StatsTransformerConfig#

Note

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

pydantic model StatsTransformerConfig[source]#

Configure a StatsTransformer.

Show JSON schema
{
   "title": "StatsTransformerConfig",
   "description": "Configure a :class:`.StatsTransformer`.",
   "type": "object",
   "properties": {
      "type_hint": {
         "title": "Type Hint",
         "default": "stats_transformer",
         "enum": [
            "stats_transformer"
         ],
         "type": "string"
      },
      "stats_uri": {
         "title": "Stats Uri",
         "description": "The URI of the output of the StatsAnalyzer. If None, and this Config is inside an RVPipeline, this field will be auto-generated.",
         "type": "string"
      },
      "scene_group": {
         "title": "Scene Group",
         "description": "Name of the group of scenes whose stats to use. Defaultsto \"train_scenes\".",
         "default": "train_scenes",
         "type": "string"
      }
   },
   "additionalProperties": false
}

Config

  • extra: str = forbid

  • validate_assignment: bool = True

Fields
field scene_group: str = 'train_scenes'#

Name of the group of scenes whose stats to use. Defaultsto “train_scenes”.

field stats_uri: Optional[str] = None#

The URI of the output of the StatsAnalyzer. If None, and this Config is inside an RVPipeline, this field will be auto-generated.

field type_hint: Literal['stats_transformer'] = 'stats_transformer'#
build()[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.

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[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.

Parameters
Return type

None

update_root(root_dir: str) → None[source]#
Parameters

root_dir (str) –

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