记录毕设

本文档详细记录了在Ubuntu环境下遇到的三个问题及其解决方案:1)Ubuntu磁盘空间显示不正确,通过使用gparted工具修复;2)PyCharm启动失败,可能是由于插件问题导致,尝试卸载相关文件夹并重新安装;3)PyTorch模型加载时出现错误,可能是因为模型结构不匹配,检查state_dict和模型结构是否对应。此外,还提供了Nvidia驱动、CUDA和PyTorch的下载链接。
部署运行你感兴趣的模型镜像

环境搭载

Linux下Nvidia驱动下载:https://blog.youkuaiyun.com/le_more/article/details/104082643
cuda版本是 https://blog.youkuaiyun.com/finviento/article/details/80835044
pytorch,torchvision下载:https://blog.youkuaiyun.com/qq_23013309/article/details/103965619

遇到的问题

1:Ubuntu空间

Ubuntu问题:明明分配了100多个G 的空间但是只显示80个G 。

解决方法,优盘启动,打开gparted,然后对该分区进行检查。

2:Ubuntu打不开pycharm

Ubuntu下打不开pycharm(升级后),出了一堆错误。不知道怎么解决。
启动时的部分log如下,解决方法:Youtrack
pycharm使用的文件夹
就是把他使用的文件夹全部删除再重装可以解决

