Unable to integrate custom model in deepstreamsdk 5

Please provide complete information as applicable to your setup.

• Hardware Platform (GPU)
• DeepStream Version : 5.0
**• NVIDIA GPU Driver Version (valid for GPU only): Driver Version: 450.66

Hello,

Iam trying to integrate my custom yolov3 model into deepstream sdk in deepstream sdk docker container.

pipeline is breaking with the Segmentation fault (core dumped) (error)

Here is the logs catched when iam running it in debug 1 mode:

command we have used: deepstream-app -c demo_mask_video_stream.txt --gst-debug=1

logs:

** INFO: <bus_callback:181>: Pipeline ready

0:02:32.552753951 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.552793543 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.552863031 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.552879225 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.552932072 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.552946198 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.552991057 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.553004558 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.553048170 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.553061969 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.553108171 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

0:02:32.553121687 1816 0x7f7d7000a230 ERROR v4l2 gstv4l2object.c:2074:gst_v4l2_object_get_interlace_mode: Driver bug detected - check driver with v4l2-compliance from http://git.linuxtv.org/v4l-utils.git

** INFO: <bus_callback:167>: Pipeline running

Segmentation fault (core dumped)

PFB logs screenshot :

Note: The custom model has two classes

Hi @ashok.pinapathula,
you can use gdb to run your progran and get the back trace of the segment fault?

Thanks!

Could you please guide me with the command to run inference in deepstream sdk

example: deepstream-app -c

$ gdb --args deepstream-app -c…

after hang, run “bt” to check the backtrace.

Iam facing the below logs when Iam running the gdb command.

Iam getting the not in executable format: File format not recognized Error

root@2d772fdbfe5f:/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo# gdb --args demo_mask_video_stream.txt
GNU gdb (Ubuntu 8.1-0ubuntu3.2) 8.1.0.20180409-git
Copyright © 2018 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later http://gnu.org/licenses/gpl.html
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type “show copying”
and “show warranty” for details.
This GDB was configured as “x86_64-linux-gnu”.
Type “show configuration” for configuration details.
For bug reporting instructions, please see:
http://www.gnu.org/software/gdb/bugs/.
Find the GDB manual and other documentation resources online at:
http://www.gnu.org/software/gdb/documentation/.
For help, type “help”.
Type “apropos word” to search for commands related to “word”…
“/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/demo_mask_video_stream.txt”: not in executable format: File format not recognized
(gdb)

The command is

$ gdb --args deepstream-app -c

e.g.

$ gdb --args deepstream-app -c demo_mask_video_stream.txt

Here is the logs that iam getting when iam running using gdb

It is showing no debugging symbols found error

logs:
root@2d772fdbfe5f:/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo# gdb --args deepstream-app -c demo_mask_video_stream.txt
GNU gdb (Ubuntu 8.1-0ubuntu3.2) 8.1.0.20180409-git
Copyright © 2018 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later http://gnu.org/licenses/gpl.html
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type “show copying”
and “show warranty” for details.
This GDB was configured as “x86_64-linux-gnu”.
Type “show configuration” for configuration details.
For bug reporting instructions, please see:
http://www.gnu.org/software/gdb/bugs/.
Find the GDB manual and other documentation resources online at:
http://www.gnu.org/software/gdb/documentation/.
For help, type “help”.
Type “apropos word” to search for commands related to “word”…
Reading symbols from deepstream-app…(no debugging symbols found)…done.
(gdb) bt
No stack.
(gdb)

Hi @ashok.pinapathula,
I would suggest you seach some introduction about gdb on network.

