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/stable/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 performs 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.

add_dynamic_flow(*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)

enable_metadata(self, enable)

Configure whether or not the metadata feature is enabled for this operator.

find_all_flow_info(self, predicate, bool])

Find all flow info objects in the operator’s next flows that match a given condition.

find_flow_info(self, predicate, bool])

Find a flow info in the operator’s next flows based on a given predicate.

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.

queue_policy(self, port_name, port_type, policy)

Set the queue policy to be used by an input (or output) port’s receiver (or transmitter).

receiver(self, port_name)

Get the receiver used by an input port.

resource(self, name)

Resources associated with the operator.

setup(spec)

Default implementation of setup method.

start()

Default implementation of start

stop()

Default implementation of stop

stop_execution(self)

Stop the execution of the operator.

transmitter(self, port_name)

Get the transmitter used by an output port.

Attributes

INPUT_EXEC_PORT_NAME

MODEL_LOCAL_PATH

OUTPUT_EXEC_PORT_NAME

args

The list of arguments associated with the component.

async_condition

The internal asynchronous condition for the operator.

conditions

Conditions associated with the operator.

description

YAML formatted string describing the operator.

execution_context

The execution context for the operator.

fragment

The fragment (holoscan.core.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.

is_metadata_enabled

Boolean indicating whether the fragment this operator belongs to has metadata transmission enabled.

metadata

The metadata dictionary (holoscan.core.MetadataDictionary) associated with the operator.

metadata_policy

The metadata policy (holoscan.core.MetadataPolicy) associated with the operator.

name

The name of the operator.

next_flows

Get the list of flow information for connections to downstream operators.

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

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

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

Enum class for operator types used by the executor.

  • NATIVE: Native operator.

  • GXF: GXF operator.

  • VIRTUAL: Virtual operator. (for internal use, not intended for use by application authors)

Members:

NATIVE

GXF

VIRTUAL

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/stable/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.

add_dynamic_flow(*args, **kwargs)

Overloaded function.

  1. add_dynamic_flow(self: holoscan.core._core.Operator, flow: holoscan.core._core.FlowInfo) -> None

  2. add_dynamic_flow(self: holoscan.core._core.Operator, flows: list[holoscan.core._core.FlowInfo]) -> None

  3. add_dynamic_flow(self: holoscan.core._core.Operator, next_op: holoscan.core._core.Operator, next_input_port_name: str = ‘’) -> None

Add a dynamic flow from this operator to another operator.

next_opholoscan.core.Operator

The downstream operator to connect to.

next_input_port_namestr, optional

The name of the input port on the downstream operator to connect to. If not specified, the first available input port will be used.

This method has several overloads to support different ways of creating dynamic flows:

  1. add_dynamic_flow(next_op: Operator, next_input_port_name: str = ‘’) - Basic connection using default output port. This is the simplest form for connecting

    two operators when you only need to specify the destination.

  2. add_dynamic_flow(curr_output_port_name: str, next_op: Operator, next_input_port_name: str = ‘’) - Connection with explicit output port specification. Use this when the source operator has

    multiple output ports and you need to specify which one to use.

  3. add_dynamic_flow(flow: FlowInfo) - Connection using a FlowInfo object, which encapsulates all connection details including:

    • Source operator and its output port specification

    • Destination operator and its input port specification

    • Port names and associated IOSpecs

    • This is useful for complex connections or when reusing connection patterns.

  4. add_dynamic_flow(flows: List[FlowInfo]) - Batch connection using multiple FlowInfo objects. Use this to set up multiple

    connections in a single call, which is more efficient than making multiple individual connections.

The FlowInfo class provides a complete description of a flow connection between operators, including all port specifications and naming. It’s particularly useful when you need to:

  • Store and reuse connection patterns

  • Create complex routing configurations

  • Handle dynamic port specifications

  • Manage multiple connections systematically

  1. add_dynamic_flow(self: holoscan.core._core.Operator, curr_output_port_name: str, next_op: holoscan.core._core.Operator, next_input_port_name: str = ‘’) -> None

property args

The list of arguments associated with the component.

arglist : holoscan.core.ArgList

property async_condition

The internal asynchronous condition for the operator.

This property provides access to the internal asynchronous condition for the operator, which controls the scheduling of the operator’s compute method.

conditionholoscan.conditions.AsynchronousCondition

An instance of holoscan.conditions.AsynchronousCondition that is the internal asynchronous condition for the operator.

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.

enable_metadata(self: holoscan.core._core.Operator, enable: bool) → None

Configure whether or not the metadata feature is enabled for this operator. If it is not set, the default value will be determined by the enable_metadata setting from the Fragment that this operator belongs to.

property execution_context

The execution context for the operator.

