tests/pkg_cmsis-nn: add cifar10 example application
This commit is contained in:
Alexandre Abadie
parent
fdd2b97387
commit
db7d8a58e8
22
tests/pkg_cmsis-nn/Makefile
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22
tests/pkg_cmsis-nn/Makefile
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BOARD ?= nucleo-l476rg
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include ../Makefile.tests_common
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USEPKG += cmsis-nn
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BLOBS += input
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# Boards that were tested and are known to work
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# This package only works with Cortex M3, M4 and M7 CPUs but there's no easy
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# way provided by the build system to filter them at that level (arch_cortexm is
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# the only feature available) for the moment.
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BOARD_WHITELIST := \
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b-l475e-iot01a \
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iotlab-m3 \
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nrf52832-mdk \
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nrf52dk \
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nucleo-l476rg \
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same54-xpro \
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stm32f723e-disco
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#
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include $(RIOTBASE)/Makefile.include
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31
tests/pkg_cmsis-nn/README.md
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tests/pkg_cmsis-nn/README.md
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## ARM CMSIS-NN package
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This application shows how to use the neural network API provided by the ARM CMSIS
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package in order to determine the type of "object" present in an RGB image.
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The image are part of the [SIFAR10 dataset](http://www.cs.toronto.edu/~kriz/cifar.html)
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which contains 10 classes of objects: plane, car, cat, bird, deer, dog, frog,
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horse, ship and truck.
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Expected output
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---------------
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```
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Predicted class: cat
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```
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Change the input image
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----------------------
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Use the `generate_image.py` script and the `-i` option to generate a new
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input image.
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For example, the following command
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```
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./generate_image.py -i 1
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```
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will generate an input containing an image with a boat.
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The generated image is displayed at the end of the script execution, for visual
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validation of the prediction made by the neural network running on the device.
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Note that each time a new image is generated, the firmware must be rebuilt so
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that it embeds the new image.
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39
tests/pkg_cmsis-nn/generate_image.py
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39
tests/pkg_cmsis-nn/generate_image.py
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#!/usr/bin/env python3
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"""Generate a binary file from a sample image of the CIFAR-10 dataset.
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Pixel of the sample are stored as uint8, images have size 32x32x3.
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"""
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import os
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import argparse
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import numpy as np
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import matplotlib.pyplot as plt
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from tensorflow.keras.datasets import cifar10
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SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
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def main(args):
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_, (cifar10_test, _) = cifar10.load_data()
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data = cifar10_test[args.index]
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data = data.astype('uint8')
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output_path = os.path.join(SCRIPT_DIR, args.output)
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np.ndarray.tofile(data, output_path)
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if args.no_plot is False:
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plt.imshow(data)
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plt.show()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--index", type=int, default=0,
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help="Image index in CIFAR test dataset")
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parser.add_argument("-o", "--output", type=str, default='input',
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help="Output filename")
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parser.add_argument("--no-plot", default=False, action='store_true',
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help="Disable image display in matplotlib")
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main(parser.parse_args())
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1
tests/pkg_cmsis-nn/input
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1
tests/pkg_cmsis-nn/input
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@l(U=t¨1f”#U„+[<5B>'Z‹*\†,X}(Qp*UsHhCf JmGj Il$Ps/Vx8_€>e‡Bm<42>KwœEq˜1_†+X+X<i<>U‚ªmœÅ]‘¾<s¤R‚ R~@k6k 8i•-Y„+V†(Y†(\„(W{&Qs$OrEiBe IlEh Il?b:Y Fd/Wv=h‰Jw˜Bo‘5`ƒ4_‚-W{Cm‘Yƒ§i’¶Y‡¯0c‘M|"T<>Cn
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133
tests/pkg_cmsis-nn/main.c
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tests/pkg_cmsis-nn/main.c
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/*
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* Copyright (C) 2019 Inria
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*
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* This file is subject to the terms and conditions of the GNU Lesser
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* General Public License v2.1. See the file LICENSE in the top level
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* directory for more details.
