I use yolov3 to train 6 types of targets. After I replaced the parameters in deepstream_app_config_yoloV3.txt and config_infer_primary_yoloV3.txt, the program ran into errors.
Total Number of weights read : 61603267
layer inp_size out_size weightPtr
(1) conv-bn-leaky 3 x 416 x 416 32 x 416 x 416 992
(2) conv-bn-leaky 32 x 416 x 416 64 x 208 x 208 19680
(3) conv-bn-leaky 64 x 208 x 208 32 x 208 x 208 21856
(4) conv-bn-leaky 32 x 208 x 208 64 x 208 x 208 40544
(5) skip 64 x 208 x 208 64 x 208 x 208 -
(6) conv-bn-leaky 64 x 208 x 208 128 x 104 x 104 114784
(7) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 123232
(8) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 197472
(9) skip 128 x 104 x 104 128 x 104 x 104 -
(10) conv-bn-leaky 128 x 104 x 104 64 x 104 x 104 205920
(11) conv-bn-leaky 64 x 104 x 104 128 x 104 x 104 280160
(12) skip 128 x 104 x 104 128 x 104 x 104 -
(13) conv-bn-leaky 128 x 104 x 104 256 x 52 x 52 576096
(14) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 609376
(15) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 905312
(16) skip 256 x 52 x 52 256 x 52 x 52 -
(17) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 938592
(18) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1234528
(19) skip 256 x 52 x 52 256 x 52 x 52 -
(20) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1267808
(21) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1563744
(22) skip 256 x 52 x 52 256 x 52 x 52 -
(23) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1597024
(24) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 1892960
(25) skip 256 x 52 x 52 256 x 52 x 52 -
(26) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 1926240
(27) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2222176
(28) skip 256 x 52 x 52 256 x 52 x 52 -
(29) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2255456
(30) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2551392
(31) skip 256 x 52 x 52 256 x 52 x 52 -
(32) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2584672
(33) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 2880608
(34) skip 256 x 52 x 52 256 x 52 x 52 -
(35) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 2913888
(36) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 3209824
(37) skip 256 x 52 x 52 256 x 52 x 52 -
(38) conv-bn-leaky 256 x 52 x 52 512 x 26 x 26 4391520
(39) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 4523616
(40) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 5705312
(41) skip 512 x 26 x 26 512 x 26 x 26 -
(42) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 5837408
(43) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 7019104
(44) skip 512 x 26 x 26 512 x 26 x 26 -
(45) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 7151200
(46) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 8332896
(47) skip 512 x 26 x 26 512 x 26 x 26 -
(48) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 8464992
(49) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 9646688
(50) skip 512 x 26 x 26 512 x 26 x 26 -
(51) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 9778784
(52) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 10960480
(53) skip 512 x 26 x 26 512 x 26 x 26 -
(54) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 11092576
(55) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 12274272
(56) skip 512 x 26 x 26 512 x 26 x 26 -
(57) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 12406368
(58) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 13588064
(59) skip 512 x 26 x 26 512 x 26 x 26 -
(60) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 13720160
(61) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 14901856
(62) skip 512 x 26 x 26 512 x 26 x 26 -
(63) conv-bn-leaky 512 x 26 x 26 1024 x 13 x 13 19624544
(64) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 20150880
(65) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 24873568
(66) skip 1024 x 13 x 13 1024 x 13 x 13 -
(67) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 25399904
(68) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 30122592
(69) skip 1024 x 13 x 13 1024 x 13 x 13 -
(70) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 30648928
(71) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 35371616
(72) skip 1024 x 13 x 13 1024 x 13 x 13 -
(73) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 35897952
(74) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 40620640
(75) skip 1024 x 13 x 13 1024 x 13 x 13 -
(76) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 41146976
(77) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 45869664
(78) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 46396000
(79) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 51118688
(80) conv-bn-leaky 1024 x 13 x 13 512 x 13 x 13 51645024
(81) conv-bn-leaky 512 x 13 x 13 1024 x 13 x 13 56367712
(82) conv-linear 1024 x 13 x 13 255 x 13 x 13 56629087
(83) yolo 255 x 13 x 13 255 x 13 x 13 56629087
(84) route - 512 x 13 x 13 56629087
(85) conv-bn-leaky 512 x 13 x 13 256 x 13 x 13 56761183
(86) upsample 256 x 13 x 13 256 x 26 x 26 -
(87) route - 768 x 26 x 26 56761183
(88) conv-bn-leaky 768 x 26 x 26 256 x 26 x 26 56958815
(89) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 58140511
(90) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 58272607
(91) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 59454303
(92) conv-bn-leaky 512 x 26 x 26 256 x 26 x 26 59586399
(93) conv-bn-leaky 256 x 26 x 26 512 x 26 x 26 60768095
(94) conv-linear 512 x 26 x 26 255 x 26 x 26 60898910
(95) yolo 255 x 26 x 26 255 x 26 x 26 60898910
(96) route - 256 x 26 x 26 60898910
(97) conv-bn-leaky 256 x 26 x 26 128 x 26 x 26 60932190
(98) upsample 128 x 26 x 26 128 x 52 x 52 -
(99) route - 384 x 52 x 52 60932190
(100) conv-bn-leaky 384 x 52 x 52 128 x 52 x 52 60981854
(101) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61277790
(102) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61311070
(103) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61607006
(104) conv-bn-leaky 256 x 52 x 52 128 x 52 x 52 61640286
(105) conv-bn-leaky 128 x 52 x 52 256 x 52 x 52 61936222
(106) conv-linear 256 x 52 x 52 255 x 52 x 52 62001757
(107) yolo 255 x 52 x 52 255 x 52 x 52 62001757
Number of unused weights left : 18446744073709153126
deepstream-app: yolo.cpp:349: nvinfer1::INetworkDefinition* Yolo::createYoloNetwork(std::vector<float>&, std::vector<nvinfer1::Weights>&): Assertion `0' failed.
Aborted (core dumped)
I have changed 2 elements in yolov3.cfg
[convolutional]
size=1
stride=1
pad=1
filters=255 ->33
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80 -> 6
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
What did i do wrong