Gst-nvinfer

The Gst-nvinfer plugin does inferencing on input data using NVIDIA^® TensorRT™.

The plugin accepts batched NV12/RGBA buffers from upstream. The NvDsBatchMeta structure must already be attached to the Gst Buffers. The low-level library (libnvds_infer) operates on any of INT8 RGB, BGR, or GRAY data with dimension of Network Height and Network Width. The Gst-nvinfer plugin performs transforms (format conversion and scaling), on the input frame based on network requirements, and passes the transformed data to the low-level library. The low-level library preprocesses the transformed frames (performs normalization and mean subtraction) and produces final float RGB/BGR/GRAY planar data which is passed to the TensorRT engine for inferencing. The output type generated by the low-level library depends on the network type. The pre-processing function is:

y = net scale factor*(x-mean)

Where:

• x is the input pixel value. It is an int8 with range [0,255].

• mean is the corresponding mean value, read either from the mean file or as offsets[c], where c is the channel to which the input pixel belongs, and offsets is the array specified in the configuration file. It is a float.

• net-scale-factor is the pixel scaling factor specified in the configuration file. It is a float.

• y is the corresponding output pixel value. It is a float.

Gst-nvinfer currently works on the following type of networks:

• Multi-class object detection

• Multi-label classification

• Segmentation (semantic)

• Instance Segmentation

The Gst-nvinfer plugin can work in three modes:

• Primary mode: Operates on full frames

• Secondary mode: Operates on objects added in the meta by upstream components

• Preprocessed Tensor Input mode: Operates on tensors attached by upstream components

When operating in preprocessed tensor input mode, the pre-processing inside Gst-nvinfer is completely skipped. The plugin looks for GstNvDsPreProcessBatchMeta attached to the input buffer and passes the tensor as is to TensorRT inference function without any modifications. This mode currently supports processing on full-frame and ROI. The GstNvDsPreProcessBatchMeta is attached by the Gst-nvdspreprocess plugin.

When the plugin is operating as a secondary classifier along with the tracker, it tries to improve performance by avoiding re-inferencing on the same objects in every frame. It does this by caching the classification output in a map with the object’s unique ID as the key. The object is inferred upon only when it is first seen in a frame (based on its object ID) or when the size (bounding box area) of the object increases by 20% or more. This optimization is possible only when the tracker is added as an upstream element. Detailed documentation of the TensorRT interface is available at: https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html The plugin supports the IPlugin interface for custom layers. Refer to section IPlugin Interface for details. The plugin also supports the interface for custom functions for parsing outputs of object detectors and initialization of non-image input layers in cases where there is more than one input layer. Refer to sources/includes/nvdsinfer_custom_impl.h for the custom method implementations for custom models.

Downstream components receive a Gst Buffer with unmodified contents plus the metadata created from the inference output of the Gst-nvinfer plugin. The plugin can be used for cascaded inferencing. That is, it can perform primary inferencing directly on input data, then perform secondary inferencing on the results of primary inferencing, and so on. See the sample application deepstream-test2 for more details.

Inputs and Outputs

This section summarizes the inputs, outputs, and communication facilities of the Gst-nvinfer plugin.

• Inputs

  • Gst Buffer

  • NvDsBatchMeta (attaching NvDsFrameMeta)

  • ONNX

  • TAO Encoded Model and Key

  • Offline: Supports engine files generated by TAO Toolkit SDK Model converters

  • Layers: Supports all layers supported by TensorRT, see: https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html.

• Control parameters

Gst-nvinfer gets control parameters from a configuration file. You can specify this by setting the property config-file-path. For details, see Gst-nvinfer File Configuration Specifications. Other control parameters that can be set through GObject properties are:

• Batch size

• Inference interval

• Attach inference tensor outputs as buffer metadata

• Attach instance mask output as in object metadata

• The parameters set through the GObject properties override the parameters in the Gst-nvinfer configuration file.

• Outputs

  • Gst Buffer

  • Depending on network type and configured parameters, one or more of:

  • NvDsObjectMeta

  • NvDsClassifierMeta

  • NvDsInferSegmentationMeta

  • NvDsInferTensorMeta

Features

The following table summarizes the features of the plugin.

