Ubuntu 16.04 使用docker资料汇总与应用docker安装caffe并使用Classifier（ros kinetic+usb_cam+caffe）

Docker是开源的应用容器引擎。若想简单了解一下，可以参考百度百科词条Docker。好像只支持64位系统。

Docker官网：https://www.docker.com/

Docker — 从入门到实践：https://yeasy.gitbooks.io/docker_practice/content/

Pdf版下载：http://download.csdn.net/detail/zhangrelay/9743400

caffe官网：http://caffe.berkeleyvision.org/installation.html

caffe_docker:https://github.com/BVLC/caffe/tree/master/docker

然后参考这篇博客就可以了：http://blog.csdn.net/sysushui/article/details/54585788

看右图数据，准确识别出是磁罗盘（>0.8）。

如： docker search caffe

$ docker search caffe
NAME                                  DESCRIPTION                                     STARS     OFFICIAL   AUTOMATED
kaixhin/caffe                         Ubuntu Core 14.04 + Caffe.                      33                   [OK]
kaixhin/cuda-caffe                    Ubuntu Core 14.04 + CUDA + Caffe.               30                   [OK]
neowaylabs/caffe-cpu                  Caffe CPU based on:  https://hub.docker.co...   4                    [OK]
kaixhin/caffe-deps                    `kaixhin/caffe` dependencies.                   1                    [OK]
mbartoli/caffe                        Caffe, CPU-only                                 1                    [OK]
drunkar/cuda-caffe-anaconda-chainer   cuda-caffe-anaconda-chainer                     1                    [OK]
kaixhin/cuda-caffe-deps               `kaixhin/cuda-caffe` dependencies.              0                    [OK]
mtngld/caffe-gpu                      Ubuntu + caffe (gpu ready)                      0                    [OK]
nitnelave/caffe                       Master branch of BVLC/caffe, on CentOS7 wi...   0                    [OK]
bvlc/caffe                            Official Caffe images                           0                    [OK]
ruimashita/caffe-gpu                  ubuntu 14.04 cuda 7 (NVIDIA driver version...   0                    [OK]
ruimashita/caffe-cpu-with-models      ubuntu 14.04 caffe  bvlc_reference_caffene...   0                    [OK]
elezar/caffe                          Caffe Docker Images                             0                    [OK]
ruimashita/caffe-gpu-with-models      ubuntu 14.04 cuda 7.0 caffe  bvlc_referenc...   0                    [OK]
floydhub/caffe                        Caffe docker image                              0                    [OK]
namikister/caffe                      Caffe with CUDA 8.0                             0                    [OK]
tingtinglu/caffe                      caffe                                           0                    [OK]
djpetti/caffe                         A simple container with Caffe, CUDA, and C...   0                    [OK]
flyingmouse/caffe                     Caffe is a deep learning framework made wi...   0                    [OK]
ruimashita/caffe-cpu                  ubuntu 14.04 caffe                              0                    [OK]
suyongsun/caffe-gpu                   Caffe image with gpu mode.                      0                    [OK]
haoyangz/caffe-cnn                    caffe-cnn                                       0                    [OK]
2breakfast/caffe-sshd                 installed sshd server on nvidia/caffe           0                    [OK]
chakkritte/docker-caffe               Docker caffe                                    0                    [OK]
ederrm/caffe                          Caffe http://caffe.berkeleyvision.org setup!    0                    [OK]
relaybot@relaybot-desktop:~$ 

选择安装即可，caffe安装CPU版本还是比较容易的。

安装完毕测试，这是在ros kinetic版本测试，和ros indigo一样。

具体请参考：

ROS + Caffe 机器人操作系统框架和深度学习框架笔记 (機器人控制與人工智能)

http://blog.csdn.net/zhangrelay/article/details/54669922

$ roscore ... logging to /home/relaybot/.ros/log/f214a97a-e0b1-11e6-833d-70f1a1ca7552/roslaunch-relaybot-desktop-32381.log Checking log directory for disk usage. This may take awhile. Press Ctrl-C to interrupt Done checking log file disk usage. Usage is <1GB. started roslaunch server http://relaybot-desktop:44408/ ros_comm version 1.12.6 SUMMARY ======== PARAMETERS  * /rosdistro: kinetic  * /rosversion: 1.12.6 NODES auto-starting new master process[master]: started with pid [32411] ROS_MASTER_URI=http://relaybot-desktop:11311/ setting /run_id to f214a97a-e0b1-11e6-833d-70f1a1ca7552 process[rosout-1]: started with pid [32424] started core service [/rosout]

