Ubuntu18.04下安装配置Caffe-SSD-GPU版本并MNIST模型测试和matlab caffe接口测试

其他 2018-12-13 09:31:16 阅读次数: 0

Ubuntu18.04->sogou->显卡驱动->matlab2018a->cuda9.0->cudnn5.01->opencv3.4.1->caffe
opencv 和caffe 比较难装。其中opencv最为慢，主要make 一次太久。

caffe 安装

首先ｍａｋｅ　ａｌｌ之前现在ｃａｆｆｅ的Ｐｙｔｈｏｎ里执行下面这句
cd /…/caffe/python

for req in $(cat requirements.txt); do pip install $req; done

https://blog.csdn.net/CAU_Ayao/article/details/84000151

报错

sudo make runtest
.build_release/tools/caffe
.build_release/tools/caffe: error while loading shared libraries: libcublas.so.9.0: cannot open shared object file: No such file or directory

sudo vim ld.so.conf

增加

include /etc/ld.so.conf.d/*.conf
/usr/local/cuda/lib64

执行

 sudo ldconfig

vim ~/.bashrc

增加

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

保存退出

source ~/.bashrc

重新执行

sudo make clean

sudo make all

sudo make test

sudo make runtest

       OK ] AnnotatedDataLayerTest/2.TestReadCropTrainSequenceUnseededLMDB (23 ms)
[ RUN      ] AnnotatedDataLayerTest/2.TestReadCropTestLevelDB
[       OK ] AnnotatedDataLayerTest/2.TestReadCropTestLevelDB (43 ms)
[----------] 12 tests from AnnotatedDataLayerTest/2 (732 ms total)

[----------] 2 tests from CuDNNSoftmaxLayerTest/1, where TypeParam = double
[ RUN      ] CuDNNSoftmaxLayerTest/1.TestForwardCuDNN
[       OK ] CuDNNSoftmaxLayerTest/1.TestForwardCuDNN (3 ms)
[ RUN      ] CuDNNSoftmaxLayerTest/1.TestGradientCuDNN
[       OK ] CuDNNSoftmaxLayerTest/1.TestGradientCuDNN (962 ms)
[----------] 2 tests from CuDNNSoftmaxLayerTest/1 (965 ms total)

[----------] Global test environment tear-down
[==========] 2361 tests from 309 test cases ran. (391325 ms total)
[  PASSED  ] 2361 tests.


