monai.deploy.operators.MonaiSegInferenceOperator

class monai.deploy.operators.MonaiSegInferenceOperator(fragment, *args, roi_size, pre_transforms, post_transforms, app_context, model_name='', overlap=0.25, sw_batch_size=4, inferer=InfererType.SLIDING_WINDOW, model_path=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/monai-deploy-app-sdk/checkouts/latest/docs/source/model/model.ts'), **kwargs)[source]

Bases: monai.deploy.operators.inference_operator.InferenceOperator

This segmentation operator uses MONAI transforms and Sliding Window Inference.

This operator preforms pre-transforms on a input image, inference using a given model, and post-transforms. The segmentation image is saved as a named Image object in memory.

If specified in the post transforms, results may also be saved to disk.

Named Input:

image: Image object of the input image.

Named Output:

seg_image: Image object of the segmentation image. Not requiring a ready receiver.

Creates a instance of this class.

Parameters
  • fragment (Fragment) – An instance of the Application class which is derived from Fragment.

  • roi_size (Union[Sequence[int], int]) – The window size to execute “SLIDING_WINDOW” evaluation. An optional input only to be passed for “SLIDING_WINDOW”. If using a “SIMPLE” Inferer, this input is ignored.

  • pre_transforms (Compose) – MONAI Compose object used for pre-transforms.

  • post_transforms (Compose) – MONAI Compose object used for post-transforms.

  • app_context (AppContext) – Object holding the I/O and model paths, and potentially loaded models.

  • model_name (str, optional) – Name of the model. Default to “” for single model app.

  • overlap (float) – The amount of overlap between scans along each spatial dimension. Defaults to 0.25. Applicable for “SLIDING_WINDOW” only.

  • sw_batch_size (int) – The batch size to run window slices. Defaults to 4. Applicable for “SLIDING_WINDOW” only.

  • inferer (InfererType) – The type of inferer to use, “SIMPLE” or “SLIDING_WINDOW”. Defaults to “SLIDING_WINDOW”.

  • model_path (Path) – Path to the model file. Defaults to model/models.ts of current working dir.

Methods

__init__(fragment, *args, roi_size, …[, …])

Creates a instance of this class.

add_arg(*args, **kwargs)

Overloaded function.

compute(op_input, op_output, context)

Infers with the input image and save the predicted image to output

compute_impl(input_image, context)

initialize()

Default implementation of initialize

post_process(data, *args, **kwargs)

Transforms the prediction results from the model(s).

pre_process(data, *args, **kwargs)

Transforms input before being used for predicting on a model.

predict(data, *args, **kwargs)

Predicts results using the models(s) with input tensors.

setup(spec)

Default implementation of setup method.

start()

Default implementation of start

stop()

Default implementation of stop

Attributes

MODEL_LOCAL_PATH

args

The list of arguments associated with the component.

conditions

Conditions associated with the operator.

description

YAML formatted string describing the operator.

fragment

The fragment that the operator belongs to.

id

The identifier of the component.

inferer

The type of inferer to use

input_dataset_key

This is the input image key name used in dictionary based MONAI pre-transforms.

name

The name of the operator.

operator_type

The operator type.

overlap

This is the overlap used during sliding window inference

pred_dataset_key

This is the prediction key name used in dictionary based MONAI post-transforms.

resources

Resources associated with the operator.

roi_size

The ROI size of tensors used in prediction.

spec

sw_batch_size

The batch size to run window slices

class OperatorType(self: holoscan.core._core.Operator.OperatorType, value: int) None

Bases: pybind11_builtins.pybind11_object

Members:

NATIVE

GXF

property name
__init__(fragment, *args, roi_size, pre_transforms, post_transforms, app_context, model_name='', overlap=0.25, sw_batch_size=4, inferer=InfererType.SLIDING_WINDOW, model_path=PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/monai-deploy-app-sdk/checkouts/latest/docs/source/model/model.ts'), **kwargs)[source]

Creates a instance of this class.

Parameters
  • fragment (Fragment) – An instance of the Application class which is derived from Fragment.

  • roi_size (Union[Sequence[int], int]) – The window size to execute “SLIDING_WINDOW” evaluation. An optional input only to be passed for “SLIDING_WINDOW”. If using a “SIMPLE” Inferer, this input is ignored.

