Publish Edge Impulse model via ModelForge

25ade36 verified 4 months ago

4.83 kB

#include "edge-impulse-sdk/classifier/ei_classifier_config.h"
#if EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES
/*
 * Copyright (C) 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_elementwise_add_s16
 * Description:  Elementwise add
 *
 * $Date:        10 May 2022
 * $Revision:    V.2.1.0
 *
 * Target Processor:  Cortex-M CPUs
 *
 * -------------------------------------------------------------------- */

#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnfunctions.h"
#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnsupportfunctions.h"

/**
 *  @ingroup groupNN
 */

/**
 * @addtogroup BasicMath
 * @{
 */

/*
 * s16 elementwise add
 *
 * Refer header file for details.
 *
 */

/* Note: __SHIFT is expected to be <=0 */

arm_cmsis_nn_status arm_elementwise_add_s16(const int16_t *input_1_vect,
                                            const int16_t *input_2_vect,
                                            const int32_t input_1_offset,
                                            const int32_t input_1_mult,
                                            const int32_t input_1_shift,
                                            const int32_t input_2_offset,
                                            const int32_t input_2_mult,
                                            const int32_t input_2_shift,
                                            const int32_t left_shift,
                                            int16_t *output,
                                            const int32_t out_offset,
                                            const int32_t out_mult,
                                            const int32_t out_shift,
                                            const int32_t out_activation_min,
                                            const int32_t out_activation_max,
                                            const int32_t block_size)
{
    (void)input_1_offset;
    (void)input_2_offset;
    (void)out_offset;
    int32_t input_1;
    int32_t input_2;
    int32_t sum;
    int32_t two_halfword_1, two_halfword_2;
    int16_t sum_1, sum_2;
    int32_t loop_count = block_size / 2;

    while (loop_count > 0)
    {
        two_halfword_1 = arm_nn_read_q15x2_ia(&input_1_vect);
        two_halfword_2 = arm_nn_read_q15x2_ia(&input_2_vect);

        input_1 = (int16_t)(two_halfword_1 & 0xFFFF) << left_shift;
        input_1 = arm_nn_requantize(input_1, input_1_mult, input_1_shift);
        input_2 = (int16_t)(two_halfword_2 & 0xFFFF) << left_shift;
        input_2 = arm_nn_requantize(input_2, input_2_mult, input_2_shift);
        sum = input_1 + input_2;
        sum = arm_nn_requantize(sum, out_mult, out_shift);
        sum = MAX(sum, out_activation_min);
        sum = MIN(sum, out_activation_max);
        sum_1 = (int16_t)sum;

        input_1 = (int16_t)(two_halfword_1 >> 16) << left_shift;
        input_1 = arm_nn_requantize(input_1, input_1_mult, input_1_shift);
        input_2 = (int16_t)(two_halfword_2 >> 16) << left_shift;
        input_2 = arm_nn_requantize(input_2, input_2_mult, input_2_shift);
        sum = input_1 + input_2;
        sum = arm_nn_requantize(sum, out_mult, out_shift);
        sum = MAX(sum, out_activation_min);
        sum = MIN(sum, out_activation_max);
        sum_2 = (int16_t)sum;

        arm_nn_write_q15x2_ia(&output, PACK_Q15x2_32x1(sum_1, sum_2));

        loop_count--;
    }
    loop_count = block_size & 0x1;

    while (loop_count > 0)
    {
        /* C = A + B */
        input_1 = *input_1_vect++ << left_shift;
        input_2 = *input_2_vect++ << left_shift;

        input_1 = arm_nn_requantize(input_1, input_1_mult, input_1_shift);
        input_2 = arm_nn_requantize(input_2, input_2_mult, input_2_shift);

        sum = input_1 + input_2;
        sum = arm_nn_requantize(sum, out_mult, out_shift);

        sum = MAX(sum, out_activation_min);
        sum = MIN(sum, out_activation_max);

        *output++ = (int16_t)sum;

