Please provide the following information when requesting support.
Hardware - GPU (A100/A30/T4/V100)
Hardware - CPU
Operating System - ubuntu
Riva Version
TLT Version (if relevant)
How to reproduce the issue ? (This is for errors. Please share the command and the detailed log here)
when i try to execute (deepstream-appsrc-test) i am getting this error Segmentation fault (core dumped) how to resolve this error
jetson@ubuntu:~/Documents/deepstream-appsrc-test$ ./deepstream-appsrc-test out.mp4 1920 1080 30 NV12
Using winsys: x11
0:00:09.764576857 19490 0x55aba36f50 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1900> [UID = 1]: deserialized trt engine from :/home/jetson/Documents/deepstream-appsrc-test/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
INFO: [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x368x640
1 OUTPUT kFLOAT conv2d_bbox 16x23x40
2 OUTPUT kFLOAT conv2d_cov/Sigmoid 4x23x40
0:00:09.766272719 19490 0x55aba36f50 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger: NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2004> [UID = 1]: Use deserialized engine model: /home/jetson/Documents/deepstream-appsrc-test/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
0:00:09.799989693 19490 0x55aba36f50 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus: [UID 1]: Load new model:dstest_appsrc_config.txt sucessfully
Running…
Segmentation fault (core dumped)
this is a configuration file [property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-file= Primary_Detector/resnet10.caffemodel
proto-file= Primary_Detector/resnet10.prototxt
model-engine-file = Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
labelfile-path = Primary_Detector/labels.txt
int8-calib-file= Primary_Detector/cal_trt.bin
force-implicit-batch-dim=1
batch-size=1
network-mode=1
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=conv2d_bbox;conv2d_cov/Sigmoid
#scaling-filter=0
#scaling-compute-hw=0
cluster-mode=2
[class-attrs-all]
pre-cluster-threshold=0.2
topk=20
nms-iou-threshold=0.5