vim ~/.bashrc

增加

export PYTHONPATH=~/caffe-ssd/python:$PYTHONPATH

激活

    source ~/.bashrc

报错：

ImportError: No module named _caffe

执行

 sudo make pycaffe

 sudo pip install -U scikit-image

sudo pip install protobuf

import caffe
成功！！！

MNIST测试

0下载数据集

sudo ./data/mnist/get_mnist.sh

0格式转换

sudo ./examples/mnist/create_mnist.sh

1训练

sudo ./examples/mnist/train_lenet.sh


I1210 14:58:30.440436   343 caffe.cpp:217] Using GPUs 0
I1210 14:58:30.462970   343 caffe.cpp:222] GPU 0: GeForce GTX 1070
I1210 14:58:30.632964   343 solver.cpp:63] Initializing solver from parameters: 
test_iter: 100
test_interval: 500
base_lr: 0.01
display: 100
max_iter: 10000
lr_policy: "inv"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
solver_mode: GPU
device_id: 0
net: "examples/mnist/lenet_train_test.prototxt"
train_state {
  level: 0
  stage: ""
}
I1210 14:58:30.633087   343 solver.cpp:106] Creating training net from net file: examples/mnist/lenet_train_test.prototxt
I1210 14:58:30.633352   343 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist
I1210 14:58:30.633363   343 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I1210 14:58:30.633415   343 net.cpp:58] Initializing net from parameters: 
name: "LeNet"
state {
  phase: TRAIN
  level: 0
  stage: ""
}
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/mnist/mnist_train_lmdb"
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}
I1210 14:58:30.633489   343 layer_factory.hpp:77] Creating layer mnist
I1210 14:58:30.633653   343 net.cpp:100] Creating Layer mnist
I1210 14:58:30.633678   343 net.cpp:408] mnist -> data
I1210 14:58:30.633693   343 net.cpp:408] mnist -> label
I1210 14:58:30.634238   354 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_train_lmdb
I1210 14:58:30.644352   343 data_layer.cpp:41] output data size: 64,1,28,28
I1210 14:58:30.645308   343 net.cpp:150] Setting up mnist
I1210 14:58:30.645336   343 net.cpp:157] Top shape: 64 1 28 28 (50176)
I1210 14:58:30.645340   343 net.cpp:157] Top shape: 64 (64)
I1210 14:58:30.645342   343 net.cpp:165] Memory required for data: 200960
I1210 14:58:30.645349   343 layer_factory.hpp:77] Creating layer conv1
I1210 14:58:30.645370   343 net.cpp:100] Creating Layer conv1
I1210 14:58:30.645375   343 net.cpp:434] conv1 <- data
I1210 14:58:30.645383   343 net.cpp:408] conv1 -> conv1
I1210 14:58:31.067790   343 net.cpp:150] Setting up conv1
I1210 14:58:31.067811   343 net.cpp:157] Top shape: 64 20 24 24 (737280)
I1210 14:58:31.067814   343 net.cpp:165] Memory required for data: 3150080
I1210 14:58:31.067827   343 layer_factory.hpp:77] Creating layer pool1
I1210 14:58:31.067836   343 net.cpp:100] Creating Layer pool1
I1210 14:58:31.067859   343 net.cpp:434] pool1 <- conv1
I1210 14:58:31.067864   343 net.cpp:408] pool1 -> pool1
I1210 14:58:31.067948   343 net.cpp:150] Setting up pool1
I1210 14:58:31.067953   343 net.cpp:157] Top shape: 64 20 12 12 (184320)
I1210 14:58:31.067956   343 net.cpp:165] Memory required for data: 3887360
I1210 14:58:31.067960   343 layer_factory.hpp:77] Creating layer conv2
I1210 14:58:31.067967   343 net.cpp:100] Creating Layer conv2
I1210 14:58:31.067970   343 net.cpp:434] conv2 <- pool1
I1210 14:58:31.067975   343 net.cpp:408] conv2 -> conv2
I1210 14:58:31.069413   343 net.cpp:150] Setting up conv2
I1210 14:58:31.069423   343 net.cpp:157] Top shape: 64 50 8 8 (204800)
I1210 14:58:31.069427   343 net.cpp:165] Memory required for data: 4706560
I1210 14:58:31.069432   343 layer_factory.hpp:77] Creating layer pool2
I1210 14:58:31.069437   343 net.cpp:100] Creating Layer pool2
I1210 14:58:31.069440   343 net.cpp:434] pool2 <- conv2
I1210 14:58:31.069443   343 net.cpp:408] pool2 -> pool2
I1210 14:58:31.069475   343 net.cpp:150] Setting up pool2
I1210 14:58:31.069480   343 net.cpp:157] Top shape: 64 50 4 4 (51200)
I1210 14:58:31.069483   343 net.cpp:165] Memory required for data: 4911360
I1210 14:58:31.069485   343 layer_factory.hpp:77] Creating layer ip1
I1210 14:58:31.069491   343 net.cpp:100] Creating Layer ip1
I1210 14:58:31.069494   343 net.cpp:434] ip1 <- pool2
I1210 14:58:31.069499   343 net.cpp:408] ip1 -> ip1
I1210 14:58:31.071511   343 net.cpp:150] Setting up ip1
