ClassConfig#

Note

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

pydantic model ClassConfig[source]#

Configure class information for a machine learning task.

Show JSON schema
{
   "title": "ClassConfig",
   "description": "Configure class information for a machine learning task.",
   "type": "object",
   "properties": {
      "names": {
         "title": "Names",
         "description": "Names of classes. The i-th class in this list will have class ID = i.",
         "type": "array",
         "items": {
            "type": "string"
         }
      },
      "colors": {
         "title": "Colors",
         "description": "Colors used to visualize classes. Can be color strings accepted by matplotlib or RGB tuples. If None, a random color will be auto-generated for each class.",
         "type": "array",
         "items": {
            "anyOf": [
               {
                  "type": "string"
               },
               {
                  "type": "array",
                  "items": {}
               }
            ]
         }
      },
      "null_class": {
         "title": "Null Class",
         "description": "Optional name of class in `names` to use as the null class. This is used in semantic segmentation to represent the label for imagery pixels that are NODATA or that are missing a label. If None and the class names include \"null\", it will automatically be used as the null class. If None, and this Config is part of a SemanticSegmentationConfig, a null class will be added automatically.",
         "type": "string"
      },
      "type_hint": {
         "title": "Type Hint",
         "default": "class_config",
         "enum": [
            "class_config"
         ],
         "type": "string"
      }
   },
   "required": [
      "names"
   ],
   "additionalProperties": false
}

Config
  • extra: str = forbid

  • validate_assignment: bool = True

Fields
Validators
field colors: Optional[List[Union[str, Tuple]]] = None#

Colors used to visualize classes. Can be color strings accepted by matplotlib or RGB tuples. If None, a random color will be auto-generated for each class.

Validated by
field names: List[str] [Required]#

Names of classes. The i-th class in this list will have class ID = i.

field null_class: Optional[str] = None#

Optional name of class in names to use as the null class. This is used in semantic segmentation to represent the label for imagery pixels that are NODATA or that are missing a label. If None and the class names include “null”, it will automatically be used as the null class. If None, and this Config is part of a SemanticSegmentationConfig, a null class will be added automatically.

Validated by
field type_hint: Literal['class_config'] = 'class_config'#
build()#

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.

ensure_null_class() None[source]#

Add a null class if one isn’t set. This method is idempotent.

Return type

None

classmethod from_file(uri: str) Config#

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

Parameters

uri (str) – URI to load from.

Return type

Config

get_class_id(name: str) int[source]#
Parameters

name (str) –

Return type

int

get_color_to_class_id() dict[source]#
Return type

dict

get_name(id: int) str[source]#
Parameters

id (int) –

Return type

str

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

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(*args, **kwargs)#

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.

validator validate_colors  »  colors[source]#

Compare length w/ names. Also auto-generate if not specified.

Parameters
Return type

Optional[List[Union[str, Tuple]]]

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

validator validate_null_class  »  null_class[source]#

Check if in names. If ‘null’ in names, use it as null class.

Parameters
Return type

Optional[str]

property color_triples: List[Tuple[float, float, float]]#

Class colors in a normalized form.

property null_class_id: int#