CastTransformerConfig#

Note

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

pydantic model CastTransformerConfig[source]#

Configure a CastTransformer.

Show JSON schema

{
   "title": "CastTransformerConfig",
   "description": "Configure a :class:`.CastTransformer`.",
   "type": "object",
   "properties": {
      "type_hint": {
         "const": "cast_transformer",
         "default": "cast_transformer",
         "title": "Type Hint",
         "type": "string"
      },
      "to_dtype": {
         "description": "dtype to cast raster to. Must be a valid Numpy dtype e.g. \"uint8\", \"float32\", etc.",
         "title": "To Dtype",
         "type": "string"
      }
   },
   "additionalProperties": false,
   "required": [
      "to_dtype"
   ]
}

Config

extra: str = forbid
validate_assignment: bool = True

Fields

to_dtype (str)
type_hint (Literal['cast_transformer'])

field to_dtype: str [Required]#: dtype to cast raster to. Must be a valid Numpy dtype e.g. “uint8”, “float32”, etc.

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

build(channel_order: list[int] | None = 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.

Parameters: channel_order (list[int] | None) –

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, scene: SceneConfig = 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) –
scene (SceneConfig) –

update_root(root_dir: str)#

Parameters: root_dir (str) –

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