ObjectDetectionPredictOptions#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model ObjectDetectionPredictOptions[source]#
Show JSON schema
{ "title": "ObjectDetectionPredictOptions", "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. Defaults to half of ``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": "object_detection_predict_options", "enum": [ "object_detection_predict_options" ], "type": "string" }, "merge_thresh": { "title": "Merge Thresh", "description": "If predicted boxes have an IOA (intersection over area) greater than merge_thresh, then they are merged into a single box during postprocessing. This is needed since the sliding window approach results in some false duplicates.", "default": 0.5, "type": "number" }, "score_thresh": { "title": "Score Thresh", "description": "Predicted boxes are only output if their score is above score_thresh.", "default": 0.5, "type": "number" } }, "additionalProperties": false }
- Config
extra: str = forbid
validate_assignment: bool = True
- Fields
- Validators
validate_stride
»stride
- field merge_thresh: float = 0.5#
If predicted boxes have an IOA (intersection over area) greater than merge_thresh, then they are merged into a single box during postprocessing. This is needed since the sliding window approach results in some false duplicates.
- field score_thresh: float = 0.5#
Predicted boxes are only output if their score is above score_thresh.
- field stride: Optional[int] = None#
Stride of the sliding window for generating chips. Defaults to half of
chip_sz
.- Validated by
validate_stride
- 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