I am using the following configuration file in deepstream objectDetector_Yolo app. There seems to be a mismatch in the size of weights and the params loaded from the configuration file. Can you look this in greater detail.
https://github.com/AlexeyAB/darknet/files/4213821/yoloV3_pan3_changes1_leaky.txt
Here is the output:
root@ashutosh-GL553VE:/opt/nvidia/deepstream/deepstream-4.0/sources/objectDetector_Yolo# deepstream-app -c deepstream_app_config_yoloV3_pan3.txt
Creating LL OSD context new
0:00:00.326109565 32071 0x5568c1741e90 INFO nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<primary_gie_classifier> NvDsInferContext[UID 1]:initialize(): Trying to create engine from model files
Loading pre-trained weights...
Loading complete!
Total Number of weights read : 12014020
layer inp_size out_size weightPtr
(1) conv-bn-leaky 3 x 544 x 544 16 x 544 x 544 496
(2) maxpool 16 x 544 x 544 16 x 272 x 272 496
(3) conv-bn-leaky 16 x 272 x 272 32 x 272 x 272 5232
(4) maxpool 32 x 272 x 272 32 x 136 x 136 5232
(5) conv-bn-leaky 32 x 136 x 136 64 x 136 x 136 23920
(6) maxpool 64 x 136 x 136 64 x 68 x 68 23920
(7) conv-bn-leaky 64 x 68 x 68 128 x 68 x 68 98160
(8) maxpool 128 x 68 x 68 128 x 34 x 34 98160
(9) conv-bn-leaky 128 x 34 x 34 256 x 34 x 34 394096
(10) maxpool 256 x 34 x 34 256 x 17 x 17 394096
(11) conv-bn-leaky 256 x 17 x 17 512 x 17 x 17 1575792
(12) maxpool 512 x 17 x 17 512 x 17 x 17 1575792
(13) conv-bn-leaky 512 x 17 x 17 1024 x 17 x 17 6298480
(14) conv-bn-leaky 1024 x 17 x 17 256 x 17 x 17 6561648
(15) conv-bn-leaky 256 x 17 x 17 512 x 17 x 17 7743344
(16) conv-bn-leaky 512 x 17 x 17 128 x 17 x 17 7809392
(17) upsample 128 x 17 x 17 128 x 34 x 34 -
(18) route - 384 x 34 x 34 7809392
(19) conv-bn-leaky 384 x 34 x 34 128 x 34 x 34 7859056
(20) conv-bn-leaky 128 x 34 x 34 256 x 34 x 34 8154992
(21) conv-bn-leaky 256 x 34 x 34 128 x 34 x 34 8188272
(22) upsample 128 x 34 x 34 128 x 68 x 68 -
(23) route - 256 x 68 x 68 8188272
(24) conv-bn-leaky 256 x 68 x 68 64 x 68 x 68 8204912
(25) conv-bn-leaky 64 x 68 x 68 128 x 68 x 68 8279152
(26) route - 32 x 272 x 272 8279152
(27) maxpool 32 x 272 x 272 32 x 17 x 17 8279152
(28) conv-bn-leaky 32 x 17 x 17 64 x 17 x 17 8281456
(29) route - 64 x 136 x 136 8281456
(30) maxpool 64 x 136 x 136 64 x 17 x 17 8281456
(31) conv-bn-leaky 64 x 17 x 17 64 x 17 x 17 8285808
(32) route - 64 x 136 x 136 8285808
(33) maxpool 64 x 136 x 136 64 x 33 x 33 8285808
(34) conv-bn-leaky 64 x 33 x 33 64 x 17 x 17 8290160
(35) route - 64 x 136 x 136 8290160
(36) maxpool 64 x 136 x 136 64 x 17 x 17 8290160
(37) conv-bn-leaky 64 x 17 x 17 64 x 17 x 17 8294512
(38) route - 64 x 136 x 136 8294512
(39) maxpool 64 x 136 x 136 64 x 17 x 17 8294512
(40) conv-bn-leaky 64 x 17 x 17 64 x 17 x 17 8298864
(41) route - 128 x 68 x 68 8298864
(42) maxpool 128 x 68 x 68 128 x 17 x 17 8298864
(43) conv-bn-leaky 128 x 17 x 17 64 x 17 x 17 8307312
(44) route - 128 x 68 x 68 8307312
(45) maxpool 128 x 68 x 68 128 x 33 x 33 8307312
(46) conv-bn-leaky 128 x 33 x 33 64 x 17 x 17 8315760
(47) route - 128 x 68 x 68 8315760
(48) maxpool 128 x 68 x 68 128 x 17 x 17 8315760
(49) conv-bn-leaky 128 x 17 x 17 64 x 17 x 17 8324208
(50) route - 256 x 34 x 34 8324208
(51) maxpool 256 x 34 x 34 256 x 17 x 17 8324208
(52) conv-bn-leaky 256 x 17 x 17 64 x 17 x 17 8340848
(53) route - 512 x 17 x 17 8340848
(54) conv-bn-leaky 512 x 17 x 17 64 x 17 x 17 8373872
(55) route - 128 x 17 x 17 8373872
(56) maxpool 128 x 17 x 17 128 x 17 x 17 8373872
(57) upsample 128 x 17 x 17 128 x 68 x 68 -
(58) route - 256 x 68 x 68 8373872
(59) conv-bn-leaky 256 x 68 x 68 128 x 68 x 68 8669296
(60) conv-linear 128 x 68 x 68 30 x 68 x 68 8673166
(61) yolo 30 x 68 x 68 30 x 68 x 68 8673166
(62) route - 128 x 17 x 17 8673166
(63) upsample 128 x 17 x 17 128 x 34 x 34 -
(64) route - 384 x 34 x 34 8673166
(65) conv-bn-leaky 384 x 34 x 34 256 x 34 x 34 9558926
(66) conv-linear 256 x 34 x 34 30 x 34 x 34 9566636
(67) yolo 30 x 34 x 34 30 x 34 x 34 9566636
(68) route - 128 x 17 x 17 9566636
(69) route - 640 x 17 x 17 9566636
(70) conv-bn-leaky 640 x 17 x 17 512 x 17 x 17 12517804
(71) conv-linear 512 x 17 x 17 24 x 17 x 17 12530116
(72) yolo 24 x 17 x 17 24 x 17 x 17 12530116
Number of unused weights left : 18446744073709035520
deepstream-app: yolo.cpp:349: nvinfer1::INetworkDefinition* Yolo::createYoloNetwork(std::vector<float>&, std::vector<nvinfer1::Weights>&): Assertion `0' failed.
Aborted (core dumped)