  • pre_transforms (Compose) – MONAI Compose object used for pre-transforms.

  • post_transforms (Compose) – MONAI Compose object used for post-transforms.

  • app_context (AppContext) – Object holding the I/O and model paths, and potentially loaded models.

  • model_name (str, optional) – Name of the model. Default to “” for single model app.

  • overlap (float) – The amount of overlap between scans along each spatial dimension. Defaults to 0.25. Applicable for “SLIDING_WINDOW” only.

  • sw_batch_size (int) – The batch size to run window slices. Defaults to 4. Applicable for “SLIDING_WINDOW” only.

  • inferer (InfererType) – The type of inferer to use, “SIMPLE” or “SLIDING_WINDOW”. Defaults to “SLIDING_WINDOW”.

  • model_path (Path) – Path to the model file. Defaults to model/models.ts of current working dir.

add_arg(*args, **kwargs)

Overloaded function.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Arg) -> None

Add an argument to the component.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.ArgList) -> None

Add a list of arguments to the component.

  1. add_arg(self: holoscan.core._core.Operator, **kwargs) -> None

Add arguments to the component via Python kwargs.

  1. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Condition) -> None

  2. add_arg(self: holoscan.core._core.Operator, arg: holoscan.core._core.Resource) -> None

Add a condition or resource to the Operator.

This can be used to add a condition or resource to an operator after it has already been constructed.

argholoscan.core.Condition or holoscan.core.Resource

The condition or resource to add.

property args

The list of arguments associated with the component.

arglist : holoscan.core.ArgList

compute(op_input, op_output, context)[source]

Infers with the input image and save the predicted image to output

Parameters
  • op_input (InputContext) – An input context for the operator.

  • op_output (OutputContext) – An output context for the operator.

  • context (ExecutionContext) – An execution context for the operator.

property conditions

Conditions associated with the operator.

property description

YAML formatted string describing the operator.

property fragment

The fragment that the operator belongs to.

name : holoscan.core.Fragment

property id

The identifier of the component.

The identifier is initially set to -1, and will become a valid value when the component is initialized.

With the default executor (holoscan.gxf.GXFExecutor), the identifier is set to the GXF component ID.

id : int

property inferer: Union[monai.deploy.operators.monai_seg_inference_operator.InfererType, str]

The type of inferer to use

Return type

Union[InfererType, str]

initialize()

Default implementation of initialize

property input_dataset_key

This is the input image key name used in dictionary based MONAI pre-transforms.

property name

The name of the operator.

name : str

property operator_type

The operator type.

holoscan.core.Operator.OperatorType enum representing the type of the operator. The two types currently implemented are native and GXF.

property overlap

This is the overlap used during sliding window inference

post_process(data, *args, **kwargs)[source]

Transforms the prediction results from the model(s).

This method must be overridden by a derived class. Expected return is monai.transforms.Compose.

Parameters

data (monai.transforms.Compose) – The pre-processing transforms in a Compose object.

Return type

Union[Any, Image, Tuple[Any, …], Dict[Any, Any]]

Returns

monai.transforms.Compose encapsulating post-processing transforms.

Raises

NotImplementedError – When the subclass does not override this method.

pre_process(data, *args, **kwargs)[source]

Transforms input before being used for predicting on a model.

This method must be overridden by a derived class. Expected return is monai.transforms.Compose.

Parameters

data (monai.data.ImageReader) – Reader used in LoadImage to load monai.deploy.core.Image as the input.

Return type

Union[Any, Image, Tuple[Any, …], Dict[Any, Any]]

Returns

monai.transforms.Compose encapsulating pre transforms

Raises

NotImplementedError – When the subclass does not override this method.

property pred_dataset_key

This is the prediction key name used in dictionary based MONAI post-transforms.

predict(data, *args, **kwargs)[source]

Predicts results using the models(s) with input tensors.

This method is currently not used in this class, instead monai.inferers.sliding_window_inference is used.

Raises

NotImplementedError – When the subclass does not override this method.

Return type

Union[Image, Any, Tuple[Any, …], Dict[Any, Any]]

property resources

Resources associated with the operator.

property roi_size

The ROI size of tensors used in prediction.

setup(spec)[source]

Default implementation of setup method.

start()

Default implementation of start

stop()

Default implementation of stop

property sw_batch_size

The batch size to run window slices