Caffe源码(caffe version commit: 09868ac , date: 2015.08.15)中有一些重要的头文件，这里介绍下include/caffe/net.hpp文件的内容：
1. include文件：
(1)、<caffe/blob.hpp>：此文件的介绍可以参考：http://blog.youkuaiyun.com/fengbingchun/article/details/59106613
(2)、<caffe/common.hpp>：此文件的介绍可以参考：http://blog.youkuaiyun.com/fengbingchun/article/details/54955236
(3)、<caffe/layer.hpp>：此文件的介绍可以参考：http://blog.youkuaiyun.com/fengbingchun/article/details/60871052
(4)、<caffe/proto/caffe.pb.h>：此文件的介绍可以参考：http://blog.youkuaiyun.com/fengbingchun/article/details/55267162
(5)、<caffe/layer_factory.hpp>：此文件的介绍可以参考：http://blog.youkuaiyun.com/fengbingchun/article/details/54310956
2. 类Net：
通过合成和自动微分，网络同时定义了一个函数和其对应的梯度。通过合成各层的输出来计算这个函数，来执行给定的任务，并通过合成各层的向后传播过程来计算来自损失函数的梯度，从而学习任务。Caffe模型是端到端的机器学习引擎。
Net是由一系列层组成的有向五环(DAG)计算图，Caffe保留了计算图中所有的中间值以确保前向和反向迭代的准确性。一个典型的Net开始于data layer------从磁盘中加载数据，终止于loss layer------计算分类和重构这些任务的目标函数。
Net由一系列层和它们之间的相互连接构成，用的是一种文本建模语言(protobuf)。Net是通过protobuf文件来描述整个Net是怎么由layer组成的。
Caffe中网络的构建与设备无关。网络构建完之后，通过设置Caffe::mode()函数中的Caffe::set_mode()即可实现在CPU或GPU上的运行。CPU与GPU无缝切换并且独立于模型定义。
前传(forward)过程为给定的待推断的输入计算输出。在前传过程中，Caffe组合每一层的计算以得到整个模型的计算”函数”。本过程自底向上进行。
反传(backward)过程根据损失来计算梯度从而进行学习。在反传过程中，Caffe通过自动求导并反向组合每一层的梯度来计算整个网络的梯度。这就是反传过程的本质。本过程自顶向下进行。
反传过程以损失开始，然后根据输出计算梯度。根据链式准则，逐层计算出模型其余部分的梯度。有参数的层，会在反传过程中根据参数计算梯度。
只要定义好了模型，Caffe中前传和反传的计算就可以立即进行，Caffe已经准备好了前传和反传的实现方法。
实现方法：
(1)、Net::Forward()和Net::Backward()方法实现网络的前传和反传，而Layer::Forward()和Layer::Backward()计算每一层的前传和后传。
(2)、每一层都有forward_{cpu,gpu}()和backward_{cpu,gpu}方法来适应不同的计算模式。由于条件限制或者为了使用便利，一个层可能仅实现了CPU或者GPU模式。
与大多数的机器学习模型一样，在Caffe中，学习是由一个损失函数驱动的(通常也被称为误差、代价或者目标函数)。一个损失函数通过将参数集(即当前的网络权值)映射到一个可以标识这些参数”不良程度”的标量值来学习目标。因此，学习的目的是找到一个网络权重的集合，使得损失函数最小。
在Caffe中，损失是通过网络的前向计算得到的。每一层由一系列的输入blobs(bottom)，然后产生一系列的输出blobs(top)。这些层的某些输出可以用来作为损失函数。典型的一对多分类任务的损失函数是softMaxWithLoss函数。
Loss weights：对于含有多个损失层的网络(例如，一个网络使用一个softMaxWithLoss输入分类并使用EuclideanLoss层进行重构)，损失权值可以被用来指定它们之间的相对重要性。
按照惯例，有着Loss后缀的Caffe层对损失函数有贡献，其它层被假定仅仅用于中间计算。然而，通过在层定义中添加一个loss_weight:<float>字段到由该层的top blob，任何层都可以作为一个loss。对于带后缀Loss的层来说，其对于该层的第一个top blob含有一个隐式的loss_weight:1；其它层对应于所有top blob有一个隐式的loss_weight: 0。
然而，任何可以反向传播的层，可允许给予一个非0的loss_weight，例如，如果需要，对网络的某些中间层所产生的激活进行正则化。对于具有相关非0损失的非单输出，损失函数可以通过对所有blob求和来进行简单地计算。
那么，在Caffe中最终的损失函数可以通过对整个网络中所有的权值损失进行求和计算获得。
为了创建一个Caffe模型，需要在一个protobuf(.prototxt)文件中定义模型的结构。在Caffe中，层和相应的参数都定义在caffe.proto文件里。
注：以上关于Net内容的介绍主要摘自由CaffeCN社区翻译的《Caffe官方教程中译本》。
<caffe/net.hpp>文件的详细介绍如下：
#ifndef CAFFE_NET_HPP_
#define CAFFE_NET_HPP_
#include <map>
#include <set>
#include <string>
#include <utility>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layer_factory.hpp"
namespace caffe {
// 在图论中，如果一个有向图从任意顶点出发无法经过若干条边回到该点，则这个图是一个有向无环图(DAG图)
/**
* @brief Connects Layer%s together into a directed acyclic graph (DAG)
* specified by a NetParameter.