I1210 14:58:31.071521   343 net.cpp:157] Top shape: 64 500 (32000)
I1210 14:58:31.071522   343 net.cpp:165] Memory required for data: 5039360
I1210 14:58:31.071528   343 layer_factory.hpp:77] Creating layer relu1
I1210 14:58:31.071533   343 net.cpp:100] Creating Layer relu1
I1210 14:58:31.071537   343 net.cpp:434] relu1 <- ip1
I1210 14:58:31.071540   343 net.cpp:395] relu1 -> ip1 (in-place)
I1210 14:58:31.071892   343 net.cpp:150] Setting up relu1
I1210 14:58:31.071898   343 net.cpp:157] Top shape: 64 500 (32000)
I1210 14:58:31.071902   343 net.cpp:165] Memory required for data: 5167360
I1210 14:58:31.071904   343 layer_factory.hpp:77] Creating layer ip2
I1210 14:58:31.071910   343 net.cpp:100] Creating Layer ip2
I1210 14:58:31.071913   343 net.cpp:434] ip2 <- ip1
I1210 14:58:31.071918   343 net.cpp:408] ip2 -> ip2
I1210 14:58:31.072499   343 net.cpp:150] Setting up ip2
I1210 14:58:31.072506   343 net.cpp:157] Top shape: 64 10 (640)
I1210 14:58:31.072510   343 net.cpp:165] Memory required for data: 5169920
I1210 14:58:31.072515   343 layer_factory.hpp:77] Creating layer loss
I1210 14:58:31.072520   343 net.cpp:100] Creating Layer loss
I1210 14:58:31.072522   343 net.cpp:434] loss <- ip2
I1210 14:58:31.072525   343 net.cpp:434] loss <- label
I1210 14:58:31.072532   343 net.cpp:408] loss -> loss
I1210 14:58:31.072542   343 layer_factory.hpp:77] Creating layer loss
I1210 14:58:31.072957   343 net.cpp:150] Setting up loss
I1210 14:58:31.072964   343 net.cpp:157] Top shape: (1)
I1210 14:58:31.072966   343 net.cpp:160]     with loss weight 1
I1210 14:58:31.072978   343 net.cpp:165] Memory required for data: 5169924
I1210 14:58:31.072981   343 net.cpp:226] loss needs backward computation.
I1210 14:58:31.072986   343 net.cpp:226] ip2 needs backward computation.
I1210 14:58:31.072989   343 net.cpp:226] relu1 needs backward computation.
I1210 14:58:31.072993   343 net.cpp:226] ip1 needs backward computation.
I1210 14:58:31.072996   343 net.cpp:226] pool2 needs backward computation.
I1210 14:58:31.072999   343 net.cpp:226] conv2 needs backward computation.
I1210 14:58:31.073002   343 net.cpp:226] pool1 needs backward computation.
I1210 14:58:31.073004   343 net.cpp:226] conv1 needs backward computation.
I1210 14:58:31.073010   343 net.cpp:228] mnist does not need backward computation.
I1210 14:58:31.073014   343 net.cpp:270] This network produces output loss
I1210 14:58:31.073019   343 net.cpp:283] Network initialization done.
I1210 14:58:31.073135   343 solver.cpp:196] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt
I1210 14:58:31.073166   343 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I1210 14:58:31.073215   343 net.cpp:58] Initializing net from parameters: 
name: "LeNet"
state {
  phase: TEST
}
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/mnist/mnist_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}
I1210 14:58:31.073285   343 layer_factory.hpp:77] Creating layer mnist
I1210 14:58:31.073367   343 net.cpp:100] Creating Layer mnist
I1210 14:58:31.073374   343 net.cpp:408] mnist -> data
I1210 14:58:31.073380   343 net.cpp:408] mnist -> label
I1210 14:58:31.073958   356 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I1210 14:58:31.074043   343 data_layer.cpp:41] output data size: 100,1,28,28
I1210 14:58:31.074859   343 net.cpp:150] Setting up mnist
I1210 14:58:31.074869   343 net.cpp:157] Top shape: 100 1 28 28 (78400)
I1210 14:58:31.074873   343 net.cpp:157] Top shape: 100 (100)
I1210 14:58:31.074875   343 net.cpp:165] Memory required for data: 314000
I1210 14:58:31.074878   343 layer_factory.hpp:77] Creating layer label_mnist_1_split
I1210 14:58:31.074885   343 net.cpp:100] Creating Layer label_mnist_1_split
I1210 14:58:31.074888   343 net.cpp:434] label_mnist_1_split <- label
I1210 14:58:31.074892   343 net.cpp:408] label_mnist_1_split -> label_mnist_1_split_0
I1210 14:58:31.074898   343 net.cpp:408] label_mnist_1_split -> label_mnist_1_split_1
I1210 14:58:31.074932   343 net.cpp:150] Setting up label_mnist_1_split
I1210 14:58:31.074936   343 net.cpp:157] Top shape: 100 (100)
I1210 14:58:31.074960   343 net.cpp:157] Top shape: 100 (100)

