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)]
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[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.
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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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** 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