$ rosrun uvc_camera uvc_camera_node [ INFO] [1485096579.984543774]: using default calibration URL [ INFO] [1485096579.984671839]: camera calibration URL: file:///home/relaybot/.ros/camera_info/camera.yaml [ INFO] [1485096579.984939036]: Unable to open camera calibration file [/home/relaybot/.ros/camera_info/camera.yaml] [ WARN] [1485096579.984987494]: Camera calibration file /home/relaybot/.ros/camera_info/camera.yaml not found. opening /dev/video0 pixfmt 0 = 'YUYV' desc = 'YUYV 4:2:2'   discrete: 640x480:   1/30 1/15   discrete: 352x288:   1/30 1/15   discrete: 320x240:   1/30 1/15   discrete: 176x144:   1/30 1/15   discrete: 160x120:   1/30 1/15   discrete: 1280x800:   2/15   discrete: 1280x1024:   2/15   int (Brightness, 0, id = 980900): -64 to 64 (1)   int (Contrast, 0, id = 980901): 0 to 64 (1)   int (Saturation, 0, id = 980902): 0 to 128 (1)   int (Hue, 0, id = 980903): -40 to 40 (1)   bool (White Balance Temperature, Auto, 0, id = 98090c): 0 to 1 (1)   int (Gamma, 0, id = 980910): 72 to 500 (1)   menu (Power Line Frequency, 0, id = 980918): 0 to 2 (1)     0: Disabled     1: 50 Hz     2: 60 Hz   int (Sharpness, 0, id = 98091b): 0 to 6 (1)   int (Backlight Compensation, 0, id = 98091c): 0 to 2 (1) select timeout in grab ^Crelaybot@relaybot-desktop:~$ rosrun uvc_camera uvc_camera_node topic:=/camera/b/image_raw [ INFO] [1485096761.665718381]: using default calibration URL [ INFO] [1485096761.665859706]: camera calibration URL: file:///home/relaybot/.ros/camera_info/camera.yaml [ INFO] [1485096761.665944994]: Unable to open camera calibration file [/home/relaybot/.ros/camera_info/camera.yaml] [ WARN] [1485096761.665980436]: Camera calibration file /home/relaybot/.ros/camera_info/camera.yaml not found. opening /dev/video0 pixfmt 0 = 'YUYV' desc = 'YUYV 4:2:2'   discrete: 640x480:   1/30 1/15   discrete: 352x288:   1/30 1/15   discrete: 320x240:   1/30 1/15   discrete: 176x144:   1/30 1/15   discrete: 160x120:   1/30 1/15   discrete: 1280x800:   2/15   discrete: 1280x1024:   2/15   int (Brightness, 0, id = 980900): -64 to 64 (1)   int (Contrast, 0, id = 980901): 0 to 64 (1)   int (Saturation, 0, id = 980902): 0 to 128 (1)   int (Hue, 0, id = 980903): -40 to 40 (1)   bool (White Balance Temperature, Auto, 0, id = 98090c): 0 to 1 (1)   int (Gamma, 0, id = 980910): 72 to 500 (1)   menu (Power Line Frequency, 0, id = 980918): 0 to 2 (1)     0: Disabled     1: 50 Hz     2: 60 Hz   int (Sharpness, 0, id = 98091b): 0 to 6 (1)   int (Backlight Compensation, 0, id = 98091c): 0 to 2 (1) select timeout in grab

rosrun topic_tools transform /image_raw /camera/rgb/image_raw sensor_msgs/Image 'm'

$ rosrun ros_caffe ros_caffe_test

WARNING: Logging before InitGoogleLogging() is written to STDERR

I0122 23:02:21.915738  2968 upgrade_proto.cpp:67] Attempting to upgrade input file specified using deprecated input fields: /home/relaybot/Rob_Soft/caffe/src/ros_caffe/data/deploy.prototxt

I0122 23:02:21.915875  2968 upgrade_proto.cpp:70] Successfully upgraded file specified using deprecated input fields.

