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daisukekobayashi / darknet-docker

Dockerfile for Darknet Yolo v4, v3 and v2 (https://github.com/AlexeyAB/darknet)
https://cloud.docker.com/repository/docker/daisukekobayashi/darknet
MIT License
91 stars 41 forks source link

Cannot get any usable output when running the darknet executable within this image #3

Closed bizmate closed 4 years ago

bizmate commented 4 years ago

I am running the cpu-cv image, i doubt gpu will work on my machine as i have a very old radeon, inside the darknet folder as needed also with the yolov3.weights but any attempt at trying to run it will only give me some basic information and no detection. Could you please suggest what I am missing based on the below runs

Run detect

docker run --rm -v $PWD:/workspace -v /media/2TBStore/Media/Video/cctv/tp/2019/11/05:/images -w /workspace daisukekobayashi/darknet:cpu-cv darknet detect data/coco.data cfg/yolov3.cfg yolov3.weights  /images/Reolink401_TP_1_01_20191105081614.jpg -ext_output
 OpenCV version: 3.2.0
 GPU isn't used 
 Not used AVX 
 Not used FMA & AVX2

Run detector test

docker run --rm -v $PWD:/workspace -v /media/2TBStore/Media/Video/cctv/tp/2019/11/05:/images -w /workspace daisukekobayashi/darknet:cpu-cv darknet detector test data/coco.data cfg/yolov3.cfg yolov3.weights  /images/Reolink401_TP_1_01_20191105081614.jpg -ext_output
 GPU isn't used 
 Not used AVX 
 Not used FMA & AVX2 
 OpenCV version: 3.2.0

Any hints?

daisukekobayashi commented 4 years ago

From your results, I guess you can't load weights correctly.

The cpu-cv image doesn't use gpu, so I think gpu doesn't matter. And darknet uses CUDA library, so if you want to use gpu images, you need Nvidia gpu.

Could you try to check the commands below.

$ git clone https://github.com/AlexeyAB/darknet.git
$ cd darknet/build/darknet/x64
$ curl -L -O https://pjreddie.com/media/files/yolov3.weights
$docker run --rm -v $PWD:/workspace -w /workspace daisukekobayashi/darknet:cpu-cv \
    darknet detector test data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg -ext_output

I'm using MacBook Pro (13 inch 2016), and I got the outputs below and predictions.jpg in the directory.

