machine-learning - 使用caffe训练我自己的jpg数据集:type "caffe.ImageDataParameter" has no field named "backend"

Question

当我运行train_caffenet.sh时，出现以下错误:

I0906 10:56:42.327703 21556 solver.cpp:91] Creating training net from net file: /home/pris/caffe-master/examples/myself/train_val.prototxt
[libprotobuf ERROR google/protobuf/text_format.cc:245] Error parsing text-format caffe.NetParameter: 26:12: Message type "caffe.ImageDataParameter" has no field named "backend".
F0906 10:56:42.327837 21556 upgrade_proto.cpp:79] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: /home/pris/caffe-master/examples/myself/train_val.prototxt
*** Check failure stack trace: ***
    @     0x7f5013ca0daa  (unknown)
    @     0x7f5013ca0ce4  (unknown)
    @     0x7f5013ca06e6  (unknown)
    @     0x7f5013ca3687  (unknown)
    @     0x7f50142b019e  caffe::ReadNetParamsFromTextFileOrDie()
    @     0x7f501429e76b  caffe::Solver<>::InitTrainNet()
    @     0x7f501429f83c  caffe::Solver<>::Init()
    @     0x7f501429fb6a  caffe::Solver<>::Solver()
    @     0x7f50143de663  caffe::Creator_SGDSolver<>()
    @           0x40e9be  caffe::SolverRegistry<>::CreateSolver()
    @           0x407b62  train()
    @           0x4059ec  main
    @     0x7f5012faef45  (unknown)
    @           0x406121  (unknown)
    @              (nil)  (unknown)
Aborted (core dumped)

我已经尝试解决这个问题几天了，但仍然无法弄清楚它是如何出错的。
这是我的train_val.prototxt，主要是根据$CAFFE_TOOT/models/bvlc_reference_caffenet

中的修改的

name: "CaffeNet"
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "/home/pris/caffe-master/data/myself/myimagenet_mean.binaryproto"
  }
# mean pixel / channel-wise mean instead of mean image
#  transform_param {
#    crop_size: 227
#    mean_value: 104
#    mean_value: 117
#    mean_value: 123
#    mirror: true
#  }
  image_data_param {
    source: "/home/pris/caffe-master/examples/myself/imagenet_train_leveldb"
    batch_size: 256
    backend: LEVELDB
  }
}
layer 
{
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include { phase: TEST }
  transform_param 
  {
    mirror: false
    crop_size: 227
    mean_file: "/home/pris/caffe-master/data/myself/myimagenet_mean.binaryproto"
  }
# mean pixel / channel-wise mean instead of mean image
#  transform_param {
#    crop_size: 227
#    mean_value: 104
#    mean_value: 117
#    mean_value: 123
#    mirror: false
#  }
  image_data_param 
  {
    source: "/home/pris/caffe-master/examples/myself/imagenet_val_leveldb"
    batch_size: 50
    backend: LEVELDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param 
  {
    lr_mult: 1
    decay_mult: 1
  }
  param 
  {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param 
  {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler 
    {
      type: "gaussian"
      std: 0.01
    }
    bias_filler 
    {
      type: "constant"
      value: 0
    }
  }
}
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"
  param 
  {
    lr_mult: 1
    decay_mult: 1
  }
  param 
  {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param 
  {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler 
    {
      type: "gaussian"
      std: 0.01
    }
    bias_filler 
    {
      type: "constant"
      value: 1
    }
  }
}
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"
  param 
  {
    lr_mult: 1
    decay_mult: 1
  }
  param 
  {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param 
  {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler 
    {
      type: "gaussian"
      std: 0.01
    }
    bias_filler 
    {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param 
  {
    lr_mult: 1
    decay_mult: 1
  }
  param 
  {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param 
  {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler 
    {
      type: "gaussian"
      std: 0.01
    }
    bias_filler 
    {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param 
  {
    lr_mult: 1
    decay_mult: 1
  }
  param 
  {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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"
  param 
  {
    lr_mult: 1
    decay_mult: 1
  }
  param 
  {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
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"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}

和sovler.prototxt

net: "/home/pris/caffe-master/examples/myself/train_val.prototxt"
test_iter: 10
test_interval: 500
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 2000
snapshot_prefix:"/home/pris/caffe-master/examples/myself/result"
solver_mode: GPU

train_caffenet.sh:

#!/usr/bin/env sh

/home/pris/caffe-master/build/tools/caffe train \
    --solver=/home/pris/caffe-master/examples/myself/solver.prototxt

如果有人能帮助我修复它，我将非常感激。

Answer 1

您正在从leveldb数据库读取训练数据，您应该使用“Data”类型的输入层，而不“ImageData”。