W0122 23:02:21.915894  2968 upgrade_proto.cpp:72] Note that future Caffe releases will only support input layers and not input fields.

I0122 23:02:21.916246  2968 net.cpp:53] Initializing net from parameters:

name: "CaffeNet"

state {

  phase: TEST

  level: 0

}

layer {

  name: "input"

  type: "Input"

  top: "data"

  input_param {

    shape {

      dim: 10

      dim: 3

      dim: 227

      dim: 227

    }

  }

}

layer {

  name: "conv1"

  type: "Convolution"

  bottom: "data"

  top: "conv1"

  convolution_param {

    num_output: 96

    kernel_size: 11

    stride: 4

  }

}

layer {

  name: "relu1"

  type: "ReLU"

  bottom: "conv1"

  top: "conv1"

}

layer {

  name: "pool1"

  type: "Pooling"

  bottom: "conv1"

  top: "pool1"

  pooling_param {

    pool: MAX

    kernel_size: 3

    stride: 2

  }

}

layer {

  name: "norm1"

  type: "LRN"

  bottom: "pool1"

  top: "norm1"

  lrn_param {

    local_size: 5

    alpha: 0.0001

    beta: 0.75

  }

}

layer {

  name: "conv2"

  type: "Convolution"

  bottom: "norm1"

  top: "conv2"

  convolution_param {

    num_output: 256

    pad: 2

    kernel_size: 5

    group: 2

  }

}

layer {

  name: "relu2"

  type: "ReLU"

  bottom: "conv2"

  top: "conv2"

}

layer {

  name: "pool2"

  type: "Pooling"

  bottom: "conv2"

  top: "pool2"

  pooling_param {

    pool: MAX

    kernel_size: 3

    stride: 2

  }

}

layer {

  name: "norm2"

  type: "LRN"

  bottom: "pool2"

  top: "norm2"

  lrn_param {

    local_size: 5

    alpha: 0.0001

    beta: 0.75

  }

}

layer {

  name: "conv3"

  type: "Convolution"

  bottom: "norm2"

  top: "conv3"

  convolution_param {

    num_output: 384

    pad: 1

    kernel_size: 3

  }

}

layer {

  name: "relu3"

  type: "ReLU"

  bottom: "conv3"

  top: "conv3"

}

layer {

  name: "conv4"

  type: "Convolution"

  bottom: "conv3"

  top: "conv4"

  convolution_param {

    num_output: 384

    pad: 1

    kernel_size: 3

    group: 2

  }

}

layer {

  name: "relu4"

  type: "ReLU"

  bottom: "conv4"

  top: "conv4"

}

layer {

  name: "conv5"

  type: "Convolution"

  bottom: "conv4"

  top: "conv5"

  convolution_param {

    num_output: 256

    pad: 1

    kernel_size: 3

    group: 2

  }

}

layer {

  name: "relu5"

  type: "ReLU"

  bottom: "conv5"

  top: "conv5"

}

layer {

  name: "pool5"

  type: "Pooling"

  bottom: "conv5"

  top: "pool5"

  pooling_param {

    pool: MAX

    kernel_size: 3

    stride: 2

  }

}

layer {

  name: "fc6"

  type: "InnerProduct"

  bottom: "pool5"

  top: "fc6"

  inner_product_param {

    num_output: 4096

  }

}

layer {

  name: "relu6"

  type: "ReLU"

  bottom: "fc6"

  top: "fc6"

}

layer {

  name: "drop6"

  type: "Dropout"

  bottom: "fc6"

  top: "fc6"

  dropout_param {

    dropout_ratio: 0.5

  }

}

layer {

  name: "fc7"

  type: "InnerProduct"

  bottom: "fc6"

  top: "fc7"

  inner_product_param {

    num_output: 4096

  }

}

layer {

  name: "relu7"

  type: "ReLU"

  bottom: "fc7"

  top: "fc7"