The flow should be:

$ gdb --args deepstream-app -c demo_mask_video_stream.txt

(gdb) r

‘crash’…
(gdb) bt // get the call stack

Thanks for your inputs.

i had run with the gdb and here is the complete trace back logs:
logs:

[Thread debugging using libthread_db enabled]
Using host libthread_db library “/lib/x86_64-linux-gnu/libthread_db.so.1”.
[New Thread 0x7f352e12a700 (LWP 1397)]
[New Thread 0x7f352d929700 (LWP 1398)]
[Thread 0x7f352d929700 (LWP 1398) exited]
[New Thread 0x7f352765e700 (LWP 1399)]
[New Thread 0x7f3526e5d700 (LWP 1400)]

*** DeepStream: Launched RTSP Streaming at rtsp://localhost:8554/ds-test ***

Unknown or legacy key specified ‘is-classifier’ for group [property]
Warn: ‘threshold’ parameter has been deprecated. Use ‘pre-cluster-threshold’ instead.
[New Thread 0x7f3508891700 (LWP 1401)]
[New Thread 0x7f3501d06700 (LWP 1402)]
[New Thread 0x7f3501505700 (LWP 1403)]
[New Thread 0x7f3500d04700 (LWP 1404)]
[New Thread 0x7f34fbfff700 (LWP 1405)]
[New Thread 0x7f34fb7fe700 (LWP 1406)]
0:00:00.814927992 1391 0x55a344f3fb50 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1715> [UID = 1]: Trying to create engine from model files
Loading pre-trained weights…
Loading weights of yolov3 complete!
Total Number of weights read : 61581727
Loading pre-trained weights…
Loading weights of yolov3 complete!
Total Number of weights read : 61581727
Building Yolo network…
layer inp_size out_size weightPtr
(0) conv-bn-leaky 3 x 416 x 416 32 x 416 x 416 992
(1) conv-bn-leaky 32 x 416 x 416 64 x 208 x 208 19680
(2) conv-bn-leaky 64 x 208 x 208 32 x 208 x 208 21856
(3) conv-bn-leaky 32 x 208 x 208 64 x 208 x 208 40544
(4) skip 64 x 208 x 208 64 x 208 x 208 -
(5) conv-bn-leaky 64 x 208 x 208 128 x 104 x 104 114784
(6) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 123232
(7) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 197472
(8) skip 128 x 104 x 104 128 x 104 x 104 -
(9) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 205920
(10) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 280160
(11) skip 128 x 104 x 104 128 x 104 x 104 -
(12) conv-bn-leaky 128 x 104 x 104 256 x 52 x 52 576096
(13) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 609376
(14) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 905312
(15) skip 256 x 52 x 52 256 x 52 x 52 -
(16) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 938592
(17) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1234528
(18) skip 256 x 52 x 52 256 x 52 x 52 -
(19) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1267808
(20) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1563744
(21) skip 256 x 52 x 52 256 x 52 x 52 -
(22) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1597024
(23) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1892960
(24) skip 256 x 52 x 52 256 x 52 x 52 -
(25) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1926240
(26) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2222176
(27) skip 256 x 52 x 52 256 x 52 x 52 -
(28) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2255456
(29) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2551392
(30) skip 256 x 52 x 52 256 x 52 x 52 -
(31) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2584672
(32) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2880608
(33) skip 256 x 52 x 52 256 x 52 x 52 -
(34) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2913888
(35) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 3209824
(36) skip 256 x 52 x 52 256 x 52 x 52 -
(37) conv-bn-leaky 256 x 52 x 52 512 x 26 x 26 4391520
(38) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 4523616
(39) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 5705312
(40) skip 512 x 26 x 26 512 x 26 x 26 -
(41) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 5837408
(42) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 7019104
(43) skip 512 x 26 x 26 512 x 26 x 26 -
(44) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 7151200
(45) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 8332896
(46) skip 512 x 26 x 26 512 x 26 x 26 -
(47) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 8464992
(48) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 9646688
(49) skip 512 x 26 x 26 512 x 26 x 26 -
(50) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 9778784
(51) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 10960480
(52) skip 512 x 26 x 26 512 x 26 x 26 -
(53) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 11092576
(54) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 12274272
(55) skip 512 x 26 x 26 512 x 26 x 26 -
(56) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 12406368
(57) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 13588064
(58) skip 512 x 26 x 26 512 x 26 x 26 -
(59) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 13720160
(60) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 14901856
(61) skip 512 x 26 x 26 512 x 26 x 26 -
(62) conv-bn-leaky 512 x 26 x 26 1024 x 13 x 13 19624544
(63) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 20150880
(64) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 24873568
(65) skip 1024 x 13 x 13 1024 x 13 x 13 -