I1210 14:58:45.304703   343 solver.cpp:259]     Train net output #0: loss = 0.00905059 (* 1 = 0.00905059 loss)
I1210 14:58:45.304708   343 sgd_solver.cpp:138] Iteration 9300, lr = 0.00610706
I1210 14:58:45.447075   343 solver.cpp:243] Iteration 9400, loss = 0.0251583
I1210 14:58:45.447098   343 solver.cpp:259]     Train net output #0: loss = 0.0251585 (* 1 = 0.0251585 loss)
I1210 14:58:45.447103   343 sgd_solver.cpp:138] Iteration 9400, lr = 0.00608343
I1210 14:58:45.587970   343 solver.cpp:358] Iteration 9500, Testing net (#0)
I1210 14:58:45.639950   343 solver.cpp:425]     Test net output #0: accuracy = 0.989
I1210 14:58:45.639971   343 solver.cpp:425]     Test net output #1: loss = 0.0345453 (* 1 = 0.0345453 loss)
I1210 14:58:45.640565   343 solver.cpp:243] Iteration 9500, loss = 0.00247001
I1210 14:58:45.640578   343 solver.cpp:259]     Train net output #0: loss = 0.00247023 (* 1 = 0.00247023 loss)
I1210 14:58:45.640599   343 sgd_solver.cpp:138] Iteration 9500, lr = 0.00606002
I1210 14:58:45.781831   343 solver.cpp:243] Iteration 9600, loss = 0.00303816
I1210 14:58:45.781853   343 solver.cpp:259]     Train net output #0: loss = 0.00303838 (* 1 = 0.00303838 loss)
I1210 14:58:45.781858   343 sgd_solver.cpp:138] Iteration 9600, lr = 0.00603682
I1210 14:58:45.923971   343 solver.cpp:243] Iteration 9700, loss = 0.002835
I1210 14:58:45.923993   343 solver.cpp:259]     Train net output #0: loss = 0.00283521 (* 1 = 0.00283521 loss)
I1210 14:58:45.924000   343 sgd_solver.cpp:138] Iteration 9700, lr = 0.00601382
I1210 14:58:46.071753   343 solver.cpp:243] Iteration 9800, loss = 0.0137216
I1210 14:58:46.071775   343 solver.cpp:259]     Train net output #0: loss = 0.0137218 (* 1 = 0.0137218 loss)
I1210 14:58:46.071780   343 sgd_solver.cpp:138] Iteration 9800, lr = 0.00599102
I1210 14:58:46.211102   343 solver.cpp:243] Iteration 9900, loss = 0.00450876
I1210 14:58:46.211122   343 solver.cpp:259]     Train net output #0: loss = 0.00450896 (* 1 = 0.00450896 loss)
I1210 14:58:46.211127   343 sgd_solver.cpp:138] Iteration 9900, lr = 0.00596843
I1210 14:58:46.351517   343 solver.cpp:596] Snapshotting to binary proto file examples/mnist/lenet_iter_10000.caffemodel
I1210 14:58:46.356554   343 sgd_solver.cpp:307] Snapshotting solver state to binary proto file examples/mnist/lenet_iter_10000.solverstate
I1210 14:58:46.358690   343 solver.cpp:332] Iteration 10000, loss = 0.00307509
I1210 14:58:46.358705   343 solver.cpp:358] Iteration 10000, Testing net (#0)
I1210 14:58:46.410430   343 solver.cpp:425]     Test net output #0: accuracy = 0.9909
I1210 14:58:46.410466   343 solver.cpp:425]     Test net output #1: loss = 0.0283011 (* 1 = 0.0283011 loss)
I1210 14:58:46.410472   343 solver.cpp:337] Optimization Done.
I1210 14:58:46.410478   343 caffe.cpp:254] Optimization Done.

