SemanticSegmentationPredictOptions#

Note

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

pydantic model SemanticSegmentationPredictOptions[source]#

Show JSON schema
{
   "title": "SemanticSegmentationPredictOptions",
   "description": "Base class that can be extended to provide custom configurations.\n\nThis adds some extra methods to Pydantic BaseModel.\nSee https://pydantic-docs.helpmanual.io/\n\nThe general idea is that configuration schemas can be defined by\nsubclassing this and adding class attributes with types and\ndefault values for each field. Configs can be defined hierarchically,\nie. a Config can have fields which are of type Config.\nValidation, serialization, deserialization, and IDE support is\nprovided automatically based on this schema.",
   "type": "object",
   "properties": {
      "chip_sz": {
         "title": "Chip Sz",
         "description": "Size of predictions chips in pixels.",
         "default": 300,
         "type": "integer"
      },
      "stride": {
         "title": "Stride",
         "description": "Stride of the sliding window for generating chips. Allows aggregating multiple predictions for each pixel if less than the chip size. Defaults to ``chip_sz``.",
         "type": "integer"
      },
      "batch_sz": {
         "title": "Batch Sz",
         "description": "Batch size to use during prediction.",
         "default": 8,
         "type": "integer"
      },
      "type_hint": {
         "title": "Type Hint",
         "default": "semantic_segmentation_predict_options",
         "enum": [
            "semantic_segmentation_predict_options"
         ],
         "type": "string"
      },
      "crop_sz": {
         "title": "Crop Sz",
         "description": "Number of rows/columns of pixels from the edge of prediction windows to discard. This is useful because predictions near edges tend to be lower quality and can result in very visible artifacts near the edges of chips. If \"auto\", will be set to half the stride if stride is less than chip_sz. Defaults to None.",
         "anyOf": [
            {
               "type": "integer",
               "exclusiveMinimum": 0
            },
            {
               "enum": [
                  "auto"
               ],
               "type": "string"
            }
         ]
      }
   },
   "additionalProperties": false
}

Config
  • extra: str = forbid

  • validate_assignment: bool = True

Fields
Validators
field batch_sz: int = 8#

Batch size to use during prediction.

field chip_sz: int = 300#

Size of predictions chips in pixels.

field crop_sz: Optional[Union[ConstrainedIntValue, Literal['auto']]] = None#

Number of rows/columns of pixels from the edge of prediction windows to discard. This is useful because predictions near edges tend to be lower quality and can result in very visible artifacts near the edges of chips. If “auto”, will be set to half the stride if stride is less than chip_sz. Defaults to None.

Validated by
field stride: Optional[int] = None#

Stride of the sliding window for generating chips. Allows aggregating multiple predictions for each pixel if less than the chip size. Defaults to chip_sz.

Validated by
  • validate_stride

field type_hint: Literal['semantic_segmentation_predict_options'] = 'semantic_segmentation_predict_options'#
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.

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

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.

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.

validator validate_crop_sz  »  crop_sz[source]#
Parameters
Return type

dict

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_stride  »  stride#
Parameters
Return type

dict