 GPU isn't used
 Used AVX
 Used FMA & AVX2
 OpenCV version: 3.2.0
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   0 - receptive field: 3 x 3
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   1 - receptive field: 5 x 5
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   2 - receptive field: 5 x 5
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   3 - receptive field: 9 x 9
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   4 - receptive field: 9 x 9
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   5 - receptive field: 13 x 13
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   6 - receptive field: 13 x 13
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   7 - receptive field: 21 x 21
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   8 - receptive field: 21 x 21
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   9 - receptive field: 21 x 21
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  10 - receptive field: 29 x 29
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  11 - receptive field: 29 x 29
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  12 - receptive field: 37 x 37
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  13 - receptive field: 37 x 37
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  14 - receptive field: 53 x 53
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  15 - receptive field: 53 x 53
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  16 - receptive field: 53 x 53
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  17 - receptive field: 69 x 69
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  18 - receptive field: 69 x 69
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  19 - receptive field: 69 x 69
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  20 - receptive field: 85 x 85
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  21 - receptive field: 85 x 85
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  22 - receptive field: 85 x 85
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  23 - receptive field: 101 x 101
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  24 - receptive field: 101 x 101
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  25 - receptive field: 101 x 101
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  26 - receptive field: 117 x 117
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  27 - receptive field: 117 x 117
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  28 - receptive field: 117 x 117
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  29 - receptive field: 133 x 133
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  30 - receptive field: 133 x 133
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  31 - receptive field: 133 x 133
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  32 - receptive field: 149 x 149
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  33 - receptive field: 149 x 149
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  34 - receptive field: 149 x 149
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  35 - receptive field: 165 x 165
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  36 - receptive field: 165 x 165
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  37 - receptive field: 181 x 181
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  38 - receptive field: 181 x 181
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  39 - receptive field: 213 x 213
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  40 - receptive field: 213 x 213
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  41 - receptive field: 213 x 213
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  42 - receptive field: 245 x 245
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  43 - receptive field: 245 x 245
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  44 - receptive field: 245 x 245
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  45 - receptive field: 277 x 277
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  46 - receptive field: 277 x 277
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  47 - receptive field: 277 x 277
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  48 - receptive field: 309 x 309
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  49 - receptive field: 309 x 309
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  50 - receptive field: 309 x 309
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  51 - receptive field: 341 x 341
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  52 - receptive field: 341 x 341
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  53 - receptive field: 341 x 341
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  54 - receptive field: 373 x 373
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  55 - receptive field: 373 x 373
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  56 - receptive field: 373 x 373
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  57 - receptive field: 405 x 405
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  58 - receptive field: 405 x 405
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  59 - receptive field: 405 x 405
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  60 - receptive field: 437 x 437
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  61 - receptive field: 437 x 437
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  62 - receptive field: 469 x 469
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  63 - receptive field: 469 x 469
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  64 - receptive field: 533 x 533
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  65 - receptive field: 533 x 533
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  66 - receptive field: 533 x 533
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  67 - receptive field: 597 x 597
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  68 - receptive field: 597 x 597
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  69 - receptive field: 597 x 597
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  70 - receptive field: 661 x 661
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  71 - receptive field: 661 x 661
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  72 - receptive field: 661 x 661
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  73 - receptive field: 725 x 725
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  74 - receptive field: 725 x 725
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  75 - receptive field: 725 x 725
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  76 - receptive field: 789 x 789
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  77 - receptive field: 789 x 789
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  78 - receptive field: 853 x 853
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  79 - receptive field: 853 x 853
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  80 - receptive field: 917 x 917
  81 conv    255       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 255 0.088 BF
  81 - receptive field: 917 x 917
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  82 - receptive field: 917 x 917
  83 route  79                                     ->   13 x  13 x 512
  83 - receptive field: 853 x 853
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  84 - receptive field: 853 x 853
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  85 - receptive field: 853 x 853
  86 route  85 61                                  ->   26 x  26 x 768
  86 - receptive field: 853 x 853
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  87 - receptive field: 853 x 853
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  88 - receptive field: 885 x 885
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  89 - receptive field: 885 x 885
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  90 - receptive field: 917 x 917
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  91 - receptive field: 917 x 917
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  92 - receptive field: 949 x 949
  93 conv    255       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 255 0.177 BF
  93 - receptive field: 949 x 949
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  94 - receptive field: 949 x 949
  95 route  91                                     ->   26 x  26 x 256
  95 - receptive field: 917 x 917
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  96 - receptive field: 917 x 917
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  97 - receptive field: 917 x 917
  98 route  97 36                                  ->   52 x  52 x 384
  98 - receptive field: 917 x 917
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
  99 - receptive field: 917 x 917
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 100 - receptive field: 933 x 933
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 101 - receptive field: 933 x 933
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 102 - receptive field: 949 x 949
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 103 - receptive field: 949 x 949
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 104 - receptive field: 965 x 965
 105 conv    255       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 255 0.353 BF
 105 - receptive field: 965 x 965
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
 106 - receptive field: 965 x 965
Total BFLOPS 65.879
avg_outputs = 532444
Loading weights from yolov3.weights...mini_batch = 1, batch = 16, time_steps = 1, train = 0
Done! Loaded 107 layers from weights-file
Unable to init server: Could not connect: Connection refused

(predictions:1): Gtk-WARNING **: 14:26:33.968: cannot open display:

 seen 64, trained: 32013 K-images (500 Kilo-batches_64)
 Detection layer: 82 - type = 28
 Detection layer: 94 - type = 28
 Detection layer: 106 - type = 28
dog.jpg: Predicted in 3694.879000 milli-seconds.
bicycle: 99%    (left_x:  117   top_y:  124   width:  452   height:  309)
dog: 100%       (left_x:  124   top_y:  224   width:  196   height:  320)
truck: 94%      (left_x:  474   top_y:   87   width:  217   height:   79)
bizmate commented 4 years ago

Thank you for your reply. It was helpful but let me detail what is happening.