2测试

2.1先新建文件test_lenet.sh：

sudo gedit test_lenet.sh

2.1增加内容

定义模型、权重

./build/tools/caffe.bin test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -iterations 100


sudo sh test_lenet2.sh


I1210 15:04:57.474340  1817 caffe.cpp:279] Use CPU.
I1210 15:04:57.668058  1817 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I1210 15:04:57.668138  1817 net.cpp:58] Initializing net from parameters: 
name: "LeNet"
state {
  phase: TEST
  level: 0
  stage: ""
}
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "examples/mnist/mnist_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}
I1210 15:04:57.668231  1817 layer_factory.hpp:77] Creating layer mnist
I1210 15:04:57.668817  1817 net.cpp:100] Creating Layer mnist
I1210 15:04:57.668828  1817 net.cpp:408] mnist -> data
I1210 15:04:57.668843  1817 net.cpp:408] mnist -> label
I1210 15:04:57.669452  1842 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I1210 15:04:57.669580  1817 data_layer.cpp:41] output data size: 100,1,28,28
I1210 15:04:57.669999  1817 net.cpp:150] Setting up mnist
I1210 15:04:57.670009  1817 net.cpp:157] Top shape: 100 1 28 28 (78400)
I1210 15:04:57.670013  1817 net.cpp:157] Top shape: 100 (100)
I1210 15:04:57.670015  1817 net.cpp:165] Memory required for data: 314000
I1210 15:04:57.670020  1817 layer_factory.hpp:77] Creating layer label_mnist_1_split
I1210 15:04:57.670051  1817 net.cpp:100] Creating Layer label_mnist_1_split
I1210 15:04:57.670058  1817 net.cpp:434] label_mnist_1_split <- label
I1210 15:04:57.670066  1817 net.cpp:408] label_mnist_1_split -> label_mnist_1_split_0
I1210 15:04:57.670074  1817 net.cpp:408] label_mnist_1_split -> label_mnist_1_split_1
I1210 15:04:57.670081  1817 net.cpp:150] Setting up label_mnist_1_split
I1210 15:04:57.670085  1817 net.cpp:157] Top shape: 100 (100)
I1210 15:04:57.670089  1817 net.cpp:157] Top shape: 100 (100)
I1210 15:04:57.670090  1817 net.cpp:165] Memory required for data: 314800
I1210 15:04:57.670094  1817 layer_factory.hpp:77] Creating layer conv1
I1210 15:04:57.670104  1817 net.cpp:100] Creating Layer conv1
I1210 15:04:57.670107  1817 net.cpp:434] conv1 <- data
I1210 15:04:57.670111  1817 net.cpp:408] conv1 -> conv1
I1210 15:04:58.105351  1817 net.cpp:150] Setting up conv1
I1210 15:04:58.105371  1817 net.cpp:157] Top shape: 100 20 24 24 (1152000)
I1210 15:04:58.105376  1817 net.cpp:165] Memory required for data: 4922800
I1210 15:04:58.105406  1817 layer_factory.hpp:77] Creating layer pool1
I1210 15:04:58.105437  1817 net.cpp:100] Creating Layer pool1
I1210 15:04:58.105440  1817 net.cpp:434] pool1 <- conv1
I1210 15:04:58.105446  1817 net.cpp:408] pool1 -> pool1
I1210 15:04:58.105464  1817 net.cpp:150] Setting up pool1
I1210 15:04:58.105469  1817 net.cpp:157] Top shape: 100 20 12 12 (288000)
I1210 15:04:58.105471  1817 net.cpp:165] Memory required for data: 6074800
I1210 15:04:58.105475  1817 layer_factory.hpp:77] Creating layer conv2
I1210 15:04:58.105501  1817 net.cpp:100] Creating Layer conv2
I1210 15:04:58.105520  1817 net.cpp:434] conv2 <- pool1
I1210 15:04:58.105525  1817 net.cpp:408] conv2 -> conv2
I1210 15:04:58.106905  1817 net.cpp:150] Setting up conv2