(66) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 25399904
(67) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 30122592
(68) skip 1024 x 13 x 13 1024 x 13 x 13 -
(69) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 30648928
(70) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 35371616
(71) skip 1024 x 13 x 13 1024 x 13 x 13 -
(72) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 35897952
(73) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 40620640
(74) skip 1024 x 13 x 13 1024 x 13 x 13 -
(75) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 41146976
(76) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 45869664
(77) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 46396000
(78) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 51118688
(79) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 51645024
(80) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 56367712
(81) conv-linear 1024 x 13 x 13 21 x 13 x 13 56389237
(82) yolo 21 x 13 x 13 21 x 13 x 13 56389237
(83) route - 512 x 13 x 13 56389237
(84) conv-bn-leaky 512 x 13 x 13 256 x 13 x 13 56521333
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm1_85: broadcasting input0 to make tensors conform, dims(input0)=[1,26,13][NONE] dims(input1)=[256,13,13][NONE].
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm2_85: broadcasting input1 to make tensors conform, dims(input0)=[256,26,13][NONE] dims(input1)=[1,13,26][NONE].
(85) upsample 256 x 13 x 13 256 x 26 x 26 -
(86) route - 768 x 26 x 26 56521333
(87) conv-bn-leaky 768 x 26 x 26 256 x 26 x 26 56718965
(88) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 57900661
(89) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 58032757
(90) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 59214453
(91) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 59346549
(92) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 60528245
(93) conv-linear 512 x 26 x 26 21 x 26 x 26 60539018
(94) yolo 21 x 26 x 26 21 x 26 x 26 60539018
(95) route - 256 x 26 x 26 60539018
(96) conv-bn-leaky 256 x 26 x 26 128 x 26 x 26 60572298
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm1_97: broadcasting input0 to make tensors conform, dims(input0)=[1,52,26][NONE] dims(input1)=[128,26,26][NONE].
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm2_97: broadcasting input1 to make tensors conform, dims(input0)=[128,52,26][NONE] dims(input1)=[1,26,52][NONE].
(97) upsample 128 x 26 x 26 128 x 52 x 52 -
(98) route - 384 x 52 x 52 60572298
(99) conv-bn-leaky 384 x 52 x 52 128 x 52 x 52 60621962
(100) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 60917898
(101) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 60951178
(102) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61247114
(103) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61280394
(104) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61576330
(105) conv-linear 256 x 52 x 52 21 x 52 x 52 61581727
(106) yolo 21 x 52 x 52 21 x 52 x 52 61581727
Output yolo blob names :
yolo_83
yolo_95
yolo_107
Total number of yolo layers: 257
Building yolo network complete!
Building the TensorRT Engine…
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm1_85: broadcasting input0 to make tensors conform, dims(input0)=[1,26,13][NONE] dims(input1)=[256,13,13][NONE].
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm2_85: broadcasting input1 to make tensors conform, dims(input0)=[256,26,13][NONE] dims(input1)=[1,13,26][NONE].
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm1_97: broadcasting input0 to make tensors conform, dims(input0)=[1,52,26][NONE] dims(input1)=[128,26,26][NONE].
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: mm2_97: broadcasting input1 to make tensors conform, dims(input0)=[128,52,26][NONE] dims(input1)=[1,26,52][NONE].
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: Reading Calibration Cache for calibrator: EntropyCalibration2
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: Generated calibration scales using calibration cache. Make sure that calibration cache has latest scales.
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: To regenerate calibration cache, please delete the existing one. TensorRT will generate a new calibration cache.
[New Thread 0x7f34faffd700 (LWP 1407)]
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
INFO: …/nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: Detected 1 inputs and 3 output network tensors.
Building complete!
0:02:17.573812609 1391 0x55a344f3fb50 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1748> [UID = 1]: serialize cuda engine to file: /opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/model_b30_gpu0_int8.engine successfully
WARNING: …/nvdsinfer/nvdsinfer_func_utils.cpp:36 [TRT]: Current optimization profile is: 0. Please ensure there are no enqueued operations pending in this context prior to switching profiles
INFO: …/nvdsinfer/nvdsinfer_model_builder.cpp:685 [Implicit Engine Info]: layers num: 4
0 INPUT kFLOAT data 3x416x416
1 OUTPUT kFLOAT yolo_83 21x13x13
2 OUTPUT kFLOAT yolo_95 21x26x26
3 OUTPUT kFLOAT yolo_107 21x52x52