First of all i was using the darknet from https://github.com/pjreddie/darknet , not sure on the exact difference between this and https://github.com/AlexeyAB/darknet.git . For once, the first does not contain folder build/darknet/x64 so i cloned the one from AlexeyAB

On linux ubuntu the command you suggested does not work. It just shows the following

$ git clone https://github.com/AlexeyAB/darknet.git darknet-ab
Cloning into 'darknet-ab'...
remote: Enumerating objects: 14285, done.
remote: Total 14285 (delta 0), reused 0 (delta 0), pack-reused 14285
Receiving objects: 100% (14285/14285), 12.83 MiB | 9.52 MiB/s, done.
Resolving deltas: 100% (9747/9747), done.

$ cd darknet-ab/build/darknet/x64

$ curl -L -O https://pjreddie.com/media/files/yolov3.weights
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  236M  100  236M    0     0   326k      0  0:12:21  0:12:21 --:--:--  321k

$ docker run --rm -v $PWD:/workspace -w /workspace daisukekobayashi/darknet:cpu-cv     darknet detector test data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg -ext_output
 OpenCV version: 3.2.0
 GPU isn't used 
 Not used AVX 
 Not used FMA & AVX2

On my mac it works but not sure why it is yielding a different result compared to the linux one. I am wondering if the difference is because of the mac vs linux but this problem would probably go away if it was all copied over inside the images and you do not need to mount the darknet folder as workspace.

I will also test i the image works with videos on the mac but ideally i would like to use it from linux and just mount media files to analyse instead of mounting darknet

Could you confirm why the darknet folder needs mounting as it would be great to just use the image straight out the box consistently in all OSs. Are you trying to minimise the image size?