I1210 15:04:58.106915  1817 net.cpp:157] Top shape: 100 50 8 8 (320000)
I1210 15:04:58.106918  1817 net.cpp:165] Memory required for data: 7354800
I1210 15:04:58.106925  1817 layer_factory.hpp:77] Creating layer pool2
I1210 15:04:58.106943  1817 net.cpp:100] Creating Layer pool2
I1210 15:04:58.106947  1817 net.cpp:434] pool2 <- conv2
I1210 15:04:58.106952  1817 net.cpp:408] pool2 -> pool2
I1210 15:04:58.106958  1817 net.cpp:150] Setting up pool2
I1210 15:04:58.106963  1817 net.cpp:157] Top shape: 100 50 4 4 (80000)
I1210 15:04:58.106966  1817 net.cpp:165] Memory required for data: 7674800
I1210 15:04:58.106968  1817 layer_factory.hpp:77] Creating layer ip1
I1210 15:04:58.106976  1817 net.cpp:100] Creating Layer ip1
I1210 15:04:58.106979  1817 net.cpp:434] ip1 <- pool2
I1210 15:04:58.106983  1817 net.cpp:408] ip1 -> ip1
I1210 15:04:58.108822  1817 net.cpp:150] Setting up ip1
I1210 15:04:58.108829  1817 net.cpp:157] Top shape: 100 500 (50000)
I1210 15:04:58.108831  1817 net.cpp:165] Memory required for data: 7874800
I1210 15:04:58.108836  1817 layer_factory.hpp:77] Creating layer relu1
I1210 15:04:58.108840  1817 net.cpp:100] Creating Layer relu1
I1210 15:04:58.108844  1817 net.cpp:434] relu1 <- ip1
I1210 15:04:58.108861  1817 net.cpp:395] relu1 -> ip1 (in-place)
I1210 15:04:58.109218  1817 net.cpp:150] Setting up relu1
I1210 15:04:58.109225  1817 net.cpp:157] Top shape: 100 500 (50000)
I1210 15:04:58.109228  1817 net.cpp:165] Memory required for data: 8074800
I1210 15:04:58.109231  1817 layer_factory.hpp:77] Creating layer ip2
I1210 15:04:58.109252  1817 net.cpp:100] Creating Layer ip2
I1210 15:04:58.109256  1817 net.cpp:434] ip2 <- ip1
I1210 15:04:58.109261  1817 net.cpp:408] ip2 -> ip2
I1210 15:04:58.109295  1817 net.cpp:150] Setting up ip2
I1210 15:04:58.109299  1817 net.cpp:157] Top shape: 100 10 (1000)
I1210 15:04:58.109302  1817 net.cpp:165] Memory required for data: 8078800
I1210 15:04:58.109306  1817 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I1210 15:04:58.109313  1817 net.cpp:100] Creating Layer ip2_ip2_0_split
I1210 15:04:58.109315  1817 net.cpp:434] ip2_ip2_0_split <- ip2
I1210 15:04:58.109319  1817 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_0
I1210 15:04:58.109325  1817 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_1
I1210 15:04:58.109330  1817 net.cpp:150] Setting up ip2_ip2_0_split
I1210 15:04:58.109334  1817 net.cpp:157] Top shape: 100 10 (1000)
I1210 15:04:58.109338  1817 net.cpp:157] Top shape: 100 10 (1000)
I1210 15:04:58.109340  1817 net.cpp:165] Memory required for data: 8086800
I1210 15:04:58.109344  1817 layer_factory.hpp:77] Creating layer accuracy
I1210 15:04:58.109349  1817 net.cpp:100] Creating Layer accuracy
I1210 15:04:58.109351  1817 net.cpp:434] accuracy <- ip2_ip2_0_split_0
I1210 15:04:58.109354  1817 net.cpp:434] accuracy <- label_mnist_1_split_0
I1210 15:04:58.109359  1817 net.cpp:408] accuracy -> accuracy
I1210 15:04:58.109364  1817 net.cpp:150] Setting up accuracy
I1210 15:04:58.109367  1817 net.cpp:157] Top shape: (1)
I1210 15:04:58.109370  1817 net.cpp:165] Memory required for data: 8086804
I1210 15:04:58.109374  1817 layer_factory.hpp:77] Creating layer loss
