Speed up by ignoring std::cout

Test Code

  1. #include <iostream>
  2.  
  3. int main(int argc, char *argv[])
  4. {
  5. 	//std::cout.setstate(std::ios_base::badbit);
  6. 	for(int i = 0; i < 100; i ++) {
  7. 		for(int i = 0; i < 100; i ++) {
  8. 			;//std::cout << "" << std::endl;
  9. 		}
  10. 	}
  11. 	return 0;
  12. }

Test it without std::cout

  1. root@imx6ul7d:~/tmp# time ./t1_1 
  2.  
  3. real    0m0.041s
  4. user    0m0.020s
  5. sys     0m0.000s

Test it with std::cout, but redirect to /dev/null

  1. root@imx6ul7d:~/tmp# time ./t1_0 > /dev/null
  2.  
  3. real    0m0.096s
  4. user    0m0.030s
  5. sys     0m0.030s

Test it with std::cout, but set io state

  1. root@imx6ul7d:~/tmp# time ./t1_2
  2.  
  3. real    0m0.061s
  4. user    0m0.040s
  5. sys     0m0.000s

Profile : Linux

Example 1

Add -pg

  1. arm-linux-gnueabihf-g++ -Wall -g -pg hello.cpp -o hello -std=c++17

Test

  1. root@imx6ul7d:~# gprof -b hello 
  2. Flat profile:
  3.  
  4. Each sample counts as 0.01 seconds.
  5.   %   cumulative   self              self     total           
  6.  time   seconds   seconds    calls   s/call   s/call  name    
  7. 100.00      3.45     3.45        1     3.45     3.45  hehe()
  8.   0.00      3.45     0.00        4     0.00     0.00  std::_Optional_base<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >::_M_is_engaged() const
  9. ......

Routes stream to specified buffer

Developer Command Prompt for VS 2019

  1. cl basic_ios_rdbuf.cpp /EHsc

MSDN Source Code

  1. #include <ios>
  2. #include <iostream>
  3. #include <fstream>
  4.  
  5. int main( ) 
  6. {
  7.    using namespace std;
  8.    ofstream file( "rdbuf.txt" );
  9.    streambuf *x = cout.rdbuf( file.rdbuf( ) );
  10.    cout << "test" << endl; // Goes to file
  11.    cout.rdbuf(x);
  12.    cout << "test2" << endl; //normal
  13. }

