| /* ---------------------------------------------------------------------- |
| * 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 |
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| * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE |
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| * -------------------------------------------------------------------- */ |
| |
| /** |
| * @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; |
| } |