My mac execution below

$ docker run --rm -v $PWD:/workspace -w /workspace daisukekobayashi/darknet:cpu-cv     darknet detector test data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg -ext_output
Unable to find image 'daisukekobayashi/darknet:cpu-cv' locally
cpu-cv: Pulling from daisukekobayashi/darknet
f08d8e2a3ba1: Pull complete 
3baa9cb2483b: Pull complete 
94e5ff4c0b15: Pull complete 
1860925334f9: Pull complete 
1c8390763484: Pull complete 
5332e910b5c4: Pull complete 
2a61797a7a19: Pull complete 
Digest: sha256:0b4507c349eb64a24ac4bee396950d88543daec6f7e4e4202cfe9f9a7feff11d
Status: Downloaded newer image for daisukekobayashi/darknet:cpu-cv
 OpenCV version: 3.2.0
 GPU isn't used 
 Used AVX 
 Used FMA & AVX2 
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   0 - receptive field: 3 x 3 
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   1 - receptive field: 5 x 5 
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   2 - receptive field: 5 x 5 
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   3 - receptive field: 9 x 9 
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   4 - receptive field: 9 x 9 
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   5 - receptive field: 13 x 13 
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   6 - receptive field: 13 x 13 
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   7 - receptive field: 21 x 21 
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   8 - receptive field: 21 x 21 
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   9 - receptive field: 21 x 21 
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  10 - receptive field: 29 x 29 
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  11 - receptive field: 29 x 29 
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  12 - receptive field: 37 x 37 
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  13 - receptive field: 37 x 37 
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  14 - receptive field: 53 x 53 
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  15 - receptive field: 53 x 53 
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  16 - receptive field: 53 x 53 
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  17 - receptive field: 69 x 69 
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  18 - receptive field: 69 x 69 
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  19 - receptive field: 69 x 69 
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  20 - receptive field: 85 x 85 
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  21 - receptive field: 85 x 85 
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  22 - receptive field: 85 x 85 
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  23 - receptive field: 101 x 101 
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  24 - receptive field: 101 x 101 
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  25 - receptive field: 101 x 101 
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  26 - receptive field: 117 x 117 
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  27 - receptive field: 117 x 117 
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  28 - receptive field: 117 x 117 
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  29 - receptive field: 133 x 133 
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  30 - receptive field: 133 x 133 
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  31 - receptive field: 133 x 133 
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  32 - receptive field: 149 x 149 
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  33 - receptive field: 149 x 149 
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  34 - receptive field: 149 x 149 
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  35 - receptive field: 165 x 165 
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  36 - receptive field: 165 x 165 
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  37 - receptive field: 181 x 181 
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  38 - receptive field: 181 x 181 
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  39 - receptive field: 213 x 213 
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  40 - receptive field: 213 x 213 
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  41 - receptive field: 213 x 213 
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  42 - receptive field: 245 x 245 
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  43 - receptive field: 245 x 245 
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  44 - receptive field: 245 x 245 
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  45 - receptive field: 277 x 277 
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  46 - receptive field: 277 x 277 
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  47 - receptive field: 277 x 277 
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  48 - receptive field: 309 x 309 
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  49 - receptive field: 309 x 309 
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  50 - receptive field: 309 x 309 
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  51 - receptive field: 341 x 341 
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  52 - receptive field: 341 x 341 
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  53 - receptive field: 341 x 341 
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  54 - receptive field: 373 x 373 
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  55 - receptive field: 373 x 373 
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  56 - receptive field: 373 x 373 
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  57 - receptive field: 405 x 405 
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  58 - receptive field: 405 x 405 
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  59 - receptive field: 405 x 405 
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  60 - receptive field: 437 x 437 
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  61 - receptive field: 437 x 437 
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  62 - receptive field: 469 x 469 
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  63 - receptive field: 469 x 469 
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  64 - receptive field: 533 x 533 
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  65 - receptive field: 533 x 533 
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  66 - receptive field: 533 x 533 
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  67 - receptive field: 597 x 597 
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  68 - receptive field: 597 x 597 
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  69 - receptive field: 597 x 597 
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  70 - receptive field: 661 x 661 
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  71 - receptive field: 661 x 661 
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  72 - receptive field: 661 x 661 
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  73 - receptive field: 725 x 725 
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  74 - receptive field: 725 x 725 
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  75 - receptive field: 725 x 725 
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  76 - receptive field: 789 x 789 
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  77 - receptive field: 789 x 789 
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  78 - receptive field: 853 x 853 
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  79 - receptive field: 853 x 853 
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  80 - receptive field: 917 x 917 
  81 conv    255       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 255 0.088 BF
  81 - receptive field: 917 x 917 
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  82 - receptive field: 917 x 917 
  83 route  79                                 ->   13 x  13 x 512 
  83 - receptive field: 853 x 853 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  84 - receptive field: 853 x 853 
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  85 - receptive field: 853 x 853 
  86 route  85 61                              ->   26 x  26 x 768 
  86 - receptive field: 853 x 853 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  87 - receptive field: 853 x 853 
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  88 - receptive field: 885 x 885 
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  89 - receptive field: 885 x 885 
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  90 - receptive field: 917 x 917 
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  91 - receptive field: 917 x 917 
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  92 - receptive field: 949 x 949 
  93 conv    255       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 255 0.177 BF
  93 - receptive field: 949 x 949 
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  94 - receptive field: 949 x 949 
  95 route  91                                 ->   26 x  26 x 256 
  95 - receptive field: 917 x 917 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  96 - receptive field: 917 x 917 
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  97 - receptive field: 917 x 917 
  98 route  97 36                              ->   52 x  52 x 384 
  98 - receptive field: 917 x 917 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
  99 - receptive field: 917 x 917 
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 100 - receptive field: 933 x 933 
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 101 - receptive field: 933 x 933 
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 102 - receptive field: 949 x 949 
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 103 - receptive field: 949 x 949 
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 104 - receptive field: 965 x 965 
 105 conv    255       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 255 0.353 BF
 105 - receptive field: 965 x 965 
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
 106 - receptive field: 965 x 965 
Total BFLOPS 65.879 
avg_outputs = 532444 
Loading weights from yolov3.weights...mini_batch = 1, batch = 16, time_steps = 1, train = 0 
Done! Loaded 107 layers from weights-file 
Unable to init server: Could not connect: Connection refused

(predictions:1): Gtk-WARNING **: 16:00:20.445: cannot open display: 

 seen 64, trained: 32013 K-images (500 Kilo-batches_64) 
 Detection layer: 82 - type = 28 
 Detection layer: 94 - type = 28 
 Detection layer: 106 - type = 28 
dog.jpg: Predicted in 3131.078000 milli-seconds.
bicycle: 99%    (left_x:  117   top_y:  124   width:  452   height:  309)
dog: 100%   (left_x:  124   top_y:  224   width:  196   height:  320)
truck: 94%  (left_x:  474   top_y:   87   width:  217   height:   79)
daisukekobayashi commented 4 years ago

Thank you for checking.

