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