}

layer {

  name: "drop7"

  type: "Dropout"

  bottom: "fc7"

  top: "fc7"

  dropout_param {

    dropout_ratio: 0.5

  }

}

layer {

  name: "fc8"

  type: "InnerProduct"

  bottom: "fc7"

  top: "fc8"

  inner_product_param {

    num_output: 1000

  }

}

layer {

  name: "prob"

  type: "Softmax"

  bottom: "fc8"

  top: "prob"

}

I0122 23:02:21.916574  2968 layer_factory.hpp:77] Creating layer input

I0122 23:02:21.916613  2968 net.cpp:86] Creating Layer input

I0122 23:02:21.916638  2968 net.cpp:382] input -> data

I0122 23:02:21.931437  2968 net.cpp:124] Setting up input

I0122 23:02:21.939075  2968 net.cpp:131] Top shape: 10 3 227 227 (1545870)

I0122 23:02:21.939122  2968 net.cpp:139] Memory required for data: 6183480

I0122 23:02:21.939157  2968 layer_factory.hpp:77] Creating layer conv1

I0122 23:02:21.939210  2968 net.cpp:86] Creating Layer conv1

I0122 23:02:21.939235  2968 net.cpp:408] conv1 <- data

I0122 23:02:21.939278  2968 net.cpp:382] conv1 -> conv1

I0122 23:02:21.939563  2968 net.cpp:124] Setting up conv1

I0122 23:02:21.939604  2968 net.cpp:131] Top shape: 10 96 55 55 (2904000)

I0122 23:02:21.939618  2968 net.cpp:139] Memory required for data: 17799480

I0122 23:02:21.939685  2968 layer_factory.hpp:77] Creating layer relu1

I0122 23:02:21.939714  2968 net.cpp:86] Creating Layer relu1

I0122 23:02:21.939730  2968 net.cpp:408] relu1 <- conv1

I0122 23:02:21.939752  2968 net.cpp:369] relu1 -> conv1 (in-place)

I0122 23:02:21.939781  2968 net.cpp:124] Setting up relu1

I0122 23:02:21.939802  2968 net.cpp:131] Top shape: 10 96 55 55 (2904000)

I0122 23:02:21.939817  2968 net.cpp:139] Memory required for data: 29415480

I0122 23:02:21.939832  2968 layer_factory.hpp:77] Creating layer pool1

I0122 23:02:21.939857  2968 net.cpp:86] Creating Layer pool1

I0122 23:02:21.939868  2968 net.cpp:408] pool1 <- conv1

I0122 23:02:21.939887  2968 net.cpp:382] pool1 -> pool1

I0122 23:02:21.939947  2968 net.cpp:124] Setting up pool1

I0122 23:02:21.939967  2968 net.cpp:131] Top shape: 10 96 27 27 (699840)

I0122 23:02:21.939980  2968 net.cpp:139] Memory required for data: 32214840

I0122 23:02:21.939992  2968 layer_factory.hpp:77] Creating layer norm1

I0122 23:02:21.940014  2968 net.cpp:86] Creating Layer norm1

I0122 23:02:21.940027  2968 net.cpp:408] norm1 <- pool1

I0122 23:02:21.940045  2968 net.cpp:382] norm1 -> norm1

I0122 23:02:21.940075  2968 net.cpp:124] Setting up norm1

I0122 23:02:21.940093  2968 net.cpp:131] Top shape: 10 96 27 27 (699840)

I0122 23:02:21.940104  2968 net.cpp:139] Memory required for data: 35014200

I0122 23:02:21.940116  2968 layer_factory.hpp:77] Creating layer conv2

I0122 23:02:21.940137  2968 net.cpp:86] Creating Layer conv2

I0122 23:02:21.940152  2968 net.cpp:408] conv2 <- norm1

I0122 23:02:21.940171  2968 net.cpp:382] conv2 -> conv2

I0122 23:02:21.940996  2968 net.cpp:124] Setting up conv2

I0122 23:02:21.941033  2968 net.cpp:131] Top shape: 10 256 27 27 (1866240)

