#include <algorithm>

#include <map>

#include <set>

#include <string>

#include <utility>

#include <vector>

#include "hdf5.h"

#include "caffe/common.hpp"

#include "caffe/layer.hpp"

#include "caffe/net.hpp"

#include "caffe/parallel.hpp"

#include "caffe/proto/caffe.pb.h"

#include "caffe/util/hdf5.hpp"

#include "caffe/util/insert_splits.hpp"

#include "caffe/util/math_functions.hpp"

#include "caffe/util/upgrade_proto.hpp"

#include "caffe/test/test_caffe_main.hpp"

namespace caffe {

template <typename Dtype>

Net<Dtype>::Net(const NetParameter& param, const Net* root_net)

: root_net_(root_net) {

Init(param);

}

template <typename Dtype>

Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)

: root_net_(root_net) {

NetParameter param;

ReadNetParamsFromTextFileOrDie(param_file, &param);

param.mutable_state()->set_phase(phase);

Init(param);

}

template <typename Dtype>

void Net<Dtype>::Init(const NetParameter& in_param) {

CHECK(Caffe::root_solver() || root_net_)

<< "root_net_ needs to be set for all non-root solvers";

// 得到是训练网络还是测试网络

phase_ = in_param.state().phase();

/*

// Filter layers based on their include/exclude rules and

// the current NetState.

// 传入网络结构参数，然后可以根据LayerParameter中的

// include和exclude来确定该层是否应该包含在网络中，返回过滤过后的网络参数

NetParameter filtered_param;

FilterNet(in_param, &filtered_param);

LOG_IF(INFO, Caffe::root_solver())

<< "Initializing net from parameters: " << std::endl

<< filtered_param.DebugString();

// Create a copy of filtered_param with splits added where necessary.

/*

NetParameter param;

InsertSplits(filtered_param, &param);

// Basically, build all the layers and set up their connections.

name_ = param.name();

map<string, int> blob_name_to_idx;

LOG(INFO) << " -> " <<" "<<(blob_name_to_idx).size()<<"heheda";

//

set<string> available_blobs;

memory_used_ = 0;

// For each layer, set up its input and output

// resize是改变容器的大小，并且使用默认构造函数创建对象

// 参数初始化

bottom_vecs_.resize(param.layer_size());

top_vecs_.resize(param.layer_size());

bottom_id_vecs_.resize(param.layer_size());

param_id_vecs_.resize(param.layer_size());

top_id_vecs_.resize(param.layer_size());

bottom_need_backward_.resize(param.layer_size());

// 循环每一层

for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {

// For non-root solvers, whether this layer is shared from root_net_.

// 默认为false

bool share_from_root = !Caffe::root_solver()

&& root_net_->layers_[layer_id]->ShareInParallel();

// Inherit phase from net if unset.

// 如果每一层没有设置phase，则从网络参数中继承

if (!param.layer(layer_id).has_phase()) {

param.mutable_layer(layer_id)->set_phase(phase_);

}

// Setup layer.

// 没一层的结构参数常量

const LayerParameter& layer_param = param.layer(layer_id);

// 是否设置了对输入求导,参考caffe.proto里LayerParameter的propagate_down参数

if (layer_param.propagate_down_size() > 0) {

CHECK_EQ(layer_param.propagate_down_size(),

layer_param.bottom_size())

<< "propagate_down param must be specified "

<< "either 0 or bottom_size times ";

}

if (share_from_root) {

LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";

layers_.push_back(root_net_->layers_[layer_id]);

layers_[layer_id]->SetShared(true);

}

else {

//把每一特定层的指针存放在容器中

layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));

}

//存放网络中每一层的名称

layer_names_.push_back(layer_param.name());

LOG_IF(INFO, Caffe::root_solver())

<< "Creating Layer " << layer_param.name();

// 判断每层是否需要反向传播

bool need_backward = false;

// Figure out this layer's input and output

// 计算这一层的输入和输出,注意第一层他妈的没有输出bottom，所以在第一层的时候并不会进入循环

// 这个地方耽误了半天，妈的

for (int bottom_id = 0; bottom_id < layer_param.bottom_size();

