commit

backup/preset/server/ccrb.crt

@@ -1,26 +0,0 @@
------BEGIN CERTIFICATE-----
-MIIEYjCCA0qgAwIBAgIJAOS8Bh4qCqhBMA0GCSqGSIb3DQEBCwUAMIGGMQswCQYD
-VQQGEwJDTjESMBAGA1UECAwJR3Vhbmdkb25nMREwDwYDVQQHDAhTaGVuemhlbjEV
-MBMGA1UECgwMQ29jb1JvYm8gTFREMRowGAYDVQQDDBFDb2NvUm9ibyBUcnVzdCBD
-QTEdMBsGCSqGSIb3DQEJARYOaXRAY29jb3JvYm8uY2MwHhcNMTgxMjI0MDg1ODIz
-WhcNMjExMDEzMDg1ODIzWjB+MQswCQYDVQQGEwJDTjESMBAGA1UECAwJR3Vhbmdk
-b25nMREwDwYDVQQHDAhTaGVuemhlbjEVMBMGA1UECgwMQ29jb1JvYm8gTFREMRIw
-EAYDVQQDDAlsb2NhbGhvc3QxHTAbBgkqhkiG9w0BCQEWDml0QGNvY29yb2JvLmNj
-MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAvk3cAwel+eWyJXAl9RB9
-rhxQHc4vDr/VshOy157/+jj6rUyDqRpdWh66DBmn429ReXzRsxhu5dhWUfWzztjW
-1pJ1159mC9TTGWWRqCKuVDNXtXq7QYD6apODjpk7U/lpKed4Gf6DWMtGk4BSAJ8g
-Y3k/R/bE6iCQHJLE/yVnWw/tw5P8lKRajUi36JzOKQ6l0cJqa1vFXjs9S64xzhlp
-HfKLPLzwEjIYVVJTShBQSVuNz9WcetQkID8FUm4uJg1XkFBJXY6pxXgTeeHpPEO2
-HgOIXaJi2clIshjgMGHkseP62coS8LyoDZ7uLJf8JhR8Rdb+Qn+n/54RbFWDGyUs
-mwIDAQABo4HZMIHWMIGlBgNVHSMEgZ0wgZqhgYykgYkwgYYxCzAJBgNVBAYTAkNO
-MRIwEAYDVQQIDAlHdWFuZ2RvbmcxETAPBgNVBAcMCFNoZW56aGVuMRUwEwYDVQQK
-DAxDb2NvUm9ibyBMVEQxGjAYBgNVBAMMEUNvY29Sb2JvIFRydXN0IENBMR0wGwYJ
-KoZIhvcNAQkBFg5pdEBjb2Nvcm9iby5jY4IJANpFK/+zAjUeMAkGA1UdEwQCMAAw
-CwYDVR0PBAQDAgTwMBQGA1UdEQQNMAuCCWxvY2FsaG9zdDANBgkqhkiG9w0BAQsF
-AAOCAQEAq7LoQ+AMK9SEw+HSPivOt8zE356FGvGxaXImwHA87vPPGozIN6nLYSey
-qtR0kxpRYU0Ki2zijqN4BZNZD7p6gRzbOA8pnO41tBUUmXIsgoYSKykuPVO8NkiA
-bdFo8m5TBOrkIJXf6rrfXOaFQbZ5n75H9N8UrdXKQz9sp13vfZYmltRt1+ya+0Xw
-h1mrnDMBZqFkttkKDu+nFoJTJcBZ0xUEh5u0gdFNZQDdQeZO+SYKtUIhdhVN+3HM
-JnQXM3eeTl/EzS8PvntQa0BV3M4jHO7W+7piOBD5exNmIFkHqd0f6h4MQ2b+rLsE
-MQFHzwA1WZLZKzUUlQe763uXn2KG5w==
------END CERTIFICATE-----

