学习Caffe代码的方法心得

版权声明:本文为博主原创文章,转载请注明源地址。 https://blog.csdn.net/10km/article/details/52728157

这几天在梳理深度学习框架Caffe的代码结构,有一些心得做一下记录。

从prototxt开始

按照我的理解,从系统整体结构来看,Caffe是个数据驱动型的系统,而非程序驱动型,如果要类比,可以类似于用于Java应用的Spring框架,我对Spring的了解也非常肤浅,不过我知道基于Sprin框架的java应用都是用xml配置文件来控制的。xml文件里详细定义了每个应用的Class以及相应的参数。。。。 从这一点来说,Caffe与Spring框架就有相似之处了,Caffe定义一个神经网络,定义训练和测试的参数都是通过ProtoBuffer格式的prototxt文件来完成。 比如对于一个训练来说,net定义,net中每个layer的参数,训练/测试数据来源,训练参数,都在定义在.prototxt文件中(训练超参数Hyper-Parameter), lenet_solver.prototxt

# net 指定训练/测试的网络定义文件
net: "examples/mnist/lenet_train_test.prototxt"
############定义各种训练参数#############
test_iter: 100
test_interval: 500
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
lr_policy: "inv"
gamma: 0.0001
power: 0.75
########以下定义训练过程控制参数#########
# 每迭代100次输出log
display: 100
# 最大迭代次数
max_iter: 10000
# 训练快照保存间隔(5000迭代保存一次)
snapshot: 5000
# 训练快照保存的位置
snapshot_prefix: "examples/mnist/lenet"
# Caffe运行模式CPU/GPU
solver_mode: GPU

在上面这个训练超参数文件中,描述了一次训练所需要的所有参数,这其中最关注的就是第一个参数net,从这里顺藤摸瓜,我们就找到了神经网络定义文件。

所以,在开始学习Caffe的代码整体结构时,并不需要急于看cpp/h代码,先看prototxt,通过solver.prototxt以及net 的prototxt文件就可以提纲挈领,从全局角度对代码结构有一个总体的了解。 另外还要提到一个文件就是 src/caffe/proto/caffe.proto,Caffe几乎所有的数据类型定义都在这里,还以前面这个lenet_solver.prototxt文件为例,这个文件被Caffe读取后,最终也会被解析成一个叫SolverParameter的数据对象,打开caffe.proto,就能找到它,代码如下(已经简化删除掉一些无关或废弃的字段,完整定义请参见caffe.proto)。

message SolverParameter {
  // prototxt格式的网络定义文件名
  optional string net = 24;
  // 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 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;
  optional int32 max_iter = 7; // the maximum number of iterations
  optional string lr_policy = 8;
  optional float gamma = 9; // The parameter to compute the learning rate.
  optional float power = 10; // The parameter to compute the learning rate.
  optional float momentum = 11; // The momentum value.
  optional float weight_decay = 12; // The weight decay.
  // the stepsize for learning rate policy "step"
  optional int32 stepsize = 13;
  // the stepsize for learning rate policy "multistep"
  repeated int32 stepvalue = 34;

  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
  // whenever their actual L2 norm is larger.
  optional float clip_gradients = 35 [default = -1];

  optional int32 snapshot = 14 [default = 0]; // The snapshot interval
  optional string snapshot_prefix = 15; // The prefix for the snapshot.
  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
  // 运算类型定义
  enum SolverMode {
    CPU = 0;
    GPU = 1;
  }
  // 运算模式 CPU/GPU
  optional SolverMode solver_mode = 17 [default = GPU];
}

那么同理,我们也可以推断lenet_solver.prototxt中定义的net文件lenet_train_test.prototxt也会被Caffe解析成一个叫NetParameter 数据对象,下面是NetParameter 的定义(已经简化删除掉一些无关或废弃的字段,完整定义请参见caffe.proto)

message NetParameter {
  // 网络名字 比如LeNet
  optional string name = 1; 
  repeated int32 input_dim = 4;
  optional bool force_backward = 5 [default = false];
  optional NetState state = 6;
  optional bool debug_info = 7 [default = false];
  // 保存所有layer对象的layer数组,prototxt中的每个layer最终都保存在这里
  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.
}

这里我们再看看lenet_train_test.prototxt,其中的name和layer就与NetParameter中的字段有了对应关系。

name: "LeNet"
// 每个layer都是NetParameter中layer数组的一个元素
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
  }
}
// 。。。略过
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}