++bottom_id) {

//LOG(INFO) << " -> " <<" "<<(blob_name_to_idx).size()<<"sbheheda";

const int blob_id = AppendBottom(param, layer_id, bottom_id,

&available_blobs, &blob_name_to_idx);

need_backward |= blob_need_backward_[blob_id];

}

// 每一层输出数据的个数

int num_top = layer_param.top_size();

// 对每层的每个输出数据

for (int top_id = 0; top_id < num_top; ++top_id) {

AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);

// Collect Input layer tops as Net inputs.

if (layer_param.type() == "Input") {

const int blob_id = blobs_.size() - 1;

net_input_blob_indices_.push_back(blob_id);

net_input_blobs_.push_back(blobs_[blob_id].get());

}

}

// If the layer specifies that AutoTopBlobs() -> true and the LayerParameter

// specified fewer than the required number (as specified by

// ExactNumTopBlobs() or MinTopBlobs()), allocate them here.

Layer<Dtype>* layer = layers_[layer_id].get();

if (layer->AutoTopBlobs()) {

const int needed_num_top =

std::max(layer->MinTopBlobs(), layer->ExactNumTopBlobs());

for (; num_top < needed_num_top; ++num_top) {

// Add "anonymous" top blobs -- do not modify available_blobs or

// blob_name_to_idx as we don't want these blobs to be usable as input

// to other layers.

AppendTop(param, layer_id, num_top, NULL, NULL);

}

}

// After this layer is connected, set it up.

if (share_from_root) {

// Set up size of top blobs using root_net_

const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];

const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];

for (int top_id = 0; top_id < base_top.size(); ++top_id) {

this_top[top_id]->ReshapeLike(*base_top[top_id]);

LOG(INFO) << "Created top blob " << top_id << " (shape: "

<< this_top[top_id]->shape_string() << ") for shared layer "

<< layer_param.name();

}

}

else {

// 设置layers实例

// 调用layer类的Setup函数进行初始化，输入参数：每个layer的输入blobs以及输出blobs

// 为每个blob设置大小

// 设置每一层的可学习参数，保存早layer的成员blobs_中

layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);

}

LOG_IF(INFO, Caffe::root_solver())

<< "Setting up " << layer_names_[layer_id];

// 对每一层的输出blobs循环

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {

if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {

blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));

}

blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);

LOG_IF(INFO, Caffe::root_solver())

<< "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();

if (layer->loss(top_id)) {

LOG_IF(INFO, Caffe::root_solver())

<< " with loss weight " << layer->loss(top_id);

}

// 计算网络所使用的字节数

memory_used_ += top_vecs_[layer_id][top_id]->count();

}

// 打印目前所需的存储

LOG_IF(INFO, Caffe::root_solver())

<< "Memory required for data: " << memory_used_ * sizeof(Dtype);

// param通常用来设置学习率之类的参数，每层的param有多少个则说明至少有这么多个

// 可学习参数

const int param_size = layer_param.param_size();

//可学习参数个数

const int num_param_blobs = layers_[layer_id]->blobs().size();

CHECK_LE(param_size, num_param_blobs)

<< "Too many params specified for layer " << layer_param.name();

ParamSpec default_param_spec;

// 对每一个可学习的参数循环

for (int param_id = 0; param_id < num_param_blobs; ++param_id) {

// 如果prototxt文件没有设置param，则使用默认param

const ParamSpec* param_spec = (param_id < param_size) ?

