NvInferAttributeModel

class savant.deepstream.nvinfer.model.NvInferAttributeModel(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, symmetric_padding=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=AttributeModelOutput(layer_names=[], converter=None, attributes='???'), format=None, 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, layer_device_precision=<factory>, enable_dla=None, use_dla_core=None, scaling_compute_hw=None, scaling_filter=None, parse_classifier_func_name=None)

NvInferAttribute model configuration template.

Use to configure classifiers, etc.

Example

- element: nvinfer@classifier
  name: Secondary_CarColor
  model:
    format: caffe
    model_file: resnet18.caffemodel
    mean_file: mean.ppm
    label_file: labels.txt
    precision: int8
    int8_calib_file: cal_trt.bin
    batch_size: 16
    input:
      object: Primary_Detector.Car
      object_min_width: 64
      object_min_height: 64
      color_format: bgr
    output:
      layer_names: [predictions/Softmax]
      attributes:
        - name: car_color
          threshold: 0.51

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

config_file: str | None = None: Model configuration file. Should be specified without a location.

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.

enable_dla: bool | None = None: Specifies that DLA engines to be used for inference.

engine_create_func_name: str | None = None: Name of the custom TensorRT CudaEngine creation function.

engine_file: str | None = None: Serialized model engine file.

format: NvInferModelFormat | None = None

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 the engine_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, symmetric_padding=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.

interval: int = 0: Specifies the number of consecutive batches to be skipped for inference.

label_file: str | None = None: Pathname of a text file containing the labels for the model.

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”).

mean_file: str | None = None: Pathname of mean data file in PPM format.

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”.

output: AttributeModelOutput = AttributeModelOutput(layer_names=[], converter=None, attributes='???'): Configuration for post-processing of an attribute model’s results.

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.

scaling_compute_hw: NvInferScalingComputeHW | None = None: Specifies the hardware to be used for scaling compute.

scaling_filter: NvInferScalingFilter | None = None: Specifies the algorithm to be used for scaling.

tlt_model_key: str | None = None: Key for the TAO toolkit encoded model.

use_dla_core: int | None = None: Specifies the DLA core to be used for inference.

workspace_size: int = 6144: Workspace size to be used by the engine, in MB

layer_device_precision: List[str]: Specifies the device type and precision for any layer in the network. List of items of format <layer1-name>:<precision>:<device-type>.

parse_classifier_func_name: str | None = None: Name of the custom classifier output parsing function. If not specified, Gst-nvinfer uses the internal parsing function for softmax layers.