morpheus.stages.postprocess.filter_detections_stage.FilterDetectionsStage

(Latest Version)
class FilterDetectionsStage(c, threshold=0.5, copy=True, filter_source=<FilterSource.Auto: 0>, field_name='probs’)[source]

Bases: morpheus.pipeline.single_port_stage.SinglePortStage

Filter message by a classification threshold.

The FilterDetectionsStage is used to filter rows from a dataframe based on values in a tensor using a specified criteria. Rows in the meta dataframe are excluded if their associated value in the probs array is less than or equal to threshold.

This stage can operate in two different modes set by the copy argument. When the copy argument is True (default), rows that meet the filter criteria are copied into a new dataframe. When False sliced views are used instead.

Setting copy=True should be used when the number of matching records is expected to be both high and in non-adjacent rows. In this mode, the stage will generate only one output message for each incoming message, regardless of the size of the input and the number of matching records. However this comes at the cost of needing to allocate additional memory and perform the copy. Note: In most other stages, messages emitted contain a reference to the original MessageMeta emitted into the pipeline by the source stage. When using copy mode this won’t be the case and could cause the original MessageMeta to be deallocated after this stage.

Setting copy=False should be used when either the number of matching records is expected to be very low or are likely to be contained in adjacent rows. In this mode, slices of contiguous blocks of rows are emitted in multiple output messages. Performing a slice is relatively low-cost, however for each incoming message the number of emitted messages could be high (in the worst case scenario as high as half the number of records in the incoming message). Depending on the downstream stages, this can cause performance issues, especially if those stages need to acquire the Python GIL.

Parameters
cmorpheus.config.Config

Pipeline configuration instance.

thresholdfloat

Threshold to classify, default is 0.5.

copybool

Whether or not to perform a copy.

filter_sourcemorpheus.common.FilterSource, case_sensitive = False

Indicate if we are operating on is an output tensor or a field in the DataFrame. Choosing Auto will default to TENSOR when the incoming message contains output tensorts and DATAFRAME otherwise.

field_namestr

Name of the tensor or DataFrame column to use as the filter criteria

Attributes
has_multi_input_ports

Indicates if this stage has multiple input ports.

has_multi_output_ports

Indicates if this stage has multiple output ports.

input_ports

Input ports to this stage.

is_built

Indicates if this stage has been built.

name

The name of the stage.

output_ports

Output ports from this stage.

unique_name

Unique name of stage.

Methods

accepted_types()

Accepted input types for this stage are returned.

build(builder[, do_propagate])

Build this stage.

can_build([check_ports])

Determines if all inputs have been built allowing this node to be built.

filter_copy(x)

This function uses a threshold value to filter the messages.

filter_slice(x)

This function uses a threshold value to filter the messages.

get_all_input_stages()

Get all input stages to this stage.

get_all_inputs()

Get all input senders to this stage.

get_all_output_stages()

Get all output stages from this stage.

get_all_outputs()

Get all output receivers from this stage.

get_needed_columns()

Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.common.TypeId.

join()

Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped.

on_start()

This function can be overridden to add usecase-specific implementation at the start of any stage in the pipeline.

start_async()

This function is called along with on_start during stage initialization.

stop()

Stages can implement this to perform cleanup steps when pipeline is stopped.

supports_cpp_node()

Specifies whether this Stage is capable of creating C++ nodes.

_build(builder, in_ports_streams)[source]

This function is responsible for constructing this stage’s internal mrc.SegmentObject object. The input of this function contains the returned value from the upstream stage.

The input values are the mrc.Builder for this stage and a StreamPair tuple which contain the input mrc.SegmentObject object and the message data type.

Parameters
buildermrc.Builder

mrc.Builder object for the pipeline. This should be used to construct/attach the internal mrc.SegmentObject.

in_ports_streamsmorpheus.pipeline.pipeline.StreamPair

List of tuples containing the input mrc.SegmentObject object and the message data type.

Returns
typing.List[morpheus.pipeline.pipeline.StreamPair]

List of tuples containing the output mrc.SegmentObject object from this stage and the message data type.

accepted_types()[source]

Accepted input types for this stage are returned.

Returns
typing.Tuple[morpheus.pipeline.messages.MultiMessage, ]

Accepted input types.

build(builder, do_propagate=True)[source]

Build this stage.

Parameters
buildermrc.Builder

MRC segment for this stage.

do_propagatebool, optional

Whether to propagate to build output stages, by default True.

can_build(check_ports=False)[source]

Determines if all inputs have been built allowing this node to be built.

Parameters
check_portsbool, optional

Check if we can build based on the input ports, by default False.

Returns
bool

True if we can build, False otherwise.

filter_copy(x)[source]

This function uses a threshold value to filter the messages.

Parameters
xmorpheus.pipeline.messages.MultiMessage

Response message with probabilities calculated from inference results.

Returns
morpheus.pipeline.messages.MultiMessage

A new message containing a copy of the rows above the threshold.

filter_slice(x)[source]

This function uses a threshold value to filter the messages.

Parameters
xmorpheus.pipeline.messages.MultiMessage

Response message with probabilities calculated from inference results.

Returns
typing.List[morpheus.pipeline.messages.MultiMessage]

List of filtered messages.

get_all_input_stages()[source]

Get all input stages to this stage.

Returns
typing.List[morpheus.pipeline.pipeline.StreamWrapper]

All input stages.

get_all_inputs()[source]

Get all input senders to this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Sender]

All input senders.

get_all_output_stages()[source]

Get all output stages from this stage.

Returns
typing.List[morpheus.pipeline.pipeline.StreamWrapper]

All output stages.

get_all_outputs()[source]

Get all output receivers from this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Receiver]

All output receivers.

get_needed_columns()[source]

Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.common.TypeId. This will ensure that the columns are allocated and populated with null values.

property has_multi_input_ports: bool

Indicates if this stage has multiple input ports.

Returns
bool

True if stage has multiple input ports, False otherwise.

property has_multi_output_ports: bool

Indicates if this stage has multiple output ports.

Returns
bool

True if stage has multiple output ports, False otherwise.

property input_ports: List[morpheus.pipeline.receiver.Receiver]

Input ports to this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Receiver]

Input ports to this stage.

property is_built: bool

Indicates if this stage has been built.

Returns
bool

True if stage is built, False otherwise.

async join()[source]

Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped. Typically this is called after stop during a graceful shutdown, but may not be called if the pipeline is terminated.

property name: str

The name of the stage. Used in logging. Each derived class should override this property with a unique name.

Returns
str

Name of a stage.

on_start()[source]

This function can be overridden to add usecase-specific implementation at the start of any stage in the pipeline.

property output_ports: List[morpheus.pipeline.sender.Sender]

Output ports from this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Sender]

Output ports from this stage.

async start_async()[source]

This function is called along with on_start during stage initialization. Allows stages to utilize the asyncio loop if needed.

stop()[source]

Stages can implement this to perform cleanup steps when pipeline is stopped.

supports_cpp_node()[source]

Specifies whether this Stage is capable of creating C++ nodes. During the build phase, this value will be combined with CppConfig.get_should_use_cpp() to determine whether or not a C++ node is created. This is an instance method to allow runtime decisions and derived classes to override base implementations.

property unique_name: str

Unique name of stage. Generated by appending stage id to stage name.

Returns
str

Unique name of stage.

© Copyright 2023, NVIDIA. Last updated on Apr 11, 2023.