&layer_param.param(param_id) : &default_param_spec;

// 学习率不等于0，表示需要对这个可学习的参数反向求导

const bool param_need_backward = param_spec->lr_mult() != 0;

need_backward |= param_need_backward;

layers_[layer_id]->set_param_propagate_down(param_id,

param_need_backward);

}

// 接下来的工作是将每层的parameter的指针塞进params_，尤其是learnable_params_。

// 对每一层的每个可学习参数循环

for (int param_id = 0; param_id < num_param_blobs; ++param_id) {

// param：整个网络参数，layer_id:层数id，param_id:可学习参数id

// 设置每一层权值的一些参数，学习率，正则率，参数id等

AppendParam(param, layer_id, param_id);

}

// Finally, set the backward flag

// 最后设置反向传播标志

layer_need_backward_.push_back(need_backward);

if (need_backward) {

for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {

blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;

}

}

}

// Go through the net backwards to determine which blobs contribute to the

// loss. We can skip backward computation for blobs that don't contribute

// to the loss.

// 寻找反向传播过程中哪些blobs对最终的loss有影响，如果某个blob对最终的loss没有贡献，

// 则不需要对这个blob求梯度

// Also checks if all bottom blobs don't need backward computation (possible

// because the skip_propagate_down param) and so we can skip bacward

// computation for the entire layer

// 还要检查是否所有的bottom blobs都不需要求梯度

set<string> blobs_under_loss;

set<string> blobs_skip_backp;

// 对每一层从后向前循环

for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {

bool layer_contributes_loss = false;

bool layer_skip_propagate_down = true;

// 对每一层的每个top blob循环

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {

const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];

if (layers_[layer_id]->loss(top_id) ||

(blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {

layer_contributes_loss = true;

}

if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {

layer_skip_propagate_down = false;

}

// 只要这一层有一个blob对loss有贡献，就说明这层对loss有贡献

if (layer_contributes_loss && !layer_skip_propagate_down)

break;

}

// If this layer can skip backward computation, also all his bottom blobs

// don't need backpropagation

// 如果这一层跳过梯度计算，那么这一层所有的输入blobs都不需要计算梯度

if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {

layer_need_backward_[layer_id] = false;

for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();

++bottom_id) {

bottom_need_backward_[layer_id][bottom_id] = false;

}

}

if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }

if (Caffe::root_solver()) {

if (layer_need_backward_[layer_id]) {

LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";

}

else {

LOG(INFO) << layer_names_[layer_id]

<< " does not need backward computation.";

}

}

for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();

++bottom_id) {

if (layer_contributes_loss) {

const string& blob_name =

blob_names_[bottom_id_vecs_[layer_id][bottom_id]];

blobs_under_loss.insert(blob_name);

}

else {

bottom_need_backward_[layer_id][bottom_id] = false;

}

if (!bottom_need_backward_[layer_id][bottom_id]) {

const string& blob_name =

blob_names_[bottom_id_vecs_[layer_id][bottom_id]];

blobs_skip_backp.insert(blob_name);

}

}

}

// 从后向前循环结束

// Handle force_backward if needed.

// 如果设置强制计算梯度

if (param.force_backward()) {

for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {

layer_need_backward_[layer_id] = true;

for (int bottom_id = 0;

bottom_id < bottom_need_backward_[layer_id].size(); ++bottom_id) {

bottom_need_backward_[layer_id][bottom_id] =

bottom_need_backward_[layer_id][bottom_id] ||

layers_[layer_id]->AllowForceBackward(bottom_id);

blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] =

blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] ||

bottom_need_backward_[layer_id][bottom_id];

}

for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();

++param_id) {

layers_[layer_id]->set_param_propagate_down(param_id, true);

}

}

}

// In the end, all remaining blobs are considered output blobs.

// 最后，输入输出blob中除了输入blob剩下的都作为网络的输出，比如loss blob

for (set<string>::iterator it = available_blobs.begin();

it != available_blobs.end(); ++it) {

LOG_IF(INFO, Caffe::root_solver())

<< "This network produces output " << *it;

net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());

net_output_blob_indices_.push_back(blob_name_to_idx[*it]);

}

for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {

blob_names_index_[blob_names_[blob_id]] = blob_id;

}

for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {

layer_names_index_[layer_names_[layer_id]] = layer_id;

}

ShareWeights();

debug_info_ = param.debug_info();

LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";