I1210 15:04:58.109380  1817 net.cpp:100] Creating Layer loss
I1210 15:04:58.109382  1817 net.cpp:434] loss <- ip2_ip2_0_split_1
I1210 15:04:58.109386  1817 net.cpp:434] loss <- label_mnist_1_split_1
I1210 15:04:58.109421  1817 net.cpp:408] loss -> loss
I1210 15:04:58.109429  1817 layer_factory.hpp:77] Creating layer loss
I1210 15:04:58.109791  1817 net.cpp:150] Setting up loss
I1210 15:04:58.109798  1817 net.cpp:157] Top shape: (1)
I1210 15:04:58.109802  1817 net.cpp:160]     with loss weight 1
I1210 15:04:58.109814  1817 net.cpp:165] Memory required for data: 8086808
I1210 15:04:58.109817  1817 net.cpp:226] loss needs backward computation.
I1210 15:04:58.109823  1817 net.cpp:228] accuracy does not need backward computation.
I1210 15:04:58.109827  1817 net.cpp:226] ip2_ip2_0_split needs backward computation.
I1210 15:04:58.109830  1817 net.cpp:226] ip2 needs backward computation.
I1210 15:04:58.109833  1817 net.cpp:226] relu1 needs backward computation.
I1210 15:04:58.109838  1817 net.cpp:226] ip1 needs backward computation.
I1210 15:04:58.109840  1817 net.cpp:226] pool2 needs backward computation.
I1210 15:04:58.109843  1817 net.cpp:226] conv2 needs backward computation.
I1210 15:04:58.109846  1817 net.cpp:226] pool1 needs backward computation.
I1210 15:04:58.109850  1817 net.cpp:226] conv1 needs backward computation.
I1210 15:04:58.109853  1817 net.cpp:228] label_mnist_1_split does not need backward computation.
I1210 15:04:58.109858  1817 net.cpp:228] mnist does not need backward computation.
I1210 15:04:58.109860  1817 net.cpp:270] This network produces output accuracy
I1210 15:04:58.109864  1817 net.cpp:270] This network produces output loss
I1210 15:04:58.109874  1817 net.cpp:283] Network initialization done.
I1210 15:04:58.111215  1817 caffe.cpp:285] Running for 100 iterations.
I1210 15:04:58.132164  1817 caffe.cpp:308] Batch 0, accuracy = 1
I1210 15:04:58.132187  1817 caffe.cpp:308] Batch 0, loss = 0.00986392
I1210 15:04:58.150775  1817 caffe.cpp:308] Batch 1, accuracy = 1
I1210 15:04:58.150795  1817 caffe.cpp:308] Batch 1, loss = 0.00429982
I1210 15:04:58.169167  1817 caffe.cpp:308] Batch 2, accuracy = 0.99
I1210 15:04:58.169183  1817 caffe.cpp:308] Batch 2, loss = 0.025451
I1210 15:04:58.186553  1817 caffe.cpp:308] Batch 3, accuracy = 0.99
I1210 15:04:58.186573  1817 caffe.cpp:308] Batch 3, loss = 0.0285686
I1210 15:04:58.204509  1817 caffe.cpp:308] Batch 4, accuracy = 0.99
I1210 15:04:58.204527  1817 caffe.cpp:308] Batch 4, loss = 0.0695804
I1210 15:04:58.222080  1817 caffe.cpp:308] Batch 5, accuracy = 0.99
I1210 15:04:58.222098  1817 caffe.cpp:308] Batch 5, loss = 0.0460763
 = 0.000167832
I1210 15:04:59.981992  1817 caffe.cpp:308] Batch 98, accuracy = 1
I1210 15:04:59.982015  1817 caffe.cpp:308] Batch 98, loss = 0.00464218
I1210 15:05:00.013013  1817 caffe.cpp:308] Batch 99, accuracy = 1
I1210 15:05:00.013046  1817 caffe.cpp:308] Batch 99, loss = 0.00950743
I1210 15:05:00.013056  1817 caffe.cpp:313] Loss: 0.0283011
I1210 15:05:00.013063  1817 caffe.cpp:325] accuracy = 0.9909
I1210 15:05:00.013078  1817 caffe.cpp:325] loss = 0.0283011 (* 1 = 0.0283011 loss)

