背景
- 在vscode 中 如果执行命令需要传递进来参数，那么直接通过命令行终端的方式不太方便。
- 通过task 任务的方式来进行启动执行，降低反复输入参数等繁琐工作。
首先可以查看vscode 官方文档
.vscode/task.json 示例
执行cpp 带参数二进制
{"version": "2.0.0","tasks": [{"label": "Run BIN","type": "shell", // shell 类型的任务"command": "${command:cmake.launchTargetPath}", // CMake 生成的二进制文件路径"args": ["${workspaceFolder}/config.toml", // 第一个参数"${workspaceFolder}/model.onnx" // 第二个参数],"options": {"cwd": "${command:cmake.launchTargetDirectory}"},"group": {"kind": "build", // 此任务的组别"isDefault": true // 设为默认任务},"problemMatcher": [], // 没有特定的错误匹配器"presentation": {"echo": true, // 在输出面板中回显命令"reveal": "always", // 始终在面板中显示"focus": false, // 不聚焦到输出面板"panel": "new", // 在新面板中显示输出"close": false, // 任务完成后关闭输出面板"clear": false // 任务启动前清空输出面板}}]
}
在执行task时，手动输入传参
- 硬编码方式虽然比较简单粗暴，但是，当使用git来管理的时候，会引入一些没有必要的改变，那么此时就需要将参数，不是使用硬编码，而是去通过启动任务时，动态加载进去。
- 那就简单介绍几种输入方式
{ // 简单理解就是输入文本框"type": "promptString","id": "outputPath","description": "Please input the output path:",},{ // 简单理解说就是选择框"type": "pickString","id": "quantize","description": "use quantize","options": [{"label": "True","value": "-q"},{"label": "False","value": ""}],"default": ""},
{"version": "2.0.0","inputs":[{"type": "promptString","id": "outputPath","description": "Please input the output path:",},{"type": "pickString","id": "quantize","description": "use quantize","options": [{"label": "True","value": "-q"},{"label": "False","value": ""}],"default": ""},{"type": "pickString","id": "task","description": "task type","options": ["weigh","arc"],"default": "arc"},{"type": "promptString","id": "config","description": "Please input using configs file",},{"type": "pickString","id": "test_data","description": "juse use test data","options": [{"label": "True","value": "-b"},{"label": "False","value": ""}],"default": ""},{"type": "pickString","id": "qat","description": "use keras quantization aware training","options": [{"label": "True","value": "--qat"},{"label": "False","value": ""}],"default": ""},],"tasks": [{"label": "torch_inf","type": "shell","command": "${command:python.interpreterPath}","args": ["${workspaceFolder}/inference_torch.py","-i","${workspaceFolder}/output/${input:task}/${input:outputPath}"],"presentation": {"echo": true,"reveal": "always","focus": false,"panel": "shared","showReuseMessage": true,"clear": true},"problemMatcher": []},{"label" : "train","type": "shell","command": "${command:python.interpreterPath}","args": ["${workspaceFolder}/test.py","-c","${workspaceFolder}/configs/${input:config}"],"presentation": {"echo": true,"reveal": "always","focus": false,"panel": "shared","showReuseMessage": true,"clear": true},"problemMatcher": []},{"label" : "exprot_tfl","type": "shell","command": "${command:python.interpreterPath}","args": ["${workspaceFolder}/export2.py","-i","${workspaceFolder}/output/${input:task}/${input:outputPath}","${input:quantize}"],"presentation": {"echo": true,"reveal": "always","focus": false,"panel": "shared","showReuseMessage": true,"clear": true},"problemMatcher": []}]
}
