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AlexeyAB / darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
http://pjreddie.com/darknet/
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Create cudnn-handle 0 非法指令 (核心已转储) #7531

Closed SpongeBab closed 3 years ago

SpongeBab commented 3 years ago

/darknet detector test cfg/coco.data cfg/yolov4.cfg CUDA-version: 11000 (11000), cuDNN: 8.1.1, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1 OpenCV version: 4.4.0 0 : compute_capability = 610, cudnn_half = 0, GPU: GeForce GTX 1070 net.optimized_memory = 0 mini_batch = 1, batch = 8, time_steps = 1, train = 0 layer filters size/strd(dil) input output 0 Create CUDA-stream - 0 Create cudnn-handle 0 非法指令 (核心已转储)

@AlexeyAB i dont know how to slove this> can you help me ? hope to your reply above is my config

SpongeBab commented 3 years ago

I guess it's same as https://github.com/AlexeyAB/darknet/issues/7153 @AlexeyAB Do you plan to solve it? or i have to downgrate my cuda and ubuntu version.cuda 10.x only support ubuntu 18.04.

SpongeBab commented 3 years ago 
(base) xiaopeng@xiaopeng-HP-Z800-Workstation:~/下载/darknet$ ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/desk2.jpg
 CUDA-version: 11000 (11000), cuDNN: 8.1.1, GPU count: 1  
 OpenCV isn't used - data augmentation will be slow 
 0 : compute_capability = 610, cudnn_half = 0, GPU: GeForce GTX 1070 
net.optimized_memory = 0 
mini_batch = 1, batch = 8, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
非法指令 (核心已转储)

and i tried with opencv 4.4.0.No help. i tried without opencv.No help. @AlexeyAB @cenit

SpongeBab commented 3 years ago

Oh。I success installing it . I can confirm that darknet does not install cuda 11.0 and above! I dowgrade my cuda and make it.

4.cfg yolov4.weights -thresh 0.25
 CUDA-version: 10010 (11000), cuDNN: 8.0.4, GPU count: 1  
 OpenCV version: 4.2.0
 0 : compute_capability = 610, cudnn_half = 0, GPU: GeForce GTX 1070 
net.optimized_memory = 0 
mini_batch = 1, batch = 8, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
conv     32       3 x 3/ 1    608 x 608 x   3 ->  608 x 608 x  32 0.639 BF
   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF
   2 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   3 route  1                                  ->  304 x 304 x  64 
   4 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   5 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF
   6 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF
   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 304 x 304 x  64 0.006 BF
   8 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   9 route  8 2                                ->  304 x 304 x 128 
  10 conv     64       1 x 1/ 1    304 x 304 x 128 ->  304 x 304 x  64 1.514 BF
  11 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF
  12 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF
  13 route  11                                 ->  152 x 152 x 128 
  14 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF
cenit commented 3 years ago

can you build successfully with cuda 11 using darknet from PR #7279 ?

SpongeBab commented 3 years ago

@cenit , I haven't tried with cuda 11. now my version is :

CUDA-version: 10010 (11000), cuDNN: 8.0.4, GPU count: 1  
 OpenCV version: 4.3.0
cenit commented 3 years ago

ok closing the issue. If it will become a problem again, please re-open with details

EVNN304 commented 3 years ago

hello, but it will pass cuda 10.2 and cudnn 8.0.2?

cenit commented 3 years ago

it may work. Please try and open another issue in case you have problems, reporting all logs

EVNN304 commented 3 years ago

Heillo, found problems

CUDA-version: 10020 (11030), cuDNN: 8.0.2, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1 OpenCV version: 3.2.0 backup: Using default '/backup/' yolov4-custom 0 : compute_capability = 610, cudnn_half = 0, GPU: NVIDIA GeForce GTX 1060 6GB net.optimized_memory = 0 mini_batch = 1, batch = 64, time_steps = 1, train = 1 layer filters size/strd(dil) input output 0 Create CUDA-stream - 0 Create cudnn-handle 0 conv 32 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF 1 conv 64 3 x 3/ 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BF 2 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 3 route 1 -> 208 x 208 x 64 4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF 6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF 8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF 9 route 8 2 -> 208 x 208 x 128 10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF 11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF 12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 13 route 11 -> 104 x 104 x 128 14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF 18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF 21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF 22 route 21 12 -> 104 x 104 x 128 23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF 24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF 25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 26 route 24 -> 52 x 52 x 256 27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF 52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF 53 route 52 25 -> 52 x 52 x 256 54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF 55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF 56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 57 route 55 -> 26 x 26 x 512 58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF 83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF 84 route 83 56 -> 26 x 26 x 512 85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF 86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF 87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 88 route 86 -> 13 x 13 x1024 89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF 102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF 103 route 102 87 -> 13 x 13 x1024 104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF 105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF 109 route 107 -> 13 x 13 x 512 110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF 111 route 107 -> 13 x 13 x 512 112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF 113 route 112 110 108 107 -> 13 x 13 x2048 114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF 115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 119 route 85 -> 26 x 26 x 512 120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 121 route 120 118 -> 26 x 26 x 512 122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF 128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 129 route 54 -> 52 x 52 x 256 130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 131 route 130 128 -> 52 x 52 x 256 132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 138 conv 18 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 18 0.025 BF 139 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20 nms_kind: greedynms (1), beta = 0.600000 140 route 136 -> 52 x 52 x 128 141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF 142 route 141 126 -> 26 x 26 x 512 143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 149 conv 18 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 18 0.012 BF 150 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10 nms_kind: greedynms (1), beta = 0.600000 151 route 147 -> 26 x 26 x 256 152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF 153 route 152 116 -> 13 x 13 x1024 154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 160 conv 18 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 18 0.006 BF 161 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Total BFLOPS 59.563 avg_outputs = 489778 Allocate additional workspace_size = 54.53 MB Loading weights from yolov4.conv.137... seen 64, trained: 0 K-images (0 Kilo-batches_64) Done! Loaded 137 layers from weights-file Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005 Detection layer: 139 - type = 28 Detection layer: 150 - type = 28 Detection layer: 161 - type = 28