2021-04-23 18:37:33,508 [   1961]  ERROR - llij.ide.plugins.PluginManager - The Datalore (id=com.jetbrains.intellij.datalore, path=~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/211.7142.13/plugins/datalore-intellij-plugin) plugin requires missing class loader for 'Python' 
java.lang.Throwable: The Datalore (id=com.jetbrains.intellij.datalore, path=~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/211.7142.13/plugins/datalore-intellij-plugin) plugin requires missing class loader for 'Python'
	at com.intellij.openapi.diagnostic.Logger.error(Logger.java:161)
	at com.intellij.ide.plugins.ClassLoaderConfigurator.addClassloaderIfDependencyEnabled(ClassLoaderConfigurator.java:530)
	at com.intellij.ide.plugins.ClassLoaderConfigurator.configure(ClassLoaderConfigurator.java:176)
	at java.base/java.util.ArrayList.forEach(ArrayList.java:1541)
	at com.intellij.ide.plugins.PluginManagerCore.initializePlugins(PluginManagerCore.java:1093)
	at com.intellij.ide.plugins.PluginManagerCore.loadAndInitializePlugins(PluginManagerCore.java:1364)
	at com.intellij.ide.plugins.PluginManagerCore.lambda$initPlugins$20(PluginManagerCore.java:803)
	at java.base/java.util.concurrent.CompletableFuture.uniApplyNow(CompletableFuture.java:680)
	at java.base/java.util.concurrent.CompletableFuture.uniApplyStage(CompletableFuture.java:658)
	at java.base/java.util.concurrent.CompletableFuture.thenApply(CompletableFuture.java:2094)
	at com.intellij.ide.plugins.PluginManagerCore.initPlugins(PluginManagerCore.java:802)
	at com.intellij.idea.ApplicationLoader.initApplication(ApplicationLoader.kt:383)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at com.intellij.idea.a.a(a.java:104)
	at com.intellij.ide.a.b.y.a(y.java:93)
	at com.intellij.ide.a.b.g.b(g.java:279)
	at com.intellij.ide.a.b.h.b(h.java:276)
	at com.intellij.ide.a.b.h.c(h.java:169)
	at com.intellij.ide.a.b.h.a(h.java:81)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at com.intellij.idea.MainImpl.start(MainImpl.java:96)
	at com.intellij.idea.StartupUtil.startApp(StartupUtil.java:310)
	at com.intellij.idea.StartupUtil.prepareApp(StartupUtil.java:250)
	at com.intellij.ide.plugins.MainRunner.lambda$start$1(MainRunner.java:41)
	at java.base/java.lang.Thread.run(Thread.java:834)
2021-04-23 18:37:33,508 [   1961]   INFO - ntellij.idea.ApplicationLoader - WM detected: GNOME Shell 
2021-04-23 18:37:33,511 [   1964]  ERROR - llij.ide.plugins.PluginManager - PyCharm 2021.1.1  Build #PY-211.7142.13 
2021-04-23 18:37:33,514 [   1967]  ERROR - llij.ide.plugins.PluginManager - JDK: 11.0.10; VM: Dynamic Code Evolution 64-Bit Server VM; Vendor: JetBrains s.r.o. 
2021-04-23 18:37:33,516 [   1969]  ERROR - llij.ide.plugins.PluginManager - OS: Linux 
2021-04-23 18:37:33,516 [   1969]  ERROR - llij.ide.plugins.PluginManager - The PyCharm Professional Customization (id=com.jetbrains.pycharm.pro.customization, path=~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/211.7142.13/plugins/pythonIDE) plugin requires missing class loader for 'Python' 
java.lang.Throwable: The PyCharm Professional Customization (id=com.jetbrains.pycharm.pro.customization, path=~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/211.7142.13/plugins/pythonIDE) plugin requires missing class loader for 'Python'
	at com.intellij.openapi.diagnostic.Logger.error(Logger.java:161)
	at com.intellij.ide.plugins.ClassLoaderConfigurator.addClassloaderIfDependencyEnabled(ClassLoaderConfigurator.java:530)
	at com.intellij.ide.plugins.ClassLoaderConfigurator.configure(ClassLoaderConfigurator.java:176)
	at java.base/java.util.ArrayList.forEach(ArrayList.java:1541)
	at com.intellij.ide.plugins.PluginManagerCore.initializePlugins(PluginManagerCore.java:1093)
	at com.intellij.ide.plugins.PluginManagerCore.loadAndInitializePlugins(PluginManagerCore.java:1364)
	at com.intellij.ide.plugins.PluginManagerCore.lambda$initPlugins$20(PluginManagerCore.java:803)
	at java.base/java.util.concurrent.CompletableFuture.uniApplyNow(CompletableFuture.java:680)
	at java.base/java.util.concurrent.CompletableFuture.uniApplyStage(CompletableFuture.java:658)
	at java.base/java.util.concurrent.CompletableFuture.thenApply(CompletableFuture.java:2094)
	at com.intellij.ide.plugins.PluginManagerCore.initPlugins(PluginManagerCore.java:802)
	at com.intellij.idea.ApplicationLoader.initApplication(ApplicationLoader.kt:383)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at com.intellij.idea.a.a(a.java:104)
	at com.intellij.ide.a.b.y.a(y.java:93)
	at com.intellij.ide.a.b.g.b(g.java:279)
	at com.intellij.ide.a.b.h.b(h.java:276)
	at com.intellij.ide.a.b.h.c(h.java:169)
	at com.intellij.ide.a.b.h.a(h.java:81)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at com.intellij.idea.MainImpl.start(MainImpl.java:96)
	at com.intellij.idea.StartupUtil.startApp(StartupUtil.java:310)
	at com.intellij.idea.StartupUtil.prepareApp(StartupUtil.java:250)
	at com.intellij.ide.plugins.MainRunner.lambda$start$1(MainRunner.java:41)
	at java.base/java.lang.Thread.run(Thread.java:834)
2021-04-23 18:37:33,517 [   1970]  ERROR - llij.ide.plugins.PluginManager - PyCharm 2021.1.1  Build #PY-211.7142.13 
2021-04-23 18:37:33,517 [   1970]  ERROR - llij.ide.plugins.PluginManager - JDK: 11.0.10; VM: Dynamic Code Evolution 64-Bit Server VM; Vendor: JetBrains s.r.o. 
2021-04-23 18:37:33,517 [   1970]  ERROR - llij.ide.plugins.PluginManager - OS: Linux 
2021-04-23 18:37:33,518 [   1971]  ERROR - llij.ide.plugins.PluginManager - The Shared Indexes for Python (id=com.jetbrains.python.sharedIndexes, path=~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/211.7142.13/plugins/python-sharedIndexes) plugin requires missing class loader for 'Python' 
java.lang.Throwable: The Shared Indexes for Python (id=com.jetbrains.python.sharedIndexes, path=~/.local/share/JetBrains/Toolbox/apps/PyCharm-P/ch-0/211.7142.13/plugins/python-sharedIndexes) plugin requires missing class loader for 'Python'
	at com.intellij.openapi.diagnostic.Logger.error(Logger.java:161)
	at com.intellij.ide.plugins.ClassLoaderConfigurator.addClassloaderIfDependencyEnabled(ClassLoaderConfigurator.java:530)
	at com.intellij.ide.plugins.ClassLoaderConfigurator.configure(ClassLoaderConfigurator.java:176)
	at java.base/java.util.ArrayList.forEach(ArrayList.java:1541)
	at com.intellij.ide.plugins.PluginManagerCore.initializePlugins(PluginManagerCore.java:1093)
	at com.intellij.ide.plugins.PluginManagerCore.loadAndInitializePlugins(PluginManagerCore.java:1364)
	at com.intellij.ide.plugins.PluginManagerCore.lambda$initPlugins$20(PluginManagerCore.java:803)
	at java.base/java.util.concurrent.CompletableFuture.uniApplyNow(CompletableFuture.java:680)