I tested on Linux (Ubuntu 18.04) and it works fine.

Seeing your Linux output, I noticed that AVX isn't enabled in your Linux output. What cpu do you use in your Linux machine ? It's maybe the reason why it doesn't work correctly in Linux.

When we use darknet, we need to specify configuration file, data file, weights and images on training and prediction, and refer those from inside docker container. And each of that is needed to change depend on what we want to train and predict. I created this image for general purpose, so I took this approach which mounts working directory every time. But if we only use coco model, your suggested approach is much easier.

Darknet only uses configuration file, data file, weights and image, so you don't need to mout the darknet folder in itself. But to simplify explanation, I described cloning darknet repository and mount it in README.

I haven't tried to minimize the image size. If I try to reduce the image size, I will remove development libraries when I use to build darknet.

Building darknet on Linux is easy, so before using docker image I recommend that you try to use official repos.

bizmate commented 4 years ago

Hi, thank you again for the fast reply and suggestions. Not sure what to check in regards to my CPU (or maybe Bios). Here is a cpuinfo output by running cat /proc/cpuinfo

processor   : 0
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 2140.500
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 0
cpu cores   : 4
apicid      : 0
initial apicid  : 0
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 1
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 1811.119
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 1
cpu cores   : 4
apicid      : 2
initial apicid  : 2
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 2
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 1938.414
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 2
cpu cores   : 4
apicid      : 4
initial apicid  : 4
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 3
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 1871.830
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 3
cpu cores   : 4
apicid      : 6
initial apicid  : 6
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 4
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 2551.262
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 0
cpu cores   : 4
apicid      : 1
initial apicid  : 1
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 5
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 1824.973
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 1
cpu cores   : 4
apicid      : 3
initial apicid  : 3
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 6
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 1948.006
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 2
cpu cores   : 4
apicid      : 5
initial apicid  : 5
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

processor   : 7
vendor_id   : GenuineIntel
cpu family  : 6
model       : 26
model name  : Intel(R) Core(TM) i7 CPU         950  @ 3.07GHz
stepping    : 5
microcode   : 0x1d
cpu MHz     : 1785.797
cache size  : 8192 KB
physical id : 0
siblings    : 8
core id     : 3
cpu cores   : 4
apicid      : 7
initial apicid  : 7
fpu     : yes
fpu_exception   : yes
cpuid level : 11
wp      : yes
flags       : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid dtherm ida flush_l1d
bugs        : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit
bogomips    : 6128.65
clflush size    : 64
cache_alignment : 64
address sizes   : 36 bits physical, 48 bits virtual
power management:

Can you see anything that might cause that AVX is disabled?

About your comment

Building darknet on Linux is easy, so before using docker image I recommend that you try to use official repos.

Not sure on the benefits of trying to build darknet myself vs using the docker version. i.e. docker definitely minimises problems by enforcing environment consistency. Meaning in general a docker command should work the same in all OSs and machines, wish some hedge cases I guess like this one. Also as a user i would like to avoid the build and install steps that are greatly minimised by using docker.

For instance I am stuck because of https://stackoverflow.com/questions/54899818/yolov3-undefined-symbol-zn9-iplimagec1erkn2cv3mate-when-build-with-opencv and I was using the pjreddie repo where a CMakeLists.txt file is not present. I will try to do it with CMake step within the Andrey's repo

bizmate commented 4 years ago

I was told also the kernel might be what is causing the problem on the docker channel on IRC freenode, so sharing what I am using on my machine

$ lsb_release -a
LSB Version:    core-9.20170808ubuntu1-noarch:printing-9.20170808ubuntu1-noarch:security-9.20170808ubuntu1-noarch
Distributor ID: Ubuntu
Description:    Ubuntu 18.04.5 LTS
Release:    18.04
Codename:   bionic

$ uname -r
4.15.0-112-generic

Are you using the same?

daisukekobayashi commented 4 years ago

Thank you for information. I understood difficulty to build darknet yourself. I've never been stuck to build Alexyeab/darknet, so I just suggested.