I0122 23:02:21.941045  2968 net.cpp:139] Memory required for data: 42479160

I0122 23:02:21.941121  2968 layer_factory.hpp:77] Creating layer relu2

I0122 23:02:21.941144  2968 net.cpp:86] Creating Layer relu2

I0122 23:02:21.941157  2968 net.cpp:408] relu2 <- conv2

I0122 23:02:21.941174  2968 net.cpp:369] relu2 -> conv2 (in-place)

I0122 23:02:21.941193  2968 net.cpp:124] Setting up relu2

I0122 23:02:21.941208  2968 net.cpp:131] Top shape: 10 256 27 27 (1866240)

I0122 23:02:21.941220  2968 net.cpp:139] Memory required for data: 49944120

I0122 23:02:21.941232  2968 layer_factory.hpp:77] Creating layer pool2

I0122 23:02:21.941248  2968 net.cpp:86] Creating Layer pool2

I0122 23:02:21.941259  2968 net.cpp:408] pool2 <- conv2

I0122 23:02:21.941275  2968 net.cpp:382] pool2 -> pool2

I0122 23:02:21.941301  2968 net.cpp:124] Setting up pool2

I0122 23:02:21.941316  2968 net.cpp:131] Top shape: 10 256 13 13 (432640)

I0122 23:02:21.941328  2968 net.cpp:139] Memory required for data: 51674680

I0122 23:02:21.941339  2968 layer_factory.hpp:77] Creating layer norm2

I0122 23:02:21.941360  2968 net.cpp:86] Creating Layer norm2

I0122 23:02:21.941372  2968 net.cpp:408] norm2 <- pool2

I0122 23:02:21.941390  2968 net.cpp:382] norm2 -> norm2

I0122 23:02:21.941411  2968 net.cpp:124] Setting up norm2

I0122 23:02:21.941426  2968 net.cpp:131] Top shape: 10 256 13 13 (432640)

I0122 23:02:21.941437  2968 net.cpp:139] Memory required for data: 53405240

I0122 23:02:21.941448  2968 layer_factory.hpp:77] Creating layer conv3

I0122 23:02:21.941468  2968 net.cpp:86] Creating Layer conv3

I0122 23:02:21.941478  2968 net.cpp:408] conv3 <- norm2

I0122 23:02:21.941495  2968 net.cpp:382] conv3 -> conv3

I0122 23:02:21.943603  2968 net.cpp:124] Setting up conv3

I0122 23:02:21.943662  2968 net.cpp:131] Top shape: 10 384 13 13 (648960)

I0122 23:02:21.943675  2968 net.cpp:139] Memory required for data: 56001080

I0122 23:02:21.943711  2968 layer_factory.hpp:77] Creating layer relu3

I0122 23:02:21.943733  2968 net.cpp:86] Creating Layer relu3

I0122 23:02:21.943747  2968 net.cpp:408] relu3 <- conv3

I0122 23:02:21.943765  2968 net.cpp:369] relu3 -> conv3 (in-place)

I0122 23:02:21.943786  2968 net.cpp:124] Setting up relu3

I0122 23:02:21.943801  2968 net.cpp:131] Top shape: 10 384 13 13 (648960)

I0122 23:02:21.943812  2968 net.cpp:139] Memory required for data: 58596920

I0122 23:02:21.943822  2968 layer_factory.hpp:77] Creating layer conv4

I0122 23:02:21.943848  2968 net.cpp:86] Creating Layer conv4

I0122 23:02:21.943861  2968 net.cpp:408] conv4 <- conv3

I0122 23:02:21.943881  2968 net.cpp:382] conv4 -> conv4

I0122 23:02:21.944964  2968 net.cpp:124] Setting up conv4

I0122 23:02:21.945030  2968 net.cpp:131] Top shape: 10 384 13 13 (648960)

I0122 23:02:21.945047  2968 net.cpp:139] Memory required for data: 61192760

I0122 23:02:21.945148  2968 layer_factory.hpp:77] Creating layer relu4

I0122 23:02:21.945188  2968 net.cpp:86] Creating Layer relu4

I0122 23:02:21.945206  2968 net.cpp:408] relu4 <- conv4

I0122 23:02:21.945230  2968 net.cpp:369] relu4 -> conv4 (in-place)

