Caffe学习之ProtoBuffer

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本文简单介绍一下caffe的protobuffer的机制

ProtocolBuffer是google开源的一种数据交换的方法，类似于xml等。在caffe里扮演着解析配置文件，传递消息和参数的作用。(便于规范序列化以及反序列化)

具体作用，以solver.prototxt为例，可以概括为一句话：

运行时，能够将solver.prototxt中的配置参数按照caffe.proto的协议解析并加载的到内存的solver对象的SolverParameter param_中去(同理net.prototxt)

这样就能够很方便管理参数，并且只需修改solver.protxt的参数大小即可。

具体来讲：

1.caffe.proto

caffe.proto 规定了数据结构，参数名。（其实可以简单的看做参数的申明）比如其中描述Solver的参数(其中又申明了NetParameter 等之类的变量，均在caffe.proto中定义)：

message SolverParameter {

  optional string net = 24;

  optional NetParameter net_param = 25;

  optional string train_net = 1; // Proto filename for the train net.
  repeated string test_net = 2; // Proto filenames for the test nets.
  optional NetParameter train_net_param = 21; // Inline train net params.
  repeated NetParameter test_net_param = 22; // Inline test net params.

  optional NetState train_state = 26;
  repeated NetState test_state = 27;

  // The number of iterations for each test net.
  repeated int32 test_iter = 3;

  // The number of iterations between two testing phases.
  optional int32 test_interval = 4 [default = 0];
  optional bool test_compute_loss = 19 [default = false];
  // If true, run an initial test pass before the first iteration,
  // ensuring memory availability and printing the starting value of the loss.
  optional bool test_initialization = 32 [default = true];
  optional float base_lr = 5; // The base learning rate
  // the number of iterations between displaying info. If display = 0, no info
  // will be displayed.
  optional int32 display = 6;
  // Display the loss averaged over the last average_loss iterations
  optional int32 average_loss = 33 [default = 1];
  optional int32 max_iter = 7; // the maximum number of iterations
  // accumulate gradients over `iter_size` x `batch_size` instances
  optional int32 iter_size = 36 [default = 1];
 ...
 ...
 ...
}

2.xxx.prototxt

我们知道训练一个网络需要定义一个net.prototxt和一个solver.prototxt。比如lenet_solver.prototxt：

# The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
# solver mode: CPU or GPU
solver_mode: GPU

solver.prototxt里面可以有申明参数，参数值的选项均在caffe.proto的SolverParameter中有所定义。
同理在写net.prototext的时候，也在caffe.proto中找到对应。

因此可想而知，如果要自定义一个Layer的话，需要在对应的caffe.proto里定义对应的Message等。这样运
行时参数才可以被解析