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/* ----------------------------------------------------------------------
* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
*
*
* Project: CMSIS NN Library
* Title: arm_nnexamples_cifar10.cpp
*
* Description: Convolutional Neural Network Example
*
* Target Processor: Cortex-M4/Cortex-M7
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* - Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* - Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
* - Neither the name of Arm LIMITED nor the names of its contributors
* may be used to endorse or promote products derived from this
* software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
* -------------------------------------------------------------------- */
/**
* @ingroup groupExamples
*/
/**
* @defgroup CNNExample Convolutional Neural Network Example
*
* \par Description:
* \par
* Demonstrates a convolutional neural network (CNN) example with the use of convolution,
* ReLU activation, pooling and fully-connected functions.
*
* \par Model definition:
* \par
* The CNN used in this example is based on CIFAR-10 example from Caffe [1].
* The neural network consists
* of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a
* fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will
* be classified into one of the 10 output classes.
* This example model implementation needs 32.3 KB to store weights, 40 KB for activations and
* 3.1 KB for storing the \c im2col data.
*
* \image html CIFAR10_CNN.gif "Neural Network model definition"
*
* \par Variables Description:
* \par
* \li \c conv1_wt, \c conv2_wt, \c conv3_wt are convolution layer weight matrices
* \li \c conv1_bias, \c conv2_bias, \c conv3_bias are convolution layer bias arrays
* \li \c ip1_wt, ip1_bias point to fully-connected layer weights and biases
* \li \c input_data points to the input image data
* \li \c output_data points to the classification output
* \li \c col_buffer is a buffer to store the \c im2col output
* \li \c scratch_buffer is used to store the activation data (intermediate layer outputs)
*
* \par CMSIS DSP Software Library Functions Used:
* \par
* - arm_convolve_HWC_q7_RGB()
* - arm_convolve_HWC_q7_fast()
* - arm_relu_q7()
* - arm_maxpool_q7_HWC()
* - arm_avepool_q7_HWC()
* - arm_fully_connected_q7_opt()
* - arm_fully_connected_q7()
*
* <b> Refer </b>
* \link arm_nnexamples_cifar10.cpp \endlink
*
* \par [1] https://github.com/BVLC/caffe
*/
#include <stdint.h>
#include <stdio.h>
#include "arm_math.h"
#include "arm_nnexamples_cifar10_parameter.h"
#include "arm_nnexamples_cifar10_weights.h"
#include "arm_nnfunctions.h"
#include "arm_nnexamples_cifar10_inputs.h"
#ifdef _RTE_
#include "RTE_Components.h"
#ifdef RTE_Compiler_EventRecorder
#include "EventRecorder.h"
#endif
#endif
// include the input and weights
static q7_t conv1_wt[CONV1_IM_CH * CONV1_KER_DIM * CONV1_KER_DIM * CONV1_OUT_CH] = CONV1_WT;
static q7_t conv1_bias[CONV1_OUT_CH] = CONV1_BIAS;
static q7_t conv2_wt[CONV2_IM_CH * CONV2_KER_DIM * CONV2_KER_DIM * CONV2_OUT_CH] = CONV2_WT;
static q7_t conv2_bias[CONV2_OUT_CH] = CONV2_BIAS;
static q7_t conv3_wt[CONV3_IM_CH * CONV3_KER_DIM * CONV3_KER_DIM * CONV3_OUT_CH] = CONV3_WT;
