上一次看了layer_factory.hpp中的内容，这一次关注一下.cpp中的内容，那么我们首先忽略python相关的文件，在整个caffe项目中似乎用到了很多boost项目中的内容来进行辅助工作，在构建python_layer的过程中也不例外，我估计这是一个warpper，所以我们先不要关注，假定现在不适用python来工作。

// 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

从头文件的包含中我们就可以看到，这里面将会注册这么几个层conv_layer，lrn_layer，pooling_layer，relu_layer，sigmoid_layer，softmax_layer，tanh_layer，如果开发者想要自己加入新的层，想要按照相关的规定来进行操作，具体接下来看，，其实我也搞不懂要进行怎样的操作，，真是o(╯□╰)o。。。。

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.";
  }
}

REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer);

为什么这个Creator这么复杂呢，其实主要是这个版本的caffe加入了CUDNN所以呢在进行层的申请的时候需要考虑是否支持CUDNN特性，我们先不要考虑CUDNN这个特性，加入这个函数中不包含CUDNN特性，那么这个函数将会非常简单，而不会包含这么多预编译指令，其实只需要使用ConvolutionLayer这个原始的构造函数即可；当然这个里面有一个dilation参数，这个是CNN网络中的一个参数选项，我还是没搞明白为什么在!use_dilation时才能够使用CUDNN引擎，蜜汁尴尬，，，，

然后你可以看到最后一个语句就是注册了层的产生器，这个产生器Creator就是刚才定义的函数，在layer_factory.hpp头文件中作者就提示过了，如果不是有multiple_backend需求，直接使用宏定义REGISTER_LAYER_CLASS(MyAwesome);即可完成任务，因为只要你的层的构造函数满足要求即可；下面的所有的层Creator都是按照这个套路来进行操作的，注意看最后的说明，如果你的层是自己定义的，那你需要在自己的cpp中来进行注册就行了，不要在这个文件中进行修改，终于明白为什么#define REGISTER_LAYER_CREATOR要使用static静态变量声明了，因为这样可以在编译的时候就导入了这个Creator了，其实这个声明放在cpp文件中只是执行一次，其他时候并不会执行。

// 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.";
  }
}

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.";
  }
}

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.";
  }
}
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.";
  }
}

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.";
  }
}

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.";
  }
}

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;
  }
}

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