Failed to create network using custom yolov3 network creation function

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

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

3

Can you check again if you used the right cfg file ? Because the conv-linear layers still seem to have 255 filters -
(106) conv-linear 256 x 52 x 52 255 x 52 x 52 62001757
(94) conv-linear 512 x 26 x 26 255 x 26 x 26 60898910
(82) conv-linear 1024 x 13 x 13 255 x 13 x 13 56629087

4

I solved this problem by re-uploading the weight file. This error may be caused by the damage of the weight file.