segmentation fault( core dumped) while using deepstream with our own model

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

Hi,
I just use my own detection model with deepstream-app on tx2. I found that if I use one source, deepstream-app will run successful. But if I use two sources, deepstream-app will run only a few frames and then abort with “Segmentation fault (core dumped).” Does anyone know how to solve this? Thanks.

2

Hi,

Would you mind to share your configure file with us so we can check it further?

Thanks.

3

Hi AastaLL,
Here are my configure files.
This is my deepstream app configure file.

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=1
gie-kitti-output-dir=streamscl

[tiled-display]
enable=1
rows=1
columns=2
width=1280
height=720
gpu-id=0
nvbuf-memory-type=0

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
num-sources=1
uri=file://../../samples/streams/sample_1080p_h264.mp4
gpu-id=0
cudadec-memtype=0

[source1]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=3
uri=file://../../samples/streams/sample_720p.mp4
num-sources=1
gpu-id=0
# (0): memtype_device   - Memory type Device
# (1): memtype_pinned   - Memory type Host Pinned
# (2): memtype_unified  - Memory type Unified
cudadec-memtype=0

[streammux]
gpu-id=0
batch-size=1
batched-push-timeout=-1
## Set muxer output width and height
width=1920
height=1080
nvbuf-memory-type=0

[sink0]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File
type=2
sync=1
source-id=0
gpu-id=0

[sink1]
enable=0
type=3
#1=mp4 2=mkv
container=1
#1=h264 2=h265
codec=1
sync=1
#iframeinterval=10
bitrate=2000000
output-file=out.mp4
source-id=0

[osd]
enable=1
gpu-id=0
border-width=3
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

[primary-gie]
enable=1
gpu-id=0
batch-size=1
gie-unique-id=1
interval=0
labelfile-path=ssd_coco_labels.txt
model-engine-file=sample_centernet_1149.uff_b1_fp32.engine
config-file=config_infer_primary_centernet.txt
nvbuf-memory-type=0

And my infer primary configure file is the following.

[property]
gpu-id=0
net-scale-factor=0.0078431372
offsets=127.5;127.5;127.5
model-color-format=0
model-engine-file=sample_centernet_1149.uff_b1_fp32.engine
labelfile-path=ssd_coco_labels.txt
uff-file=sample_centernet_1149.uff
uff-input-dims=3;384;384;0
uff-input-blob-name=img_tensor
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=5
interval=0
gie-unique-id=1
is-classifier=0
output-blob-names=cdet_inference_model/head_heatmap_out/Sigmoid;cdet_inference_model/head_heatmap_maxpooling_out/MaxPool;cdet_inference_model/head_offset_out/Conv2D;cdet_inference_model/head_size_out/Conv2D
parse-bbox-func-name=NvDsInferParseCustomCenterNet
custom-lib-path=nvdsinfer_custom_impl_centernet/libnvdsinfer_custom_impl_centernet.so

[class-attrs-all]
threshold=0.5
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

## Per class configuration
#[class-attrs-2]
#threshold=0.6
#roi-top-offset=20
#roi-bottom-offset=10
#detected-min-w=40
#detected-min-h=40
#detected-max-w=400
#detected-max-h=800

Thank you so much.

4

Hi,

We try to reproduce this issue on our environment.
Could you help to check if this issue also occurs with our official model if two input sources?

Thanks.

5

Hi AastaLLL,

I’ve tried your official model with two input sources, the issue didn’t occur.

Thanks.