File size: 5,388 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | #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
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