再进一步NetParameter中layer的类型LayerParameter数组,那么在caffe.proto中找到LayerParameter,就可以搞知道LayerParameter的所有字段及含义描述:

message LayerParameter {
  optional string name = 1; // the layer name
  optional string type = 2; // the layer type
  repeated string bottom = 3; // the name of each bottom blob
  repeated string top = 4; // the name of each top blob

  // The train / test phase for computation.
  optional Phase phase = 10;

  // The amount of weight to assign each top blob in the objective.
  // Each layer assigns a default value, usually of either 0 or 1,
  // to each top blob.
  repeated float loss_weight = 5;

  // 略过.....

  optional MILParameter mil_param = 0x004d494c; //"MIL"
}

。。。由此类推,你可以找到每个数据对象的定义及说明,由此也能找到每个数据对象的cpp/h文件。

layer factory

LayerParameter定义中的type是一个字符串,指定了这个层的类型,C++又不像java有Class.forName()这样的方法可以直接将一个类名实例化为一个Class,那么这个type中的名字是如何与实际的C++对象联系在一起的呢?这是我一直想搞清楚的问题。 Caffe维护了一个name->创建layer对象函数指针的map.所以Caffe可以根据type字段指定的名字,创建对应的layer对象。具体实现代码参见caffe/layer_factory.hpp和caffe/layer_factory.cpp


caffe/layer_factory.hpp ,中文为本文作者添加

#ifndef CAFFE_LAYER_FACTORY_H_
#define CAFFE_LAYER_FACTORY_H_

#include <map>
#include <string>
#include <vector>

#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

namespace caffe {

template <typename Dtype>
class Layer;

template <typename Dtype>
class LayerRegistry {
 public:
  // 定义创建layer对象的函数指针类型
  typedef shared_ptr<Layer<Dtype> > (*Creator)(const LayerParameter&);
  // type -> layer对象的函数指针的映射类型
  typedef std::map<string, Creator> CreatorRegistry;

  static CreatorRegistry& Registry() {
    // 全局静态变量(map实例)
    static CreatorRegistry* g_registry_ = new CreatorRegistry();
    return *g_registry_;
  }

  // Adds a creator.向map中加入一个映射
  static void AddCreator(const string& type, Creator creator) {
    CreatorRegistry& registry = Registry();
    CHECK_EQ(registry.count(type), 0)
        << "Layer type " << type << " already registered.";
    registry[type] = creator;
  }

  // Get a layer using a LayerParameter.
  static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
    if (Caffe::root_solver()) {
      LOG(INFO) << "Creating layer " << param.name();
    }
    const string& type = param.type();
    CreatorRegistry& registry = Registry();
    CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
        << " (known types: " << LayerTypeListString() << ")";
    return registry[type](param);
  }

  static vector<string> LayerTypeList() {
    CreatorRegistry& registry = Registry();
    vector<string> layer_types;
    for (typename CreatorRegistry::iterator iter = registry.begin();
         iter != registry.end(); ++iter) {
      layer_types.push_back(iter->first);
    }
    return layer_types;
  }

 private:
  // Layer registry should never be instantiated - everything is done with its
  // static variables.
  LayerRegistry() {}

  static string LayerTypeListString() {
    vector<string> layer_types = LayerTypeList();
    string layer_types_str;
    for (vector<string>::iterator iter = layer_types.begin();
         iter != layer_types.end(); ++iter) {
      if (iter != layer_types.begin()) {
        layer_types_str += ", ";
      }
      layer_types_str += *iter;
    }
    return layer_types_str;
  }
};


template <typename Dtype>
class LayerRegisterer {
 public:
  LayerRegisterer(const string& type,
                  shared_ptr<Layer<Dtype> > (*creator)(const LayerParameter&)) {
    // LOG(INFO) << "Registering layer type: " << type;
    // 将指定创建layer对象的函数指针加入map
    LayerRegistry<Dtype>::AddCreator(type, creator);
  }
};

//宏定义用于将创建layer对象的函数指针加入map
#define REGISTER_LAYER_CREATOR(type, creator)                                  \
  static LayerRegisterer<float> g_creator_f_##type(#type, creator<float>);     \
  static LayerRegisterer<double> g_creator_d_##type(#type, creator<double>)    \

#define REGISTER_LAYER_CLASS(type)                                             \
  template <typename Dtype>                                                    \
  shared_ptr<Layer<Dtype> > Creator_##type##Layer(const LayerParameter& param) \
  {                                                                            \
    return shared_ptr<Layer<Dtype> >(new type##Layer<Dtype>(param));           \
  }                                                                            \
  REGISTER_LAYER_CREATOR(type, Creator_##type##Layer)