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*/
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/**
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* @ingroup tests
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* @{
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*
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* @file
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* @brief Sample application for ARM CMSIS-NN package
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*
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* This example is adapted from ARM CMSIS CIFAR10 example to RIOT by Alexandre Abadie
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* https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10
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*
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* @author Alexandre Abadie <alexandre.abadie@inria.fr>
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*/
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#include <stdint.h>
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#include <stdio.h>
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#include "arm_math.h"
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#include "parameter.h"
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#include "weights.h"
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#include "arm_nnfunctions.h"
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#include "blob/input.h"
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/* There are 10 different classes of objects in the CIFAR10 dataset */
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#define CLASSES_NUMOF 10
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/* include the input and weights */
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static const q7_t conv1_wt[CONV1_IM_CH * CONV1_KER_DIM * CONV1_KER_DIM * CONV1_OUT_CH] = CONV1_WT;
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static const q7_t conv1_bias[CONV1_OUT_CH] = CONV1_BIAS;
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static const q7_t conv2_wt[CONV2_IM_CH * CONV2_KER_DIM * CONV2_KER_DIM * CONV2_OUT_CH] = CONV2_WT;
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static const q7_t conv2_bias[CONV2_OUT_CH] = CONV2_BIAS;
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static const q7_t conv3_wt[CONV3_IM_CH * CONV3_KER_DIM * CONV3_KER_DIM * CONV3_OUT_CH] = CONV3_WT;
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static const q7_t conv3_bias[CONV3_OUT_CH] = CONV3_BIAS;
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static const q7_t ip1_wt[IP1_DIM * IP1_OUT] = IP1_WT;
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static const q7_t ip1_bias[IP1_OUT] = IP1_BIAS;
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/* Here the image_data should be the raw uint8 type RGB image in [RGB, RGB, RGB ... RGB] format */
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// static const uint8_t image_data[CONV1_IM_CH * CONV1_IM_DIM * CONV1_IM_DIM] = IMG_DATA;
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static q7_t output_data[IP1_OUT];
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/* vector buffer: max(im2col buffer,average pool buffer, fully connected buffer) */
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static q7_t col_buffer[2 * 5 * 5 * 32 * 2];
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static q7_t img_buffer1[32 * 32 * 10 * 4];
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static q7_t *img_buffer2 = (q7_t *)(img_buffer1 + (32 * 32 * 32));
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static const char classes[CLASSES_NUMOF][6] = {
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"plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" };
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int main(void)
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{
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printf("start execution\n");
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uint8_t *image_data = (uint8_t *)input;
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/* input pre-processing */
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int mean_data[3] = INPUT_MEAN_SHIFT;
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unsigned int scale_data[3] = INPUT_RIGHT_SHIFT;
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for (unsigned i = 0; i < input_len; i += 3) {
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img_buffer2[i] = (q7_t)__SSAT( ((((int)image_data[i] - mean_data[0]) << 7) + (0x1 << (scale_data[0] - 1)))
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>> scale_data[0], 8);
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img_buffer2[i + 1] = (q7_t)__SSAT( ((((int)image_data[i + 1] - mean_data[1]) << 7) + (0x1 << (scale_data[1] - 1)))
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>> scale_data[1], 8);
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img_buffer2[i + 2] = (q7_t)__SSAT( ((((int)image_data[i + 2] - mean_data[2]) << 7) + (0x1 << (scale_data[2] - 1)))
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>> scale_data[2], 8);
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}
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/* conv1 img_buffer2 -> img_buffer1 */
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arm_convolve_HWC_q7_RGB(img_buffer2, CONV1_IM_DIM, CONV1_IM_CH, conv1_wt, CONV1_OUT_CH, CONV1_KER_DIM, CONV1_PADDING,
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CONV1_STRIDE, conv1_bias, CONV1_BIAS_LSHIFT, CONV1_OUT_RSHIFT, img_buffer1, CONV1_OUT_DIM,
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(q15_t *)col_buffer, NULL);
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arm_relu_q7(img_buffer1, CONV1_OUT_DIM * CONV1_OUT_DIM * CONV1_OUT_CH);
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/* pool1 img_buffer1 -> img_buffer2 */
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arm_maxpool_q7_HWC(img_buffer1, CONV1_OUT_DIM, CONV1_OUT_CH, POOL1_KER_DIM,