I0122 23:02:21.945258  2968 net.cpp:124] Setting up relu4

I0122 23:02:21.945277  2968 net.cpp:131] Top shape: 10 384 13 13 (648960)

I0122 23:02:21.945291  2968 net.cpp:139] Memory required for data: 63788600

I0122 23:02:21.945303  2968 layer_factory.hpp:77] Creating layer conv5

I0122 23:02:21.945334  2968 net.cpp:86] Creating Layer conv5

I0122 23:02:21.945353  2968 net.cpp:408] conv5 <- conv4

I0122 23:02:21.945376  2968 net.cpp:382] conv5 -> conv5

I0122 23:02:21.946549  2968 net.cpp:124] Setting up conv5

I0122 23:02:21.946606  2968 net.cpp:131] Top shape: 10 256 13 13 (432640)

I0122 23:02:21.946622  2968 net.cpp:139] Memory required for data: 65519160

I0122 23:02:21.946672  2968 layer_factory.hpp:77] Creating layer relu5

I0122 23:02:21.946698  2968 net.cpp:86] Creating Layer relu5

I0122 23:02:21.946717  2968 net.cpp:408] relu5 <- conv5

I0122 23:02:21.946743  2968 net.cpp:369] relu5 -> conv5 (in-place)

I0122 23:02:21.946771  2968 net.cpp:124] Setting up relu5

I0122 23:02:21.946792  2968 net.cpp:131] Top shape: 10 256 13 13 (432640)

I0122 23:02:21.946812  2968 net.cpp:139] Memory required for data: 67249720

I0122 23:02:21.946826  2968 layer_factory.hpp:77] Creating layer pool5

I0122 23:02:21.946848  2968 net.cpp:86] Creating Layer pool5

I0122 23:02:21.946864  2968 net.cpp:408] pool5 <- conv5

I0122 23:02:21.946885  2968 net.cpp:382] pool5 -> pool5

I0122 23:02:21.946935  2968 net.cpp:124] Setting up pool5

I0122 23:02:21.946971  2968 net.cpp:131] Top shape: 10 256 6 6 (92160)

I0122 23:02:21.946986  2968 net.cpp:139] Memory required for data: 67618360

I0122 23:02:21.947003  2968 layer_factory.hpp:77] Creating layer fc6

I0122 23:02:21.947028  2968 net.cpp:86] Creating Layer fc6

I0122 23:02:21.947044  2968 net.cpp:408] fc6 <- pool5

I0122 23:02:21.947065  2968 net.cpp:382] fc6 -> fc6

I0122 23:02:21.989847  2968 net.cpp:124] Setting up fc6

I0122 23:02:21.989913  2968 net.cpp:131] Top shape: 10 4096 (40960)

I0122 23:02:21.989919  2968 net.cpp:139] Memory required for data: 67782200

I0122 23:02:21.989943  2968 layer_factory.hpp:77] Creating layer relu6

I0122 23:02:21.989967  2968 net.cpp:86] Creating Layer relu6

I0122 23:02:21.989975  2968 net.cpp:408] relu6 <- fc6

I0122 23:02:21.989989  2968 net.cpp:369] relu6 -> fc6 (in-place)

I0122 23:02:21.990003  2968 net.cpp:124] Setting up relu6

I0122 23:02:21.990010  2968 net.cpp:131] Top shape: 10 4096 (40960)

I0122 23:02:21.990015  2968 net.cpp:139] Memory required for data: 67946040

I0122 23:02:21.990020  2968 layer_factory.hpp:77] Creating layer drop6

I0122 23:02:21.990031  2968 net.cpp:86] Creating Layer drop6

I0122 23:02:21.990036  2968 net.cpp:408] drop6 <- fc6

I0122 23:02:21.990043  2968 net.cpp:369] drop6 -> fc6 (in-place)

I0122 23:02:21.990067  2968 net.cpp:124] Setting up drop6

I0122 23:02:21.990074  2968 net.cpp:131] Top shape: 10 4096 (40960)