}  // namespace caffe

#endif  // CAFFE_LAYER_FACTORY_H_

caffe/layer_factory.cpp ,中文为本文作者添加

// Make sure we include Python.h before any system header
// to avoid _POSIX_C_SOURCE redefinition
#ifdef WITH_PYTHON_LAYER
#include <boost/python.hpp>
#endif
#include <string>

#include "caffe/layer.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/layers/conv_layer.hpp"
#include "caffe/layers/lrn_layer.hpp"
#include "caffe/layers/pooling_layer.hpp"
#include "caffe/layers/relu_layer.hpp"
#include "caffe/layers/sigmoid_layer.hpp"
#include "caffe/layers/softmax_layer.hpp"
#include "caffe/layers/tanh_layer.hpp"
#include "caffe/proto/caffe.pb.h"

#ifdef USE_CUDNN
#include "caffe/layers/cudnn_conv_layer.hpp"
#include "caffe/layers/cudnn_lcn_layer.hpp"
#include "caffe/layers/cudnn_lrn_layer.hpp"
#include "caffe/layers/cudnn_pooling_layer.hpp"
#include "caffe/layers/cudnn_relu_layer.hpp"
#include "caffe/layers/cudnn_sigmoid_layer.hpp"
#include "caffe/layers/cudnn_softmax_layer.hpp"
#include "caffe/layers/cudnn_tanh_layer.hpp"
#endif

#ifdef WITH_PYTHON_LAYER
#include "caffe/layers/python_layer.hpp"
#endif

namespace caffe {

// Get convolution layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetConvolutionLayer(
    const LayerParameter& param) {
  ConvolutionParameter conv_param = param.convolution_param();
  ConvolutionParameter_Engine engine = conv_param.engine();
#ifdef USE_CUDNN
  bool use_dilation = false;
  for (int i = 0; i < conv_param.dilation_size(); ++i) {
    if (conv_param.dilation(i) > 1) {
      use_dilation = true;
    }
  }
#endif
  if (engine == ConvolutionParameter_Engine_DEFAULT) {
    engine = ConvolutionParameter_Engine_CAFFE;
#ifdef USE_CUDNN
    if (!use_dilation) {
      engine = ConvolutionParameter_Engine_CUDNN;
    }
#endif
  }
  if (engine == ConvolutionParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == ConvolutionParameter_Engine_CUDNN) {
    if (use_dilation) {
      LOG(FATAL) << "CuDNN doesn't support the dilated convolution at Layer "
                 << param.name();
    }
    return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"Convolution"加入定义
REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);

// Get pooling layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
  PoolingParameter_Engine engine = param.pooling_param().engine();
  if (engine == PoolingParameter_Engine_DEFAULT) {
    engine = PoolingParameter_Engine_CAFFE;
#ifdef USE_CUDNN
    engine = PoolingParameter_Engine_CUDNN;
#endif
  }
  if (engine == PoolingParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == PoolingParameter_Engine_CUDNN) {
    if (param.top_size() > 1) {
      LOG(INFO) << "cuDNN does not support multiple tops. "
                << "Using Caffe's own pooling layer.";
      return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
    }
    // CuDNN assumes layers are not being modified in place, thus
    // breaking our index tracking for updates in some cases in Caffe.
    // Until there is a workaround in Caffe (index management) or
    // cuDNN, use Caffe layer to max pooling, or don't use in place
    // layers after max pooling layers
    if (param.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) {
        return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
    } else {
        return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
    }
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"Pooling"加入定义
REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);

// Get LRN layer according to engine
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetLRNLayer(const LayerParameter& param) {
  LRNParameter_Engine engine = param.lrn_param().engine();

  if (engine == LRNParameter_Engine_DEFAULT) {
#ifdef USE_CUDNN
    engine = LRNParameter_Engine_CUDNN;
#else
    engine = LRNParameter_Engine_CAFFE;
#endif
  }

  if (engine == LRNParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == LRNParameter_Engine_CUDNN) {
    LRNParameter lrn_param = param.lrn_param();

    if (lrn_param.norm_region() ==LRNParameter_NormRegion_WITHIN_CHANNEL) {
      return shared_ptr<Layer<Dtype> >(new CuDNNLCNLayer<Dtype>(param));
    } else {
      // local size is too big to be handled through cuDNN
      if (param.lrn_param().local_size() > CUDNN_LRN_MAX_N) {
        return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
      } else {
        return shared_ptr<Layer<Dtype> >(new CuDNNLRNLayer<Dtype>(param));
      }
    }
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"LRN"加入定义
REGISTER_LAYER_CREATOR(LRN, GetLRNLayer);