}

template <typename Dtype>

void Net<Dtype>::FilterNet(const NetParameter& param,

NetParameter* param_filtered) {

NetState net_state(param.state());

param_filtered->CopyFrom(param);

param_filtered->clear_layer();

for (int i = 0; i < param.layer_size(); ++i) {

const LayerParameter& layer_param = param.layer(i);

const string& layer_name = layer_param.name();

CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)

<< "Specify either include rules or exclude rules; not both.";

// If no include rules are specified, the layer is included by default and

// only excluded if it meets one of the exclude rules.

bool layer_included = (layer_param.include_size() == 0);

for (int j = 0; layer_included && j < layer_param.exclude_size(); ++j) {

if (StateMeetsRule(net_state, layer_param.exclude(j), layer_name)) {

layer_included = false;

}

}

for (int j = 0; !layer_included && j < layer_param.include_size(); ++j) {

if (StateMeetsRule(net_state, layer_param.include(j), layer_name)) {

layer_included = true;

}

}

if (layer_included) {

param_filtered->add_layer()->CopyFrom(layer_param);

}

}

}

template <typename Dtype>

bool Net<Dtype>::StateMeetsRule(const NetState& state,

const NetStateRule& rule, const string& layer_name) {

// Check whether the rule is broken due to phase.

if (rule.has_phase()) {

if (rule.phase() != state.phase()) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState phase (" << state.phase()

<< ") differed from the phase (" << rule.phase()

<< ") specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to min level.

if (rule.has_min_level()) {

if (state.level() < rule.min_level()) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState level (" << state.level()

<< ") is above the min_level (" << rule.min_level()

<< ") specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to max level.

if (rule.has_max_level()) {

if (state.level() > rule.max_level()) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState level (" << state.level()

<< ") is above the max_level (" << rule.max_level()

<< ") specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to stage. The NetState must

// contain ALL of the rule's stages to meet it.

for (int i = 0; i < rule.stage_size(); ++i) {

// Check that the NetState contains the rule's ith stage.

bool has_stage = false;

for (int j = 0; !has_stage && j < state.stage_size(); ++j) {

if (rule.stage(i) == state.stage(j)) { has_stage = true; }

}

if (!has_stage) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState did not contain stage '" << rule.stage(i)

<< "' specified by a rule in layer " << layer_name;

return false;

}

}

// Check whether the rule is broken due to not_stage. The NetState must

// contain NONE of the rule's not_stages to meet it.

for (int i = 0; i < rule.not_stage_size(); ++i) {

// Check that the NetState contains the rule's ith not_stage.

bool has_stage = false;

for (int j = 0; !has_stage && j < state.stage_size(); ++j) {

if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }

}

if (has_stage) {

LOG_IF(INFO, Caffe::root_solver())

<< "The NetState contained a not_stage '" << rule.not_stage(i)

<< "' specified by a rule in layer " << layer_name;

return false;

}

}

return true;

}

template <typename Dtype>

void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,

const int top_id, set<string>* available_blobs,

map<string, int>* blob_name_to_idx) {

// 每层参数

shared_ptr<LayerParameter> layer_param(

new LayerParameter(param.layer(layer_id)));

// 每个输出blob的名称

const string& blob_name = (layer_param->top_size() > top_id) ?

layer_param->top(top_id) : "(automatic)";

// Check if we are doing in-place computation

// 检查是否为同址计算（in-place computation，返回值覆盖原值而不占用新的内存）。

// （比如Dropout运算，激活函数ReLu，Sigmoid等），其中输入bolb的名称和输出相同

if (blob_name_to_idx && layer_param->bottom_size() > top_id &&

blob_name == layer_param->bottom(top_id)) {

// In-place computation

LOG_IF(INFO, Caffe::root_solver())

<< layer_param->name() << " -> " << blob_name << " (in-place)";

// get获取拥有的资源的地址。

top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());

top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);

}

else if (blob_name_to_idx &&

blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {

// If we are not doing in-place computation but have duplicated blobs,

// raise an error.