static q7_t conv3_bias[CONV3_OUT_CH] = CONV3_BIAS;
static q7_t ip1_wt[IP1_DIM * IP1_OUT] = IP1_WT;
static q7_t ip1_bias[IP1_OUT] = IP1_BIAS;
/* Here the image_data should be the raw uint8 type RGB image in [RGB, RGB, RGB ... RGB] format */
uint8_t image_data[CONV1_IM_CH * CONV1_IM_DIM * CONV1_IM_DIM] = IMG_DATA;
q7_t output_data[IP1_OUT];
//vector buffer: max(im2col buffer,average pool buffer, fully connected buffer)
q7_t col_buffer[2 * 5 * 5 * 32 * 2];
q7_t scratch_buffer[32 * 32 * 10 * 4];
int main()
{
#ifdef RTE_Compiler_EventRecorder
EventRecorderInitialize (EventRecordAll, 1); // initialize and start Event Recorder
#endif
printf("start execution\n");
/* start the execution */
q7_t *img_buffer1 = scratch_buffer;
q7_t *img_buffer2 = img_buffer1 + 32 * 32 * 32;
/* input pre-processing */
int mean_data[3] = INPUT_MEAN_SHIFT;
unsigned int scale_data[3] = INPUT_RIGHT_SHIFT;
for (int i=0;i<32*32*3; i+=3) {
img_buffer2[i] = (q7_t)__SSAT( ((((int)image_data[i] - mean_data[0])<<7) + (0x1<<(scale_data[0]-1)))
>> scale_data[0], 8);
img_buffer2[i+1] = (q7_t)__SSAT( ((((int)image_data[i+1] - mean_data[1])<<7) + (0x1<<(scale_data[1]-1)))
>> scale_data[1], 8);
img_buffer2[i+2] = (q7_t)__SSAT( ((((int)image_data[i+2] - mean_data[2])<<7) + (0x1<<(scale_data[2]-1)))
>> scale_data[2], 8);
}
// conv1 img_buffer2 -> img_buffer1
arm_convolve_HWC_q7_RGB(img_buffer2, CONV1_IM_DIM, CONV1_IM_CH, conv1_wt, CONV1_OUT_CH, CONV1_KER_DIM, CONV1_PADDING,
CONV1_STRIDE, conv1_bias, CONV1_BIAS_LSHIFT, CONV1_OUT_RSHIFT, img_buffer1, CONV1_OUT_DIM,
(q15_t *) col_buffer, NULL);
arm_relu_q7(img_buffer1, CONV1_OUT_DIM * CONV1_OUT_DIM * CONV1_OUT_CH);
// pool1 img_buffer1 -> img_buffer2
arm_maxpool_q7_HWC(img_buffer1, CONV1_OUT_DIM, CONV1_OUT_CH, POOL1_KER_DIM,
POOL1_PADDING, POOL1_STRIDE, POOL1_OUT_DIM, NULL, img_buffer2);
// conv2 img_buffer2 -> img_buffer1
arm_convolve_HWC_q7_fast(img_buffer2, CONV2_IM_DIM, CONV2_IM_CH, conv2_wt, CONV2_OUT_CH, CONV2_KER_DIM,
CONV2_PADDING, CONV2_STRIDE, conv2_bias, CONV2_BIAS_LSHIFT, CONV2_OUT_RSHIFT, img_buffer1,
CONV2_OUT_DIM, (q15_t *) col_buffer, NULL);
arm_relu_q7(img_buffer1, CONV2_OUT_DIM * CONV2_OUT_DIM * CONV2_OUT_CH);
// pool2 img_buffer1 -> img_buffer2
arm_maxpool_q7_HWC(img_buffer1, CONV2_OUT_DIM, CONV2_OUT_CH, POOL2_KER_DIM,
POOL2_PADDING, POOL2_STRIDE, POOL2_OUT_DIM, col_buffer, img_buffer2);
// conv3 img_buffer2 -> img_buffer1
arm_convolve_HWC_q7_fast(img_buffer2, CONV3_IM_DIM, CONV3_IM_CH, conv3_wt, CONV3_OUT_CH, CONV3_KER_DIM,
CONV3_PADDING, CONV3_STRIDE, conv3_bias, CONV3_BIAS_LSHIFT, CONV3_OUT_RSHIFT, img_buffer1,
CONV3_OUT_DIM, (q15_t *) col_buffer, NULL);
arm_relu_q7(img_buffer1, CONV3_OUT_DIM * CONV3_OUT_DIM * CONV3_OUT_CH);
// pool3 img_buffer-> img_buffer2
arm_maxpool_q7_HWC(img_buffer1, CONV3_OUT_DIM, CONV3_OUT_CH, POOL3_KER_DIM,
POOL3_PADDING, POOL3_STRIDE, POOL3_OUT_DIM, col_buffer, img_buffer2);
arm_fully_connected_q7_opt(img_buffer2, ip1_wt, IP1_DIM, IP1_OUT, IP1_BIAS_LSHIFT, IP1_OUT_RSHIFT, ip1_bias,
output_data, (q15_t *) img_buffer1);
arm_softmax_q7(output_data, 10, output_data);
for (int i = 0; i < 10; i++)
{
printf("%d: %d\n", i, output_data[i]);
}
return 0;
}
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