        /* Decrement loop counter */
        loop_count--;
    }

    return (ARM_CMSIS_NN_SUCCESS);
}

/**
 * @} end of BasicMath group
 */

#endif // EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES

	#include "edge-impulse-sdk/classifier/ei_classifier_config.h"
	#if EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES
	/*
	* Copyright (C) 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_elementwise_add_s16
	* Description: Elementwise add
	*
	* $Date: 10 May 2022
	* $Revision: V.2.1.0
	*
	* Target Processor: Cortex-M CPUs
	*
	* -------------------------------------------------------------------- */

	#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnfunctions.h"
	#include "edge-impulse-sdk/CMSIS/NN/Include/arm_nnsupportfunctions.h"

	/**
	* @ingroup groupNN
	*/

	/**
	* @addtogroup BasicMath
	* @{
	*/

	/*
	* s16 elementwise add
	*
	* Refer header file for details.
	*
	*/

	/* Note: __SHIFT is expected to be <=0 */

	arm_cmsis_nn_status arm_elementwise_add_s16(const int16_t *input_1_vect,
	const int16_t *input_2_vect,
	const int32_t input_1_offset,
	const int32_t input_1_mult,
	const int32_t input_1_shift,
	const int32_t input_2_offset,
	const int32_t input_2_mult,
	const int32_t input_2_shift,
	const int32_t left_shift,
	int16_t *output,
	const int32_t out_offset,
	const int32_t out_mult,
	const int32_t out_shift,
	const int32_t out_activation_min,
	const int32_t out_activation_max,
	const int32_t block_size)
	{
	(void)input_1_offset;
	(void)input_2_offset;
	(void)out_offset;
	int32_t input_1;
	int32_t input_2;
	int32_t sum;
	int32_t two_halfword_1, two_halfword_2;
	int16_t sum_1, sum_2;
	int32_t loop_count = block_size / 2;

	while (loop_count > 0)
	{
	two_halfword_1 = arm_nn_read_q15x2_ia(&input_1_vect);
	two_halfword_2 = arm_nn_read_q15x2_ia(&input_2_vect);

	input_1 = (int16_t)(two_halfword_1 & 0xFFFF) << left_shift;
	input_1 = arm_nn_requantize(input_1, input_1_mult, input_1_shift);
	input_2 = (int16_t)(two_halfword_2 & 0xFFFF) << left_shift;
	input_2 = arm_nn_requantize(input_2, input_2_mult, input_2_shift);
	sum = input_1 + input_2;
	sum = arm_nn_requantize(sum, out_mult, out_shift);
	sum = MAX(sum, out_activation_min);
	sum = MIN(sum, out_activation_max);
	sum_1 = (int16_t)sum;

	input_1 = (int16_t)(two_halfword_1 >> 16) << left_shift;
	input_1 = arm_nn_requantize(input_1, input_1_mult, input_1_shift);
	input_2 = (int16_t)(two_halfword_2 >> 16) << left_shift;
	input_2 = arm_nn_requantize(input_2, input_2_mult, input_2_shift);
	sum = input_1 + input_2;
	sum = arm_nn_requantize(sum, out_mult, out_shift);
	sum = MAX(sum, out_activation_min);
	sum = MIN(sum, out_activation_max);
	sum_2 = (int16_t)sum;

	arm_nn_write_q15x2_ia(&output, PACK_Q15x2_32x1(sum_1, sum_2));

	loop_count--;
	}
	loop_count = block_size & 0x1;

	while (loop_count > 0)
	{
	/* C = A + B */
	input_1 = *input_1_vect++ << left_shift;
	input_2 = *input_2_vect++ << left_shift;

	input_1 = arm_nn_requantize(input_1, input_1_mult, input_1_shift);
	input_2 = arm_nn_requantize(input_2, input_2_mult, input_2_shift);

	sum = input_1 + input_2;
	sum = arm_nn_requantize(sum, out_mult, out_shift);

	sum = MAX(sum, out_activation_min);
	sum = MIN(sum, out_activation_max);

	*output++ = (int16_t)sum;

	/* Decrement loop counter */
	loop_count--;
	}

	return (ARM_CMSIS_NN_SUCCESS);
	}

	/**
	* @} end of BasicMath group
	*/

	#endif // EI_CLASSIFIER_TFLITE_LOAD_CMSIS_NN_SOURCES