// Get relu layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
  ReLUParameter_Engine engine = param.relu_param().engine();
  if (engine == ReLUParameter_Engine_DEFAULT) {
    engine = ReLUParameter_Engine_CAFFE;
#ifdef USE_CUDNN
    engine = ReLUParameter_Engine_CUDNN;
#endif
  }
  if (engine == ReLUParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new ReLULayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == ReLUParameter_Engine_CUDNN) {
    return shared_ptr<Layer<Dtype> >(new CuDNNReLULayer<Dtype>(param));
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"ReLU"加入定义
REGISTER_LAYER_CREATOR(ReLU, GetReLULayer);

// Get sigmoid layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetSigmoidLayer(const LayerParameter& param) {
  SigmoidParameter_Engine engine = param.sigmoid_param().engine();
  if (engine == SigmoidParameter_Engine_DEFAULT) {
    engine = SigmoidParameter_Engine_CAFFE;
#ifdef USE_CUDNN
    engine = SigmoidParameter_Engine_CUDNN;
#endif
  }
  if (engine == SigmoidParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new SigmoidLayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == SigmoidParameter_Engine_CUDNN) {
    return shared_ptr<Layer<Dtype> >(new CuDNNSigmoidLayer<Dtype>(param));
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"Sigmoid"加入定义
REGISTER_LAYER_CREATOR(Sigmoid, GetSigmoidLayer);

// Get softmax layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetSoftmaxLayer(const LayerParameter& param) {
  SoftmaxParameter_Engine engine = param.softmax_param().engine();
  if (engine == SoftmaxParameter_Engine_DEFAULT) {
    engine = SoftmaxParameter_Engine_CAFFE;
#ifdef USE_CUDNN
    engine = SoftmaxParameter_Engine_CUDNN;
#endif
  }
  if (engine == SoftmaxParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new SoftmaxLayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == SoftmaxParameter_Engine_CUDNN) {
    return shared_ptr<Layer<Dtype> >(new CuDNNSoftmaxLayer<Dtype>(param));
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"Softmax"加入定义
REGISTER_LAYER_CREATOR(Softmax, GetSoftmaxLayer);

// Get tanh layer according to engine.
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetTanHLayer(const LayerParameter& param) {
  TanHParameter_Engine engine = param.tanh_param().engine();
  if (engine == TanHParameter_Engine_DEFAULT) {
    engine = TanHParameter_Engine_CAFFE;
#ifdef USE_CUDNN
    engine = TanHParameter_Engine_CUDNN;
#endif
  }
  if (engine == TanHParameter_Engine_CAFFE) {
    return shared_ptr<Layer<Dtype> >(new TanHLayer<Dtype>(param));
#ifdef USE_CUDNN
  } else if (engine == TanHParameter_Engine_CUDNN) {
    return shared_ptr<Layer<Dtype> >(new CuDNNTanHLayer<Dtype>(param));
#endif
  } else {
    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"TanH"加入定义
REGISTER_LAYER_CREATOR(TanH, GetTanHLayer);

#ifdef WITH_PYTHON_LAYER
template <typename Dtype>
shared_ptr<Layer<Dtype> > GetPythonLayer(const LayerParameter& param) {
  Py_Initialize();
  try {
    bp::object module = bp::import(param.python_param().module().c_str());
    bp::object layer = module.attr(param.python_param().layer().c_str())(param);
    return bp::extract<shared_ptr<PythonLayer<Dtype> > >(layer)();
  } catch (bp::error_already_set) {
    PyErr_Print();
    throw;
  }
}
// 使用layer_factory.hpp定义的REGISTER_LAYER_CREATOR宏将"Python"加入定义
REGISTER_LAYER_CREATOR(Python, GetPythonLayer);
#endif

// Layers that use their constructor as their default creator should be
// registered in their corresponding cpp files. Do not register them here.
}  // namespace caffe

这个文件的主要作用就是将net中用到的这些layer类型的添加到map,搞明白这个原理,我们也可以根据自己需要创建自己layer,然后如上加入map,就可以在自己的网络定义中使用自定义的layer。

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