3: pytorch 模型保存与加载

RuntimeError: Error(s) in loading state_dict for ResNet:
	Missing key(s) in state_dict: "extractor.0.weight", "extractor.0.bias", "extractor.1.weight", "extractor.1.bias", "extractor.1.running_mean", "extractor.1.running_var", "extractor.4.C1.weight", "extractor.4.C1.bias", "extractor.4.C2.weight", "extractor.4.C2.bias", "extractor.4.BN1.weight", "extractor.4.BN1.bias", "extractor.4.BN1.running_mean", "extractor.4.BN1.running_var", "extractor.4.BN2.weight", "extractor.4.BN2.bias", "extractor.4.BN2.running_mean", "extractor.4.BN2.running_var", "extractor.4.operation.0.weight", "extractor.4.operation.0.bias", "extractor.4.operation.1.weight", "extractor.4.operation.1.bias", "extractor.4.operation.1.running_mean", "extractor.4.operation.1.running_var", "extractor.4.operation.3.weight", "extractor.4.operation.3.bias", "extractor.4.operation.4.weight", "extractor.4.operation.4.bias", "extractor.4.operation.4.running_mean", "extractor.4.operation.4.running_var", "extractor.5.C1.weight", "extractor.5.C1.bias", "extractor.5.C2.weight", "extractor.5.C2.bias", "extractor.5.BN1.weight", "extractor.5.BN1.bias", "extractor.5.BN1.running_mean", "extractor.5.BN1.running_var", "extractor.5.BN2.weight", "extractor.5.BN2.bias", "extractor.5.BN2.running_mean", "extractor.5.BN2.running_var", "extractor.5.shortcut.weight", "extractor.5.BNshortcut.weight", "extractor.5.BNshortcut.bias", "extractor.5.BNshortcut.running_mean", "extractor.5.BNshortcut.running_var", "extractor.5.operation.0.weight", "extractor.5.operation.0.bias", "extractor.5.operation.1.weight", "extractor.5.operation.1.bias", "extractor.5.operation.1.running_mean", "extractor.5.operation.1.running_var", "extractor.5.operation.3.weight", "extractor.5.operation.3.bias", "extractor.5.operation.4.weight", "extractor.5.operation.4.bias", "extractor.5.operation.4.running_mean", "extractor.5.operation.4.running_var", "extractor.6.C1.weight", "extractor.6.C1.bias", "extractor.6.C2.weight", "extractor.6.C2.bias", "extractor.6.BN1.weight", "extractor.6.BN1.bias", "extractor.6.BN1.running_mean", "extractor.6.BN1.running_var", "extractor.6.BN2.weight", "extractor.6.BN2.bias", "extractor.6.BN2.running_mean", "extractor.6.BN2.running_var", "extractor.6.shortcut.weight", "extractor.6.BNshortcut.weight", "extractor.6.BNshortcut.bias", "extractor.6.BNshortcut.running_mean", "extractor.6.BNshortcut.running_var", "extractor.6.operation.0.weight", "extractor.6.operation.0.bias", "extractor.6.operation.1.weight", "extractor.6.operation.1.bias", "extractor.6.operation.1.running_mean", "extractor.6.operation.1.running_var", "extractor.6.operation.3.weight", "extractor.6.operation.3.bias", "extractor.6.operation.4.weight", "extractor.6.operation.4.bias", "extractor.6.operation.4.running_mean", "extractor.6.operation.4.running_var", "extractor.7.C1.weight", "extractor.7.C1.bias", "extractor.7.C2.weight", "extractor.7.C2.bias", "extractor.7.BN1.weight", "extractor.7.BN1.bias", "extractor.7.BN1.running_mean", "extractor.7.BN1.running_var", "extractor.7.BN2.weight", "extractor.7.BN2.bias", "extractor.7.BN2.running_mean", "extractor.7.BN2.running_var", "extractor.7.shortcut.weight", "extractor.7.BNshortcut.weight", "extractor.7.BNshortcut.bias", "extractor.7.BNshortcut.running_mean", "extractor.7.BNshortcut.running_var", "extractor.7.operation.0.weight", "extractor.7.operation.0.bias", "extractor.7.operation.1.weight", "extractor.7.operation.1.bias", "extractor.7.operation.1.running_mean", "extractor.7.operation.1.running_var", "extractor.7.operation.3.weight", "extractor.7.operation.3.bias", "extractor.7.operation.4.weight", "extractor.7.operation.4.bias", "extractor.7.operation.4.running_mean", "extractor.7.operation.4.running_var", "classifier.weight", "classifier.bias". 
	Unexpected key(s) in state_dict: "module.extractor.0.weight", "module.extractor.0.bias", "module.extractor.1.weight", "module.extractor.1.bias", "module.extractor.1.running_mean", "module.extractor.1.running_var", "module.extractor.1.num_batches_tracked", "module.extractor.4.C1.weight", "module.extractor.4.C1.bias", "module.extractor.4.C2.weight", "module.extractor.4.C2.bias", "module.extractor.4.BN1.weight", "module.extractor.4.BN1.bias", "module.extractor.4.BN1.running_mean", "module.extractor.4.BN1.running_var", "module.extractor.4.BN1.num_batches_tracked", "module.extractor.4.BN2.weight", "module.extractor.4.BN2.bias", "module.extractor.4.BN2.running_mean", "module.extractor.4.BN2.running_var", "module.extractor.4.BN2.num_batches_tracked", "module.extractor.4.operation.0.weight", "module.extractor.4.operation.0.bias", "module.extractor.4.operation.1.weight", "module