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0:02:17.660029505 1391 0x55a344f3fb50 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<primary_gie> [UID 1]: Load new model:/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/config_infer_primary_yoloV3.txt sucessfully
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Runtime commands:
h: Print this help
q: Quit

    p: Pause
    r: Resume

NOTE: To expand a source in the 2D tiled display and view object details, left-click on the source.
To go back to the tiled display, right-click anywhere on the window.

**PERF: FPS 0 (Avg) FPS 1 (Avg) FPS 2 (Avg) FPS 3 (Avg) FPS 4 (Avg) FPS 5 (Avg) FPS 6 (Avg) FPS 7 (Avg) FPS 8 (Avg) FPS 9 (Avg) FPS 10 (Avg) FPS 11 (Avg) FPS 12 (Avg) FPS 13 (Avg) FPS 14 (Avg)
**PERF: 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)
** INFO: <bus_callback:181>: Pipeline ready

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[New Thread 0x7f3225fff700 (LWP 1534)]
[New Thread 0x7f32257fe700 (LWP 1535)]
** INFO: <bus_callback:167>: Pipeline running

Thread 13 “deepstream-app” received signal SIGSEGV, Segmentation fault.
[Switching to Thread 0x7f34f1fff700 (LWP 1408)]
0x00007f35002a6f49 in decodeYoloV3Tensor(float const*, std::vector<int, std::allocator > const&, std::vector<float, std::allocator > const&, unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, unsigned int const&, unsigned int const&) ()
from /opt/nvidia/deepstream/deepstream-5.0/lib/libnvdsinfer_custom_impl_Yolo.so
(gdb) bt
#0 0x00007f35002a6f49 in decodeYoloV3Tensor(float const*, std::vector<int, std::allocator > const&, std::vector<float, std::allocator > const&, unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, unsigned int const&, unsigned int const&) ()
at /opt/nvidia/deepstream/deepstream-5.0/lib/libnvdsinfer_custom_impl_Yolo.so
#1 0x00007f35002a7453 in NvDsInferParseYoloV3(std::vector<NvDsInferLayerInfo, std::allocator > const&, NvDsInferNetworkInfo const&, NvDsInferParseDetectionParams const&, std::vector<NvDsInferObjectDetectionInfo, std::allocator >&, std::vector<float, std::allocator > const&, std::vector<std::vector<int, std::allocator >, std::allocator<std::vector<int, std::allocator > > > const&) ()
at /opt/nvidia/deepstream/deepstream-5.0/lib/libnvdsinfer_custom_impl_Yolo.so
#2 0x00007f35002a7878 in NvDsInferParseCustomYoloV3 () at /opt/nvidia/deepstream/deepstream-5.0/lib/libnvdsinfer_custom_impl_Yolo.so
#3 0x00007f350ae13346 in nvdsinfer::DetectPostprocessor::fillDetectionOutput(std::vector<NvDsInferLayerInfo, std::allocator > const&, NvDsInferDetectionOutput&) () at ///opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_infer.so
#4 0x00007f350ade5433 in nvdsinfer::DetectPostprocessor::parseEachBatch(std::vector<NvDsInferLayerInfo, std::allocator > const&, NvDsInferFrameOutput&) () at ///opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_infer.so
#5 0x00007f350ade4918 in nvdsinfer::InferPostprocessor::postProcessHost(nvdsinfer::NvDsInferBatch&, NvDsInferContextBatchOutput&) ()
at ///opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_infer.so
#6 0x00007f350adeba46 in nvdsinfer::NvDsInferContextImpl::dequeueOutputBatch(NvDsInferContextBatchOutput&) ()
at ///opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_infer.so
#7 0x00007f350b4aea56 in gst_nvinfer_output_loop(void*) () at /usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_infer.so
#8 0x00007f3587fa0175 in () at /usr/lib/x86_64-linux-gnu/libglib-2.0.so.0
#9 0x00007f3575aef6db in start_thread () at /lib/x86_64-linux-gnu/libpthread.so.0
#10 0x00007f3587a0888f in clone () at /lib/x86_64-linux-gnu/libc.so.6
(gdb)