*
* TODO(dox): more thorough description.
*/
//
template <typename Dtype>
class Net {
public:
// 显示构造函数，内部调用Init函数
explicit Net(const NetParameter& param, const Net* root_net = NULL);
explicit Net(const string& param_file, Phase phase, const Net* root_net = NULL);
// 虚析构函数
virtual ~Net() {}
/// @brief Initialize a network with a NetParameter.
// Net初始化:创建blobs和layers以搭建整个网络DAG图，以及调用layer的SetUp函数，
// 初始化时也会做另一些记录，例如确认整个网络结构的正确与否等，
// 另外，初始化期间，Net会打印其初始化日志到INFO信息中
void Init(const NetParameter& param);
/**
* @brief Run Forward with the input Blob%s already fed separately.
*
* You can get the input blobs using input_blobs().
*/
// 前向传播，以下相关的前向传播函数，内部最终均会调用ForwardFromTo函数
const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
/**
* The From and To variants of Forward and Backward operate on the
* (topological) ordering by which the net is specified. For general DAG
* networks, note that (1) computing from one layer to another might entail
* extra computation on unrelated branches, and (2) computation starting in
* the middle may be incorrect if all of the layers of a fan-in are not
* included.
*/
Dtype ForwardFromTo(int start, int end);
Dtype ForwardFrom(int start);
Dtype ForwardTo(int end);
/// @brief Run forward using a set of bottom blobs, and return the result.
const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom, Dtype* loss = NULL);
/**
* @brief Run forward using a serialized BlobProtoVector and return the
* result as a serialized BlobProtoVector
*/
string Forward(const string& input_blob_protos, Dtype* loss = NULL);
/**
* @brief Zeroes out the diffs of all net parameters.
* Should be run before Backward.
*/
// 对Net中的所有diff_数据清零
void ClearParamDiffs();
/**
* The network backward should take no input and output, since it solely
* computes the gradient w.r.t the parameters, and the data has already been
* provided during the forward pass.
*/
// 反向传播,以下相关的反向传播函数，内部最终均会调用BackwardFromTo函数
void Backward();
void BackwardFromTo(int start, int end);
void BackwardFrom(int start);
void BackwardTo(int end);
/**
* @brief Reshape all layers from bottom to top.
*
* This is useful to propagate changes to layer sizes without running
* a forward pass, e.g. to compute output feature size.
*/
// 调整layes shape
void Reshape();
// 前向反向传播
Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
Dtype loss;
Forward(bottom, &loss);
Backward();
return loss;
}
/// @brief Updates the network weights based on the diff values computed.
// 更新Net权值和偏置
void Update();
/**
* @brief Shares weight data of owner blobs with shared blobs.
*
* Note: this is called by Net::Init, and thus should normally not be
* called manually.
*/
// 共享权值和偏置数据
void ShareWeights();
/**
* @brief For an already initialized net, implicitly copies (i.e., using no
* additional memory) the pre-trained layers from another Net.
*/
// 从另一个Net拷贝train layers
void ShareTrainedLayersWith(const Net* other);
// For an already initialized net, CopyTrainedLayersFrom() copies the already
// trained layers from another net parameter instance.
/**
* @brief For an already initialized net, copies the pre-trained layers from
* another Net.
*/
// 从另一个Net拷贝train layers，加载已训练好的模型
void CopyTrainedLayersFrom(const NetParameter& param);
void CopyTrainedLayersFrom(const string trained_filename);
void CopyTrainedLayersFromBinaryProto(const string trained_filename);
void CopyTrainedLayersFromHDF5(const string trained_filename);
/// @brief Writes the net to a proto.
// 写Net到NetParameter
void ToProto(NetParameter* param, bool write_diff = false) const;
/// @brief Writes the net to an HDF5 file.
// 写Net weights到HDF5文件
void ToHDF5(const string& filename, bool write_diff = false) const;
/// @brief returns the network name.
// 获得Net名
inline const string& name() const { return name_; }
/// @brief returns the layer names
// 获得所有layer名
inline const vector<string>& layer_names() const { return layer_names_; }
/// @brief returns the blob names
// 获得blob名
inline const vector<string>& blob_names() const { return blob_names_; }
/// @brief returns the blobs
// 获得blob
inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const { return blobs_; }
/// @brief returns the layers
// 获得layer
inline const vector<shared_ptr<Layer<Dtype> > >& layers() const { return layers_; }
/// @brief returns the phase: TRAIN or TEST
// 获得Net phase状态：train or test
inline Phase phase() const { return phase_; }
/**
* @brief returns the bottom vecs for each layer -- usually you won't
* need this unless you do per-layer checks such as gradients.
*/
// 获得每一个layer的bottom vector
inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const { return bottom_vecs_; }
/**
* @brief returns the top vecs for each layer -- usually you won't
* need this unless you do per-layer checks such as gradients.
*/
// 获得每一个layer的top vector
inline const vector<vector<Blob<Dtype>*> >& top_vecs() const { return top_vecs_; }
inline const vector<vector<bool> >& bottom_need_backward() const { return bottom_need_backward_; }
inline const vector<Dtype>& blob_loss_weights() const { return blob_loss_weights_; }
inline const vector<bool>& layer_need_backward() const { return layer_need_backward_; }
/// @brief returns the parameters
// 获得各种参数值
inline const vector<shared_ptr<Blob<Dtype> > >& params() const { return params_; }
inline const vector<Blob<Dtype>*>& learnable_params() const { return learnable_params_; }
/// @brief returns the learnable parameter learning rate multipliers
inline const vector<float>& params_lr() const { return params_lr_; }
inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
/// @brief returns the learnable parameter decay multipliers
inline const vector<float>& params_weight_decay() const { return params_weight_decay_; }
inline const vector<bool>& has_params_decay() const { return has_params_decay_; }
const map<string, int>& param_names_index() const { return param_names_index_; }
inline const vector<int>& param_owners() const { return param_owners_; }
/// @brief Input and output blob numbers
// input blob数目
inline int num_inputs() const { return net_input_blobs_.size(); }
// output blob数目
inline int num_outputs() const { return net_output_blobs_.size(); }
inline const vector<Blob<Dtype>*>& input_blobs() const { return net_input_blobs_; }
inline const vector<Blob<Dtype>*>& output_blobs() const { return net_output_blobs_; }
inline const vector<int>& input_blob_indices() const { return net_input_blob_indices_; }
inline const vector<int>& output_blob_indices() const { return net_output_blob_indices_; }
bool has_blob(const string& blob_name) const;
const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
bool has_layer(const string& layer_name) const;
const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;
// 设置是否显示debug info
void set_debug_info(const bool value) { debug_info_ = value; }
// Helpers for Init.
/**
* @brief Remove layers that the user specified should be excluded given the current
* phase, level, and stage.
*/
// 移除指定的layers
static void FilterNet(const NetParameter& param, NetParameter* param_filtered);
/// @brief return whether NetState state meets NetStateRule rule
static bool StateMeetsRule(const NetState& state, const NetStateRule& rule, const string& layer_name);
protected:
// Helpers for Init.
/// @brief Append a new input or top blob to the net.
// 追加top blob
void AppendTop(const NetParameter& param, const int layer_id,
const int top_id, set<string>* available_blobs,
map<string, int>* blob_name_to_idx);
/// @brief Append a new bottom blob to the net.
// 追加bottom blob
int AppendBottom(const NetParameter& param, const int layer_id,
const int bottom_id, set<string>* available_blobs,
map<string, int>* blob_name_to_idx);
/// @brief Append a new parameter blob to the net.
// 追加blob参数
void AppendParam(const NetParameter& param, const int layer_id, const int param_id);
// 显示debug info
/// @brief Helper for displaying debug info in Forward about input Blobs.
void InputDebugInfo(const int layer_id);
/// @brief Helper for displaying debug info in Forward.
void ForwardDebugInfo(const int layer_id);
/// @brief Helper for displaying debug info in Backward.
void BackwardDebugInfo(const int layer_id);
/// @brief Helper for displaying debug info in Update.
void UpdateDebugInfo(const int param_id);
// Caffe中类的成员变量名都带有后缀"_"，这样就容易区分临时变量和类成员变量
/// @brief The network name
string name_; // Net名
/// @brief The phase: TRAIN or TEST
Phase phase_; // Net Phase状态：train or test
/// @brief Individual layers in the net
vector<shared_ptr<Layer<Dtype> > > layers_; // layers
vector<string> layer_names_; // layers名
map<string, int> layer_names_index_; // layers 索引
vector<bool> layer_need_backward_; // 指定layers是否需要backward
/// @brief the blobs storing intermediate results between the layer.
vector<shared_ptr<Blob<Dtype> > > blobs_; // 存储每一个layer产生的中间结果
vector<string> blob_names_; // blobs名
map<string, int> blob_names_index_; // blobs 索引
vector<bool> blob_need_backward_; // 指定blobs是否需要backward
/// bottom_vecs stores the vectors containing the input for each layer.
/// They don't actually host the blobs (blobs_ does), so we simply store pointers.
vector<vector<Blob<Dtype>*> > bottom_vecs_; // 存储每一个layer input bottom blobs 指针
vector<vector<int> > bottom_id_vecs_; // 存储每一个bottom blobs id
vector<vector<bool> > bottom_need_backward_; // 指定bottom blobs是否需要backward
/// top_vecs stores the vectors containing the output for each layer
vector<vector<Blob<Dtype>*> > top_vecs_; // 存储每一个layer output top blobs 指针
vector<vector<int> > top_id_vecs_; // 存储每一个layer output top blobs id
/// Vector of weight in the loss (or objective) function of each net blob,
/// indexed by blob_id.
vector<Dtype> blob_loss_weights_; // layer 的loss函数值
vector<vector<int> > param_id_vecs_; //
vector<int> param_owners_;
vector<string> param_display_names_;
vector<pair<int, int> > param_layer_indices_;
map<string, int> param_names_index_;
/// blob indices for the input and the output of the net
vector<int> net_input_blob_indices_;
vector<int> net_output_blob_indices_;
vector<Blob<Dtype>*> net_input_blobs_;
vector<Blob<Dtype>*> net_output_blobs_;
/// The parameters in the network.
vector<shared_ptr<Blob<Dtype> > > params_; //
vector<Blob<Dtype>*> learnable_params_;
/**
* The mapping from params_ -> learnable_params_: we have
* learnable_param_ids_.size() == params_.size(),
* and learnable_params_[learnable_param_ids_[i]] == params_[i].get()
* if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer
* and learnable_params_[learnable_param_ids_[i]] gives its owner.
*/
vector<int> learnable_param_ids_;
/// the learning rate multipliers for learnable_params_
vector<float> params_lr_;
vector<bool> has_params_lr_;
/// the weight decay multipliers for learnable_params_
vector<float> params_weight_decay_;
vector<bool> has_params_decay_;
/// The bytes of memory used by this net
size_t memory_used_;
/// Whether to compute and display debug info for the net.
bool debug_info_; // 是否显示debug info
/// The root net that actually holds the shared layers in data parallelism
const Net* const root_net_;
// 禁止使用Net类的拷贝和赋值操作
DISABLE_COPY_AND_ASSIGN(Net);
};
} // namespace caffe
#endif // CAFFE_NET_HPP_message NetParameter { // Net参数
optional string name = 1; // consider giving the network a name,Net名
// The input blobs to the network.
repeated string input = 3; // Net的输入blobs
// The shape of the input blobs.
repeated BlobShape input_shape = 8; // 输入blobs的shape
// 4D input dimensions -- deprecated. Use "shape" instead.
// If specified, for each input blob there should be four
// values specifying the num, channels, height and width of the input blob.
// Thus, there should be a total of (4 * #input) numbers.
repeated int32 input_dim = 4; // 输入blobs的维度，已被废弃，推荐用BlobShape代替
// Whether the network will force every layer to carry out backward operation.
// If set False, then whether to carry out backward is determined
// automatically according to the net structure and learning rates.
optional bool force_backward = 5 [default = false]; // 是否每一层都需要执行反向操作
// The current "state" of the network, including the phase, level, and stage.
// Some layers may be included/excluded depending on this state and the states
// specified in the layers' include and exclude fields.
optional NetState state = 6; // Net三种状态：Phase、level、stage
// Print debugging information about results while running Net::Forward,
// Net::Backward, and Net::Update.
optional bool debug_info = 7 [default = false]; // 是否打印Net的前向、反向、更新的结果
// The layers that make up the net. Each of their configurations, including
// connectivity and behavior, is specified as a LayerParameter.
repeated LayerParameter layer = 100; // ID 100 so layers are printed last. layer参数
// DEPRECATED: use 'layer' instead.
repeated V1LayerParameter layers = 2; // 已被废弃，用LayerParameter代替
}#include "funset.hpp"
#include <string>
#include <vector>
#include <map>
#include "common.hpp"
int test_caffe_net2()
{
caffe::Caffe::set_mode(caffe::Caffe::CPU); // set run caffe mode
// reference: caffe/src/caffe/test/test_net.cpp
std::string prototxt{ "E:/GitCode/Caffe_Test/test_data/model/test_net_8.prototxt" };
caffe::Phase phase = caffe::Phase::TRAIN;
// 1. Net(const string& param_file, Phase phase, const Net* root_net = NULL)
boost::shared_ptr<caffe::Net<float>> net(new caffe::Net<float>(prototxt, phase, nullptr));
//caffe::Caffe::set_random_seed(1701);
{
std::vector<caffe::Blob<float>*> bottom;
// 2. Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom)
float loss = net->ForwardBackward(bottom);
fprintf(stderr, "loss: %f\n", loss);
}
{
// 3. Dtype ForwardFromTo(int start, int end)
float loss = net->ForwardFromTo(0, net->layers().size() - 1);
// 4. void BackwardFromTo(int start, int end)
net->BackwardFromTo(net->layers().size() - 1, 0);
fprintf(stderr, "loss: %f\n", loss);
}
{
// 5. Dtype ForwardTo(int end)
float loss = net->ForwardTo(net->layers().size() - 2);
// 6. void BackwardFrom(int start)
net->BackwardFrom(net->layers().size() - 2);
fprintf(stderr, "loss: %f\n", loss);
}
{
// 7. Dtype ForwardFrom(int start)
float loss = net->ForwardFrom(1);
// 8. void BackwardTo(int end)
net->BackwardTo(1);
fprintf(stderr, "loss: %f\n", loss);
}
{
// 9. vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL)
float loss;
std::vector<caffe::Blob<float>*> net_output_blobs = net->ForwardPrefilled(&loss);
// 10. void Backward()
net->Backward();
fprintf(stderr, "net output blobs size: %d; loss: %f\n", net_output_blobs.size(), loss);
}
{
// 11. string Forward(const string& input_blob_protos, Dtype* loss = NULL)
std::string input_blob_protos{ " " };
float loss;
std::string output = net->Forward(input_blob_protos, &loss);
net->Backward();
fprintf(stderr, "output string: %s; loss: %f\n", output.c_str(), loss);
}
{
// 12. vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom, Dtype* loss = NULL)
std::vector<caffe::Blob<float>*> bottom;
float loss;
std::vector<caffe::Blob<float>*> net_output_blobs = net->Forward(bottom, &loss);
net->Backward();
fprintf(stderr, "net output blobs size: %d; loss: %f\n", net_output_blobs.size(), loss);