.extractor.4.operation.1.bias", "module.extractor.4.operation.1.running_mean", "module.extractor.4.operation.1.running_var", "module.extractor.4.operation.1.num_batches_tracked", "module.extractor.4.operation.3.weight", "module.extractor.4.operation.3.bias", "module.extractor.4.operation.4.weight", "module.extractor.4.operation.4.bias", "module.extractor.4.operation.4.running_mean", "module.extractor.4.operation.4.running_var", "module.extractor.4.operation.4.num_batches_tracked", "module.extractor.5.C1.weight", "module.extractor.5.C1.bias", "module.extractor.5.C2.weight", "module.extractor.5.C2.bias", "module.extractor.5.BN1.weight", "module.extractor.5.BN1.bias", "module.extractor.5.BN1.running_mean", "module.extractor.5.BN1.running_var", "module.extractor.5.BN1.num_batches_tracked", "module.extractor.5.BN2.weight", "module.extractor.5.BN2.bias", "module.extractor.5.BN2.running_mean", "module.extractor.5.BN2.running_var", "module.extractor.5.BN2.num_batches_tracked", "module.extractor.5.shortcut.weight", "module.extractor.5.BNshortcut.weight", "module.extractor.5.BNshortcut.bias", "module.extractor.5.BNshortcut.running_mean", "module.extractor.5.BNshortcut.running_var", "module.extractor.5.BNshortcut.num_batches_tracked", "module.extractor.5.operation.0.weight", "module.extractor.5.operation.0.bias", "module.extractor.5.operation.1.weight", "module.extractor.5.operation.1.bias", "module.extractor.5.operation.1.running_mean", "module.extractor.5.operation.1.running_var", "module.extractor.5.operation.1.num_batches_tracked", "module.extractor.5.operation.3.weight", "module.extractor.5.operation.3.bias", "module.extractor.5.operation.4.weight", "module.extractor.5.operation.4.bias", "module.extractor.5.operation.4.running_mean", "module.extractor.5.operation.4.running_var", "module.extractor.5.operation.4.num_batches_tracked", "module.extractor.6.C1.weight", "module.extractor.6.C1.bias", "module.extractor.6.C2.weight", "module.extractor.6.C2.bias", "module.extractor.6.BN1.weight", "module.extractor.6.BN1.bias", "module.extractor.6.BN1.running_mean", "module.extractor.6.BN1.running_var", "module.extractor.6.BN1.num_batches_tracked", "module.extractor.6.BN2.weight", "module.extractor.6.BN2.bias", "module.extractor.6.BN2.running_mean", "module.extractor.6.BN2.running_var", "module.extractor.6.BN2.num_batches_tracked", "module.extractor.6.shortcut.weight", "module.extractor.6.BNshortcut.weight", "module.extractor.6.BNshortcut.bias", "module.extractor.6.BNshortcut.running_mean", "module.extractor.6.BNshortcut.running_var", "module.extractor.6.BNshortcut.num_batches_tracked", "module.extractor.6.operation.0.weight", "module.extractor.6.operation.0.bias", "module.extractor.6.operation.1.weight", "module.extractor.6.operation.1.bias", "module.extractor.6.operation.1.running_mean", "module.extractor.6.operation.1.running_var", "module.extractor.6.operation.1.num_batches_tracked", "module.extractor.6.operation.3.weight", "module.extractor.6.operation.3.bias", "module.extractor.6.operation.4.weight", "module.extractor.6.operation.4.bias", "module.extractor.6.operation.4.running_mean", "module.extractor.6.operation.4.running_var", "module.extractor.6.operation.4.num_batches_tracked", "module.extractor.7.C1.weight", "module.extractor.7.C1.bias", "module.extractor.7.C2.weight", "module.extractor.7.C2.bias", "module.extractor.7.BN1.weight", "module.extractor.7.BN1.bias", "module.extractor.7.BN1.running_mean", "module.extractor.7.BN1.running_var", "module.extractor.7.BN1.num_batches_tracked", "module.extractor.7.BN2.weight", "module.extractor.7.BN2.bias", "module.extractor.7.BN2.running_mean", "module.extractor.7.BN2.running_var", "module.extractor.7.BN2.num_batches_tracked", "module.extractor.7.shortcut.weight", "module.extractor.7.BNshortcut.weight", "module.extractor.7.BNshortcut.bias", "module.extractor.7.BNshortcut.running_mean", "module.extractor.7.BNshortcut.running_var", "module.extractor.7.BNshortcut.num_batches_tracked", "module.extractor.7.operation.0.weight", "module.extractor.7.operation.0.bias", "module.extractor.7.operation.1.weight", "module.extractor.7.operation.1.bias", "module.extractor.7.operation.1.running_mean", "module.extractor.7.operation.1.running_var", "module.extractor.7.operation.1.num_batches_tracked", "module.extractor.7.operation.3.weight", "module.extractor.7.operation.3.bias", "module.extractor.7.operation.4.weight", "module.extractor.7.operation.4.bias", "module.extractor.7.operation.4.running_mean", "module.extractor.7.operation.4.running_var", "module.extractor.7.operation.4.num_batches_tracked", "module.classifier.weight", "module.classifier.bias". 

