SemanticSegmentationPredictOptions

Note

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

pydantic model SemanticSegmentationPredictOptions

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",
         "enum": [
            "semantic_segmentation_predict_options"
         ],
         "title": "Type Hint",
         "type": "string"
      },
      "crop_sz": {
         "anyOf": [
            {
               "minimum": 0,
               "type": "integer"
            },
            {
               "const": "auto",
               "enum": [
                  "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

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