SemanticSegmentationEvaluatorConfig#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model SemanticSegmentationEvaluatorConfig[source]#
Configure a
SemanticSegmentationEvaluator
.Show JSON schema
{ "title": "SemanticSegmentationEvaluatorConfig", "description": "Configure a :class:`.SemanticSegmentationEvaluator`.", "type": "object", "properties": { "output_uri": { "title": "Output Uri", "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.", "type": "string" }, "type_hint": { "title": "Type Hint", "default": "semantic_segmentation_evaluator", "enum": [ "semantic_segmentation_evaluator" ], "type": "string" } }, "additionalProperties": false }
- Config
extra: str = forbid
validate_assignment: bool = True
- Fields
- field output_uri: Optional[str] = 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: Optional[Tuple[str, Iterable[str]]] = None) SemanticSegmentationEvaluator [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.
- Parameters
- Return type
- 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.
- dict(with_rv_metadata: bool = False, **kwargs) dict #
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- 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().
Adapted from: https://github.com/samuelcolvin/pydantic/issues/1864#issuecomment-679044432
- to_file(uri: str, with_rv_metadata: bool = True) None #
Save a Config to a JSON file, optionally with RV metadata.
- update(pipeline: Optional[RVPipelineConfig] = 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 (Optional[RVPipelineConfig]) –
- 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.