解决办法:解决

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Cuda

PyTorch 是一个开源的 Python 机器学习库,基于 Torch 库,底层由 C++ 实现,应用于人工智能领域,如计算机视觉和自然语言处理

### 计算机专业毕业设计指导与记录文档 计算机专业毕业设计是学生综合运用所学知识解决实际问题的重要环节,其过程包括选题、需求分析、系统设计、编码实现、测试和文档撰写等多个方面[^1]。以下是关于毕业设计指导与记录的具体内容: #### 一、毕业设计指导 1. **选题策略** 选题应结合个人兴趣与专业知识,确保题目具有一定的技术深度和实际应用价值。选题时可以参考当前的技术热点或行业需求,同时注意避免过于宽泛或难以实现的题目[^1]。 2. **需求分析** 在明确选题后,进行详细的需求分析。这一步骤需要与导师充分沟通,了解项目背景、目标用户以及功能需求。需求分析的结果通常以文档形式呈现,包含功能需求、非功能需求及约束条件等。 3. **系统设计** 系统设计阶段需要完成总体架构设计、模块划分以及关键技术方案的选择。设计文档应清晰描述系统的各个组成部分及其相互关系,为后续开发提供依据。 4. **编码实现** 编码阶段要求遵循良好的编程规范,使用合适的开发工具和框架。建议定期向导师汇报进度,并及时解决遇到的问题。 5. **测试与优化** 测试阶段需制定详细的测试计划,涵盖单元测试、集成测试和系统测试。通过测试发现并修复问题,确保系统功能正常且性能达标[^1]。 6. **文档撰写** 毕业设计的文档撰写是整个过程中不可或缺的一部分,包括开题报告、中期检查报告、最终论文等。文档需结构清晰、逻辑严谨,能够全面反映项目的实施过程与成果。 #### 二、毕业设计记录 为了保证毕业设计的质量,建议采用以下方式进行记录: - **指导记录表**:通过填写《计算机专业毕业设计指导记录表》[^2],记录每次与导师沟通的内容、存在的问题及解决方案。 - **日志管理**:每日或每周记录工作进展,包括完成的任务、遇到的困难及解决方法。这不仅有助于自我总结,也为后续的论文撰写提供了素材。 - **版本控制**:使用Git等版本控制工具管理代码和文档,便于追溯历史版本及协作开发。 ```python # 示例:使用Git进行版本控制的基本操作 git init # 初始化仓库 git add . # 添加文件到暂存区 git commit -m "提交说明" # 提交更改 git push origin main # 推送至远程仓库 ``` #### 三、教学模式与质量提升 以CDIO(Conceive, Design, Implement, Operate)工程教育理念为核心的教学模式,结合项目式教学,能够显著提高毕业设计的教学质量[^2]。这种模式强调学生的实践能力和创新能力培养,通过真实项目驱动学习,使学生在毕业设计中更好地将理论与实践相结合。 ---
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