IPC Performance : Socket on ARM

Test Environment

CPU: Freescale i.MX6UL rev1.1 528 MHz
DRAM: 512 MiB

Test Code

  1. #include <stdlib.h>
  2. #include <sys/types.h>
  3. #include <sys/socket.h> 
  4. #include <netinet/in.h>
  5. #include <errno.h>
  6. #include <string.h>
  7. #include <unistd.h>
  8. #include <fcntl.h>
  9.  
  10. #define CLTIP "127.0.0.1"
  11. #define SRVPORT 10172
  12. #define MAX_NUM 1024
  13.  
  14. int main(int argc, char* argv[])
  15. {
  16. 	struct timeval tv_sta, tv_end;
  17.  
  18. 	gettimeofday(&tv_sta, NULL);
  19.  
  20. 	int i = 0;
  21. 	int total = 1;
  22. 	int is_printf = 1;
  23. 	if (argc > 1) {
  24. 		total = atoi(argv[1]);
  25. 	}
  26. 	if (argc > 2) {
  27. 		is_printf = atoi(argv[2]);
  28. 	}
  29.  
  30. 	for (i = 0; i < total; i++) {
  31.  
  32. 		int clientsock = socket(PF_INET, SOCK_STREAM, 0);
  33. 		if (clientsock < 0)
  34. 		{
  35. 			printf("socket creation failed\n");
  36. 			exit(-1);
  37. 		}
  38. 		if (is_printf)
  39. 			printf("socket create successfully.\n");
  40.  
  41. 		struct sockaddr_in clientAddr;
  42. 		clientAddr.sin_family = AF_INET;
  43. 		clientAddr.sin_port = htons((u_short)SRVPORT);
  44. 		clientAddr.sin_addr.s_addr = inet_addr(CLTIP);
  45.  
  46. 		int flags = fcntl(clientsock, F_GETFL, 0);
  47. 		fcntl(clientsock, F_SETFL, flags | O_NONBLOCK);
  48.  
  49. 		int n;
  50. 		if ((n = connect(clientsock, (struct sockaddr*) & clientAddr, sizeof(struct sockaddr))) < 0)
  51. 		{
  52. 			if (errno != EINPROGRESS) {
  53. 				printf("Connect error.IP[%s], port[%d]\n", CLTIP, clientAddr.sin_port);
  54. 				exit(-1);
  55. 			}
  56. 		}
  57.  
  58. 		if (n != 0) {
  59.  
  60. 			fd_set rset, wset;
  61. 			FD_ZERO(&rset);
  62. 			FD_SET(clientsock, &rset);
  63. 			wset = rset;
  64. 			struct timeval tval;
  65. 			tval.tv_sec = 1;
  66. 			tval.tv_usec = 0;
  67. 			if ((n = select(clientsock + 1, &rset, &wset, NULL, 3 ? &tval : NULL)) == 0) {
  68. 				printf("Connect TIMEOUT.IP[%s], port[%d]\n", CLTIP, clientAddr.sin_port);
  69. 				exit(-1);
  70. 			}
  71. 			if (FD_ISSET(clientsock, &rset) || FD_ISSET(clientsock, &wset)) {
  72. 				int error;
  73. 				int len = sizeof(error);
  74. 				if (getsockopt(clientsock, SOL_SOCKET, SO_ERROR, &error, &len) < 0) {
  75. 					printf("pending error");
  76. 					exit(-1);
  77. 				}
  78. 			}
  79. 			else {
  80. 				printf("select error: clientsock not set");
  81. 				exit(-1);
  82. 			}
  83.  
  84. 		}
  85. 		fcntl(clientsock, F_SETFL, flags);
  86.  
  87. 		if (is_printf)
  88. 			printf("Connect to IP[%s], port[%d]\n", CLTIP, clientAddr.sin_port);
  89.  
  90. 		const char* sendBuf = "{\"signalType\":\"CONFIG\", \"signalData\":[{\"id\":\"CONF_NAME\", \"value\":\"PPS_MChip1\"}]}";
  91. 		char recvBuf[MAX_NUM] = { 0 };
  92.  
  93. 		//while(gets(sendBuf) != '\0')
  94. 		{
  95. 			if (send(clientsock, sendBuf, strlen(sendBuf), 0) == -1)
  96. 			{
  97. 				printf("send error!\n");
  98. 			}
  99. 			if (is_printf)
  100. 				printf("send to server:%s\n", sendBuf);
  101. 			//memset(sendBuf, 0, sizeof(sendBuf));
  102.  
  103. 			{
  104. 				int timeOut = 3000;
  105. 				int nRet = 0;
  106. 				struct timeval timeout;
  107. 				fd_set recvset;
  108. 				FD_ZERO(&recvset);
  109. 				FD_SET(clientsock, &recvset);
  110. 				timeout.tv_sec = timeOut / 1000;
  111. 				timeout.tv_usec = timeOut % 1000;
  112. 				nRet = select(clientsock + 1, &recvset, 0, 0, &timeout);
  113. 				if (nRet <= 0)
  114. 				{
  115. 					printf("clientsock recv select is error!\n");
  116. 					exit(-1);
  117. 				}
  118. 			}
  119.  
  120. 			if (recv(clientsock, recvBuf, MAX_NUM, 0) == -1)
  121. 			{
  122. 				printf("rev error!\n");
  123. 			}
  124. 			if (is_printf)
  125. 				printf("receive from server:%s\n", recvBuf);
  126. 			if (strcmp(recvBuf, "Goodbye,my dear client!") == 0)
  127. 			{
  128. 				//break;
  129. 			}
  130. 			memset(recvBuf, 0, sizeof(recvBuf));
  131. 		}
  132. 		close(clientsock);
  133.  
  134. 	}
  135.  
  136. 	gettimeofday(&tv_end, NULL);
  137.  
  138. 	int total_millisecond = (tv_end.tv_sec * 1000 + tv_end.tv_usec / 1000) - (tv_sta.tv_sec * 1000 + tv_sta.tv_usec / 1000);
  139. 	int average_millisecond = (total_millisecond / total);
  140.  
  141. 	printf("total_millisecond : %ld total_nums : %d\n", total_millisecond, total);
  142. 	printf("average_millisecond   : %ld\n", average_millisecond);
  143.  
  144. 	return 0;
  145. }