This property provides access to the execution context of the operator, which contains information about the current execution environment including scheduling details.

holoscan.core.ExecutionContext

The execution context object for this operator.

find_all_flow_info(self: holoscan.core._core.Operator, predicate: Callable[[holoscan.core._core.FlowInfo], bool]) → list[holoscan.core._core.FlowInfo]

Find all flow info objects in the operator’s next flows that match a given condition.

predicatecallable

A function that takes a FlowInfo object and returns a boolean.

list[holoscan.core.FlowInfo]

List of matching FlowInfo objects.

find_flow_info(self: holoscan.core._core.Operator, predicate: Callable[[holoscan.core._core.FlowInfo], bool]) → holoscan.core._core.FlowInfo

Find a flow info in the operator’s next flows based on a given predicate.

predicatecallable

A function that takes a FlowInfo object and returns a boolean.

holoscan.core.FlowInfo or None

The first matching FlowInfo object, or None if not found.

property fragment

The fragment (holoscan.core.Fragment) that the operator belongs to.

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 is_metadata_enabled

Boolean indicating whether the fragment this operator belongs to has metadata transmission enabled.

property metadata

The metadata dictionary (holoscan.core.MetadataDictionary) associated with the operator.

property metadata_policy

The metadata policy (holoscan.core.MetadataPolicy) associated with the operator.

The supported policies are:

  • MetadataPolicy.REJECT: Reject the new value if the key already exists

  • MetadataPolicy.UPDATE: Replace existing value with the new one if the key already exists

  • MetadataPolicy.INPLACE_UPDATE: Update the value stored within an existing MetadataObject in-place if the key already exists (in contrast to UPDATE which always replaces the existing MetadataObject with a new one).

  • MetadataPolicy.RAISE: Raise an exception if the key already exists

property name

The name of the operator.

property next_flows

Get the list of flow information for connections to downstream operators.

list[holoscan.core.FlowInfo]

List of flow information objects describing connections to downstream operators.

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]]

queue_policy(self: holoscan.core._core.Operator, port_name: str, port_type: holoscan.core._core.IOSpec.IOType = <IOType.INPUT: 0>, policy: holoscan.core._core.IOSpec.QueuePolicy = <QueuePolicy.FAULT: 2>) → None

Set the queue policy to be used by an input (or output) port’s receiver (or transmitter).

port_namestr

The name of the port.

port_typeIOSpec.IOType, optional

Enum indicating whether port_name corresponds to an input port or output port.

policyIOSpec.QueuePolicy, optional

The queue policy to set. Valid values are:

  • QueuePolicy.POP : If the queue is full, pop the oldest item, then add the new one.

  • QueuePolicy.REJECT : If the queue is full, reject (discard) the new item.

  • QueuePolicy.FAULT : If the queue is full, log a warning and reject the new item.

transmitterholoscan.resources.Transmitter or None

The transmitter used by this output port. Will be None if the port does not exist.

receiver(self: holoscan.core._core.Operator, port_name: str) → Optional[holoscan::Receiver]

Get the receiver used by an input port.

port_namestr

The name of the input port.

receiverholoscan.resources.Receiver

The receiver used by this input port. Will be None if the port does not exist.

resource(self: holoscan.core._core.Operator, name: str) → Optional[object]

Resources associated with the operator.

namestr

The name of the resource to retrieve

holoscan.core.Resource or None

The resource with the given name. If no resource with the given name is found, None is returned.

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.

property spec

The operator spec (holoscan.core.OperatorSpec) associated with the operator.

start()

Default implementation of start

stop()

Default implementation of stop

stop_execution(self: holoscan.core._core.Operator) → None

Stop the execution of the operator.

This method is used to stop the execution of the operator by setting the internal async condition to EVENT_NEVER state, which sets the scheduling condition to NEVER. Once stopped, the operator will not be scheduled for execution (the compute() method will not be called).

Note that executing this method does not trigger the operator’s stop() method. The stop() method is called only when the scheduler deactivates all operators together.

property sw_batch_size

The batch size to run window slices

transmitter(self: holoscan.core._core.Operator, port_name: str) → Optional[holoscan::Transmitter]

Get the transmitter used by an output port.

port_namestr

The name of the output port.

transmitterholoscan.resources.Transmitter or None

The transmitter used by this output port. Will be None if the port does not exist.

monai.deploy.operators.MonaiBundleInferenceOperator monai.deploy.operators.PNGConverterOperator