Thanks for your needful help

it’s ‘segment fault’ in this function. could you share your demo_mask_video_stream.txt ?

Hello ,

Here is my demo_mask_video_stream.txt file:

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
#gie-kitti-output-dir=streamscl

[tiled-display]
enable=1
rows=5
columns=6
width=1280
height=720
gpu-id=0
#(0): nvbuf-mem-default - Default memory allocated, specific to particular platform
#(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory, applicable for Tesla
#(2): nvbuf-mem-cuda-device - Allocate Device cuda memory, applicable for Tesla
#(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory, applicable for Tesla
#(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson
nvbuf-memory-type=0

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=3
#uri=file://…/…/streams/sample_1080p_h264.mp4
uri=file:/opt/nvidia/deepstream/deepstream-5.0/samples/streams/sample_1080p_h264.mp4
num-sources=15
#drop-frame-interval=2
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

[source1]
enable=0
#Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP
type=3
uri=file://…/…/streams/sample_1080p_h264.mp4
num-sources=15
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

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

[sink1]
enable=0
type=1
#1=mp4 2=mkv
container=1
#1=h264 2=h265
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=0
#iframeinterval=10
bitrate=2000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0
output-file=out.mp4
source-id=0

[sink2]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming
type=4
#1=h264 2=h265
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=0
bitrate=4000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0

set below properties in case of RTSPStreaming

rtsp-port=8554
udp-port=5400

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

[streammux]
gpu-id=0
##Boolean property to inform muxer that sources are live
live-source=0
batch-size=30
##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 to maintain aspect ratio wrt source, and allow black borders, works
##along with width, height properties
enable-padding=0
nvbuf-memory-type=0

If set to TRUE, system timestamp will be attached as ntp timestamp

If set to FALSE, ntp timestamp from rtspsrc, if available, will be attached

attach-sys-ts-as-ntp=1

config-file property is mandatory for any gie section.

Other properties are optional and if set will override the properties set in

the infer config file.

[primary-gie]
enable=1
gpu-id=0
#model-engine-file=…/…/models/Primary_Detector/resnet10.caffemodel_b30_gpu0_int8.engine
#Required to display the PGIE labels, should be added even when using config-file
#property
batch-size=30
#Required by the app for OSD, not a plugin property
bbox-border-color0=1;0;0;1
bbox-border-color1=0;1;1;1
bbox-border-color2=0;0;1;1
bbox-border-color3=0;1;0;1
interval=0
#Required by the app for SGIE, when used along with config-file property
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV3.txt

[tests]
file-loop=0

Here is my config_infer_primary_yoloV3.txt file:

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
#0=RGB, 1=BGR
model-color-format=0
custom-network-config=/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/mask-detection-yolov3/yolov3.cfg
model-file=/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/mask-detection-yolov3/yolov3_face_mask.weights
#model-engine-file=yolov3_b1_gpu0_int8.engine
labelfile-path=/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/mask-detection-yolov3/obj.names
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