Test Result

  1. root@imx6ul7d:~/tmp# ./a.out 1 0
  2. total_millisecond : 13 total_nums : 1
  3. average_millisecond   : 13
  4. root@imx6ul7d:~/tmp# ./a.out 10 0
  5. total_millisecond : 104 total_nums : 10
  6. average_millisecond   : 10
  7. root@imx6ul7d:~/tmp# ./a.out 100 0 
  8. total_millisecond : 1023 total_nums : 100
  9. average_millisecond   : 10
  10. root@imx6ul7d:~/tmp# ./a.out 1000 0
  11. total_millisecond : 10801 total_nums : 1000
  12. average_millisecond   : 10

How to expose a local server behind a NAT or firewall to the internet

Download FRP Server & Client

Modify frps.ini

  1. [root@VM_0_5_centos frp_0.25.3_linux_amd64]# cat frps.ini
  2. [common]
  3. bind_port = xxxxxx
  4. dashboard_port = xxxxxx
  5. dashboard_user = xxxxxxxxxxxxxxxx
  6. dashboard_pwd = xxxxxxxxxxxxxxxx
  7. allow_ports = xxxxx-xxxxx
  8. token = xxxxxxxxxxxxxxxx
  9. max_pool_count = 10
  10. pool_count = 3

Start frps

  1. [root@VM_0_5_centos frp_0.25.3_linux_amd64]# ./frps -c ./frps.ini

Modify frpc.ini

  1. root@imx6ul7d:~/frp# cat frpc.ini 
  2. [common]
  3. server_addr = qinuu.com
  4. server_port = xxxxxx
  5. token = xxxxxxxxxxxxxxxx
  6. [ssh]
  7. type = tcp
  8. local_ip = 127.0.0.1
  9. local_port = 22
  10. remote_port = xxxxxx

Start frpc

root@imx6ul7d:~/frp# ./frpc -c ./frpc.ini

Start a systemd service

[root@VM_0_5_centos frp_0.25.3_linux_amd64]# cat /lib/systemd/system/frps.service
[Unit]
Description=Frp Server Service
After=network.target
 
[Service]
Type=simple
User=nobody
Restart=on-failure
RestartSec=5s
ExecStart=/usr/local/frp/frp_0.25.3_linux_amd64/frps -c /usr/local/frp/frp_0.25.3_linux_amd64/frps.ini
 
[Install]
WantedBy=multi-user.targethis
 
[root@VM_0_5_centos frp_0.25.3_linux_amd64]# systemctl start frps
[root@VM_0_5_centos frp_0.25.3_linux_amd64]# systemctl enable frps
[root@VM_0_5_centos frp_0.25.3_linux_amd64]# systemctl status frps

Cross compile wget with openssl for ARM

Config OpenSSL

tar xvf openssl-1.1.1b.tar.gz 
cd openssl-1.1.1b/
mkdir __install
./config --prefix=$PWD/__install no-asm shared

Edit Makefile

PLATFORM=linux-armv4
CROSS_COMPILE=arm-linux-gnueabihf-
delete "-m32" "-m64"

Compile OpenSSL

make
make install

Config Wget && Make

tar zxvf wget-1.20.1.tar.gz
cd wget-1.20.1
./configure --prefix=$PWD/__install --host=arm-linux CC=arm-linux-gnueabihf-gcc --with-ssl=openssl --with-libssl-prefix=/root/Downloads/wget_arm/openssl-1.1.1b/__install --without-zlib
make 
make install

./xxx: /usr/lib/libstdc++.so.6: version `GLIBCXX_3.4.22′ not found (required by ./xxx)

Copy libstdc++.so.6.0.24 from cross compiler (7.3.1)

cd /usr/lib
mv libstdc++.so.6 libstdc++.so.6.bak
ln -s libstdc++.so.6.0.24 libstdc++.so.6
chmod 777 libstdc++.so.6

Now, it will be like this !

root@imx6ul7d:/usr/lib# ls -al libstdc++.so.6*
lrwxrwxrwx 1 root root      19 Sep 16 23:03 libstdc++.so.6 -> libstdc++.so.6.0.24
-rwxr-xr-x 1 root root 1240644 Sep  9 02:04 libstdc++.so.6.0.21
-rwxrwxrwx 1 root root 9459568 Sep 16 23:35 libstdc++.so.6.0.24
lrwxrwxrwx 1 root root      19 Jan  1  1970 libstdc++.so.6.bak -> libstdc++.so.6.0.21
-rwxr-xr-x 1 root root 9459568 Sep 16 23:11 libstdc++.so.6.bak_24

TensorFlow on Android (Refer to codelabs : tensorflow-for-poets)

The google demo is good, but there are some error when you follow it, so I copied and update it.

