Chengfang Lu
7c43b44c57
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
Configs
Config files in PytorchAutoDrive (./configs/) are used to define models,
how they are trained, tested, visualized, etc.
Registry Mechanism
Different to existing class-based registers, we can also register functions. For functions, you only write static args in your config, while passing the dynamic ones on-the-fly by:
REGISTRY.from_dict(
<config dict for a function/class>,
kwarg1=1, kwarg2=2, ...
)
Note that each argument must be keyword (k=v), and some kwargs can overwrite dict configs.
Use An Existing Config
Modify customized options like the root of your datasets (in configs/*/common/_*.py).
Write A New Config
Copy the config file most similar to your use case and modify it.
Note that you can simply import config parts from common or other config files, it is like writing Python.
Register A New Class/Func
Choose the appropriate registry and register your Class/Func by:
@REGISTRY.register()
Remember you still need to import this Class/Func for the registering to take effects.
How To Read The Code
Since you can't just click 'go to definition' in your IDE,
it is suggested to search the directory for each Class/Function by name in configs.