AVX was enabled in my environment , so I wondered the difference. The image with cpu or cpu-cv tag was built with AVX optimization. Looking at your information, Core i7 950 doesn't support AVX. I don't know what happens when we enable AVX to non supported cpu. So I built some images for debug and pushed to docker hub, so could you try to check whether it works or not.

Please specify above tag each instead of cpu-cv in command. The image which is disabled AVX from cpu-cv is cpu-noopt-cv.

I'm using Ubuntu 18.04.2 (Xeon E5-2698v4) and Ubuntu 18.04.4 (i7-8700), and both work fine.

bizmate commented 4 years ago

Thank you very much again for your reply.

It is quite strange but now with the cpu-cv-debug image i can run the test on the dog file. It also works with the cpu-noopt-cv tag.

And it also works with my images such as 

$ docker run --rm -v $PWD:/workspace -v /media/2TBStore/Media/Video/cctv/tp/2019/11/05:/images -w /workspace daisukekobayashi/darknet:cpu-cv-debug darknet detector test data/coco.data cfg/yolov3.cfg yolov3.weights  /images/Reolink401_TP_1_01_20191105081614.jpg -ext_output
 OpenCV version: 3.2.0d
 DEBUG=1 
 GPU isn't used 
   layer   filters  size/strd(dil)      input                output
   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     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv    255       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 255 0.088 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79                                 ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61                              ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv    255       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 255 0.177 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91                                 ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36                              ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv    255       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 255 0.353 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.879 
avg_outputs = 532444 
Loading weights from yolov3.weights...mini_batch = 1, batch = 1, time_steps = 1, train = 0 
Done! Loaded 107 layers from weights-file 
Unable to init server: Could not connect: Connection refused

(predictions:1): Gtk-WARNING **: 22:54:57.337: cannot open display: 

 seen 64, trained: 32013 K-images (500 Kilo-batches_64) 
 Detection layer: 82 - type = 28 
 Detection layer: 94 - type = 28 
 Detection layer: 106 - type = 28 
/images/Reolink401_TP_1_01_20191105081614.jpg: Predicted in 4043.585000 milli-seconds.
chair: 99%  (left_x: 1374   top_y:  830   width:  361   height:  503)
chair: 37%  (left_x: 1691   top_y:  798   width:  290   height:  566)
chair: 81%  (left_x: 1840   top_y:  808   width:  692   height:  621)

Not sure what the debug option does but it makes it work. Is it possible that something has changed in darknet that might have fixed it because using the non debug images still fails for me.

Also one more thing. I am also trying to do video detection using darknet detect demo ... I point it to my video but someone the output is truncated/stops before any detection is happening. I can open a separate ticket or ask in a different channe if you prefer but for the moment here is the output i get when i try to run it against a video or mine

docker run --rm -v $PWD:/workspace -v /media/2TBStore/Media/Video/cctv/tp/2019/11/05:/images -w /workspace daisukekobayashi/darknet:cpu-cv-debug darknet detector demo data/coco.data cfg/yolov3.cfg yolov3.weights  /images/Reolink401_TP_1_01_20191105081614.mp4 -ext_output
 OpenCV version: 3.2.0d
 DEBUG=1 
 GPU isn't used 
   layer   filters  size/strd(dil)      input                output
   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     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv    255       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 255 0.088 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79                                 ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61                              ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv    255       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 255 0.177 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91                                 ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36                              ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv    255       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 255 0.353 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.879 
avg_outputs = 532444 
Loading weights from yolov3.weights...Demo
mini_batch = 1, batch = 1, time_steps = 1, train = 0 
Done! Loaded 107 layers from weights-file 
Unable to init server: Could not connect: Connection refused

(Demo:1): Gtk-WARNING **: 22:58:35.924: cannot open display: 

 seen 64, trained: 32013 K-images (500 Kilo-batches_64) 
video file: /images/Reolink401_TP_1_01_20191105081614.mp4
Video stream: 2560 x 1440
bizmate commented 4 years ago

Hi, I have just learned about the -dont_show option and that allows the video detection to proceed without problems. I am happy to close the ticket although I think it would be great to establish with the debug images work vs the non debug ones. I dont think such a flag should cause a difference in functionality. I am happy to try and dig further if you want me to run some more commands on my machine.

daisukekobayashi commented 4 years ago

Thank you for testing. I'm glad to hear that you could use darknet.