Install TensorFlow As Normal

  1. pip install tensorflow==1.7.1

Download Google Demo

  1. git clone https://github.com/googlecodelabs/tensorflow-for-poets-2
  2. cd tensorflow-for-poets-2

Download TF_Files

  1. wget http://download.tensorflow.org/example_images/flower_photos.tgz
  2. tar -xvf flower_photos.tgz -C tf_files

Set ARCHITECTURE

  1. IMAGE_SIZE=224
  2. ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

Start TensorBoard

  1. tensorboard --logdir tf_files/training_summaries &

Help : retraining script

  1. python -m scripts.retrain -h

Train

  1. python -m scripts.retrain \
  2.   --bottleneck_dir=tf_files/bottlenecks \
  3.   --how_many_training_steps=500 \
  4.   --model_dir=tf_files/models/ \
  5.   --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  6.   --output_graph=tf_files/retrained_graph.pb \
  7.   --output_labels=tf_files/retrained_labels.txt \
  8.   --architecture="${ARCHITECTURE}" \
  9.   --image_dir=tf_files/flower_photos

Train with default 4,000 iterations

  1. python -m scripts.retrain \
  2.   --bottleneck_dir=tf_files/bottlenecks \
  3.   --model_dir=tf_files/models/"${ARCHITECTURE}" \
  4.   --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  5.   --output_graph=tf_files/retrained_graph.pb \
  6.   --output_labels=tf_files/retrained_labels.txt \
  7.   --architecture="${ARCHITECTURE}" \
  8.   --image_dir=tf_files/flower_photos

Train with Your Own Data

--image_dir= The root folder of the subdirectories which is used as label names by the classification script
This image is copied from google’s codelabs !

The Retrained Model

  1. tf_files/retrained_graph.pb
  2. tf_files/retrained_labels.txt

Test

  1. (venv) water@water-G11CD:~/tensorflow-for-poets-2$ python -m scripts.label_image     --graph=tf_files/retrained_graph.pb      --image=tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg
  2.  
  3. 2019-01-23 14:13:50.234939: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
  4.  
  5. Evaluation time (1-image): 0.094s
  6.  
  7. daisy (score=0.99938)
  8. sunflowers (score=0.00033)
  9. dandelion (score=0.00028)
  10. roses (score=0.00000)
  11. tulips (score=0.00000)

Now, Prepare for Android

Install PILLOW

  1. pip install PILLOW

Convert the model to TFLite format

  1. IMAGE_SIZE=224
  2. toco   --input_file=tf_files/retrained_graph.pb   --output_file=tf_files/optimized_graph.lite   --input_format=TENSORFLOW_GRAPHDEF   --output_format=TFLITE   --input_shape=1,${IMAGE_SIZE},${IMAGE_SIZE},3   --input_array=input   --output_array=final_result   --inference_type=FLOAT   --input_data_type=FLOAT

The TFLite Model

  1. tf_files/optimized_graph.lite

Install AndroidStudio

  1. unzip android-studio-ide-182.5199772-linux.zip 
  2. cd android-studio/
  3. cd bin/
  4. ./studio.sh

Maybe You Need to Set Proxy

If you use lantern, you can do it like this!

Open Project

Open an existing Android Studio project

Choose android/tflite

Gradle Sync, YES

Build, Make Project

  1. android/tflite/app/build/outputs/apk/debug/app-debug.apk

Test on Your Phone

Add your model files to the project, and then Make Project, Test Again

  1. cp tf_files/optimized_graph.lite android/tflite/app/src/main/assets/graph.lite
  2. cp tf_files/retrained_labels.txt android/tflite/app/src/main/assets/labels.txt