NvInferComplexModel
![Inheritance diagram of NvInferComplexModel](../../../_images/inheritance-769e2940b54bb510bb66574f49a8f99a6adc171c.png)
NvInferComplexModel inheritance diagram
- class savant.deepstream.nvinfer.model.NvInferComplexModel(local_path=None, remote=None, model_file=None, batch_size=1, precision=ModelPrecision.FP16, input=NvInferModelInput(object='auto.frame', layer_name=None, shape=None, maintain_aspect_ratio=False, scale_factor=1.0, offsets=(0.0, 0.0, 0.0), color_format=<ModelColorFormat.RGB: 0>, preprocess_object_meta=None, preprocess_object_image=None, object_min_width=None, object_min_height=None, object_max_width=None, object_max_height=None), output=NvInferComplexModelOutput(layer_names=[], converter='???', attributes='???', objects='???', num_detected_classes=None), format='???', config_file=None, int8_calib_file=None, engine_file=None, proto_file=None, custom_config_file=None, mean_file=None, label_file=None, tlt_model_key=None, gpu_id=0, interval=0, workspace_size=6144, custom_lib_path=None, engine_create_func_name=None)
NvInferComplexModel configuration template.
Complex model combines object and attribute models.
For example face detector that produces bounding boxes and landmarks:
- element: nvinfer@complex_model name: face_detector model: format: onnx config_file: config.txt output: layer_names: ['bboxes', 'scores', 'landmarks'] converter: module: module.face_detector_coverter class_name: FaceDetectorConverter objects: - class_id: 0 label: face selector: module: savant.selector class_name: BBoxSelector kwargs: confidence_threshold: 0.5 nms_iou_threshold: 0.5 attributes: - name: landmarks
- batch_size: int = 1
Number of frames or objects to be inferred together in a batch.
Note
In case the model is an NvInferModel and it is configured to use the TRT engine file directly, the default value for
batch_size
will be taken from the engine file name, by parsing it according to the scheme {model_name}_b{batch_size}_gpu{gpu_id}_{precision}.engine
- custom_config_file: str | None = None
Configuration file for custom model, eg for YOLO. By default, the model file name (
model_file
) will be used with the extension.cfg
.
- custom_lib_path: str | None = None
Absolute pathname of a library containing custom method implementations for custom models.
- engine_create_func_name: str | None = None
Name of the custom TensorRT CudaEngine creation function.
- format: NvInferModelFormat = '???'
Model file format.
Example
format: onnx # format: caffe # etc. # look in enum for full list of format options
- gpu_id: int = 0
Device ID of GPU to use for pre-processing/inference (dGPU only).
Note
In case the model is configured to use the TRT engine file directly, the default value for
gpu_id
will be taken from theengine_file
, by parsing it according to the scheme {model_name}_b{batch_size}_gpu{gpu_id}_{precision}.engine
- input: NvInferModelInput = NvInferModelInput(object='auto.frame', layer_name=None, shape=None, maintain_aspect_ratio=False, scale_factor=1.0, offsets=(0.0, 0.0, 0.0), color_format=<ModelColorFormat.RGB: 0>, preprocess_object_meta=None, preprocess_object_image=None, object_min_width=None, object_min_height=None, object_max_width=None, object_max_height=None)
Optional configuration of input data and custom preprocessing methods for a model. If not set, then input will default to entire frame.
- int8_calib_file: str | None = None
INT8 calibration file for dynamic range adjustment with an FP32 model. Required only for models in INT8.
- local_path: str | None = None
Path where all the necessary model files are placed. By default, the value of module parameter “model_path” and element name will be used (“model_path / element_name”).
- model_file: str | None = None
The model file, eg yolov4.onnx.
Note
The model file is specified without a location. The absolute path to the model file will be defined as “
local_path
/model_file
”.
- precision: ModelPrecision = 2
Data format to be used by inference.
Example
precision: fp16 # precision: int8 # precision: fp32
Note
In case the model is an NvInferModel and it is configured to use the TRT engine file directly, the default value for
precision
will be taken from the engine file name, by parsing it according to the scheme {model_name}_b{batch_size}_gpu{gpu_id}_{precision}.engine
- proto_file: str | None = None
Caffe model prototxt file. By default, the model file name (
model_file
) will be used with the extension.prototxt
.
- remote: RemoteFile | None = None
Configuration of model files remote location. Supported schemes: s3, http, https, ftp.
- output: NvInferComplexModelOutput = NvInferComplexModelOutput(layer_names=[], converter='???', attributes='???', objects='???', num_detected_classes=None)
Configuration for post-processing of a complex model’s results.