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
validate_stride
»stride
- 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.
- 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.
- 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.
- validate_list(field: str, valid_options: List[str])#
Validate a list field.
- Parameters
- Raises
ConfigError – if field is invalid