Gst-nvinfer plugin features

Feature	Description	Release
Explicit Full Dimension Network Support	Refer to https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#work_dynamic_shapes for more details.	DS 5.0
Non-maximum Suppression (NMS)	New bounding box clustering algorithm.	DS 5.0
On-the-fly model update (Engine file only)	Update the model-engine-file on-the-fly in a running pipeline.	DS 5.0
Configurable frame scaling params	Configurable options to select the compute hardware and the filter to use while scaling frame/object crops to network resolution	DS 5.0
TAO toolkit encoded model support	—	DS 4.0
Gray input model support	Support for models with single channel gray input	DS 4.0
Tensor output as meta	Raw tensor output is attached as meta data to Gst Buffers and flowed through the pipeline	DS 4.0
Segmentation model	Supports segmentation model	DS 4.0
Maintain input aspect ratio	Configurable support for maintaining aspect ratio when scaling input frame to network resolution	DS 4.0
Custom cuda engine creation interface	Interface for generating CUDA engines from TensorRT INetworkDefinition and IBuilder APIs instead of model files	DS 4.0
ONNX Model support	—	DS 3.0
Multiple modes of operation	Support for cascaded inferencing	DS 2.0
Asynchronous mode of operation for secondary inferencing	Infer asynchronously for secondary classifiers	DS 2.0
Grouping using CV::Group rectangles	For detector bounding box clustering	DS 2.0
Configurable batch-size processing	User can configure batch size for processing	DS 2.0
No Restriction on number of output blobs	Supports any number of output blobs	DS 3.0
Configurable number of detected classes (detectors)	Supports configurable number of detected classes	DS 3.0
Support for Classes: configurable (> 32)	Supports any number of classes	DS 3.0
Application access to raw inference output	Application can access inference output buffers for user specified layer	DS 3.0
Support for single shot detector (SSD)	—	DS 3.0
Secondary GPU Inference Engines (GIEs) operate as detector on primary bounding box	Supports secondary inferencing as detector	DS 2.0
Multiclass secondary support	Supports multiple classifier network outputs	DS 2.0
Grouping using DBSCAN	For detector bounding box clustering	DS 3.0
Loading an external lib containing IPlugin implementation for custom layers (IPluginCreator & IPluginFactory)	Supports loading (dlopen()) a library containing IPlugin implementation for custom layers	DS 3.0
Multi GPU	Select GPU on which we want to run inference	DS 2.0
Detection width height configuration	Filter out detected objects based on min/max object size threshold	DS 2.0
Allow user to register custom parser	Supports final output layer bounding box parsing for custom detector network	DS 2.0
Bounding box filtering based on configurable object size	Supports inferencing in secondary mode objects meeting min/max size threshold	DS 2.0
Configurable operation interval	Interval for inferencing (number of batched buffers skipped)	DS 2.0
Select Top and bottom regions of interest (RoIs)	Removes detected objects in top and bottom areas	DS 2.0
Operate on Specific object type (Secondary mode)	Process only objects of define classes for secondary inferencing	DS 2.0
Configurable blob names for parsing bounding box (detector)	Support configurable names for output blobs for detectors	DS 2.0
Allow configuration file input	Support configuration file as input (mandatory in DS 3.0)	DS 2.0
Allow selection of class id for operation	Supports secondary inferencing based on class ID	DS 2.0
Support for Full Frame Inference: Primary as a classifier	Can work as classifier as well in primary mode	DS 2.0
Multiclass secondary support	Support multiple classifier network outputs	DS 2.0
Secondary GIEs operate as detector on primary bounding box Support secondary inferencing as detector	—	DS 2.0
Supports FP16, FP32 and INT8 models FP16 and INT8 are platform dependent	—	DS 2.0
Supports TensorRT Engine file as input	—	DS 2.0
Inference input layer initialization Initializing non-video input layers in case of more than one input layers	—	DS 3.0
Support for FasterRCNN	—	DS 3.0
Support for Yolo detector (YoloV3/V3-tiny/V2/V2-tiny)	—	DS 4.0
Support for yolov3-spp detector	—	DS 5.0
Support Instance segmentation with MaskRCNN	Support for instance segmentation using MaskRCNN. It includes output parser and attach mask in object metadata.	DS 5.0
Support for NHWC network input	—	DS 6.0
Added support for TAO ONNX model	—	DS 6.0
Support for input tensor meta	Inferences using already preprocessed raw tensor from input tensor meta (attached as user meta at batch level) and skips preprocessing in nvinfer. In this mode, the batch-size of nvinfer must be equal to the sum of ROIs set in the gst-nvdspreprocess plugin config file.	DS 6.0
Support for clipping bounding boxes to ROI boundary	—	DS 6.2

Gst-nvinfer File Configuration Specifications

The Gst-nvinfer configuration file uses a “Key File” format described in https://specifications.freedesktop.org/desktop-entry-spec/latest. The [property] group configures the general behavior of the plugin. It is the only mandatory group. The [class-attrs-all] group configures detection parameters for all classes. The [class-attrs-<class-id>] group configures detection parameters for a class specified by <class-id>. For example, the [class-attrs-23] group configures detection parameters for class ID 23. This type of group has the same keys as [class-attrs-all]. The following two tables respectively describe the keys supported for [property] groups and [class-attrs-…] groups.

