I brought a new rtx3070,installed cuda 10.1(also try 10.2).cudnn7(also try 8 with cuda10.2),after successfuly compiled darknet.exe,i want to test a picture,but it just get stuck there and doesn't,could anybody help me ,or there is something wrong with my rtx3070
wentao-yolo-1996
All 6 comments
When you opened the bug it asks the following information. Could you provide it all?
If you want to report a bug - provide:
* description of a bug
* what command do you use?
* do you use Win/Linux/Mac?
* attach screenshot of a bug with previous messages in terminal
* in what cases a bug occurs, and in which not?
* if possible, specify date/commit of Darknet that works without this bug
* show such screenshot with info
./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg
CUDA-version: 10000 (10000), cuDNN: 7.4.2, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 4.2.0
0 : compute_capability = 750, cudnn_half = 1, GPU: GeForce RTX 2070
net.optimized_memory = 0
mini_batch = 1, batch = 8, time_steps = 1, train = 0
layer filters size/strd(dil) input output
bizmate
on 13 Nov 2020
sorry for didn't give the correct bug information.I use a new rtx3070 gpu in win10/vs2019 to do my work.my problem is ,after i installed cuda(10.2)with cudnn8,opencv(3.4.12),and i successfully compiled the darknet ,get the darknet.exe file,than i use the following command
darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25 -ext_output dog.jpg
and it worked as follow
F:\yolo\darknet-master\darknet-master\build\darknet\x64>darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25 -ext_output dog.jpg
CUDA-version: 10020 (11010), cuDNN: 8.0.2, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 3.4.12
0 : compute_capability = 860, cudnn_half = 1, GPU: GeForce RTX 3070
net.optimized_memory = 0
mini_batch = 1, batch = 8, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0
it just stoped there,i waited a few minutes,got the result
F:\yolo\darknet-master\darknet-master\build\darknet\x64>darknet.exe detector test cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -i 0 -thresh 0.25 -ext_output dog.jpg
CUDA-version: 10020 (11010), cuDNN: 8.0.2, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 3.4.12
0 : compute_capability = 860, cudnn_half = 1, GPU: GeForce RTX 3070
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 2 416 x 416 x 3 -> 208 x 208 x 32 0.075 BF
1 conv 64 3 x 3/ 2 208 x 208 x 32 -> 104 x 104 x 64 0.399 BF
2 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
3 route 2 1/2 -> 104 x 104 x 32
4 conv 32 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 32 0.199 BF
5 conv 32 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 32 0.199 BF
6 route 5 4 -> 104 x 104 x 64
7 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
8 route 2 7 -> 104 x 104 x 128
9 max 2x 2/ 2 104 x 104 x 128 -> 52 x 52 x 128 0.001 BF
10 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
11 route 10 1/2 -> 52 x 52 x 64
12 conv 64 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 64 0.199 BF
13 conv 64 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 64 0.199 BF
14 route 13 12 -> 52 x 52 x 128
15 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
16 route 10 15 -> 52 x 52 x 256
17 max 2x 2/ 2 52 x 52 x 256 -> 26 x 26 x 256 0.001 BF
18 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
19 route 18 1/2 -> 26 x 26 x 128
20 conv 128 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 128 0.199 BF
21 conv 128 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 128 0.199 BF
22 route 21 20 -> 26 x 26 x 256
23 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
24 route 18 23 -> 26 x 26 x 512
25 max 2x 2/ 2 26 x 26 x 512 -> 13 x 13 x 512 0.000 BF
26 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
27 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
28 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
29 conv 255 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF
30 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
31 route 27 -> 13 x 13 x 256
32 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
33 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
34 route 33 23 -> 26 x 26 x 384
35 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
36 conv 255 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF
37 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 6.910
avg_outputs = 310203
Allocate additional workspace_size = 0.01 MB
Loading weights from yolov4-tiny.weights...
seen 64, trained: 32012 K-images (500 Kilo-batches_64)
Done! Loaded 38 layers from weights-file
Detection layer: 30 - type = 27
Detection layer: 37 - type = 27
Detection layer: 161 - type = 27
dog.jpg: Predicted in 23721.728000 milli-seconds.
bicycle: 92% (left_x: 114 top_y: 128 width: 458 height: 299)
dog: 98% (left_x: 129 top_y: 225 width: 184 height: 316)
truck: 92% (left_x: 464 top_y: 77 width: 221 height: 93)
pottedplant: 33% (left_x: 681 top_y: 109 width: 37 height: 46)
i followed the recommend setting step,but why did it cost so much time?what should i do to resolve this problem? @bizmate
wentao-yolo-1996
on 13 Nov 2020
How slow/fast is your F drive? Tried running it from another drive?
In one command you are using yolov4 and in the other yolov4-tiny? Which one is the right one that you are troubleshooting?
Can you time how long it takes? ie in linux i would just run time in front of the command.
If you are familiar with docker, does it run well with it? You can see an example in this script
https://github.com/bizmate/bash-essentials/blob/master/docs/DARKNET_DETECT_AND_TRASH.md
but you need docker to be properly configured to use your GPU (check official documentation)
bizmate
on 14 Nov 2020
My F drive is quite fast,and i try to put it in another drive,the same problem.
I used yolov4 and yolov4-tiny to test the different costs of time.the yolov4-tiny.weights cost about 23721milli-seconds and the yolov4.weights costs almost three times.
i don't know much of docker,but i heared that RTX 30 series need cuda11 to match the ampere architecture,so i tried to install
cuda11.0 and 11.1,however,both of them couldn't even compile,still confused.
wentao-yolo-1996
on 14 Nov 2020
That is why i dont use darknet without docker. Too many compilation troubleshooting errors. I have logged several on this project but they are not answered. Unless you can fix the cuda installation try docker
bizmate
on 15 Nov 2020
Finally i solved my problem.hope my solution could help others.
after reinstall cuda11.1,cudnn8.0,and correctly set opencv, i change the setting of darknet.vcxproj
usecompute_86,sm_86(as my compute capability) instead of compute_30,sm_30;compute_75,sm_75,also the cuda version,and it compiled successfully!
if somebody meet the similar problem,just try it!
wentao-yolo-1996
on 16 Nov 2020
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Most helpful comment
Finally i solved my problem.hope my solution could help others.
after reinstall cuda11.1,cudnn8.0,and correctly set opencv, i change the setting of
darknet.vcxprojuse
compute_86,sm_86(as my compute capability) instead ofcompute_30,sm_30;compute_75,sm_75,also the cuda version,and it compiled successfully!if somebody meet the similar problem,just try it!