ObjectDetectionPredictOptions

Note

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

pydantic model ObjectDetectionPredictOptions

Show JSON schema

{
   "title": "ObjectDetectionPredictOptions",
   "type": "object",
   "properties": {
      "chip_sz": {
         "default": 300,
         "description": "Size of predictions chips in pixels.",
         "title": "Chip Sz",
         "type": "integer"
      },
      "stride": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Stride of the sliding window for generating chips. Defaults to half of ``chip_sz``.",
         "title": "Stride"
      },
      "batch_sz": {
         "default": 8,
         "description": "Batch size to use during prediction.",
         "title": "Batch Sz",
         "type": "integer"
      },
      "type_hint": {
         "const": "object_detection_predict_options",
         "default": "object_detection_predict_options",
         "title": "Type Hint",
         "type": "string"
      },
      "merge_thresh": {
         "default": 0.5,
         "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.",
         "title": "Merge Thresh",
         "type": "number"
      },
      "score_thresh": {
         "default": 0.5,
         "description": "Predicted boxes are only output if their score is above score_thresh.",
         "title": "Score Thresh",
         "type": "number"
      }
   },
   "additionalProperties": false
}

Config

• extra: str = forbid

• validate_assignment: bool = True

Fields

• batch_sz (int)

• chip_sz (int)

• merge_thresh (float)

• score_thresh (float)

• stride (int | None)

• type_hint (Literal['object_detection_predict_options'])

Validators

• validate_stride » all fields

field batch_sz: int = 8

Batch size to use during prediction.

Validated by

• validate_stride

field chip_sz: int = 300

Size of predictions chips in pixels.

Validated by

• validate_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.

Validated by

• validate_stride

field score_thresh: float = 0.5

Predicted boxes are only output if their score is above score_thresh.

Validated by

• validate_stride

field stride: int | None = None

Stride of the sliding window for generating chips. Defaults to half of chip_sz.

Validated by

• validate_stride

field type_hint: Literal['object_detection_predict_options'] = 'object_detection_predict_options'

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 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(*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

• 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  »  all fields

Return type

Self