I0122 23:02:21.990079  2968 net.cpp:139] Memory required for data: 68109880

I0122 23:02:21.990084  2968 layer_factory.hpp:77] Creating layer fc7

I0122 23:02:21.990094  2968 net.cpp:86] Creating Layer fc7

I0122 23:02:21.990099  2968 net.cpp:408] fc7 <- fc6

I0122 23:02:21.990111  2968 net.cpp:382] fc7 -> fc7

I0122 23:02:22.008998  2968 net.cpp:124] Setting up fc7

I0122 23:02:22.009058  2968 net.cpp:131] Top shape: 10 4096 (40960)

I0122 23:02:22.009106  2968 net.cpp:139] Memory required for data: 68273720

I0122 23:02:22.009145  2968 layer_factory.hpp:77] Creating layer relu7

I0122 23:02:22.009173  2968 net.cpp:86] Creating Layer relu7

I0122 23:02:22.009187  2968 net.cpp:408] relu7 <- fc7

I0122 23:02:22.009209  2968 net.cpp:369] relu7 -> fc7 (in-place)

I0122 23:02:22.009232  2968 net.cpp:124] Setting up relu7

I0122 23:02:22.009248  2968 net.cpp:131] Top shape: 10 4096 (40960)

I0122 23:02:22.009259  2968 net.cpp:139] Memory required for data: 68437560

I0122 23:02:22.009269  2968 layer_factory.hpp:77] Creating layer drop7

I0122 23:02:22.009286  2968 net.cpp:86] Creating Layer drop7

I0122 23:02:22.009299  2968 net.cpp:408] drop7 <- fc7

I0122 23:02:22.009322  2968 net.cpp:369] drop7 -> fc7 (in-place)

I0122 23:02:22.009346  2968 net.cpp:124] Setting up drop7

I0122 23:02:22.009362  2968 net.cpp:131] Top shape: 10 4096 (40960)

I0122 23:02:22.009371  2968 net.cpp:139] Memory required for data: 68601400

I0122 23:02:22.009382  2968 layer_factory.hpp:77] Creating layer fc8

I0122 23:02:22.009399  2968 net.cpp:86] Creating Layer fc8

I0122 23:02:22.009410  2968 net.cpp:408] fc8 <- fc7

I0122 23:02:22.009428  2968 net.cpp:382] fc8 -> fc8

I0122 23:02:22.017177  2968 net.cpp:124] Setting up fc8

I0122 23:02:22.017282  2968 net.cpp:131] Top shape: 10 1000 (10000)

I0122 23:02:22.017313  2968 net.cpp:139] Memory required for data: 68641400

I0122 23:02:22.017356  2968 layer_factory.hpp:77] Creating layer prob

I0122 23:02:22.017395  2968 net.cpp:86] Creating Layer prob

I0122 23:02:22.017411  2968 net.cpp:408] prob <- fc8

I0122 23:02:22.017433  2968 net.cpp:382] prob -> prob

I0122 23:02:22.017469  2968 net.cpp:124] Setting up prob

I0122 23:02:22.017491  2968 net.cpp:131] Top shape: 10 1000 (10000)

I0122 23:02:22.017504  2968 net.cpp:139] Memory required for data: 68681400

I0122 23:02:22.017516  2968 net.cpp:202] prob does not need backward computation.

I0122 23:02:22.017554  2968 net.cpp:202] fc8 does not need backward computation.

I0122 23:02:22.017566  2968 net.cpp:202] drop7 does not need backward computation.

I0122 23:02:22.017577  2968 net.cpp:202] relu7 does not need backward computation.

I0122 23:02:22.017588  2968 net.cpp:202] fc7 does not need backward computation.

I0122 23:02:22.017598  2968 net.cpp:202] drop6 does not need backward computation.

I0122 23:02:22.017609  2968 net.cpp:202] relu6 does not need backward computation.

I0122 23:02:22.017619  2968 net.cpp:202] fc6 does not need backward computation.

I0122 23:02:22.017630  2968 net.cpp:202] pool5 does not need backward computation.

I0122 23:02:22.017642  2968 net.cpp:202] relu5 does not need backward computation.