backup/preset/server/ccrb.key

@@ -1,27 +0,0 @@
------BEGIN RSA PRIVATE KEY-----
-MIIEpQIBAAKCAQEAvk3cAwel+eWyJXAl9RB9rhxQHc4vDr/VshOy157/+jj6rUyD
-qRpdWh66DBmn429ReXzRsxhu5dhWUfWzztjW1pJ1159mC9TTGWWRqCKuVDNXtXq7
-QYD6apODjpk7U/lpKed4Gf6DWMtGk4BSAJ8gY3k/R/bE6iCQHJLE/yVnWw/tw5P8
-lKRajUi36JzOKQ6l0cJqa1vFXjs9S64xzhlpHfKLPLzwEjIYVVJTShBQSVuNz9Wc
-etQkID8FUm4uJg1XkFBJXY6pxXgTeeHpPEO2HgOIXaJi2clIshjgMGHkseP62coS
-8LyoDZ7uLJf8JhR8Rdb+Qn+n/54RbFWDGyUsmwIDAQABAoIBAE3OcZS/jMnWcvbB
-mpNO0cPdCZiWE0FZh5qOWUG1XX1FzKAMN4xtHhvZkkGeP7lKaypbsTUjWTwaLnjG
-F2UL3RLPwOSO5F8T/ddVYq82tsXwU/z8GBGZFUl71H76AXq875VVcRJXsAYe3lUy
-UtS0Or7Q9OGMcEg1VhaIn4ieF1rwPE6QvsWiXzDcqvA9GFqLfVHE4i88CwkeBKdB
-S52HkXruVExekwITJxo6pcj61BRD8xLypFyy923+ONPSjACtetbukA9QFOsOvFSQ
-kymCaR65in5smkMrFeiTa6srDtIvUxFMdhnOmPbsmWsrR2k98ZMi+85k1OOG8vXO
-JeIAxSECgYEA7b6tTyql2RCiPoS2HiCJVsjcpwz/Pv0BduR6zNNn59HzVO3hZ+td
-Y3mAtU2ix7WK5A+9ezw4ge40TrolHxYgpUBkCrATVBxOKKChvNhRQ/Tc/26ewDrl
-1U0l0B/qi30+INwbC6gXSV2vvL5rRcCjBfTVOJ2xAxLtfcCVkMVyufECgYEAzOql
-aSC5FoSSaaL1FHMRlQjbe8UG2fdlMNAuSzkqVa2QfQefvTpFk3RD4hJR38HHenph
-m1xdlqoGSp1pjcGFCj5UfSSee01kIFa3CZ9eGqxb8JXnJex7oGC+F8mzwjYqYetr
-wTi46PeX+w+Gnq2e1gI8/QjNlL8F6KCqhRoo40sCgYEAvq/iaA4H6dt6lnRxciac
-oXwH5wD4tTfO9eJ6UFD8FScDacpGschJJmEtB75WYqJM3XF2hCKgHC7Hy6Fn5tAj
-rQfBdgUV66+sTM/SChriy4PZwhKiyAI1l+kB/DWtNOZmb4q7MpVG4DSBoPyGI/NH
-jp56aqeoc5O54tQV4oFL7LECgYEAibUel/l/qNxKsGbsoGN761XaeeC8a/pPrHAa
-zD6MbquHMB7RgI8Kdr9pZyG8l8xXy702nvuBgWCdfcMvTi5Aw9ODX94eCurFYN4l
-f3fz+6Tc3F9AyUGVCwA4fy/zd46DCO72qRD/YsARCI00gKpY1aUHa1xhG5cL13sx
-XOVpmOUCgYEA2mh8UCY5ANQ3McXBx346+RxKLZnDkntyS6A9JbZSWgp2ca8TfpXS
-XQgdmDkz6VNO3I6tasqRqZVcTFpVSRpkmyCob7cu7XpZ5hUZjK4a+5xUR+KBZ1LI
-OAFAJ9JjDwcTEujArla4aUAntxvKBWGv2LHKVluy5aOGGfKTzUDWbro=
------END RSA PRIVATE KEY-----

