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README
Apache-2.0

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欢迎来到MindSpore ModelZoo

MindSpore models仓中提供了不同任务领域,经典的SOTA模型实现和端到端解决方案。目的是方便MindSpore用户更加方便的利用MindSpore进行研究和产品开发。

为了让开发者更好地体验MindSpore框架优势,我们将陆续增加更多的典型网络和相关预训练模型。如果您对ModelZoo有任何需求,请通过GiteeMindSpore与我们联系,我们将及时处理。

目录 描述
official • 业界SOTA算法模型实现
• MindSpore团队官方维护
research • 业界前沿研究类算法模型实现
• 研究人员/机构维护
community • github/gitee 生态AI/ML repos powered by MindSpore

WHAT IS NEW

我们对经典SOTA模型进行了重构,模块化数据处理,模型定义,训练流程等常用组件,推出MindSpore CV/NLP/Audio/Yolo/OCR等系列,参考

原models仓模型实现是基于MindSpore原生API,并且有一定训练推理加速优化。

免责声明

MindSpore仅提供下载和预处理公共数据集的脚本。我们不拥有这些数据集,也不对它们的质量负责或维护。请确保您具有在数据集许可下使用该数据集的权限。在这些数据集上训练的模型仅用于非商业研究和教学目的。

致数据集拥有者:如果您不希望将数据集包含在MindSpore中,或者希望以任何方式对其进行更新,我们将根据要求删除或更新所有公共内容。请通过GitHub或Gitee与我们联系。非常感谢您对这个社区的理解和贡献。

MindSpore已获得Apache 2.0许可,请参见LICENSE文件。

许可证

Apache 2.0许可证

FAQ

想要获取更多关于MindSpore框架使用本身的FAQ问题的,可以参考官网FAQ

  • Q: 直接使用models下的模型出现内存不足错误,例如Failed to alloc memory pool memory, 该怎么处理?

    A: 直接使用models下的模型出现内存不足的典型原因是由于运行模式(PYNATIVE_MODE)、运行环境配置、License控制(AI-TOKEN)的不同造成的:

    • PYNATIVE_MODE通常比GRAPH_MODE使用更多内存,尤其是在需要进行反向传播计算的训练图中,当前有2种方法可以尝试解决该问题。 方法1:你可以尝试使用一些更小的batch size; 方法2:添加context.set_context(mempool_block_size="XXGB"),其中,“XX”当前最大有效值可设置为“31”。 如果将方法1与方法2结合使用,效果会更好。
    • 运行环境由于NPU的核数、内存等配置不同也会产生类似问题。
    • License控制(AI-TOKEN)的不同档位会造成执行过程中内存开销不同,也可以尝试使用一些更小的batch size。
  • Q: 一些网络运行中报错接口不存在,例如cannot import,该怎么处理?

    A: 优先检查一下获取网络脚本的分支,与所使用的MindSpore版本是否一致,部分新分支中的模型脚本会使用一些新版本MindSpore才支持的接口,从而在使用老版本MindSpore时会发生报错.

  • Q: 一些模型描述中提到的RANK_TABLE_FILE文件,是什么?

    A: RANK_TABLE_FILE是一个Ascend环境上用于指定分布式集群信息的文件,更多信息可以参考生成工具hccl_toos分布式并行训练教程

  • Q: 在windows环境上要怎么运行网络脚本?

    A: 多数模型都是使用bash作为启动脚本,在Windows环境上无法直接使用bash命令,你可以考虑直接运行python命令而不是bash启动脚本 ,如果你确实想需要使用bash脚本,你可以考虑使用以下几种方法来运行模型:

    1. 使用虚拟环境,可以构造一个linux的虚拟机或docker容器,然后在虚拟环境中运行脚本
    2. 使用WSL,可以开启Windows的linux子系统来在Windows系统中运行linux,然后再WSL中运行脚本。
    3. 使用Windows Bash,需要获取一个可以直接在Windows上运行bash的环境,常见的选择是cygwingit bash
    4. 跳过bash脚本,直接调用python程序。
  • Q: 网络在310推理时出现编译失败,报错信息指向gflags,例如undefined reference to 'google::FlagRegisterer::FlagRegisterer',该怎么处理?

    A: 优先检查一下环境GCC版本和gflags版本是否匹配,可以参考官方链接安装对应的GCC版本,gflags安装gflags。你需要保证所使用的组件之间是ABI兼容的,更多信息可以参考_GLIBCXX_USE_CXX11_ABI

  • Q: 在Mac系统上加载mindrecord格式的数据集出错,例如Invalid file, failed to open files for reading mindrecord files.,该怎么处理?

    A: 优先使用ulimit -a检查系统限制,如果file descriptors数量为256(默认值),需要使用ulimit -n 1024将其设置为1024(或者更大的值)。之后再检查文件是否损坏或者被修改。

  • Q: 我在多台服务器构成的大集群上进行训练,但是得到的精度比预期要低,该怎么办?

    A: 当前模型库中的大部分模型只在单机内进行过验证,最大使用8卡进行训练。由于MindSpore训练时指定的batch_size是单卡的,所以当单机8卡升级到多机时,会导致全局的global_batch_size变大,这就导致需要针对当前多机场景的global_batch_size进行重新调参优化。

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