ChipClassificationGeoJSONStoreConfig#

Note

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

pydantic model ChipClassificationGeoJSONStoreConfig[source]#

Configure a ChipClassificationGeoJSONStore.

Show JSON schema

{
   "title": "ChipClassificationGeoJSONStoreConfig",
   "description": "Configure a :class:`.ChipClassificationGeoJSONStore`.",
   "type": "object",
   "properties": {
      "type_hint": {
         "const": "chip_classification_geojson_store",
         "default": "chip_classification_geojson_store",
         "enum": [
            "chip_classification_geojson_store"
         ],
         "title": "Type Hint",
         "type": "string"
      },
      "uri": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "URI of GeoJSON file with predictions. If None, and this Config is part of a SceneConfig inside an RVPipelineConfig, it will be auto-generated.",
         "title": "Uri"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid
validate_assignment: bool = True

Fields:

type_hint (Literal['chip_classification_geojson_store'])
uri (str | None)

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

field uri: str | None = None#: URI of GeoJSON file with predictions. If None, and this Config is part of a SceneConfig inside an RVPipelineConfig, it will be auto-generated.

build(class_config, crs_transformer, bbox=None, tmp_dir=None)[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.

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=None, scene=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.

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