const int32_t *bias,
const cmsis_nn_dims *output_dims,
q7_t *output)
{
arm_cmsis_nn_status status = ARM_CMSIS_NN_SUCCESS;
if (1 == dw_conv_params->ch_mult && input_dims->n == 1 && dw_conv_params->dilation.w == 1 &&
dw_conv_params->dilation.h == 1)
{
#if !defined(ARM_MATH_MVEI)
if ((filter_dims->w == 3) && (filter_dims->h == 3) && (dw_conv_params->padding.h <= 1) &&
(dw_conv_params->padding.w <= 1))
{
status = arm_depthwise_conv_3x3_s8(ctx,
dw_conv_params,
quant_params,
input_dims,
input,
filter_dims,
filter,
bias_dims,
bias,
output_dims,
output);
}
else
#endif
{
status = arm_depthwise_conv_s8_opt(ctx,
dw_conv_params,
quant_params,
input_dims,
input,
filter_dims,
filter,
bias_dims,
bias,
output_dims,
output);
}
}
else
{
status = arm_depthwise_conv_s8(ctx,
dw_conv_params,
quant_params,
input_dims,
input,
filter_dims,
filter,
bias_dims,
bias,
output_dims,
output);
}
/* Return to application */
return status;
}
int32_t arm_depthwise_conv_wrapper_s8_get_buffer_size(const cmsis_nn_dw_conv_params *dw_conv_params,
const cmsis_nn_dims *input_dims,
const cmsis_nn_dims *filter_dims,
const cmsis_nn_dims *output_dims)
{
(void)dw_conv_params;
int32_t size = 0;
if (input_dims->c == output_dims->c && input_dims->n == 1 && dw_conv_params->dilation.w == 1 &&
dw_conv_params->dilation.h == 1)
{
size = arm_depthwise_conv_s8_opt_get_buffer_size(input_dims, filter_dims);
}
return size;
}
/**
* @} end of NNConv group
*/
#endif // EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES