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",
   "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. Allows aggregating multiple predictions for each pixel if less than the chip size. Defaults to ``chip_sz``.",
         "title": "Stride"
      },
      "batch_sz": {
         "default": 8,
         "description": "Batch size to use during prediction.",
         "title": "Batch Sz",
         "type": "integer"
      },
      "type_hint": {
         "const": "semantic_segmentation_predict_options",
         "default": "semantic_segmentation_predict_options",
         "title": "Type Hint",
         "type": "string"
      },
      "crop_sz": {
         "anyOf": [
            {
               "minimum": 0,
               "type": "integer"
            },
            {
               "const": "auto",
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "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.",
         "title": "Crop Sz"
      }
   },
   "additionalProperties": false
}

Config

extra: str = forbid
validate_assignment: bool = True

Fields

batch_sz ()
chip_sz ()
crop_sz ()
stride ()
type_hint (Literal['semantic_segmentation_predict_options'])

Validators

set_auto_crop_sz » all fields
validate_stride » all fields

field batch_sz: int = 8#

Batch size to use during prediction.

Validated by

set_auto_crop_sz
validate_stride

field chip_sz: int = 300#

Size of predictions chips in pixels.

Validated by

set_auto_crop_sz
validate_stride

field crop_sz: NonNegInt | Literal['auto'] | None = 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

set_auto_crop_sz
validate_stride

field stride: int | None = 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

set_auto_crop_sz
validate_stride

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

Validated by

set_auto_crop_sz
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().

validator set_auto_crop_sz » all fields[source]#

Return type: Self

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