SemanticSegmentationChipOptions#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model SemanticSegmentationChipOptions[source]#
Chipping options for semantic segmentation.
Show JSON schema
{ "title": "SemanticSegmentationChipOptions", "description": "Chipping options for semantic segmentation.", "type": "object", "properties": { "window_method": { "description": "Window method to use for chipping.", "default": "sliding", "allOf": [ { "$ref": "#/definitions/SemanticSegmentationWindowMethod" } ] }, "target_class_ids": { "title": "Target Class Ids", "description": "List of class ids considered as targets (ie. those to prioritize when creating chips) which is only used in conjunction with the target_count_threshold and negative_survival_probability options. Applies to the random_sample window method.", "type": "array", "items": { "type": "integer" } }, "negative_survival_prob": { "title": "Negative Survival Prob", "description": "Probability of keeping a negative chip.", "default": 1.0, "type": "number" }, "chips_per_scene": { "title": "Chips Per Scene", "description": "Number of chips to generate per scene. Applies to the random_sample window method.", "default": 1000, "type": "integer" }, "target_count_threshold": { "title": "Target Count Threshold", "description": "Minimum number of pixels covering target_classes that a chip must have. Applies to the random_sample window method.", "default": 1000, "type": "integer" }, "stride": { "title": "Stride", "description": "Stride of windows across image. Defaults to half the chip size. Applies to the sliding_window method.", "type": "integer" }, "type_hint": { "title": "Type Hint", "default": "semantic_segmentation_chip_options", "enum": [ "semantic_segmentation_chip_options" ], "type": "string" } }, "additionalProperties": false, "definitions": { "SemanticSegmentationWindowMethod": { "title": "SemanticSegmentationWindowMethod", "description": "Enum for window methods\n\nAttributes:\n sliding: use a sliding window\n random_sample: randomly sample windows", "enum": [ "sliding", "random_sample" ] } } }
- Config
extra: str = forbid
validate_assignment: bool = True
- Fields
- field chips_per_scene: int = 1000#
Number of chips to generate per scene. Applies to the random_sample window method.
- field stride: Optional[int] = None#
Stride of windows across image. Defaults to half the chip size. Applies to the sliding_window method.
- field target_class_ids: Optional[List[int]] = None#
List of class ids considered as targets (ie. those to prioritize when creating chips) which is only used in conjunction with the target_count_threshold and negative_survival_probability options. Applies to the random_sample window method.
- field target_count_threshold: int = 1000#
Minimum number of pixels covering target_classes that a chip must have. Applies to the random_sample window method.
- field type_hint: Literal['semantic_segmentation_chip_options'] = 'semantic_segmentation_chip_options'#
- field window_method: SemanticSegmentationWindowMethod = SemanticSegmentationWindowMethod.sliding#
Window method to use for chipping.
- 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.
- 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
- 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.