}
// 13. void ShareWeights()
net->ShareWeights();
// 14. void Update()
net->Update();
// 15. void Reshape()
net->Reshape();
// 16. void ClearParamDiffs()
net->ClearParamDiffs();
// 17. void CopyTrainedLayersFrom(const NetParameter& param)
caffe::NetParameter net_param;
net->ToProto(&net_param, false);
net->CopyTrainedLayersFrom(net_param);
// 加载已训练好的模型
// 18. void CopyTrainedLayersFrom(const string trained_filename)
std::string trained_filename{ " " };
//net->CopyTrainedLayersFrom(trained_filename);
// 19. void CopyTrainedLayersFromBinaryProto(const string trained_filename)
//net->CopyTrainedLayersFromBinaryProto(trained_filename);
// 20. void CopyTrainedLayersFromHDF5(const string trained_filename)
//net->CopyTrainedLayersFromHDF5(trained_filename);
// 21. void ShareTrainedLayersWith(const Net* other)
caffe::Net<float> net1(prototxt, phase, nullptr);
net->ShareTrainedLayersWith(&net1);
// 22. static void FilterNet(const NetParameter& param, NetParameter* param_filtered)
caffe::NetParameter param1, param2;
net->FilterNet(param1, ¶m2);
// 23. static bool StateMeetsRule(const NetState& state, const NetStateRule& rule, const string& layer_name)
const caffe::NetState state;
const caffe::NetStateRule rule;
const std::string layer_name;
bool ret = net->StateMeetsRule(state, rule, layer_name);
fprintf(stderr, "state meet rule: %d\n", ret);
return 0;
}
int test_caffe_net1()
{
caffe::Caffe::set_mode(caffe::Caffe::CPU); // set run caffe mode
// reference: caffe/src/caffe/test/test_net.cpp
std::string prototxt{"E:/GitCode/Caffe_Test/test_data/model/test_net_8.prototxt"}; // 1~8
caffe::NetParameter param;
caffe::ReadNetParamsFromTextFileOrDie(prototxt, ¶m);
// 1. Net(const NetParameter& param, const Net* root_net = NULL)
boost::shared_ptr<caffe::Net<float>> net(new caffe::Net<float>(param, nullptr));
// 2. const string& name()
std::string name = net->name();
fprintf(stderr, "Net name: %s\n", name.c_str());
// 3. const vector<string>& layer_names()
std::vector<std::string> layer_names = net->layer_names();
fprintf(stderr, "print all layer names: layer size: %d\n", layer_names.size());
for (auto layer_name : layer_names) {
fprintf(stderr, " %s\n", layer_name.c_str());
}
// 4. const vector<string>& blob_names()
std::vector<std::string> blob_names = net->blob_names();
fprintf(stderr, "print all blob names: blob size: %d\n", blob_names.size());
for (auto blob_name : blob_names) {
fprintf(stderr, " %s\n", blob_name.c_str());
}
// 5. const vector<shared_ptr<Blob<Dtype> > >& blobs()
std::vector<boost::shared_ptr<caffe::Blob<float>>> blobs = net->blobs();
fprintf(stderr, "print all blobs dim: blob size: %d\n", blobs.size());
for (auto blob : blobs) {
std::vector<int> shape = blob->shape();
fprintf(stderr, "blob dim: %d, ", shape.size());
for (auto value : shape) {
fprintf(stderr, " %d ", value);
}
fprintf(stderr, "\n");
}
// 6. const vector<shared_ptr<Layer<Dtype> > >& layers()
std::vector<boost::shared_ptr<caffe::Layer<float>>> layers = net->layers();
fprintf(stderr, "print all layers bottom and top blobs num: layer size: %d\n", layers.size());
for (const auto layer : layers) {
fprintf(stderr, "layer type: %s, bottom blob num: %d, top blob num: %d\n",
layer->type(), layer->ExactNumBottomBlobs(), layer->ExactNumTopBlobs());
}
// 7. Phase phase()
caffe::Phase phase = net->phase();
fprintf(stderr, "net phase: %d\n", phase);
// 8. const vector<vector<Blob<Dtype>*> >& bottom_vecs()
std::vector<std::vector<caffe::Blob<float>*>> bottom_vecs = net->bottom_vecs();
