ClassInferenceTransformerConfig#

Note

All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model ClassInferenceTransformerConfig[source]#

Configure a ClassInferenceTransformer.

Show JSON schema
{
   "title": "ClassInferenceTransformerConfig",
   "description": "Configure a :class:`.ClassInferenceTransformer`.",
   "type": "object",
   "properties": {
      "type_hint": {
         "title": "Type Hint",
         "default": "class_inference_transformer",
         "enum": [
            "class_inference_transformer"
         ],
         "type": "string"
      },
      "default_class_id": {
         "title": "Default Class Id",
         "description": "The default class_id to use if class cannot be inferred using other mechanisms. If a feature has an inferred class_id of None, then it will be deleted.",
         "type": "integer"
      },
      "class_id_to_filter": {
         "title": "Class Id To Filter",
         "description": "Map from class_id to JSON filter used to infer missing class_ids. Each key should be a class id, and its value should be a boolean expression which is run against the property field for each feature. This allows matching different features to different class IDs based on its properties. The expression schema is that described by https://docs.mapbox.com/mapbox-gl-js/style-spec/other/#other-filter",
         "type": "object",
         "additionalProperties": {
            "type": "array",
            "items": {}
         }
      }
   },
   "additionalProperties": false
}

Config

  • extra: str = forbid

  • validate_assignment: bool = True

Fields
field class_id_to_filter: Optional[Dict[int, list]] = None#

Map from class_id to JSON filter used to infer missing class_ids. Each key should be a class id, and its value should be a boolean expression which is run against the property field for each feature. This allows matching different features to different class IDs based on its properties. The expression schema is that described by https://docs.mapbox.com/mapbox-gl-js/style-spec/other/#other-filter

field default_class_id: Optional[int] = None#

The default class_id to use if class cannot be inferred using other mechanisms. If a feature has an inferred class_id of None, then it will be deleted.

field type_hint: Literal['class_inference_transformer'] = 'class_inference_transformer'#
build(class_config: Optional[ClassConfig] = None) → ClassInferenceTransformer[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

class_config (Optional[ClassConfig]) –

Return type

ClassInferenceTransformer

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#

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
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.

validate_list(field: str, valid_options: List[str])#

Validate a list field.

Parameters

  • field (str) – name of field to validate

  • valid_options (List[str]) – values that field is allowed to take

Raises

ConfigError – if field is invalid