LOG(FATAL) << "Top blob '" << blob_name

<< "' produced by multiple sources.";

}

else {

// Normal output.

if (Caffe::root_solver()) {

LOG(INFO) << layer_param->name() << " -> " << blob_name;

}

// 数据指针

shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());

// blobs只是存储中间结果；每次遍历到一个top blob都会更新blob_id

const int blob_id = blobs_.size();

blobs_.push_back(blob_pointer);

blob_names_.push_back(blob_name);

blob_need_backward_.push_back(false);

// blob_name_to_idx是一个局部变量，其实它是在当前layer的top blob 和下一层的bottom blob间起着一个桥梁作用。

// blob_name_to_idx中元素的pair是从网络最开始一层一层搭建的过程中压入map的，其中的name和id都是不重复的。name是关键字——不重复是map数据结构的必然要求，id也是不重复的——0,1,2...

// blob_name_to_idx和blobs_一样，在"Normal output"的情形下，每次遍历到一个top blob的时候都会更新

// 添加新元素-->map可以通过下标访问符为（关联）容器添加新元素

if (blob_name_to_idx) {

(*blob_name_to_idx)[blob_name] = blob_id; }

top_id_vecs_[layer_id].push_back(blob_id);

top_vecs_[layer_id].push_back(blob_pointer.get());

}

if (available_blobs) { available_blobs->insert(blob_name); }

}

template <typename Dtype>

int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,

const int bottom_id, set<string>* available_blobs,

map<string, int>* blob_name_to_idx) {

//得到这一层的参数

const LayerParameter& layer_param = param.layer(layer_id);

//得到输入bottom的名称

const string& blob_name = layer_param.bottom(bottom_id);

if (available_blobs->find(blob_name) == available_blobs->end()) {

LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"

<< layer_param.name() << "', bottom index " << bottom_id << ")";

}

// blob_name_to_idx是一个map,其关键字是不重复的。blob_name_to_idx

LOG(INFO) << " -> " << blob_name<<" "<<(*blob_name_to_idx).size()<<"heheda";

const int blob_id = (*blob_name_to_idx)[blob_name];

LOG(INFO) << " -> " << blob_name<<" "<<blob_id<<(*blob_name_to_idx).size()<<"heheda";

//

LOG_IF(INFO, Caffe::root_solver())

<< layer_names_[layer_id] << " <- " << blob_name;

bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());

bottom_id_vecs_[layer_id].push_back(blob_id);

available_blobs->erase(blob_name);

bool need_backward = blob_need_backward_[blob_id];

// Check if the backpropagation on bottom_id should be skipped

if (layer_param.propagate_down_size() > 0) {

need_backward = layer_param.propagate_down(bottom_id);

}

bottom_need_backward_[layer_id].push_back(need_backward);

return blob_id;

}

template <typename Dtype>

void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,

const int param_id) {

const LayerParameter& layer_param = layers_[layer_id]->layer_param();

const int param_size = layer_param.param_size();

string param_name =

(param_size > param_id) ? layer_param.param(param_id).name() : "";

if (param_name.size()) {

param_display_names_.push_back(param_name);

} else {

ostringstream param_display_name;

param_display_name << param_id;

param_display_names_.push_back(param_display_name.str());

}

const int net_param_id = params_.size();

// 存放每个可学习参数

params_.push_back(layers_[layer_id]->blobs()[param_id]);

param_id_vecs_[layer_id].push_back(net_param_id);

// pair实质上是一个结构体，其主要的两个成员变量是first和second，这两个变量可以直接使用。

// 初始化一个pair可以使用构造函数，也可以使用std::make_pair函数，

param_layer_indices_.push_back(make_pair(layer_id, param_id));

ParamSpec default_param_spec;

const ParamSpec* param_spec = (layer_param.param_size() > param_id) ?

&layer_param.param(param_id) : &default_param_spec;

if (!param_size || !param_name.size() || (param_name.size() &&

param_names_index_.find(param_name) == param_names_index_.end())) {

// This layer "owns" this parameter blob -- it is either anonymous

// (i.e., not given a param_name) or explicitly given a name that we

// haven't already seen.

