StatsTransformerConfig#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model StatsTransformerConfig[source]#
Configure a
StatsTransformer
.Show JSON schema
{ "title": "StatsTransformerConfig", "description": "Configure a :class:`.StatsTransformer`.", "type": "object", "properties": { "type_hint": { "title": "Type Hint", "default": "stats_transformer", "enum": [ "stats_transformer" ], "type": "string" }, "stats_uri": { "title": "Stats Uri", "description": "The URI of the output of the StatsAnalyzer. If None, and this Config is inside an RVPipeline, this field will be auto-generated.", "type": "string" }, "scene_group": { "title": "Scene Group", "description": "Name of the group of scenes whose stats to use. Defaultsto \"train_scenes\".", "default": "train_scenes", "type": "string" } }, "additionalProperties": false }
- Config
extra: str = forbid
validate_assignment: bool = True
- Fields
- field scene_group: str = 'train_scenes'#
Name of the group of scenes whose stats to use. Defaultsto “train_scenes”.
- field stats_uri: Optional[str] = None#
The URI of the output of the StatsAnalyzer. If None, and this Config is inside an RVPipeline, this field will be auto-generated.
- build()[source]#
Build an instance of the corresponding type of object using this config.
For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.
- recursive_validate_config()#
Recursively validate hierarchies of Configs.
This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.
- revalidate()#
Re-validate an instantiated Config.
Runs all Pydantic validators plus self.validate_config().
Adapted from: https://github.com/samuelcolvin/pydantic/issues/1864#issuecomment-679044432
- update(pipeline: Optional[RVPipelineConfig] = None, scene: Optional[SceneConfig] = None) None [source]#
Update any fields before validation.
Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.
- Parameters
pipeline (Optional[RVPipelineConfig]) –
scene (Optional[SceneConfig]) –
- Return type
None
- validate_config()#
Validate fields that should be checked after update is called.
This is to complement the builtin validation that Pydantic performs at the time of object construction.