ClassificationEvaluatorConfig#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model ClassificationEvaluatorConfig[source]#
Configure a
ClassificationEvaluator
.Show JSON schema
{ "title": "ClassificationEvaluatorConfig", "description": "Configure a :class:`.ClassificationEvaluator`.", "type": "object", "properties": { "output_uri": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "URI of directory where evaluator output will be saved. Evaluations for each scene-group will be save in a JSON file at <output_uri>/<scene-group-name>/eval.json. If None, and this Config is part of an RVPipeline, this field will be auto-generated.", "title": "Output Uri" }, "type_hint": { "const": "classification_evaluator", "default": "classification_evaluator", "enum": [ "classification_evaluator" ], "title": "Type Hint", "type": "string" } }, "additionalProperties": false }
- Config:
extra: str = forbid
validate_assignment: bool = True
- Fields:
- field output_uri: str | None = None#
URI of directory where evaluator output will be saved. Evaluations for each scene-group will be save in a JSON file at <output_uri>/<scene-group-name>/eval.json. If None, and this Config is part of an RVPipeline, this field will be auto-generated.
- build(class_config: ClassConfig, scene_group: tuple[str, Iterable[str]] | None = None) Evaluator #
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.
- classmethod deserialize(inp: str | dict | Config) Self #
Deserialize Config from a JSON file or dict, upgrading if possible.
If
inp
is already aConfig
, it is returned as is.
- classmethod from_dict(cfg_dict: dict) Self #
Deserialize Config from a dict.
- Parameters:
cfg_dict (dict) – Dict to deserialize.
- Return type:
Self
- classmethod from_file(uri: str) Self #
Deserialize Config from a JSON file, upgrading if possible.
- Parameters:
uri (str) – URI to load from.
- Return type:
Self
- 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().
- to_file(uri: str, with_rv_metadata: bool = True) None #
Save a Config to a JSON file, optionally with RV metadata.
- update(pipeline: RVPipelineConfig | None = None) None #
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 (RVPipelineConfig | None) –
- 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.