lab2 kernel image download

SipeedR329主线armbian内核、系统、驱动的开发⽅法

我的开发环境是 ubuntu20.04 ，请在具备以下⽂章的开发基础上，再继续往下会⽐较好。

选⽤官⽅版型：

⾸先拿到原始镜像 sipeed-r329-maixsense-armbian-maixpy3-0.4.0.img ，⽤ dd 烧录到⼤于 4G 的卡⾥就⾏，我丢群⾥了。

2022-03-03 本次更新只是包含了 maixpy3 所需要的所有基础环境，并在⾥⾯放了⼀个 resnet50 和 ⼈脸检测关键点供参考测试，加⼊了 uart 和 i2c 设

备，aipu 未更新。

sipeed ⽤户镜像 root 账号没有 root 密码，输⼊ root 直接进⼊系统，⾃带 maixpy3 0.4.0 所需的基础环境，仅⽤于⽤户参考。

原始镜像为 Armbian_21.08.0-trunk_Maixsense_bullseye_edge_5.14.1.img.xz (700M) ⾃⾏下载站的 sipeed 企业盘中获取。

镜像编辑和导出的⽅法

在 ubuntu 上可以直接使⽤ disk 装载镜像编辑⽂件（disk）。

挂载到 loop 设备和加载到⽂件系统

sudo losetup -P /dev/loop404 ./r329-armbian-maixpy3-0.4.0-20220223.img && sudo mkdir -p ./r329 && sudo mount /dev/loop404p1 ./r329 && ls ./r

329

卸载的⽂件系统，并删除⽬录。

sudo umount /dev/loop404p1 && sudo losetup -d /dev/loop404 && rm -rf ./r329

卸载后会⾃动同步，此时烧录即可使⽤。

扩容⽂件系统的⽅法

注意分区的损坏了就不能挂载，分区的⼤⼩改变不代表⽂件系统⼤⼩改变，所以需要以下代码检查和扩容⽂件系统。

1 Section 0 – 1: Clone a BSP Create, Build, and Run a New OS Design Learning Objectives 󰁺 Create an OS Design using Visual Studio 󰁺 Identify the catalog features included in the design 󰁺 Extend the standard design by adding catalog items 󰁺 Build configuration for the run-time image and build a run-time image 󰁺 Run the OS image on the target device Estimated time to complete this lab: 45 minutes Exercise 1: Clone BSP In this exercise, you will use the Clone BSP tool in Visual Studio 2005 to create a copy of the existing Device Emulator Board Support Package (BSP). We can modify this copy instead of modifying the original that was delivered as a part of the Windows Embedded CE 6.0 tools. 󰂾 Clone the DeviceEmulator BSP 1. Launch Microsoft Visual Studio 2005. Note If this is the first time Visual Studio is launched after installation the Choose Default Environment Setting dialog will be displayed. For the purposed of this course select Platform Builder Development Settings and select Start Visual Studio. 2. Select the Tools | Platform Builder for CE6.0 | Clone BSP from the menu in Visual Studio to bring up the Clone BSP dialog box. 3. In the Clone BSP dialog, drop down the Source Board Support Package and select Device Emulator: ARMV4I BSP for clone. Note If you are working with a hardware reference platform, select the BSP corresponding to your specific hardware. 4. Type TrainingBSP in the Name field in the New Board Support Package Info area. 5. Type a description for your new BSP in the Description field. 2 6. Type TrainingBSP in the Platform Directory field. 7. Type GeneriCo in the Vendor field. 8. Type 1.0 in the Version field. 9. Click the Clone Button. The Clone BSP tool will create a new Board Support Package based on the DeviceEmulator Board Support Package. 10. Acknowledge the Clone BSP success message by selecting OK. 11. Open the C:\WINCE600\PLATFORM\TrainingBSP\FILES folder using Windows Explorer. Note The labs in this course refer to the default installation path for OS Build Tree, which is “C:\WINCE600”. If your path is something other than the default you will need to make the adjustment anytime the path is noted. 12. Rename the deviceemulator-preri.bat file to trainingbsp-preri.bat. Note The previous step was necessary due to a bug in the Clone BSP tool. It is only necessary if the source BSP contains certain build related batch files in the FILES directory. This is not common, but is the case with the DeviceEmulator BSP. Failure to make the change will cause a build error in a later step. The DeviceEmulator Board Support Package has now been cloned into a new Board Support Package called TrainingBSP. The TrainingBSP Board Support Package will be used in the remaining labs.

【神经⽹络篇】--基于数据集cifa10的经典模型实例

⼀、前述

本⽂分享⼀篇基于数据集cifa10的经典模型架构和代码。

⼆、代码

import tensorflow as tf

import numpy as np

import math

import time

from tutorials.image.cifar10 import cifar10

from tutorials.image.cifar10 import cifar10_input

# 本节使⽤的数据集是CIFAR-10，这是⼀个经典的数据集，包含60000张32*32的彩⾊图像，其中训练集50000张，测试集10000张

# ⼀共标注为10类，每⼀类图⽚6000张。10类分别是 airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck

# 我们载⼊⼀些常⽤库，⽐如NumPy和time，并载⼊TensorFlow Models中⾃动下载、读取CIFAR-10数据的类

max_steps = 3000#迭代3000次

batch_size = 128#每次128张图⽚

# 下载cifar10数据集的默认路径

data_dir = 'D:/cifar10_data/cifar-10-batches-bin'

def variable_with_weight_losses(shape, stddev, wl):#w1是L1正则中的系数

# 定义初始化weights的函数，和之前⼀样依然使⽤tf.truncated_normal截断的正太分布来初始化权值

var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))

if wl is not None:

# 给weight加⼀个L2的loss，相当于做了⼀个L2的正则化处理

# 在机器学习中，不管是分类还是回归任务，都可能因为特征过多⽽导致过拟合，⼀般可以通过减少特征或者惩罚不重要特征的权重来缓解这个问题

dm37xx android2.3.4增加recovery升级功能（一） —代码修改

今天在给TI omap3的android系统增加recovery模式升级功能时发现，这部分TI根本没有做，只能自己来处理了，参照以前freescale i.mx53平台的做法，分别修改bootloader及recovery包，现在将工作过程记录如下：

1、bootloader部分修改

1.1、增加cache及recovery分区

/* Initialize the name of fastboot flash name mappings */

fastboot_ptentry ptn[] = {

{

.name = "xloader",

.start = 0x0000000,

.length = 0x0020000,

/* Written into the first 4 0x20000 blocks

Use HW ECC */

.flags = FASTBOOT_PTENTRY_FLAGS_WRITE_I |

FASTBOOT_PTENTRY_FLAGS_WRITE_HW_ECC |

FASTBOOT_PTENTRY_FLAGS_HW_ECC_LAYOUT_2 |

FASTBOOT_PTENTRY_FLAGS_REPEAT_4,

},

{

.name = "bootloader",

.start = 0x0080000,

.length = 0x01C0000,

/* Skip bad blocks on write

Use HW ECC */

.flags = FASTBOOT_PTENTRY_FLAGS_WRITE_I |

FASTBOOT_PTENTRY_FLAGS_WRITE_HW_ECC |

FASTBOOT_PTENTRY_FLAGS_HW_ECC_LAYOUT_2,