0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)

cluster-mode=2
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseCustomYoloV3
custom-lib-path=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet
#scaling-filter=0
#scaling-compute-hw=0

[class-attrs-all]
nms-iou-threshold=0.3
threshold=0.7

Thanks

Ok, I believe it’s because you customized the yolov3.cfg model, which cause current decodeYoloV3Tensor() failed to parse your customized cfg file and them “segment fault.”
You need to debug into decodeYoloV3Tensor() function and check which layer it fails on and then make the change to your yolov3.cfg or decodeYoloV3Tensor() function.

Hello,

When iam trying to integrate my custom .trt file (YOLOv3 model) as a model engine instead of weights and config file in primary infer file iam getting below error

Here is the logs caught by running with gdb:

Unknown or legacy key specified ‘is-classifier’ for group [property]
[New Thread 0x7fd69c36b700 (LWP 12960)]
[Thread 0x7fd69c36b700 (LWP 12960) exited]
[New Thread 0x7fd69c36b700 (LWP 12961)]
[Thread 0x7fd69c36b700 (LWP 12961) exited]
[New Thread 0x7fd69c36b700 (LWP 12962)]
[New Thread 0x7fd69bb6a700 (LWP 12963)]
[New Thread 0x7fd69b369700 (LWP 12964)]
[New Thread 0x7fd69ab68700 (LWP 12965)]
[New Thread 0x7fd69a367700 (LWP 12966)]
[Thread 0x7fd69a367700 (LWP 12966) exited]
[Thread 0x7fd69ab68700 (LWP 12965) exited]
[Thread 0x7fd69b369700 (LWP 12964) exited]
[Thread 0x7fd69bb6a700 (LWP 12963) exited]
[Thread 0x7fd69c36b700 (LWP 12962) exited]
[Thread 0x7fd6c2a6f700 (LWP 12959) exited]
[Thread 0x7fd6c3270700 (LWP 12958) exited]
[Thread 0x7fd6c6555700 (LWP 12956) exited]
[Inferior 1 (process 12947) exited with code 0377]

Here is the infer config file:

[property]
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=/opt/nvidia/deepstream/deepstream-5.0/new-project-/yolov3_face_mask.trt
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

0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)

cluster-mode=2
maintain-aspect-ratio=1
parse-bbox-func-name=NvDsInferParseCustomYoloV3
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet
#scaling-filter=0
#scaling-compute-hw=0

[class-attrs-all]
nms-iou-threshold=0.3

Here is my deepstream config file:

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5
#gie-kitti-output-dir=streamscl

[tiled-display]
enable=1
rows=1
columns=1
width=1280
height=720
gpu-id=0
#(0): nvbuf-mem-default - Default memory allocated, specific to particular platform
#(1): nvbuf-mem-cuda-pinned - Allocate Pinned/Host cuda memory, applicable for Tesla
#(2): nvbuf-mem-cuda-device - Allocate Device cuda memory, applicable for Tesla
#(3): nvbuf-mem-cuda-unified - Allocate Unified cuda memory, applicable for Tesla
#(4): nvbuf-mem-surface-array - Allocate Surface Array memory, applicable for Jetson
nvbuf-memory-type=0

[source0]
enable=1
#Type - 1=CameraV4L2 2=URI 3=MultiURI
type=3
uri=file://…/…/samples/streams/sample_1080p_h264.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

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

[sink1]
enable=0
type=3
#1=mp4 2=mkv
container=1
#1=h264 2=h265
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=0
#iframeinterval=10
bitrate=2000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0
output-file=out.mp4
source-id=0

[sink2]
enable=1
#Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming
type=4
#1=h264 2=h265
codec=1
#encoder type 0=Hardware 1=Software
enc-type=0
sync=0
bitrate=4000000
#H264 Profile - 0=Baseline 2=Main 4=High
#H265 Profile - 0=Main 1=Main10
profile=0

set below properties in case of RTSPStreaming

rtsp-port=8554
udp-port=5400

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

[streammux]
gpu-id=0
##Boolean property to inform muxer that sources are live
live-source=0
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 to maintain aspect ratio wrt source, and allow black borders, works
##along with width, height properties
enable-padding=0
nvbuf-memory-type=0

config-file property is mandatory for any gie section.

