WindowSamplingConfig#

Note

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

pydantic model WindowSamplingConfig[source]#

Configure the sampling of chip windows.

Show JSON schema

{
   "title": "WindowSamplingConfig",
   "description": "Configure the sampling of chip windows.",
   "type": "object",
   "properties": {
      "method": {
         "default": "sliding",
         "allOf": [
            {
               "$ref": "#/definitions/WindowSamplingMethod"
            }
         ]
      },
      "size": {
         "title": "Size",
         "description": "If method = sliding, this is the size of sliding window. If method = random, this is the size that all the windows are resized to before they are returned. If method = random and neither size_lims nor h_lims and w_lims have been specified, then size_lims is set to (size, size + 1).",
         "anyOf": [
            {
               "type": "integer",
               "exclusiveMinimum": 0
            },
            {
               "type": "array",
               "minItems": 2,
               "maxItems": 2,
               "items": [
                  {
                     "type": "integer",
                     "exclusiveMinimum": 0
                  },
                  {
                     "type": "integer",
                     "exclusiveMinimum": 0
                  }
               ]
            }
         ]
      },
      "stride": {
         "title": "Stride",
         "description": "Stride of sliding window. Only used if method = sliding.",
         "anyOf": [
            {
               "type": "integer",
               "exclusiveMinimum": 0
            },
            {
               "type": "array",
               "minItems": 2,
               "maxItems": 2,
               "items": [
                  {
                     "type": "integer",
                     "exclusiveMinimum": 0
                  },
                  {
                     "type": "integer",
                     "exclusiveMinimum": 0
                  }
               ]
            }
         ]
      },
      "padding": {
         "title": "Padding",
         "description": "How many pixels are windows allowed to overflow the edges of the raster source.",
         "anyOf": [
            {
               "type": "integer",
               "minimum": 0
            },
            {
               "type": "array",
               "minItems": 2,
               "maxItems": 2,
               "items": [
                  {
                     "type": "integer",
                     "minimum": 0
                  },
                  {
                     "type": "integer",
                     "minimum": 0
                  }
               ]
            }
         ]
      },
      "pad_direction": {
         "title": "Pad Direction",
         "description": "If \"end\", only pad ymax and xmax (bottom and right). If \"start\", only pad ymin and xmin (top and left). If \"both\", pad all sides. Has no effect if paddiong is zero. Defaults to \"end\".",
         "default": "end",
         "enum": [
            "both",
            "start",
            "end"
         ],
         "type": "string"
      },
      "size_lims": {
         "title": "Size Lims",
         "description": "[min, max) interval from which window sizes will be uniformly randomly sampled. The upper limit is exclusive. To fix the size to a constant value, use size_lims = (sz, sz + 1). Only used if method = random. Specify either size_lims or h_lims and w_lims, but not both. If neither size_lims nor h_lims and w_lims have been specified, then this will be set to (size, size + 1).",
         "type": "array",
         "minItems": 2,
         "maxItems": 2,
         "items": [
            {
               "type": "integer",
               "exclusiveMinimum": 0
            },
            {
               "type": "integer",
               "exclusiveMinimum": 0
            }
         ]
      },
      "h_lims": {
         "title": "H Lims",
         "description": "[min, max] interval from which window heights will be uniformly randomly sampled. Only used if method = random.",
         "type": "array",
         "minItems": 2,
         "maxItems": 2,
         "items": [
            {
               "type": "integer",
               "exclusiveMinimum": 0
            },
            {
               "type": "integer",
               "exclusiveMinimum": 0
            }
         ]
      },
      "w_lims": {
         "title": "W Lims",
         "description": "[min, max] interval from which window widths will be uniformly randomly sampled. Only used if method = random.",
         "type": "array",
         "minItems": 2,
         "maxItems": 2,
         "items": [
            {
               "type": "integer",
               "exclusiveMinimum": 0
            },
            {
               "type": "integer",
               "exclusiveMinimum": 0
            }
         ]
      },
      "max_windows": {
         "title": "Max Windows",
         "description": "Max number of windows to sample. Only used if method = random.",
         "default": 10000,
         "minimum": 0,
         "type": "integer"
      },
      "max_sample_attempts": {
         "title": "Max Sample Attempts",
         "description": "Max attempts when trying to find a window within the AOI of a scene. Only used if method = random and the scene has aoi_polygons specified.",
         "default": 100,
         "exclusiveMinimum": 0,
         "type": "integer"
      },
      "efficient_aoi_sampling": {
         "title": "Efficient Aoi Sampling",
         "description": "If the scene has AOIs, sampling windows at random anywhere in the extent and then checking if they fall within any of the AOIs can be very inefficient. This flag enables the use of an alternate algorithm that only samples window locations inside the AOIs. Only used if method = random and the scene has aoi_polygons specified. Defaults to True",
         "default": true,
         "type": "boolean"
      },
      "within_aoi": {
         "title": "Within Aoi",
         "description": "If True and if the scene has an AOI, only sample windows that lie fully within the AOI. If False, windows only partially intersecting the AOI will also be allowed.",
         "default": true,
         "type": "boolean"
      },
      "type_hint": {
         "title": "Type Hint",
         "default": "window_sampling",
         "enum": [
            "window_sampling"
         ],
         "type": "string"
      }
   },
   "required": [
      "size"
   ],
   "additionalProperties": false,
   "definitions": {
      "WindowSamplingMethod": {
         "title": "WindowSamplingMethod",
         "description": "Enum for window sampling methods.\n\nAttributes:\n    sliding: Sliding windows.\n    random: Randomly sampled windows.",
         "enum": [
            "sliding",
            "random"
         ]
      }
   }
}

