search

leggedrobotics / darknet_ros

YOLO ROS: Real-Time Object Detection for ROS
BSD 3-Clause "New" or "Revised" License
2.21k stars 1.18k forks source link

launch file error after loading weights #251

Open mmahdavian opened 4 years ago

mmahdavian commented 4 years ago

Hello

I am trying to use the package on a yolov3 model that I have trained myself, but I have following error. Do you know what might be the reason?

SUMMARY
========

PARAMETERS
 * /darknet_ros/actions/camera_reading/name: /darknet_ros/chec...
 * /darknet_ros/config_path: /home/mohammad/ca...
 * /darknet_ros/image_view/enable_console_output: True
 * /darknet_ros/image_view/enable_opencv: True
 * /darknet_ros/image_view/wait_key_delay: 1
 * /darknet_ros/publishers/bounding_boxes/latch: False
 * /darknet_ros/publishers/bounding_boxes/queue_size: 1
 * /darknet_ros/publishers/bounding_boxes/topic: /darknet_ros/boun...
 * /darknet_ros/publishers/detection_image/latch: True
 * /darknet_ros/publishers/detection_image/queue_size: 1
 * /darknet_ros/publishers/detection_image/topic: /darknet_ros/dete...
 * /darknet_ros/publishers/object_detector/latch: False
 * /darknet_ros/publishers/object_detector/queue_size: 1
 * /darknet_ros/publishers/object_detector/topic: /darknet_ros/foun...
 * /darknet_ros/subscribers/camera_reading/queue_size: 1
 * /darknet_ros/subscribers/camera_reading/topic: /camera/rgb/image...
 * /darknet_ros/weights_path: /home/mohammad/ca...
 * /darknet_ros/yolo_model/config_file/name: yolov3-spp.cfg
 * /darknet_ros/yolo_model/detection_classes/names: ['person', 'bicyc...
 * /darknet_ros/yolo_model/threshold/value: 0.3
 * /darknet_ros/yolo_model/weight_file/name: yolov3-spp.weights
 * /rosdistro: kinetic
 * /rosversion: 1.12.14

NODES
  /
    darknet_ros (darknet_ros/darknet_ros)

ROS_MASTER_URI=http://localhost:11311

process[darknet_ros-1]: started with pid [11401]
[ INFO] [1594609317.031678020]: [YoloObjectDetector] Node started.
[ INFO] [1594609317.035157487]: [YoloObjectDetector] Xserver is running.
[ INFO] [1594609317.036212699]: [YoloObjectDetector] init().
YOLO V3
layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   608 x 608 x   3   ->   608 x 608 x  32  0.639 BFLOPs
    1 conv     64  3 x 3 / 2   608 x 608 x  32   ->   304 x 304 x  64  3.407 BFLOPs
    2 conv     32  1 x 1 / 1   304 x 304 x  64   ->   304 x 304 x  32  0.379 BFLOPs
    3 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64  3.407 BFLOPs
    4 res    1                 304 x 304 x  64   ->   304 x 304 x  64
    5 conv    128  3 x 3 / 2   304 x 304 x  64   ->   152 x 152 x 128  3.407 BFLOPs
    6 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
    7 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
    8 res    5                 152 x 152 x 128   ->   152 x 152 x 128
    9 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
   10 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
   11 res    8                 152 x 152 x 128   ->   152 x 152 x 128
   12 conv    258  3 x 3 / 2   152 x 152 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   13 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   14 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   15 res   12                  76 x  76 x 258   ->    76 x  76 x 258
   16 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   17 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   18 res   15                  76 x  76 x 258   ->    76 x  76 x 258
   19 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   20 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   21 res   18                  76 x  76 x 258   ->    76 x  76 x 258
   22 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   23 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   24 res   21                  76 x  76 x 258   ->    76 x  76 x 258
   25 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   26 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   27 res   24                  76 x  76 x 258   ->    76 x  76 x 258
   28 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   29 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   30 res   27                  76 x  76 x 258   ->    76 x  76 x 258
   31 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   32 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   33 res   30                  76 x  76 x 258   ->    76 x  76 x 258
   34 conv    128  1 x 1 / 1    76 x  76 x 258   ->    76 x  76 x 128  0.381 BFLOPs
   35 conv    258  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 258  3.433 BFLOPs
   36 res   33                  76 x  76 x 258   ->    76 x  76 x 258
   37 conv    512  3 x 3 / 2    76 x  76 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   38 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   39 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   40 res   37                  38 x  38 x 512   ->    38 x  38 x 512
   41 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   42 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   43 res   40                  38 x  38 x 512   ->    38 x  38 x 512
   44 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   45 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   46 res   43                  38 x  38 x 512   ->    38 x  38 x 512
   47 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   48 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   49 res   46                  38 x  38 x 512   ->    38 x  38 x 512
   50 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   51 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   52 res   49                  38 x  38 x 512   ->    38 x  38 x 512
   53 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   54 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   55 res   52                  38 x  38 x 512   ->    38 x  38 x 512
   56 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   57 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   58 res   55                  38 x  38 x 512   ->    38 x  38 x 512
   59 conv    258  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 258  0.381 BFLOPs
   60 conv    512  3 x 3 / 1    38 x  38 x 258   ->    38 x  38 x 512  3.433 BFLOPs
   61 res   58                  38 x  38 x 512   ->    38 x  38 x 512
   62 conv   1024  3 x 3 / 2    38 x  38 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   63 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   64 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   65 res   62                  19 x  19 x1024   ->    19 x  19 x1024
   66 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   67 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   68 res   65                  19 x  19 x1024   ->    19 x  19 x1024
   69 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   70 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   71 res   68                  19 x  19 x1024   ->    19 x  19 x1024
   72 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   73 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   74 res   71                  19 x  19 x1024   ->    19 x  19 x1024
   75 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   76 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   77 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   78 max          5 x 5 / 1    19 x  19 x 512   ->    23 x  23 x 512
   79 route  77
   80 max          9 x 9 / 1    19 x  19 x 512   ->    27 x  27 x 512
   81 route  77
   82 max          13 x 13 / 1    19 x  19 x 512   ->    31 x  31 x 512
   83 route  82 80 78 77
   84 Layer before convolutional layer must output image.: File exists
[darknet_ros-1] process has died [pid 11401, exit code 255, cmd /home/mohammad/catkin_ws/devel/lib/darknet_ros/darknet_ros camera/rgb/image_raw:=/camera/rgb/image_raw __name:=darknet_ros __log:=/home/mohammad/.ros/log/29e731cc-c4b3-11ea-86fe-5c80b69e8dce/darknet_ros-1.log].
log file: /home/mohammad/.ros/log/29e731cc-c4b3-11ea-86fe-5c80b69e8dce/darknet_ros-1*.log
all processes on machine have died, roslaunch will exit
shutting down processing monitor...
... shutting down processing monitor complete
done

Thank You

renowator commented 4 years ago

Seems like your .cfg file is not done parsing and gets an error