//

param_owners_.push_back(-1);

if (param_name.size()) {

param_names_index_[param_name] = net_param_id;

}

const int learnable_param_id = learnable_params_.size();

// 智能指针转换为普通指针，Why？

learnable_params_.push_back(params_[net_param_id].get());

learnable_param_ids_.push_back(learnable_param_id);

has_params_lr_.push_back(param_spec->has_lr_mult());

has_params_decay_.push_back(param_spec->has_decay_mult());

params_lr_.push_back(param_spec->lr_mult());

params_weight_decay_.push_back(param_spec->decay_mult());

}

// 真个else直到结束，一般不会执行这一部分，共享网络如siamese model会执行

else

{

// Named param blob with name we've seen before: share params

const int owner_net_param_id = param_names_index_[param_name];

param_owners_.push_back(owner_net_param_id);

const pair<int, int>& owner_index =

param_layer_indices_[owner_net_param_id];

const int owner_layer_id = owner_index.first;

const int owner_param_id = owner_index.second;

LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name

<< "' owned by "

<< "layer '" << layer_names_[owner_layer_id] << "', param "

<< "index " << owner_param_id;

Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();

Blob<Dtype>* owner_blob =

layers_[owner_layer_id]->blobs()[owner_param_id].get();

const int param_size = layer_param.param_size();

if (param_size > param_id && (layer_param.param(param_id).share_mode() ==

ParamSpec_DimCheckMode_PERMISSIVE)) {

// Permissive dimension checking -- only check counts are the same.

CHECK_EQ(this_blob->count(), owner_blob->count())

<< "Cannot share param '" << param_name << "' owned by layer '"

<< layer_names_[owner_layer_id] << "' with layer '"

<< layer_names_[layer_id] << "'; count mismatch. Owner layer param "

<< "shape is " << owner_blob->shape_string() << "; sharing layer "

<< "shape is " << this_blob->shape_string();

} else {

// Strict dimension checking -- all dims must be the same.

CHECK(this_blob->shape() == owner_blob->shape())

<< "Cannot share param '" << param_name << "' owned by layer '"

<< layer_names_[owner_layer_id] << "' with layer '"

<< layer_names_[layer_id] << "'; shape mismatch. Owner layer param "

<< "shape is " << owner_blob->shape_string() << "; sharing layer "

<< "expects shape " << this_blob->shape_string();

}

const int learnable_param_id = learnable_param_ids_[owner_net_param_id];

learnable_param_ids_.push_back(learnable_param_id);

if (param_spec->has_lr_mult()) {

if (has_params_lr_[learnable_param_id]) {

CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])

<< "Shared param '" << param_name << "' has mismatched lr_mult.";

} else {

has_params_lr_[learnable_param_id] = true;

params_lr_[learnable_param_id] = param_spec->lr_mult();

}

}

if (param_spec->has_decay_mult()) {

if (has_params_decay_[learnable_param_id]) {

CHECK_EQ(param_spec->decay_mult(),

params_weight_decay_[learnable_param_id])

<< "Shared param '" << param_name << "' has mismatched decay_mult.";

} else {

has_params_decay_[learnable_param_id] = true;

params_weight_decay_[learnable_param_id] = param_spec->decay_mult();

}

}

}

}

template <typename Dtype>

Dtype Net<Dtype>::ForwardFromTo(int start, int end) {

CHECK_GE(start, 0);

CHECK_LT(end, layers_.size());

Dtype loss = 0;

for (int i = start; i <= end; ++i) {

// LOG(ERROR) << "Forwarding " << layer_names_[i];

// 对每一层进行前向计算，返回每层的loss，其实只有最后一层loss不为0

Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);

loss += layer_loss;

if (debug_info_) { ForwardDebugInfo(i); }

}

return loss;

}

template <typename Dtype>

Dtype Net<Dtype>::ForwardFrom(int start) {

return ForwardFromTo(start, layers_.size() - 1);

}

template <typename Dtype>

Dtype Net<Dtype>::ForwardTo(int end) {

return ForwardFromTo(0, end);

