The debug_log lead to preprocess plugin seg fault

[jiangyuzhe1992]

Please provide complete information as applicable to your setup.

• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.1
• JetPack Version (valid for Jetson only)
• TensorRT Version 8.2.5-1+cuda11.4
• NVIDIA GPU Driver Version (valid for GPU only) 510.68.02
• Issue Type( questions, new requirements, bugs) questions
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
export GST_DEBUG=5
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
When i export GST_DEBUG=5, the ds-app start to print debug log. But it make a Segmentation fault in the preprocess plugin. I add some prints , and found it may core dump at a debug log.

Here is some debug log:

I run the code in a docker image : nvcr.io/nvidia/deepstream:6.1-devel

When i found it may this log lead to core dump , i annotation this log . It can work normaly.

Wait for your answer. thanks

[yuweiw]

Which demo did you run, our demo or your own demo?
Could you help to use our demo to repro this crash problem?

[jiangyuzhe1992]

@yuweiw

ou run, our demo or your own demo?
Could you help to use our demo to repro this crash problem?

i run the deepstream-app program

here is the app config file :

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 5=CSI
type=3
uri=file:/work/deepstream-6.1-origin/samples/streams/sample_1080p_h264.mp4
#uri=rtsp://admin:zhy12345@172.16.72.64:554/Streaming/Channels/101
#uri=rtmp://192.168.0.14:1935/live/live999
num-sources=1
gpu-id=0
cudadec-memtype=0
[sink0]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 5=Overlay 8=rtmpStreaming
type=8
source-id=0
#Indicates how fast the stream is to be rendered. 0: As fast as possible 1: Synchronously
sync=0
gpu-id=0
nvbuf-memory-type=0
codec=1
enc-type=0
qos=0
bitrate=4000000
iframeinterval=30
#rtsp-port=8857
#udp-port=5400
rtmp-address=192.168.0.87
rtmp-port=26666
[sink4]
enable=1
type=6
topic=yolo-meta-app-custom
msg-conv-config=dstest5_msgconv_sample_config.txt
msg-conv-msg2p-new-api=1
msg-conv-payload-type=257
msg-conv-msg2p-lib=/work/deepstream/sources/libs/zhy_nvmsgconv/libzhy_nvds_msgconv.so
msg-broker-proto-lib=/opt/nvidia/deepstream/deepstream-6.1/lib/libnvds_redis_proto.so
msg-broker-conn-str=192.168.0.87;36379
[osd]
enable=1
gpu-id=0
border-width=5
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Serif
show-clock=0
clock-x-offset=800
clock-y-offset=820
clock-text-size=12
clock-color=1;0;0;0
nvbuf-memory-type=0
label-file=./deepStream-Yolo-zhy/labels.txt
count-ids=0;2
opencv-img-save=0
save-interval=10
save-img-path=/work/

[streammux]
gpu-id=0
#Boolean property to inform muxer that sources are live
live-source=1
batch-size=1
#time out in usec, to wait after the first buffer is available
#to push the batch even if the complete batch is not formed
batched-push-timeout=40000
set muxer output width and height
width=1920
height=1080
enable-padding=0
nvbuf-memory-type=0

[primary-gie]
enable=1
gpu-id=0
batch-size=4
gie-unique-id=1
nvbuf-memory-type=3
input-tensor-meta=1
config-file=config_infer_primary_yoloV5.txt

[pre-process]
enable=1
gpu-id=0
config-file=config_preprocess.txt

[tests]
file-loop=1

The preprocess config file is:

[property]
enable=1
target-unique-ids=1
# 0=NCHW, 1=NHWC, 2=CUSTOM
network-input-order=0
# if enabled maintain the aspect ratio while scaling
maintain-aspect-ratio=1
# if enabled pad symmetrically with maintain-aspect-ratio enabled
symmetric-padding=1
# processig width/height at which image scaled
processing-width=960
processing-height=960
scaling-buf-pool-size=6
tensor-buf-pool-size=6
# tensor shape based on network-input-order
network-input-shape= 4;3;960;960
# 0=RGB, 1=BGR, 2=GRAY
network-color-format=0
# 0=FP32, 1=UINT8, 2=INT8, 3=UINT32, 4=INT32, 5=FP16
tensor-data-type=0
tensor-name=data
# 0=NVBUF_MEM_DEFAULT 1=NVBUF_MEM_CUDA_PINNED 2=NVBUF_MEM_CUDA_DEVICE 3=NVBUF_MEM_CUDA_UNIFIED
scaling-pool-memory-type=0
# 0=NvBufSurfTransformCompute_Default 1=NvBufSurfTransformCompute_GPU 2=NvBufSurfTransformCompute_VIC
scaling-pool-compute-hw=0
# Scaling Interpolation method
# 0=NvBufSurfTransformInter_Nearest 1=NvBufSurfTransformInter_Bilinear 2=NvBufSurfTransformInter_Algo1
# 3=NvBufSurfTransformInter_Algo2 4=NvBufSurfTransformInter_Algo3 5=NvBufSurfTransformInter_Algo4
# 6=NvBufSurfTransformInter_Default
scaling-filter=0
custom-lib-path=/work/deepstream/sources/gst-plugins/gst-nvdspreprocess/nvdspreprocess_lib/libcustom2d_preprocess.so
custom-tensor-preparation-function=CustomTensorPreparation
[user-configs]
pixel-normalization-factor=0.003921568
#mean-file=
#offsets=
[group-0]
src-ids=0
custom-input-transformation-function=CustomAsyncTransformation
process-on-roi=1
roi-params-src-0=200;200;1000;600

The pgie config file is:

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-color-format=0
custom-network-config=/work/deepstream/sources/apps/sample_apps/deepstream-app/deepStream-Yolo-zhy/best.cfg
#model-file=best.wts
model-engine-file=…/model_b4_gpu0_fp16.engine
#model-engine-file=wurenji_1121.engine
#int8-calib-file=int8calib.table
labelfile-path=labels.txt
batch-size=1
network-mode=2
num-detected-classes=1
interval=0
gie-unique-id=1
process-mode=1
network-type=0
cluster-mode=2
maintain-aspect-ratio=1
input-tensor-from-meta=1
parse-bbox-func-name=NvDsInferParseYolo
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet

[class-attrs-all]
nms-iou-threshold=0.45
pre-cluster-threshold=0.25

[yuweiw]

@jiangyuzhe1992 , Thanks for your debugging, it’s a code error. Cause it’s open source, you can modify it by

    GST_DEBUG_OBJECT (nvdspreprocess, "parsed user-config key = %s value = %s\n", *key, val.c_str());

We’ll update it in the future version.

[jiangyuzhe1992]

thanks for your reply.