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

pydantic model NanTransformerConfig[source]#

Configure a NanTransformer.

Show JSON schema
   "title": "NanTransformerConfig",
   "description": "Configure a :class:`.NanTransformer`.",
   "type": "object",
   "properties": {
      "type_hint": {
         "title": "Type Hint",
         "default": "nan_transformer",
         "enum": [
         "type": "string"
      "to_value": {
         "title": "To Value",
         "description": "Turn all NaN values into this value.",
         "default": 0.0,
         "type": "number"
   "additionalProperties": false

  • extra: str = forbid

  • validate_assignment: bool = True

field to_value: Optional[float] = 0.0#

Turn all NaN values into this value.

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

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.


Recursively validate hierarchies of Configs.

This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.


Re-validate an instantiated Config.

Runs all Pydantic validators plus self.validate_config().

Adapted from:

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 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.

  • field (str) – name of field to validate

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


ConfigError – if field is invalid