File size: 4,830 Bytes
25ade36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | #include "edge-impulse-sdk/classifier/ei_classifier_config.h"
#if EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES
/*
* SPDX-FileCopyrightText: Copyright 2010-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
*
* 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_s16.c
* Description: Wrapper API to select appropriate depthwise conv API based
* on dimensions.
*
* $Date: 6 July 2022
* $Revision: V.1.0.1
*
* Target Processor: Cortex-M CPUs
*
* -------------------------------------------------------------------- */
#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
#define USE_FAST_DW_CONV_FUNCTION(dw_conv_params, filter_dims, input_dims) \
(dw_conv_params->ch_mult == 1 && dw_conv_params->dilation.w == 1 && dw_conv_params->dilation.h == 1 && \
filter_dims->w * filter_dims->h * input_dims->c < 512)
/*
* s16 Depthwise conv wrapper function
*
* Refer header file for details.
*
*/
arm_cmsis_nn_status arm_depthwise_conv_wrapper_s16(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 q15_t *input,
const cmsis_nn_dims *filter_dims,
const q7_t *filter,
const cmsis_nn_dims *bias_dims,
const int64_t *bias,
const cmsis_nn_dims *output_dims,
q15_t *output)
{
arm_cmsis_nn_status status = ARM_CMSIS_NN_SUCCESS;
if (USE_FAST_DW_CONV_FUNCTION(dw_conv_params, filter_dims, input_dims))
{
status = arm_depthwise_conv_fast_s16(ctx,
dw_conv_params,
quant_params,
input_dims,
input,
filter_dims,
filter,
bias_dims,
bias,
output_dims,
output);
}
else
{
status = arm_depthwise_conv_s16(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_s16_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;
(void)input_dims;
(void)filter_dims;
(void)output_dims;
int32_t size = 0;
if (USE_FAST_DW_CONV_FUNCTION(dw_conv_params, filter_dims, input_dims))
{
size = arm_depthwise_conv_fast_s16_get_buffer_size(input_dims, filter_dims);
}
return size;
}
/**
* @} end of NNConv group
*/
#endif // EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES
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