backup/preset/server/wirelessServer.py

@@ -1,88 +0,0 @@
-import asyncio
-import websockets
-import ssl
-import os
-import signal
-import linecache
-
-
-ssl._create_default_https_context = ssl._create_unverified_context
-ssl_context = ssl.SSLContext(ssl.PROTOCOL_SSLv23)
-ssl_context.load_cert_chain("/root/preset/server/ccrb.crt", "/root/preset/server/ccrb.key")
-p1 = None
-p2 = None
-p3 = None
-countn = 0
-
-async def send(websocket):
-    global p2
-    global p1
-    global countn
-    await asyncio.create_subprocess_shell("echo > /root/event.log &")
-    #open("/root/event.log", 'w').close()
-    while True:
-        await asyncio.sleep(0.03)
-        if True:
-            try:
-                proxy_recv_msg = ""
-                linecache.clearcache()
-                logarr = linecache.getlines("/root/event.log")[-20:]
-                open("/root/event.log", 'w').close()
-                #os.remove(r'/root/event.log')
-                #await asyncio.create_subprocess_shell("echo '' > /root/event.log")
-                #print(linecache.getlines("/root/event.log")[-10:])
-                proxy_recv_msg = "".join(logarr).rstrip('\x00')
-                #print(len(logarr))
-                if proxy_recv_msg != "" and proxy_recv_msg != "\n":                                                                         
-                    try:                                                                                         
-                        await websocket.send(proxy_recv_msg)
-                    except websockets.ConnectionClosed:
-                        try:
-                            if p1.returncode == None:                                                                                                                                                
-                                os.killpg(p1.pid, signal.SIGUSR1)
-                            #open("/root/event.log", 'w').close()
-                            #await asyncio.create_subprocess_shell("echo "" > /root/event.log")                                                                                                                
-                        finally:                                                                                                                                             
-                            pass                                                           
-                        pass 
-            finally:                                                                                         
-                pass
-
-async def receive(websocket):
-    global p2
-    global p3
-    global p1    
-#    if p2 == None and p3 == None:
-#        p2 = await asyncio.create_subprocess_shell("echo '' > /tmp/event.log && tail -f /tmp/event.log", stdout=asyncio.subprocess.PIPE, preexec_fn = os.setsid)
-#        p3 = p2
-#    else:
-#        p2 = p3
-
-    while True:
-        await asyncio.sleep(0.5)
-        recv_text = await websocket.recv()
-        if p1 != None:
-            try:
-                if p1.returncode == None:
-                    p1.kill()
-                    os.killpg(p1.pid, signal.SIGUSR1)
-            finally:
-                pass
-        p1 = await asyncio.create_subprocess_shell(recv_text, stdout=None, preexec_fn = os.setsid)
-        print(p1.pid)
-
-async def main_logic(websocket, path):
-        await runstart(websocket, path)
-        asyncio.run(runstart(websocket, path))
-
-async def runstart(websocket, path):
-        a = asyncio.create_task(send(websocket))
-        b = asyncio.create_task(receive(websocket))
-        await a
-        await b
-
-
-start_server = websockets.serve(main_logic, '0.0.0.0', 5678, ssl=ssl_context)
-
-asyncio.get_event_loop().run_until_complete(start_server)        
-asyncio.get_event_loop().run_forever()

