NvInferDetector
![Inheritance diagram of NvInferDetector](../../../_images/inheritance-17e45c9717812f04577e4b9457870b55f48c04b4.png)
NvInferDetector inheritance diagram
- class savant.deepstream.nvinfer.model.NvInferDetector(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=NvInferObjectModelOutput(layer_names=[], converter=None, objects=None, 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, parse_bbox_func_name=None)
Standard detector with orthogonal bboxes configuration template.
Example
- element: nvinfer@detector name: Primary_Detector model: format: caffe model_file: resnet10.caffemodel batch_size: 1 precision: int8 int8_calib_file: cal_trt.bin label_file: labels.txt input: scale_factor: 0.0039215697906911373 output: num_detected_classes: 4 layer_names: [conv2d_bbox, conv2d_cov/Sigmoid]
- 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.
- parse_bbox_func_name: str | None = None
Name of the custom bounding box parsing function. If not specified, Gst-nvinfer uses the internal function for the resnet model provided by the SDK.
- output: NvInferObjectModelOutput = NvInferObjectModelOutput(layer_names=[], converter=None, objects=None, num_detected_classes=None)
Results post-processing configuration.