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": {
         "const": "class_inference_transformer",
         "default": "class_inference_transformer",
         "enum": [
            "class_inference_transformer"
         ],
         "title": "Type Hint",
         "type": "string"
      },
      "default_class_id": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "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. Defaults to ``None``.",
         "title": "Default Class Id"
      },
      "class_id_to_filter": {
         "anyOf": [
            {
               "additionalProperties": {
                  "items": {},
                  "type": "array"
               },
               "type": "object"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "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. Defaults to ``None``.",
         "title": "Class Id To Filter"
      },
      "class_name_mapping": {
         "anyOf": [
            {
               "additionalProperties": {
                  "type": "string"
               },
               "type": "object"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "``old_name --> new_name`` mapping for values in the ``class_name`` or ``label`` property of the GeoJSON features. The ``new_name`` must be a valid class name in the ``ClassConfig``. This can also be used to merge multiple classes into one e.g.: ``dict(car=\"vehicle\", truck=\"vehicle\")``. Defaults to None.",
         "title": "Class Name Mapping"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid
validate_assignment: bool = True

Fields:

class_id_to_filter (dict[int, list] | None)
class_name_mapping (dict[str, str] | None)
default_class_id (int | None)
type_hint (Literal['class_inference_transformer'])

field class_id_to_filter: dict[int, list] | None = 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. Defaults to None.

field class_name_mapping: dict[str, str] | None = None#: old_name --> new_name mapping for values in the class_name or label property of the GeoJSON features. The new_name must be a valid class name in the ClassConfig. This can also be used to merge multiple classes into one e.g.: dict(car="vehicle", truck="vehicle"). Defaults to None.

field default_class_id: int | None = 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. Defaults to None.

field type_hint: Literal['class_inference_transformer'] = 'class_inference_transformer'#

build(class_config: ClassConfig | None = 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 (ClassConfig | None) –
Return type:: ClassInferenceTransformer

classmethod deserialize(inp: str | dict | Config) → Self#

Deserialize Config from a JSON file or dict, upgrading if possible.

If inp is already a Config, it is returned as is.

Parameters:: inp (str | dict | Config) – a URI to a JSON file or a dict.
Return type:: Self

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.

Parameters:

uri (str) – URI to save to.
with_rv_metadata (bool) – If True, inject Raster Vision metadata such as plugin_versions, so that the config can be upgraded when loaded.

Return type:

None

update(pipeline: RVPipelineConfig | None = None, scene: SceneConfig | 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) –
scene (SceneConfig | 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.

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