Gst-nvinfer Class-attributes Group Supported Keys

Name	Description	Type and Range	Example Notes	(Primary/Secondary)
threshold	Detection threshold	Float, ≥0	threshold=0.5	Object detector Both
pre-cluster-threshold	Detection threshold to be applied prior to clustering operation	Float, ≥0	pre-cluster-threshold= 0.5	Object detector Both
post-cluster-threshold	Detection threshold to be applied post clustering operation	Float, ≥0	post-cluster-threshold= 0.5	Object detector Both
eps	Epsilon values for OpenCV grouprectangles() function and DBSCAN algorithm	Float, ≥0	eps=0.2	Object detector Both
group-threshold	Threshold value for rectangle merging for OpenCV grouprectangles() function	Integer, ≥0	group-threshold=1 0 disables the clustering functionality	Object detector Both
minBoxes	Minimum number of points required to form a dense region for DBSCAN algorithm	Integer, ≥0	minBoxes=1 0 disables the clustering functionality	Object detector Both
dbscan-min-score	Minimum sum of confidence of all the neighbors in a cluster for it to be considered a valid cluster.	Float, ≥0	dbscan-min-score= 0.7	Object detector Both
nms-iou-threshold	Maximum IOU score between two proposals after which the proposal with the lower confidence will be rejected.	Float, ≥0	nms-iou-threshold= 0.2	Object detector Both
roi-top-offset	Offset of the RoI from the top of the frame. Only objects within the RoI are output.	Integer, ≥0	roi-top-offset= 200	Object detector Both
roi-bottom-offset	Offset of the RoI from the bottom of the frame. Only objects within the RoI are output.	Integer, ≥0	roi-bottom-offset= 200	Object detector Both
detected-min-w	Minimum width in pixels of detected objects to be output by the GIE	Integer, ≥0	detected-min-w= 64	Object detector Both
detected-min-h	Minimum height in pixels of detected objects to be output by the GIE	Integer, ≥0	detected-min-h= 64	Object detector Both
detected-max-w	Maximum width in pixels of detected objects to be output by the GIE	Integer, ≥0	detected-max-w=200 0 disables the property	Object detector Both
detected-max-h	Maximum height in pixels of detected objects to be output by the GIE	Integer, ≥0	detected-max-h=200 0 disables the property	Object detector Both
topk	Keep only top K objects with highest detection scores.	Integer, ≥0. -1 to disable	topk=10	Object detector Both

Note

UFF model support is removed from TRT 10.3.

Gst Properties

The values set through Gst properties override the values of properties in the configuration file. The application does this for certain properties that it needs to set programmatically. The following table describes the Gst-nvinfer plugin’s Gst properties.