fprintf(stderr, "print layer bottom blob: layer size: %d\n", bottom_vecs.size());
for (auto layer : bottom_vecs) {
for (auto blob : layer) {
fprintf(stderr, "layer blob shape: %s\n", (blob->shape_string()).c_str());
}
}
// 9. const vector<vector<Blob<Dtype>*> >& top_vecs()
std::vector<std::vector<caffe::Blob<float>*>> top_vecs = net->top_vecs();
fprintf(stderr, "print layer top blol: layer size: %d\n", top_vecs.size());
for (auto layer : top_vecs) {
for (const auto blob : layer) {
fprintf(stderr, "layer top shape: %s\n", (blob->shape_string()).c_str());
}
}
// 10. const vector<vector<bool> >& bottom_need_backward()
std::vector<std::vector<bool>> bottom_need_backward = net->bottom_need_backward();
fprintf(stderr, "print bottom need backward info: layer size: %d\n", bottom_need_backward.size());
for (auto layer : bottom_need_backward) {
for (auto flag : layer) {
fprintf(stderr, " %s ", flag ? "true" : "false");
}
fprintf(stderr, "\n");
}
fprintf(stderr, "\n");
// 11. const vector<Dtype>& blob_loss_weights()
std::vector<float> blob_loss_weights = net->blob_loss_weights();
fprintf(stderr, "print blob loss weights: blob size: %d\n", blob_loss_weights.size());
for (auto weight : blob_loss_weights) {
fprintf(stderr, "weight: %f\n", weight);
}
// 12. const vector<bool>& layer_need_backward()
std::vector<bool> layer_need_backward = net->layer_need_backward();
fprintf(stderr, "print layer need backward: layer size: %d\n", layer_need_backward.size());
for (auto flag : layer_need_backward) {
fprintf(stderr, "layer need backward: %s\n", flag ? "true" : "false");
}
// 13. const vector<shared_ptr<Blob<Dtype> > >& params()
std::vector<boost::shared_ptr<caffe::Blob<float>>> params = net->params();
fprintf(stderr, "print net params info: blob size: %d\n", params.size());
for (auto blob : params) {
fprintf(stderr, "blob shape: %s\n", blob->shape_string().c_str());
}
// 14. const vector<Blob<Dtype>*>& learnable_params()
std::vector<caffe::Blob<float>*> learnable_params = net->learnable_params();
fprintf(stderr, "print learnable params info: blob size: %d\n", learnable_params.size());
for (const auto blob : learnable_params) {
fprintf(stderr, "blob shape: %s\n", blob->shape_string().c_str());
}
// 15. const vector<float>& params_lr()
std::vector<float> params_lr = net->params_lr();
fprintf(stderr, "print learnable rate info: size: %d\n", params_lr.size());
for (auto value : params_lr) {
fprintf(stderr, "learnable rate: %f\n", value);
}
// 16. const vector<bool>& has_params_lr()
std::vector<bool> has_params_lr = net->has_params_lr();
fprintf(stderr, "print has learnable rate info: size: %d\n", has_params_lr.size());
for (auto flag : has_params_lr) {
fprintf(stderr, "has learnable rate: %s\n", flag ? "true" : "false");
}
// 17. const vector<float>& params_weight_decay()
std::vector<float> params_weight_decay = net->params_weight_decay();
fprintf(stderr, "print weight decay info: size: %d\n", params_weight_decay.size());
for (auto value : params) {
fprintf(stderr, "weight decay: %f\n", value);
}
// 18. const vector<bool>& has_params_decay()
std::vector<bool> has_params_decay = net->has_params_decay();
fprintf(stderr, "print has decay info: size: %d\n", has_params_decay.size());
for (auto value : has_params_decay) {
fprintf(stderr, "has decay: %s\n", value ? "true" : "false");
}
// 19. const map<string, int>& param_names_index()
const std::map<std::string, int> param_names_index = net->param_names_index();
fprintf(stderr, "print param names index info: size: %d\n", param_names_index.size());
auto it = param_names_index.begin();