}

template <typename Dtype>

const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {

// ？？应该是训练过程的前向传播

if (loss != NULL) {

*loss = ForwardFromTo(0, layers_.size() - 1);

}

else {

ForwardFromTo(0, layers_.size() - 1);

}

return net_output_blobs_;

}

template <typename Dtype>

const vector<Blob<Dtype>*>& Net<Dtype>::Forward(

const vector<Blob<Dtype>*> & bottom, Dtype* loss) {

LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "

<< "will be removed in a future version. Use Forward(loss).";

// Copy bottom to net bottoms

for (int i = 0; i < bottom.size(); ++i) {

net_input_blobs_[i]->CopyFrom(*bottom[i]);

}

return Forward(loss);

}

template <typename Dtype>

void Net<Dtype>::BackwardFromTo(int start, int end) {

CHECK_GE(end, 0);

CHECK_LT(start, layers_.size());

for (int i = start; i >= end; --i) {

if (layer_need_backward_[i]) {

// 对每一层经行反向传播计算

layers_[i]->Backward(

top_vecs_[i], bottom_need_backward_[i], bottom_vecs_[i]);

if (debug_info_) { BackwardDebugInfo(i); }

}

}

}

template <typename Dtype>

void Net<Dtype>::ForwardDebugInfo(const int layer_id) {

for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {

const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];

const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];

const Dtype data_abs_val_mean = blob.asum_data() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Forward] "

<< "Layer " << layer_names_[layer_id]

<< ", top blob " << blob_name

<< " data: " << data_abs_val_mean;

}

for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();

++param_id) {

const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];

const int net_param_id = param_id_vecs_[layer_id][param_id];

const string& blob_name = param_display_names_[net_param_id];

const Dtype data_abs_val_mean = blob.asum_data() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Forward] "

<< "Layer " << layer_names_[layer_id]

<< ", param blob " << blob_name

<< " data: " << data_abs_val_mean;

}

}

template <typename Dtype>

void Net<Dtype>::BackwardDebugInfo(const int layer_id) {

const vector<Blob<Dtype>*>& bottom_vec = bottom_vecs_[layer_id];

for (int bottom_id = 0; bottom_id < bottom_vec.size(); ++bottom_id) {

if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }

const Blob<Dtype>& blob = *bottom_vec[bottom_id];

const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];

const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Backward] "

<< "Layer " << layer_names_[layer_id]

<< ", bottom blob " << blob_name

<< " diff: " << diff_abs_val_mean;

}

for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();

++param_id) {

if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }

const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];

const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Backward] "

<< "Layer " << layer_names_[layer_id]

<< ", param blob " << param_id

<< " diff: " << diff_abs_val_mean;

}

}

template <typename Dtype>

void Net<Dtype>::UpdateDebugInfo(const int param_id) {

const Blob<Dtype>& blob = *params_[param_id];

const int param_owner = param_owners_[param_id];

const string& layer_name = layer_names_[param_layer_indices_[param_id].first];

const string& param_display_name = param_display_names_[param_id];

const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();

if (param_owner < 0) {

const Dtype data_abs_val_mean = blob.asum_data() / blob.count();

LOG_IF(INFO, Caffe::root_solver())

<< " [Update] Layer " << layer_name

<< ", param " << param_display_name

<< " data: " << data_abs_val_mean

<< "; diff: " << diff_abs_val_mean;

} else {

const string& owner_layer_name =

layer_names_[param_layer_indices_[param_owner].first];

LOG_IF(INFO, Caffe::root_solver())

<< " [Update] Layer " << layer_name

<< ", param blob " << param_display_name

<< " (owned by layer " << owner_layer_name << ", " << "param "

<< param_display_names_[param_owners_[param_id]] << ")"

<< " diff: " << diff_abs_val_mean;

}

}

template <typename Dtype>

void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {

int num_source_layers = other->layers().size();

for (int i = 0; i < num_source_layers; ++i) {

Layer<Dtype>* source_layer = other->layers()[i].get();

const string& source_layer_name = other->layer_names()[i];

int target_layer_id = 0;

while (target_layer_id != layer_names_.size() &&

layer_names_[target_layer_id] != source_layer_name) {

++target_layer_id;

