# SimpleDL
### 介绍
用于对基于pytorch深度学习框架的快速开发。提供一种优雅快速的开发方式----您仅需要修改配置文件或者少量代码即可完成模型的训练验证等工作。
### 软件架构
软件架构说明
### 安装教程
1. xxxx
2. xxxx
3. xxxx
### 使用说明
> ### model（神经网络模型）的配置：
> model包含三个关键字，type、args、input_name，分别用来指定模型的类、模型的初始化参数以及输入模型的内容
> *************************************
> + type：指定模型的类。
> + args：type类的初始参数。参数为字典类型，关键字为参数关键字，值为该关键字值。不可以省略args 参数，若如参数传入则需要指定args为空字典
> + input_name：输入模型数据所对应的关键字。！！数据集加载时以字典方式加载多个待用数据，可能包括图像、标签、坐标等等，传入模型时需要指定模型所接受数据在数据集中所对应的关键字。
更多详细的数据集加载问题请参考[data(数据集加载)配置](#fo)
> + 例子：
> ```yaml
> "model":{
> type: torchvision.models.resnet.resnet34,
> "args": {
> },
> input_name: "img"
> }
> ```
> ### data(数据集加载)配置：
>
> *************************************
> + 例子
> ```yaml
> "dataset":{
> type: SimpleDL.dataset.BasicDataset,
> "args": {
> base_path: "/home/zhoudonghui/dataset",
> behavior: [
> {type: SimpleDL.dataset.LoadImageBehavior, args:{transforms: *transforms}, out_name: "img"},
> {type: SimpleDL.dataset.ClassifiBehavior, args:{}, out_name: "label"},
> ]
> }
> }
> ```
> *************************************
#### 参与贡献
1. Fork 本仓库
2. 新建 Feat_xxx 分支
3. 提交代码
4. 新建 Pull Request
# fo
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"description": "# SimpleDL\n\n### \u4ecb\u7ecd\n\u7528\u4e8e\u5bf9\u57fa\u4e8epytorch\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u5feb\u901f\u5f00\u53d1\u3002\u63d0\u4f9b\u4e00\u79cd\u4f18\u96c5\u5feb\u901f\u7684\u5f00\u53d1\u65b9\u5f0f----\u60a8\u4ec5\u9700\u8981\u4fee\u6539\u914d\u7f6e\u6587\u4ef6\u6216\u8005\u5c11\u91cf\u4ee3\u7801\u5373\u53ef\u5b8c\u6210\u6a21\u578b\u7684\u8bad\u7ec3\u9a8c\u8bc1\u7b49\u5de5\u4f5c\u3002\n\n### \u8f6f\u4ef6\u67b6\u6784\n\u8f6f\u4ef6\u67b6\u6784\u8bf4\u660e\n\n\n### \u5b89\u88c5\u6559\u7a0b\n\n1. xxxx\n2. xxxx\n3. xxxx\n\n### \u4f7f\u7528\u8bf4\u660e\n> ### model\uff08\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff09\u7684\u914d\u7f6e\uff1a \n> model\u5305\u542b\u4e09\u4e2a\u5173\u952e\u5b57\uff0ctype\u3001args\u3001input_name\uff0c\u5206\u522b\u7528\u6765\u6307\u5b9a\u6a21\u578b\u7684\u7c7b\u3001\u6a21\u578b\u7684\u521d\u59cb\u5316\u53c2\u6570\u4ee5\u53ca\u8f93\u5165\u6a21\u578b\u7684\u5185\u5bb9\n> *************************************\n> + type\uff1a\u6307\u5b9a\u6a21\u578b\u7684\u7c7b\u3002\n> + args\uff1atype\u7c7b\u7684\u521d\u59cb\u53c2\u6570\u3002\u53c2\u6570\u4e3a\u5b57\u5178\u7c7b\u578b\uff0c\u5173\u952e\u5b57\u4e3a\u53c2\u6570\u5173\u952e\u5b57\uff0c\u503c\u4e3a\u8be5\u5173\u952e\u5b57\u503c\u3002\u4e0d\u53ef\u4ee5\u7701\u7565args \u53c2\u6570\uff0c\u82e5\u5982\u53c2\u6570\u4f20\u5165\u5219\u9700\u8981\u6307\u5b9aargs\u4e3a\u7a7a\u5b57\u5178\n> + input_name\uff1a\u8f93\u5165\u6a21\u578b\u6570\u636e\u6240\u5bf9\u5e94\u7684\u5173\u952e\u5b57\u3002\uff01\uff01\u6570\u636e\u96c6\u52a0\u8f7d\u65f6\u4ee5\u5b57\u5178\u65b9\u5f0f\u52a0\u8f7d\u591a\u4e2a\u5f85\u7528\u6570\u636e\uff0c\u53ef\u80fd\u5305\u62ec\u56fe\u50cf\u3001\u6807\u7b7e\u3001\u5750\u6807\u7b49\u7b49\uff0c\u4f20\u5165\u6a21\u578b\u65f6\u9700\u8981\u6307\u5b9a\u6a21\u578b\u6240\u63a5\u53d7\u6570\u636e\u5728\u6570\u636e\u96c6\u4e2d\u6240\u5bf9\u5e94\u7684\u5173\u952e\u5b57\u3002\n\u66f4\u591a\u8be6\u7ec6\u7684\u6570\u636e\u96c6\u52a0\u8f7d\u95ee\u9898\u8bf7\u53c2\u8003[data(\u6570\u636e\u96c6\u52a0\u8f7d)\u914d\u7f6e](#fo)\n> + \u4f8b\u5b50\uff1a\n> ```yaml\n> \"model\":{\n> type: torchvision.models.resnet.resnet34,\n> \"args\": {\n> },\n> input_name: \"img\"\n> }\n> ``` \n\n\n> ### data(\u6570\u636e\u96c6\u52a0\u8f7d)\u914d\u7f6e\uff1a\n> \n> *************************************\n> + \u4f8b\u5b50\n> ```yaml\n> \"dataset\":{\n> type: SimpleDL.dataset.BasicDataset,\n> \"args\": {\n> base_path: \"/home/zhoudonghui/dataset\",\n> behavior: [\n> {type: SimpleDL.dataset.LoadImageBehavior, args:{transforms: *transforms}, out_name: \"img\"},\n> {type: SimpleDL.dataset.ClassifiBehavior, args:{}, out_name: \"label\"},\n> ]\n> }\n> }\n> ```\n> *************************************\n\n\n\n\n#### \u53c2\u4e0e\u8d21\u732e\n\n1. Fork \u672c\u4ed3\u5e93\n2. \u65b0\u5efa Feat_xxx \u5206\u652f\n3. \u63d0\u4ea4\u4ee3\u7801\n4. \u65b0\u5efa Pull Request\n\n# fo\n\n\n",
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