while (it != param_names_index.end()) {
fprintf(stderr, "param names index: %s : %d\n", it->first.c_str(), it->second);
++it;
}
// 20. const vector<int>& param_owners()
std::vector<int> param_owers = net->param_owners();
fprintf(stderr, "print param owers info: size: %d\n", param_owers.size());
for (auto value : param_owers) {
fprintf(stderr, "param owers: %d\n", value);
}
// 21. int num_inputs() const
int num_inputs = net->num_inputs();
fprintf(stderr, "num inputs: %d\n", num_inputs);
// 22. int num_outputs() const
int num_outputs = net->num_outputs();
fprintf(stderr, "num outputs: %d\n", num_outputs);
// 23. const vector<Blob<Dtype>*>& input_blobs()
const std::vector<caffe::Blob<float>*> input_blobs = net->input_blobs();
fprintf(stderr, "print input blobs info: %d\n", input_blobs.size());
for (auto blob : input_blobs) {
fprintf(stderr, "input blob shape: %s\n", blob->shape_string().c_str());
}
// 24. const vector<Blob<Dtype>*>& output_blobs()
const std::vector<caffe::Blob<float>*> output_blobs = net->output_blobs();
fprintf(stderr, "print output blobs info: %d\n", output_blobs.size());
for (auto blob : output_blobs) {
fprintf(stderr, "output blob shape: %s\n", blob->shape_string().c_str());
}
// 25. const vector<int>& input_blob_indices()
std::vector<int> input_blob_indices = net->input_blob_indices();
fprintf(stderr, "print input blob indices info: size: %d\n", input_blob_indices.size());
for (auto value : input_blob_indices) {
fprintf(stderr, "input blob indices: %d\n", value);
}
// 26. const vector<int>& output_blob_indices()
std::vector<int> output_blob_indices = net->output_blob_indices();
fprintf(stderr, "print output blob indices info: size: %d\n", output_blob_indices.size());
for (auto value : output_blob_indices) {
fprintf(stderr, "output blob indices: %d\n", value);
}
// 27. bool has_blob(const string& blob_name)
bool has_blob1 = net->has_blob("data");
bool has_blob2 = net->has_blob("loss");
fprintf(stderr, "net has blob data: %d, has blob loss: %d\n", has_blob1, has_blob2);
// 28. const shared_ptr<Blob<Dtype> > blob_by_name
const std::vector<std::string> blob_by_names{ "innerproduct", "loss" };
for (auto name : blob_by_names) {
const boost::shared_ptr<caffe::Blob<float>> blob = net->blob_by_name(name);
if (blob != nullptr)
fprintf(stderr, "blob shape: %s\n", blob->shape_string().c_str());
else
fprintf(stderr, "unknown blob name: %s\n", name.c_str());
}
// 29. bool has_layer(const string& layer_name)
const std::vector<std::string> has_layers{"innerproduct", "top_loss"};
for (auto name : has_layers) {
bool has_layer = net->has_layer(name);
fprintf(stderr, "has layer %s: %d\n", name.c_str(), has_layer);
}
// 30. const shared_ptr<Layer<Dtype> > layer_by_name
const std::vector<std::string> layer_by_names{ "data", "top_loss" };
for (auto name : layer_by_names) {
const boost::shared_ptr<caffe::Layer<float>> layer = net->layer_by_name(name);
if (layer != nullptr)
fprintf(stderr, "layer type: %s\n", layer->type());
else
fprintf(stderr, "unknown layer name: %s\n", name.c_str());
}
// 31. void set_debug_info(const bool value)
net->set_debug_info(true);
// 32. void ToHDF5(const string& filename, bool write_diff = false)
// std::string hdf5_name{"E:/GitCode/Caffe_Test/test_data/hdf5.h5"};
// net->ToHDF5(hdf5_name, false); // Note: some .prototxt will crash
// 33. void ToProto(NetParameter* param, bool write_diff = false)
caffe::NetParameter param2;
net->ToProto(¶m2, false);
fprintf(stderr, "new net name: %s\n", param2.name().c_str());
return 0;
}
扫一扫
3301
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