I0122 23:02:22.017652  2968 net.cpp:202] conv5 does not need backward computation.

I0122 23:02:22.017663  2968 net.cpp:202] relu4 does not need backward computation.

I0122 23:02:22.017674  2968 net.cpp:202] conv4 does not need backward computation.

I0122 23:02:22.017685  2968 net.cpp:202] relu3 does not need backward computation.

I0122 23:02:22.017696  2968 net.cpp:202] conv3 does not need backward computation.

I0122 23:02:22.017707  2968 net.cpp:202] norm2 does not need backward computation.

I0122 23:02:22.017720  2968 net.cpp:202] pool2 does not need backward computation.

I0122 23:02:22.017734  2968 net.cpp:202] relu2 does not need backward computation.

I0122 23:02:22.017746  2968 net.cpp:202] conv2 does not need backward computation.

I0122 23:02:22.017757  2968 net.cpp:202] norm1 does not need backward computation.

I0122 23:02:22.017770  2968 net.cpp:202] pool1 does not need backward computation.

I0122 23:02:22.017783  2968 net.cpp:202] relu1 does not need backward computation.

I0122 23:02:22.017796  2968 net.cpp:202] conv1 does not need backward computation.

I0122 23:02:22.017809  2968 net.cpp:202] input does not need backward computation.

I0122 23:02:22.017819  2968 net.cpp:244] This network produces output prob

I0122 23:02:22.017868  2968 net.cpp:257] Network initialization done.

I0122 23:02:22.196004  2968 upgrade_proto.cpp:44] Attempting to upgrade input file specified using deprecated transformation parameters: /home/relaybot/Rob_Soft/caffe/src/ros_caffe/data/bvlc_reference_caffenet.caffemodel

I0122 23:02:22.196061  2968 upgrade_proto.cpp:47] Successfully upgraded file specified using deprecated data transformation parameters.

W0122 23:02:22.196069  2968 upgrade_proto.cpp:49] Note that future Caffe releases will only support transform_param messages for transformation fields.

I0122 23:02:22.196074  2968 upgrade_proto.cpp:53] Attempting to upgrade input file specified using deprecated V1LayerParameter: /home/relaybot/Rob_Soft/caffe/src/ros_caffe/data/bvlc_reference_caffenet.caffemodel

I0122 23:02:22.506147  2968 upgrade_proto.cpp:61] Successfully upgraded file specified using deprecated V1LayerParameter

I0122 23:02:22.507925  2968 net.cpp:746] Ignoring source layer data

I0122 23:02:22.597734  2968 net.cpp:746] Ignoring source layer loss

W0122 23:02:22.716584  2968 net.hpp:41] DEPRECATED: ForwardPrefilled() will be removed in a future version. Use Forward().

Test default image under /data/cat.jpg

0.3134 - "n02123045 tabby, tabby cat" 0.2380 - "n02123159 tiger cat" 0.1235 - "n02124075 Egyptian cat" 0.1003 - "n02119022 red fox, Vulpes vulpes" 0.0715 - "n02127052 lynx, catamount" W0122 23:07:35.308277  2968 net.hpp:41] DEPRECATED: ForwardPrefilled() will be removed in a future version. Use Forward(). W0122 23:12:52.805382  2968 net.hpp:41] DEPRECATED: ForwardPrefilled() will be removed in a future version. Use Forward().

$ rostopic list

/camera/rgb/image_raw /camera_info /image_raw /image_raw/compressed /image_raw/compressed/parameter_descriptions /image_raw/compressed/parameter_updates /image_raw/compressedDepth /image_raw/compressedDepth/parameter_descriptions /image_raw/compressedDepth/parameter_updates /image_raw/theora /image_raw/theora/parameter_descriptions /image_raw/theora/parameter_updates /rosout /rosout_agg

$ rostopic echo /caffe_ret

--- data: [0.557911 - n04286575 spotlight, spot] [0.115966 - n03729826 matchstick] [0.0737537 - n02948072 candle, taper, wax light] [0.040883 - n09472597 volcano] [0.028961 - n03666591 lighter, light, igniter, ignitor] ---

$ rosrun rqt_graph rqt_graph

-End-