Gst-nvinfer Gst Properties

Property	Meaning	Type and Range	Example notes
config-file-path	Absolute pathname of configuration file for the Gst-nvinfer element	String	config-file-path=­config_infer_primary.txt
process-mode	Infer Processing Mode 1=Primary Mode 2=Secondary Mode	Integer, 1 or 2	process-mode=1
unique-id	Unique ID identifying metadata generated by this GIE	Integer, | 0 to 4,294,967,295	unique-id=1
infer-on-gie-id	See operate-on-gie-id in the configuration file table	Integer, 0 to 4,294,967,295	infer-on-gie-id=1
operate-on-class-ids	See operate-on-class-ids in the configuration file table	An array of colon- separated integers (class-ids)	operate-on-class-ids=1:2:4
filter-out-class-ids	See filter-out-class-ids in the configuration file table	Semicolon delimited integer array	filter-out-class-ids=1;2
model-engine-file	Absolute pathname of the pre-generated serialized engine file for the mode	String	model-engine-file=­model_b1_fp32.engine
batch-size	Number of frames/objects to be inferred together in a batch	Integer, 1 – 4,294,967,295	batch-size=4
Interval	Number of consecutive batches to be skipped for inference	Integer, 0 to 32	interval=0
gpu-id	Device ID of GPU to use for pre-processing/inference (dGPU only)	Integer, 0-4,294,967,295	gpu-id=1
raw-output-file-write	Pathname of raw inference output file	Boolean	raw-output-file-write=1
raw-output-generated-callback	Pointer to the raw output generated callback function	Pointer	Cannot be set through gst-launch
raw-output-generated-userdata	Pointer to user data to be supplied with raw-output-generated-callback	Pointer	Cannot be set through gst-launch
output-tensor-meta	Indicates whether to attach tensor outputs as meta on GstBuffer.	Boolean	output-tensor-meta=0
output-instance-mask	Gst-nvinfer attaches instance mask output in object metadata.	Boolean	output-instance-mask=1
input-tensor-meta	Use preprocessed input tensors attached as metadata instead of preprocessing inside the plugin	Boolean	input-tensor-meta=1
crop-objects-to-roi-boundary	Clip the object bounding boxes to fit within the specified ROI boundary	Boolean	crop-objects-to-roi-boundary=1

Clustering algorithms supported by nvinfer

cluster-mode = 0 | GroupRectangles

GroupRectangles is a clustering algorithm from OpenCV library which clusters rectangles of similar size and location using the rectangle equivalence criteria. Link to API documentation - https://docs.opencv.org/3.4/d5/d54/group__objdetect.html#ga3dba897ade8aa8227edda66508e16ab9

cluster-mode = 1 | DBSCAN

Density-based spatial clustering of applications with noise or DBSCAN is a clustering algorithm which which identifies clusters by checking if a specific rectangle has a minimum number of neighbors in its vicinity defined by the eps value. The algorithm further normalizes each valid cluster to a single rectangle which is outputted as valid bounding box if it has a confidence greater than that of the threshold.

cluster-mode = 2 | NMS

Non maximum suppression or NMS is a clustering algorithm which filters overlapping rectangles based on a degree of overlap(IOU) which is used as threshold. Rectangles with the highest confidence score is first preserved while the rectangles which overlap greater than the threshold are removed iteratively.

cluster-mode = 3 | Hybrid

Hybrid clustering algorithm is a method which uses both DBSCAN and NMS algorithms in a two step process. DBSCAN is first applied to form unnormalized clusters in proposals whilst removing the outliers. NMS is later applied on these clusters to select the final rectangles for output.

cluster-mode=4 | No clustering

No clustering is applied and all the bounding box rectangle proposals are returned as it is.

Tensor Metadata

The Gst-nvinfer plugin can attach raw output tensor data generated by a TensorRT inference engine as metadata. It is added as an NvDsInferTensorMeta in the frame_user_meta_list member of NvDsFrameMeta for primary (full frame) mode, or in the obj_user_meta_list member of NvDsObjectMeta for secondary (object) mode.

To read or parse inference raw tensor data of output layers

1. Enable property output-tensor-meta or enable the same-named attribute in the configuration file for the Gst-nvinfer plugin.

2. When operating as primary GIE,` NvDsInferTensorMeta` is attached to each frame’s (each NvDsFrameMeta object’s) frame_user_meta_list. When operating as secondary GIE, NvDsInferTensorMeta is attached to each each NvDsObjectMeta object’s obj_user_meta_list.

Metadata attached by Gst-nvinfer can be accessed in a GStreamer pad probe attached downstream from the Gst-nvinfer instance.

3. The NvDsInferTensorMeta object’s metadata type is set to NVDSINFER_TENSOR_OUTPUT_META. To get this metadata you must iterate over the NvDsUserMeta user metadata objects in the list referenced by frame_user_meta_list or obj_user_meta_list.

For more information about Gst-infer tensor metadata usage, see the source code in sources/apps/sample_apps/deepstream_infer_tensor_meta-test.cpp, provided in the DeepStream SDK samples.

Segmentation Metadata

The Gst-nvinfer plugin attaches the output of the segmentation model as user meta in an instance of NvDsInferSegmentationMeta with meta_type set to NVDSINFER_SEGMENTATION_META. The user meta is added to the frame_user_meta_list member of NvDsFrameMeta for primary (full frame) mode, or the obj_user_meta_list member of NvDsObjectMeta for secondary (object) mode. For guidance on how to access user metadata, see User/Custom Metadata Addition inside NvDsBatchMeta and Tensor Metadata sections.