I saw Makefile on Alexyab/darknet, and I found why cpu-cv-debug works. AVX flag is enabled only when DEBUG flag is disabled.

So although there is a difference between release and debug build, cpu-noopt-cv equals to cpu-cv-debug. In both build AVX is disabled, so I think that your problem is due to AVX optimization.

bizmate commented 4 years ago

Do you know if there is any way to speed up scanning of video files with docker? At the moment it takes in excess of 80 minutes for a 30 seconds video at FPS:0.1 AVG_FPS:0.1. (Yolo v4 makes no difference in performance in my case using docker)

docker run --rm -v $PWD:/workspace -v /media/2TBStore/Media/Video/cctv/tp/2019/11/05:/images -w /workspace daisukekobayashi/darknet:cpu-cv-debug darknet detector demo data/coco.data cfg/yolov4.cfg yolov4.weights  /images/Reolink401_TP_1_01_20191105081614.mp4 -dont_show -ext_output
 OpenCV version: 3.2.0d
 DEBUG=1 
 GPU isn't used 
   layer   filters  size/strd(dil)      input                output
   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 
  .......
 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
Total BFLOPS 128.459 
avg_outputs = 1068395 
Loading weights from yolov4.weights...Demo
mini_batch = 1, batch = 8, time_steps = 1, train = 0 
nms_kind: greedynms (1), beta = 0.600000 
nms_kind: greedynms (1), beta = 0.600000 
nms_kind: greedynms (1), beta = 0.600000 
Done! Loaded 162 layers from weights-file 

 seen 64, trained: 32032 K-images (500 Kilo-batches_64) 
video file: /images/Reolink401_TP_1_01_20191105081614.mp4
Video stream: 2560 x 1440 
Objects:

FPS:0.0      AVG_FPS:0.0
Objects:

diningtable: 26%    (left_x:  152   top_y: 1040   width: 1987   height:  407)
chair: 97%  (left_x: 1376   top_y:  830   width:  354   height:  522)
chair: 55%  (left_x: 1847   top_y:  743   width:  693   height:  691)

FPS:0.0      AVG_FPS:0.1
Objects:

diningtable: 26%    (left_x:  149   top_y: 1041   width: 1991   height:  406)
chair: 97%  (left_x: 1376   top_y:  830   width:  353   height:  523)
chair: 49%  (left_x: 1851   top_y:  739   width:  690   height:  697)

FPS:0.0      AVG_FPS:0.1

also unfortunately neither the versions below work for me if installed directly on my machine https://github.com/pjreddie/darknet

./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights /media/2TBStore/Media/Video/cctv/tp/2019/11/05/Reolink401_TP_1_01_20191105081614.mp4
./darknet: symbol lookup error: ./darknet: undefined symbol: _ZN9_IplImageC1ERKN2cv3MatE

nor the one at https://github.com/AlexeyAB/darknet.git 

./darknet  detector demo build/darknet/x64/data/coco.data build/darknet/x64/cfg/yolov3.cfg build/darknet/x64/yolov3.weights  /media/2TBStore/Media/Video/cctv/tp/2019/11/05/Reolink401_TP_1_01_20191105081614.mp4 -dont_show -ext_output
 GPU isn't used 
 Not used AVX 
 Not used FMA & AVX2 
 OpenCV version: 3.4.11
Illegal instruction

OpenCV info on my machine

$ echo -e "import cv2; print( cv2.getBuildInformation());" | python | nc termbin.com 9999

https://termbin.com/e64u

daisukekobayashi commented 4 years ago

I think that CPU isn't suitable for heavy deep learning processing. So I recommend that you use GPU.

And you are using yolov4. There is a tiny model for yolov4. It's lighter and less accurate model than yolov4. If the accuracy is allowed in your situation, you can use it.

I have no idea why you can't build darknet, so please check official issues.

daisukekobayashi commented 4 years ago

I added noopt tag to build hook and updated docker hub repo to build image automatically.

bizmate commented 4 years ago

Thank you, just to let you know I eventually managed to get darknet working on my local machine by installing openCV 4.4. I didnt have an explicit error while running make so it is annoying that nothing suggests where the problem might be coming from but at least i have a way of running it now.