}

if (target_layer_id == layer_names_.size()) {

LOG(INFO) << "Ignoring source layer " << source_layer_name;

continue;

}

DLOG(INFO) << "Copying source layer " << source_layer_name;

vector<shared_ptr<Blob<Dtype> > >& target_blobs =

layers_[target_layer_id]->blobs();

CHECK_EQ(target_blobs.size(), source_layer->blobs().size())

<< "Incompatible number of blobs for layer " << source_layer_name;

for (int j = 0; j < target_blobs.size(); ++j) {

Blob<Dtype>* source_blob = source_layer->blobs()[j].get();

CHECK(target_blobs[j]->shape() == source_blob->shape())

<< "Cannot share param " << j << " weights from layer '"

<< source_layer_name << "'; shape mismatch. Source param shape is "

<< source_blob->shape_string() << "; target param shape is "

<< target_blobs[j]->shape_string();

target_blobs[j]->ShareData(*source_blob);

}

}

}

template <typename Dtype>

void Net<Dtype>::BackwardFrom(int start) {

BackwardFromTo(start, 0);

}

template <typename Dtype>

void Net<Dtype>::BackwardTo(int end) {

BackwardFromTo(layers_.size() - 1, end);

}

template <typename Dtype>

void Net<Dtype>::Backward() {

BackwardFromTo(layers_.size() - 1, 0);

if (debug_info_) {

Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;

for (int i = 0; i < learnable_params_.size(); ++i) {

asum_data += learnable_params_[i]->asum_data();

asum_diff += learnable_params_[i]->asum_diff();

sumsq_data += learnable_params_[i]->sumsq_data();

sumsq_diff += learnable_params_[i]->sumsq_diff();

}

const Dtype l2norm_data = std::sqrt(sumsq_data);

const Dtype l2norm_diff = std::sqrt(sumsq_diff);

LOG(ERROR) << " [Backward] All net params (data, diff): "

<< "L1 norm = (" << asum_data << ", " << asum_diff << "); "

<< "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";

}

}

template <typename Dtype>

void Net<Dtype>::Reshape() {

for (int i = 0; i < layers_.size(); ++i) {

layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);

}

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {

int num_source_layers = param.layer_size();

for (int i = 0; i < num_source_layers; ++i) {

const LayerParameter& source_layer = param.layer(i);

const string& source_layer_name = source_layer.name();

int target_layer_id = 0;

while (target_layer_id != layer_names_.size() &&

layer_names_[target_layer_id] != source_layer_name) {

++target_layer_id;

}

if (target_layer_id == layer_names_.size()) {

LOG(INFO) << "Ignoring source layer " << source_layer_name;

continue;

}

DLOG(INFO) << "Copying source layer " << source_layer_name;

vector<shared_ptr<Blob<Dtype> > >& target_blobs =

layers_[target_layer_id]->blobs();

CHECK_EQ(target_blobs.size(), source_layer.blobs_size())

<< "Incompatible number of blobs for layer " << source_layer_name;

for (int j = 0; j < target_blobs.size(); ++j) {

if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {

Blob<Dtype> source_blob;

const bool kReshape = true;

source_blob.FromProto(source_layer.blobs(j), kReshape);

LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"

<< source_layer_name << "'; shape mismatch. Source param shape is "

<< source_blob.shape_string() << "; target param shape is "

<< target_blobs[j]->shape_string() << ". "

<< "To learn this layer's parameters from scratch rather than "

<< "copying from a saved net, rename the layer.";

}

const bool kReshape = false;

target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);

}

}

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {

if (trained_filename.size() >= 3 &&

trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {

CopyTrainedLayersFromHDF5(trained_filename);

} else {

CopyTrainedLayersFromBinaryProto(trained_filename);

}

}

template <typename Dtype>

void Net<Dtype>::CopyTrainedLayersFromBinaryProto(

const string trained_filename) {