Config

extra: str = forbid
validate_assignment: bool = True

Fields

efficient_aoi_sampling (bool)
h_lims (Optional[Tuple[pydantic.types.PositiveInt, pydantic.types.PositiveInt]])
max_sample_attempts (pydantic.types.PositiveInt)
max_windows (rastervision.core.rv_pipeline.chip_options.ConstrainedIntValue)
method (rastervision.core.rv_pipeline.chip_options.WindowSamplingMethod)
pad_direction (Literal['both', 'start', 'end'])
padding (Optional[Union[rastervision.core.rv_pipeline.chip_options.ConstrainedIntValue, Tuple[rastervision.core.rv_pipeline.chip_options.ConstrainedIntValue, rastervision.core.rv_pipeline.chip_options.ConstrainedIntValue]]])
size (Union[pydantic.types.PositiveInt, Tuple[pydantic.types.PositiveInt, pydantic.types.PositiveInt]])
size_lims (Optional[Tuple[pydantic.types.PositiveInt, pydantic.types.PositiveInt]])
stride (Optional[Union[pydantic.types.PositiveInt, Tuple[pydantic.types.PositiveInt, pydantic.types.PositiveInt]]])
type_hint (Literal['window_sampling'])
w_lims (Optional[Tuple[pydantic.types.PositiveInt, pydantic.types.PositiveInt]])
within_aoi (bool)

field efficient_aoi_sampling: bool = True#

If the scene has AOIs, sampling windows at random anywhere in the extent and then checking if they fall within any of the AOIs can be very inefficient. This flag enables the use of an alternate algorithm that only samples window locations inside the AOIs. Only used if method = random and the scene has aoi_polygons specified. Defaults to True

Validated by

validate_options

field h_lims: Optional[Tuple[PositiveInt, PositiveInt]] = None#

[min, max] interval from which window heights will be uniformly randomly sampled. Only used if method = random.

Validated by

validate_options

field max_sample_attempts: PositiveInt = 100#

Max attempts when trying to find a window within the AOI of a scene. Only used if method = random and the scene has aoi_polygons specified.

Constraints

exclusiveMinimum = 0

Validated by

validate_options

field max_windows: ConstrainedIntValue = 10000#

Max number of windows to sample. Only used if method = random.

Constraints

minimum = 0

Validated by

validate_options

field method: WindowSamplingMethod = WindowSamplingMethod.sliding#

Validated by

validate_options

field pad_direction: Literal['both', 'start', 'end'] = 'end'#

If “end”, only pad ymax and xmax (bottom and right). If “start”, only pad ymin and xmin (top and left). If “both”, pad all sides. Has no effect if paddiong is zero. Defaults to “end”.

Validated by

validate_options

field padding: Optional[Union[ConstrainedIntValue, Tuple[ConstrainedIntValue, ConstrainedIntValue]]] = None#

How many pixels are windows allowed to overflow the edges of the raster source.

Validated by

validate_options

field size: Union[PositiveInt, Tuple[PositiveInt, PositiveInt]] [Required]#

If method = sliding, this is the size of sliding window. If method = random, this is the size that all the windows are resized to before they are returned. If method = random and neither size_lims nor h_lims and w_lims have been specified, then size_lims is set to (size, size + 1).

Validated by

validate_options

field size_lims: Optional[Tuple[PositiveInt, PositiveInt]] = None#

[min, max) interval from which window sizes will be uniformly randomly sampled. The upper limit is exclusive. To fix the size to a constant value, use size_lims = (sz, sz + 1). Only used if method = random. Specify either size_lims or h_lims and w_lims, but not both. If neither size_lims nor h_lims and w_lims have been specified, then this will be set to (size, size + 1).

Validated by

validate_options

field stride: Optional[Union[PositiveInt, Tuple[PositiveInt, PositiveInt]]] = None#

Stride of sliding window. Only used if method = sliding.

Validated by

validate_options

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

Validated by

validate_options

field w_lims: Optional[Tuple[PositiveInt, PositiveInt]] = None#

[min, max] interval from which window widths will be uniformly randomly sampled. Only used if method = random.

Validated by

validate_options

field within_aoi: bool = True#

If True and if the scene has an AOI, only sample windows that lie fully within the AOI. If False, windows only partially intersecting the AOI will also be allowed.

Validated by

validate_options

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().

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.

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_options » all fields[source]#

Parameters: values (dict) –
Return type: dict