RasterSourceConfig#

Note

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

pydantic model RasterSourceConfig[source]#

Configure a RasterSource.

Show JSON schema

{
   "title": "RasterSourceConfig",
   "description": "Configure a :class:`.RasterSource`.",
   "type": "object",
   "properties": {
      "channel_order": {
         "anyOf": [
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The sequence of channel indices to use when reading imagery.",
         "title": "Channel Order"
      },
      "transformers": {
         "default": [],
         "items": {
            "$ref": "#/$defs/RasterTransformerConfig"
         },
         "title": "Transformers",
         "type": "array"
      },
      "bbox": {
         "anyOf": [
            {
               "maxItems": 4,
               "minItems": 4,
               "prefixItems": [
                  {
                     "type": "integer"
                  },
                  {
                     "type": "integer"
                  },
                  {
                     "type": "integer"
                  },
                  {
                     "type": "integer"
                  }
               ],
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "User-specified bbox in pixel coords in the form (ymin, xmin, ymax, xmax). Useful for cropping the raster source so that only part of the raster is read from.",
         "title": "Bbox"
      },
      "type_hint": {
         "const": "raster_source",
         "default": "raster_source",
         "enum": [
            "raster_source"
         ],
         "title": "Type Hint",
         "type": "string"
      }
   },
   "$defs": {
      "RasterTransformerConfig": {
         "additionalProperties": false,
         "description": "Configure a :class:`.RasterTransformer`.",
         "properties": {
            "type_hint": {
               "const": "raster_transformer",
               "default": "raster_transformer",
               "enum": [
                  "raster_transformer"
               ],
               "title": "Type Hint",
               "type": "string"
            }
         },
         "title": "RasterTransformerConfig",
         "type": "object"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid
validate_assignment: bool = True

Fields:

bbox (tuple[int, int, int, int] | None)
channel_order (list[int] | None)
transformers (list[rastervision.core.data.raster_transformer.raster_transformer_config.RasterTransformerConfig])
type_hint (Literal['raster_source'])

field bbox: tuple[int, int, int, int] | None = None#: User-specified bbox in pixel coords in the form (ymin, xmin, ymax, xmax). Useful for cropping the raster source so that only part of the raster is read from.

field channel_order: list[int] | None = None#: The sequence of channel indices to use when reading imagery.

field transformers: list[RasterTransformerConfig] = []#

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

build(tmp_dir: str | None = None, use_transformers: bool = True) → RasterSource[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:

tmp_dir (str | None) –
use_transformers (bool) –

Return type:

RasterSource

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 = None, scene: SceneConfig | None = 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:

pipeline (RVPipelineConfig | None) –
scene (SceneConfig | None) –

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