backup/preset/training/res/README.md

@@ -0,0 +1,175 @@
+## 在 CocoPi 上使用 numpy + mnist 训练模型导出导入验证的教程
+
+测试集位于 mnist_test.csv ；
+
+训练集位于 mnist_train.zip (csv格式超过可上传最大值）；
+
+code包括神经网络模型和用测试、训练数据跑出的最优模型；
+
+数据集：MINIST；
+
+不可使用 pytorch，tensorflow 等 python package，可以使用numpy；
+
+## 准备数据集
+
+CSV 文件内容介绍：(https://blog.csdn.net/CVSvsvsvsvs/article/details/85127096)
+
+![](./dataset.png)
+
+更多数据集可以自己寻找或制作：[制作minist格式的图像数据集](https://blog.csdn.net/vertira/article/details/122326362)
+
+## 在板子上训练
+
+这里演示十分钟即可在 wiki.sipeed.com/m2dock 上训练 mnist 模型，精简数据集为 100 测试 10 验证，可快速体验效果。
+
+00_demo.ipynb 01_min_train.py 02_val_mnist.py
+
+完整的演示过程 00_demo.ipynb 其他脚本可以直接在板子依次运行。
+
+使用 numpy 训练要打开 numpy.random 模块
+
+`rm -rf /usr/lib/python3.8/site-packages/numpy/random/__init__.py`
+
+想恢复就用
+
+`touch /usr/lib/python3.8/site-packages/numpy/random/__init__.py`
+
+该功能只影响开机加载 numpy 的速度以及内存占用，因为 maixpy3 底层是应用了 numpy 进行部分后处理转换的，所以会发现部署 AI 应用的时候开机速度很慢。
+
+
+```bash
+
+cd NeuralNetwork
+
+adb push mnist_test_10.csv mnist_train_100.csv 01_min_train.py 02_val_mnist.py /root
+
+adb push resc /root/resc
+
+adb shell
+
+rm -rf /usr/lib/python3.8/site-packages/numpy/random/__init__.py
+
+cd /root/
+
+python 01_min_train.py # 训练模型并导出
+
+python 02_val_mnist.py # 加载模型并验证
+
+```
+
+运行结果如下：
+
+```bash
+juwan@juwan-n85-dls:~$ cd NeuralNetwork
+juwan@juwan-n85-dls:~/NeuralNetwork$
+juwan@juwan-n85-dls:~/NeuralNetwork$ adb push mnist_test_10.csv mnist_train_100.csv 01_min_train.py 02_val_mnist.py /root
+mnist_test_10.csv: 1 file pushed. 3.4 MB/s (18006 bytes in 0.005s)
+mnist_train_100.csv: 1 file pushed. 4.2 MB/s (182023 bytes in 0.041s)
+01_min_train.py: 1 file pushed. 2.0 MB/s (4940 bytes in 0.002s)
+02_val_mnist.py: 1 file pushed. 2.1 MB/s (3972 bytes in 0.002s)
+4 files pushed. 3.8 MB/s (208941 bytes in 0.053s)
+juwan@juwan-n85-dls:~/NeuralNetwork$
+juwan@juwan-n85-dls:~/NeuralNetwork$ adb push resc /root/resc
+resc/: 6 files pushed. 3.6 MB/s (13866458 bytes in 3.723s)
+juwan@juwan-n85-dls:~/NeuralNetwork$
+juwan@juwan-n85-dls:~/NeuralNetwork$ adb shell
+
+
+BusyBox v1.27.2 () built-in shell (ash)
+
+------run profile file-----
+=========================================================
+    ______                         ______
+   /\  _  \                       /\  _  \  __
+   \ \ \/\_\    ____    ____   ___\ \ \_\ \/\_\
+    \ \ \/_/_  / __ \  / __ \ / __'\ \  __/\/_/_
+     \ \ \_\ \/\ \_\ \/\ \__//\ \_\ \ \ \/   /\ \
+      \ \____/\ \____/\ \____\ \____/\ \_\   \ \_\
+       \/___/  \/___/  \/____/\/___/  \/_/    \/_/
+  __
+ /\ \       __                           ----------------
+ \ \ \     /\_\    ____  __  __  __  _    pi.cocorobo.hk
+  \ \ \    \/_/_ /  _  \/\ \/\ \/\ \/ \  ----------------
+   \ \ \____ /\ \/\ \/\ \ \ \_\ \/>  </   SYSTEM VERSION
+    \ \_____\\ \_\ \_\ \_\ \____//\_/\_\  - V6 23.0825 -
+     \/_____/ \/_/\/_/\/_/\/___/ \//\/_/ ----------------
+
+=========================================================
+
+root@CocoPi:/#
+root@CocoPi:/# rm -rf /usr/lib/python3.8/site-packages/numpy/random/__init__.py
+root@CocoPi:/#
+root@CocoPi:/# cd /root/
+root@CocoPi:/#
+root@CocoPi:~# python 01_min_train.py # 训练模型并导出
+(100, 785)
+(10, 785)
+准确度50.00%
+隐藏层节点数512,学习率0.100000,准确度60.00%
+隐藏层节点数512,学习率0.200000,准确度50.00%
+隐藏层节点数512,学习率0.300000,准确度60.00%
+隐藏层节点数256,学习率0.100000,准确度60.00%
+隐藏层节点数256,学习率0.200000,准确度60.00%
+隐藏层节点数256,学习率0.300000,准确度50.00%
+隐藏层节点数128,学习率0.100000,准确度60.00%
+隐藏层节点数128,学习率0.200000,准确度60.00%
+隐藏层节点数128,学习率0.300000,准确度60.00%
+第1次训练,准确度50.00%
+第2次训练,准确度60.00%
+第3次训练,准确度70.00%
+第4次训练,准确度70.00%
+第5次训练,准确度70.00%
+第6次训练,准确度70.00%
+第7次训练,准确度70.00%
+第8次训练,准确度70.00%
+第9次训练,准确度70.00%
+第10次训练,准确度70.00%
+root@sipeed:~#
+root@sipeed:~# python 02_val_mnist.py # 加载模型并验证
+NeuralNetwork:
+input_nodes = 784, hidden_nodes = 128,
+outputnodes = 10, learningrate = 0.025
+(1, 784)
+save:  ./imgs/sxsz.ttf_0_6.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_1_6.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_2_8.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_3_3.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_4_9.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_5_6.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_6_6.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_7_1.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_8_6.png
+(1, 784)
+save:  ./imgs/sxsz.ttf_9_7.png
+(1, 784)
+save:  ./imgs/sans.ttf_0_2.png
+(1, 784)
+save:  ./imgs/sans.ttf_1_1.png
+(1, 784)
+save:  ./imgs/sans.ttf_2_2.png
+(1, 784)
+save:  ./imgs/sans.ttf_3_3.png
+(1, 784)
+save:  ./imgs/sans.ttf_4_3.png
+(1, 784)
+save:  ./imgs/sans.ttf_5_6.png
+(1, 784)
+save:  ./imgs/sans.ttf_6_6.png
+(1, 784)
+save:  ./imgs/sans.ttf_7_2.png
+(1, 784)
+save:  ./imgs/sans.ttf_8_6.png
+(1, 784)
+save:  ./imgs/sans.ttf_9_4.png
+root@sipeed:~#
+```
+
+可见 sans,ttf 字体的识别效果在 0 1 2 3 6 有一定的正确性，如果想要更好的，可以拿完整的 60000 : 10000 训练，这里为了快速演示效果，采用了 100 ： 10

backup/preset/training/res/mnist_test_10.csv

@@ -0,0 +1,10 @@
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