MindSpore Vision是一个CV的工具箱。据说它可以帮助大家来复现经典模型，并开发自己的模型（这个张小白没本事）。那只有先看看能不能复现了。它可以提供 分类，主干网络、引擎、数据集、工具等高级功能，其架构如下：我们下面就用MindSpore 1.7的Windows版本试一下吧。先用conda创建一个Python 3.7.5的环境：conda create -n vision python=3.7.5 -y
然后进入该环境，确认是Python 3.7.5conda activate visionpython -V
再到官网 mindspore.cn/install 获取 mindspore 1.7.0 for windows CPU版本的安装命令：
接着执行这段命令：pip install ms-release.obs.cn-north-4.myhuaweicloud.com/1.7.0/MindS… --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i pypi.tuna.tsinghua.edu.cn/simple![](p3-juejin.byteimg.com/tos-cn-i-k3… 打开 mindspore.cn/versions
找到 1.7.0版本对应的vision安装盘的路径（右键复制链接地址）再去conda环境执行这个命令：pip install ms-release.obs.cn-north-4.myhuaweicloud.com/1.7.0/Visio… --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i pypi.tuna.tsinghua.edu.cn/simple![](p3-juejin.byteimg.com/tos-cn-i-k3…](p3-juejin.byteimg.com/tos-cn-i-k3… MindSpore Vision副产品后，需要再下载一下vision的代码仓：
找到LeNet的目录：
先执行 训练，以获得 数据集和生成ckpt。当然需要记得加上target_target=CPU参数，否则代码默认是GPUpython lenet_mnist_train.py --data_url ./mnist --download True --device_target=CPU
耐心等待训练结束：
。。
这个时候会生成 lenet目录下的ckpt文件：
在训练之前会下载MNIST数据集，并被解压到指定位置：训练集：
测试集：
下面开始执行评估：python lenet_mnist_eval.py --data_url ./mnist --pretrained True --download True -device_target=CPU
报错了，那么我们来跟踪一下：在 L:\Users\xishu\anaconda3\envs\vision\Lib\site-packages\mindspore\train\serialization.py代码中增加以下日志：
再次运行：python lenet_mnist_eval.py --data_url ./mnist --pretrained True --download True -device_target=CPU
发现输入的时候ckpt_file_name就不对。暂时强行改为 前面训练好的ckpt文件：```
ckpt_file_name, filter_prefix = _check_checkpoint_param("./lenet/lenet-10_1875.ckpt", filter_prefix)
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