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POOL1_PADDING, POOL1_STRIDE, POOL1_OUT_DIM, NULL, img_buffer2);
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/* conv2 img_buffer2 -> img_buffer1 */
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arm_convolve_HWC_q7_fast(img_buffer2, CONV2_IM_DIM, CONV2_IM_CH, conv2_wt, CONV2_OUT_CH, CONV2_KER_DIM,
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CONV2_PADDING, CONV2_STRIDE, conv2_bias, CONV2_BIAS_LSHIFT, CONV2_OUT_RSHIFT, img_buffer1,
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CONV2_OUT_DIM, (q15_t *)col_buffer, NULL);
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arm_relu_q7(img_buffer1, CONV2_OUT_DIM * CONV2_OUT_DIM * CONV2_OUT_CH);
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/* pool2 img_buffer1 -> img_buffer2 */
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arm_maxpool_q7_HWC(img_buffer1, CONV2_OUT_DIM, CONV2_OUT_CH, POOL2_KER_DIM,
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POOL2_PADDING, POOL2_STRIDE, POOL2_OUT_DIM, col_buffer, img_buffer2);
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/* conv3 img_buffer2 -> img_buffer1 */
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arm_convolve_HWC_q7_fast(img_buffer2, CONV3_IM_DIM, CONV3_IM_CH, conv3_wt, CONV3_OUT_CH, CONV3_KER_DIM,
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CONV3_PADDING, CONV3_STRIDE, conv3_bias, CONV3_BIAS_LSHIFT, CONV3_OUT_RSHIFT, img_buffer1,
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CONV3_OUT_DIM, (q15_t *)col_buffer, NULL);
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arm_relu_q7(img_buffer1, CONV3_OUT_DIM * CONV3_OUT_DIM * CONV3_OUT_CH);
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/* pool3 img_buffer-> img_buffer2 */
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arm_maxpool_q7_HWC(img_buffer1, CONV3_OUT_DIM, CONV3_OUT_CH, POOL3_KER_DIM,
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POOL3_PADDING, POOL3_STRIDE, POOL3_OUT_DIM, col_buffer, img_buffer2);
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arm_fully_connected_q7_opt(img_buffer2, ip1_wt, IP1_DIM, IP1_OUT, IP1_BIAS_LSHIFT, IP1_OUT_RSHIFT, ip1_bias,
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output_data, (q15_t *)img_buffer1);
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arm_softmax_q7(output_data, CLASSES_NUMOF, output_data);
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int val = -1;
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uint8_t class_idx = 0;
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for (unsigned i = 0; i < CLASSES_NUMOF; i++) {
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if (output_data[i] > val) {
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val = output_data[i];
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class_idx = i;
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}
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}
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if (val > 0) {
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printf("Predicted class: %s\n", classes[class_idx]);
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}
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else {
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puts("No match found");
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}
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return 0;
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}
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73
tests/pkg_cmsis-nn/parameter.h
Normal file
73
tests/pkg_cmsis-nn/parameter.h
Normal file
@ -0,0 +1,73 @@
|
||||
/*
|
||||
* Copyright (C) 2019 Inria
|
||||
*
|
||||
* This file is subject to the terms and conditions of the GNU Lesser
|
||||
* General Public License v2.1. See the file LICENSE in the top level
|
||||
* directory for more details.
|
||||
*/
|
||||
|
||||
/**
|
||||
* @ingroup tests
|
||||
* @{
|
||||
*
|
||||
* @file
|
||||
* @brief CNN parameters
|
||||
*/
|
||||
|
||||
#ifndef PARAMETER_H
|
||||
#define PARAMETER_H
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define CONV1_IM_DIM 32
|
||||
#define CONV1_IM_CH 3
|
||||
#define CONV1_KER_DIM 5
|
||||
#define CONV1_PADDING 2
|
||||
#define CONV1_STRIDE 1
|
||||
#define CONV1_OUT_CH 32
|
||||
#define CONV1_OUT_DIM 32
|
||||
|
||||
#define POOL1_KER_DIM 3
|
||||
#define POOL1_STRIDE 2
|
||||
#define POOL1_PADDING 0
|
||||
#define POOL1_OUT_DIM 16
|
||||
|
||||
#define CONV2_IM_DIM 16
|
||||
#define CONV2_IM_CH 32
|
||||
#define CONV2_KER_DIM 5
|
||||
#define CONV2_PADDING 2
|
||||
#define CONV2_STRIDE 1
|
||||
#define CONV2_OUT_CH 16
|
||||
#define CONV2_OUT_DIM 16
|
||||
|
||||
#define POOL2_KER_DIM 3
|
||||
#define POOL2_STRIDE 2
|
||||
#define POOL2_PADDING 0
|
||||
#define POOL2_OUT_DIM 8
|
||||
|
||||
#define CONV3_IM_DIM 8
|
||||
#define CONV3_IM_CH 16
|
||||
#define CONV3_KER_DIM 5
|
||||
#define CONV3_PADDING 2
|
||||
#define CONV3_STRIDE 1
|
||||
#define CONV3_OUT_CH 32
|
||||
#define CONV3_OUT_DIM 8
|
||||
|
||||
#define POOL3_KER_DIM 3
|
||||
#define POOL3_STRIDE 2
|
||||
#define POOL3_PADDING 0
|
||||
#define POOL3_OUT_DIM 4
|
||||
|
||||
#define IP1_DIM 4*4*32
|
||||
#define IP1_IM_DIM 4
|
||||
#define IP1_IM_CH 32
|
||||
#define IP1_OUT 10
|
||||
|
||||
#ifdef __cplusplus
|
||||
} /* end extern "C" */
|
||||
#endif
|
||||
|
||||
#endif /* PARAMETER_H */
|
||||
/** @} */
|
||||
12
tests/pkg_cmsis-nn/tests/01-run.py
Executable file
12
tests/pkg_cmsis-nn/tests/01-run.py
Executable file
@ -0,0 +1,12 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import sys
|
||||
from testrunner import run
|
||||
|
||||
|
||||
def testfunc(child):
|
||||
child.expect_exact("Predicted class: cat")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(run(testfunc))
|
||||
1772
tests/pkg_cmsis-nn/weights.h
Normal file
1772
tests/pkg_cmsis-nn/weights.h
Normal file
File diff suppressed because it is too large
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