matlab caffe接口测试

在网址下载２００Ｍ左右的模型。
https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet
放在对应的位置

在该目录下/home/ＸＸ/Downloads/caffe-ssd/matlab/demo
执行
classification_demo.m

报错

Undefined function or variable 'caffe_'.
Error in caffe.set_mode_cpu (line 5)
caffe_('set_mode_cpu');

Error in classification_demo (line 70)
  caffe.set_mode_cpu();

sudo ln -sf /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /usr/local/MATLAB/R2018a/bin/glnxa64/libstdc++.so.6

在ｃａｆｆｅ文件夹下执行

sudo make mattest -j8

使用ｇｐｕ,在第６５行增加设置

% Set caffe mode

65 use_gpu=1;

if exist(‘use_gpu’, ‘var’) && use_gpu

再次执行：classification_demo.m

using caffe/examples/images/cat.jpg as input image
Elapsed time is 0.011105 seconds.
Elapsed time is 0.028331 seconds.
Cleared 0 solvers and 1 stand-alone nets

ans =

  1000×1 single column vector

    0.0000
    0.0000
    0.0000
    0.0000
    0.0000
    0.0000
    ...
    0.0000
    0.0000
    0.0000

猜你喜欢

转载自blog.csdn.net/qq_35608277/article/details/84938244
今日推荐
基于大语言模型的开源知识库问答系统 MaxKB GitHub Star 数量突破 5,000 个！
美国拟限制 AI 大模型出口中国和俄罗斯
苹果将与 OpenAI 达成协议，将 ChatGPT 应用于 iPhone
openKylin 社区生态委员会第六次会议圆满召开
阿里云正式发布通义千问 2.5
Python 3.13 发布首个 Beta：实验性自由线程模式和 JIT、改进交互式解释器
Stack Overflow 拿我的代码去训练 AI 大模型，还封了我的账号
Pop!_OS 的 COSMIC 桌面完成 App Store 上架工作
《2024 年一季度互联网投融资运行情况》研究报告
报告：Django 仍然是 74% 开发者的首选
15 年前上了“FFmpeg 耻辱柱”，今天他还得谢谢咱——腾讯QQPlayer一雪前耻？
TIOBE 5 月榜单：Fortran “复活”进入 Top 10
周排行
记一下去大梅沙的准备（2018-05-26）
Spring 注解 事务
基于HTTP协议的客户端缓存
阿里云rds 备份和还原
[PHP] 几个拖慢 PHP 程序/API 运行速度的点
python 代码风格------------PEP8规则
js控制json生成菜单——自制菜单（一）
将字符串: 'k:1|k1:2|k2:3|k3:4 ' ,处理成 python 字典: {'k':1, 'k1':2, ...}
微信小程序转支付宝小程序
Qt551.窗口滚动条
每日归档
更多
2024-05-13(18)
2024-05-12(0)
2024-05-11(38)
2024-05-10(38)
2024-05-09(35)
2024-05-08(42)
2024-05-07(14)
2024-05-06(40)
2024-05-05(0)
2024-05-04(7)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022