Other properties are optional and if set will override the properties set in

the infer config file.

[primary-gie]
enable=1
gpu-id=0
#model-engine-file=model_b1_gpu0_int8.engine
labelfile-path=labels.txt
batch-size=1
#Required by the app for OSD, not a plugin property
bbox-border-color0=1;0;0;1
bbox-border-color1=0;1;1;1
bbox-border-color2=0;0;1;1
bbox-border-color3=0;1;0;1
interval=2
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_main.txt

[tracker]
enable=1
tracker-width=640
tracker-height=384
ll-lib-file=/opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_klt.so

[tests]
file-loop=0

Thanks

what’s the error you are referring to ?

my program getting exited with the below logs:

Unknown or legacy key specified ‘is-classifier’ for group [property]
[New Thread 0x7fd69c36b700 (LWP 12960)]
[Thread 0x7fd69c36b700 (LWP 12960) exited]

and iam neither getting any output logs nor getting the output

same as Is-classifier property: Unknown or legacy key specified

@mchi
Customer is facing this problem, when I tried I got the following error with the same docker and same application when executed with the provided example without any modifications.

root@a49046a07cd0:/opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo# deepstream-app -c deepstream_app_config_yoloV3_tiny.txt
Unknown or legacy key specified 'is-classifier' for group [property]
Warn: 'threshold' parameter has been deprecated. Use 'pre-cluster-threshold' instead.
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream-5.0/lib/libnvds_mot_klt.so
gstnvtracker: Optional NvMOT_RemoveStreams not implemented
gstnvtracker: Batch processing is OFF
gstnvtracker: Past frame output is OFF
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:62 Could not open lib: /opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so, error string: /opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so: cannot open shared object file: No such file or directory
0:00:00.528657140    96 0x557c26b49c70 ERROR                nvinfer gstnvinfer.cpp:613:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1139> [UID = 1]: Could not open custom lib: (null)
0:00:00.528734803    96 0x557c26b49c70 WARN                 nvinfer gstnvinfer.cpp:809:gst_nvinfer_start:<primary_gie> error: Failed to create NvDsInferContext instance
0:00:00.528753241    96 0x557c26b49c70 WARN                 nvinfer gstnvinfer.cpp:809:gst_nvinfer_start:<primary_gie> error: Config file path: /opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/config_infer_primary_yoloV3_tiny.txt, NvDsInfer Error: NVDSINFER_CUSTOM_LIB_FAILED
** ERROR: <main:655>: Failed to set pipeline to PAUSED
Quitting
ERROR from primary_gie: Failed to create NvDsInferContext instance
Debug info: gstnvinfer.cpp(809): gst_nvinfer_start (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie:
Config file path: /opt/nvidia/deepstream/deepstream-5.0/sources/objectDetector_Yolo/config_infer_primary_yoloV3_tiny.txt, NvDsInfer Error: NVDSINFER_CUSTOM_LIB_FAILED
App run failed

Help the customer on priority please - the model is getting ready for 2000+ cameras.

Hi @supatel,
Could you file a new topic? Yours seems different from @ashok.pinapathula’s issue.

Hi @mchi,
this is the same problem where provided models (Yolo-v3) fail to work with the Deepstream.
Internal bug filed - 200685251

There is no update from you for a period, assuming this is not an issue any more.
Hence we are closing this topic. If need further support, please open a new one.
Thanks

Hi @ashok.pinapathula,
Is your issue solved?

Thanks!