Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson )
• DeepStream Version
• JetPack Version (valid for Jetson only)
• TensorRT Version
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions
We are testing on how the DLA gives advantage in jetson devices , for that we tested the sample app objectDetector_Yolo inside the jetson devices and did following changes to model config file as stated in below reference
[property]
enable-dla=1
use-dla-core=1
While the app is running i couldn’t see any of the DLA is in on state , that was as in the picture below
Below is the model config file
####################################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2029-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
####################################################################################################
# Following properties are mandatory when engine files are not specified:
# int8-calib-file(Only in INT8), model-file-format
# Caffemodel mandatory properties: model-file, proto-file, output-blob-names
# UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names
# ONNX: onnx-file
#
# Mandatory properties for detectors:
# num-detected-classes
#
# Optional properties for detectors:
# cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)
# custom-lib-path
# parse-bbox-func-name
#
# Mandatory properties for classifiers:
# classifier-threshold, is-classifier
#
# Optional properties for classifiers:
# classifier-async-mode(Secondary mode only, Default=false)
#
# Optional properties in secondary mode:
# operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),
# input-object-min-width, input-object-min-height, input-object-max-width,
# input-object-max-height
#
# Following properties are always recommended:
# batch-size(Default=1)
#
# Other optional properties:
# net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
# model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,
# mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),
# custom-lib-path, network-mode(Default=0 i.e FP32)
#
# The values in the config file are overridden by values set through GObject
# properties.
[property]
enable-dla=1
use-dla-core=1
gpu-id=0
net-scale-factor=0.0039215697906911373
#0=RGB, 1=BGR
model-color-format=0
custom-network-config=yolov3.cfg
model-file=yolov3.weights
model-engine-file=yolov3_b1_gpu0_int8.engine
labelfile-path=labels.txt
int8-calib-file=yolov3-calibration.table.trt7.0
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=1
num-detected-classes=80
gie-unique-id=1
network-type=0
is-classifier=0
## 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
cluster-mode=2
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseCustomYoloV3
#parse-bbox-func-name=NvDsInferParseCustomYoloV3_cuda
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet
#scaling-filter=0
#scaling-compute-hw=0
disable-output-host-copy=0
[class-attrs-all]
nms-iou-threshold=0.3
threshold=0.7
Can you advice on how to enable the DLA and run the deepstream with that
