caffe code 理解-net.hpp-net.cpp

net.hpp/cpp中主要含有：前向后向传播函数，网络IO函数，每层的参数检测和读取函数，建立和维护每层参数的函数以及vector容器。

caffe支持的网络是有向无环图结构。网络中每一层都是一个节点，网络含有起点和终点，并且起点和终点不一定只有一个，信息在前向传递时在网络中每个节点都会至少会经过一次，并且不一定只有一次。但是只有一个起点时网络每个节点只能经过一次。后向传播时同理。

下面主要用在代码中添加注释的方法来说明。

首先来看net中维护的各种vector以及变量

/// @brief The network name
string name_;
/// @brief The phase: TRAIN or TEST
Phase phase_;
/// @brief Individual layers in the net, each indicator specify a layer class.
vector<shared_ptr<Layer<Dtype> > > layers_;
// the name of each layer, indexed by layer id.
vector<string> layer_names_;
// the map of layer name and layer id.
map<string, int> layer_names_index_;
// indicating whether a layer need backword
vector<bool> layer_need_backward_;
/// @brief the blobs storing intermediate results between the layer.
vector<shared_ptr<Blob<Dtype> > > blobs_;
// the name of each blob, indexed by blob id.
vector<string> blob_names_;
// the map of blob name and blob id.
map<string, int> blob_names_index_;
// indicating whether a blob need backword
vector<bool> blob_need_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_;
vector<vector<int> > bottom_id_vecs_;
vector<vector<bool> > bottom_need_backward_;
/// top_vecs stores the vectors containing the output for each layer
vector<vector<Blob<Dtype>*> > top_vecs_;
vector<vector<int> > top_id_vecs_;
/// Vector of weight in the loss (or objective) function of each net blob,
/// indexed by blob_id.
vector<Dtype> blob_loss_weights_;
// vector of param id for each param. it is needed because caffe support parameter sharing.
vector<vector<int> > param_id_vecs_;
// the number of the onwer of parameter.
vector<int> param_owners_;
// the display name of parameter, because the splite algorithm and other reason cause the name of parameter is not the same as specified the prototxt.
vector<string> param_display_names_;
// the param and its owner pair, onwer indicates by layer id.
vector<pair<int, int> > param_layer_indices_;
//map of param name and param id.
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_;

下面是构造net相关的函数
//construct function of Net. Parsed parameter or parameter file name are both available.
// Netparameter is a class to describe the structure. Sometimes it contains weights of net.
// it is not a net setted up, but some parameters.
// it is created by google protobuff project, and the defination of it can be found in caffe.pb.h
// root_net_ is used for multi-gpu computing, if root_net is not NULL, if will share weight between gpu
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, ¶m);
param.mutable_state()->set_phase(phase);
Init(param);
}
/// @brief Initialize a network with a NetParameter.
template <typename Dtype>
void Net<Dtype>::Init(const NetParameter& in_param) {
//if has root_solver, root_net is needed
CHECK(Caffe::root_solver() || root_net_)
<< "root_net_ needs to be set for all non-root solvers";
// Set phase from the state. phase is train or test
phase_ = in_param.state().phase();
// Filter layers based on their include/exclude rules and
// the current NetState.
NetParameter filtered_param;
// this function is used to check whether the parameter of a layer is legal
// and then find whether the layer is needed in this phase
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;
// this function is used when a blob act as the bottom of two or more layers. it will duplicate a bolb and share weights betwwen duplicates.
// it is needed because caffe assume all blobs can be used only once.
InsertSplits(filtered_param, ¶m);
// Basically, build all the layers and set up their connections.
name_ = param.name();
map<string, int> blob_name_to_idx;
set<string> available_blobs;
memory_used_ = 0;
// For each layer, set up its input and output
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());
// each layer will be setup in following
for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
// For non-root solvers, whether this layer is shared from root_net_.
bool share_from_root = !Caffe::root_solver()
&& root_net_->layers_[layer_id]->ShareInParallel();
// Inherit phase from net if unset.
if (!param.layer(layer_id).has_phase()) {
param.mutable_layer(layer_id)->set_phase(phase_);
}
// Setup layer.
const LayerParameter& layer_param = param.layer(layer_id);
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 the net shared form root, this layer will be shared too or it will be created.
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 {
layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
}
//push layer name
layer_names_.push_back(layer_param.name());
LOG_IF(INFO, Caffe::root_solver())
<< "Creating Layer " << layer_param.name();
bool need_backward = false;

// Figure out this layer's input and output. caffe net mainten blobs in bottom or top, and other blobs should be taken cared by layer.
// each bottom blob should corresponde to one and only one top blob, or error occur.
for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
++bottom_id) {
// add a new blob to bottom.
const int blob_id = AppendBottom(param, layer_id, bottom_id,
&available_blobs, &blob_name_to_idx);
// If a blob needs backward, this layer should provide it.
need_backward |= blob_need_backward_[blob_id];
}
int num_top = layer_param.top_size();
for (int top_id = 0; top_id < num_top; ++top_id) {
// add a new blob to top.
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 {
layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
}
LOG_IF(INFO, Caffe::root_solver())
<< "Setting up " << layer_names_[layer_id];
//add the blob id to blob_loss_weights_ vector and check wether the blob is associated with loss
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);
}
memory_used_ += top_vecs_[layer_id][top_id]->count();
}
LOG_IF(INFO, Caffe::root_solver())
<< "Memory required for data: " << memory_used_ * sizeof(Dtype);
const int param_size = layer_param.param_size();
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 is a class to chech wehther a param should backpropagate
// if ir_mul is not 0, a param should be backpropagate
ParamSpec default_param_spec;
for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
const ParamSpec* param_spec = (param_id < param_size) ?
&layer_param.param(param_id) : &default_param_spec;
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);
}
//add new param blob
for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
AppendParam(param, layer_id, param_id);
}
// Finally, set the backward flag
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;
}
}
}//layer setup done

// 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.
// 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
set<string> blobs_under_loss;
set<string> blobs_skip_backp;
// in the following we figure out whether a layer needs backpropagate
for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
bool layer_contributes_loss = false;
bool layer_skip_propagate_down = true;
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 one of the top blobs of layer contribute to loss, this layer contribute to loss.
if (layers_[layer_id]->loss(top_id) ||
(blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
layer_contributes_loss = true;
}
//if one of the top blobs is not skipped from backpropogate, this layer should not skip.
if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {
layer_skip_propagate_down = false;
}
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
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 the layer do not contribute to loss, it need not bakpropagate.
if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
// if this net is not a root net, all setting should be the same as root net.
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 a layer contribute to loss, set all its bottom blob contribute to loss.
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 a bottom blob do not need backpropagate, insert it in skip vector.
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);
}
}
}
// Handle force_backward if needed.
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.
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;
}
//handle all of the sharing weights event.
ShareWeights();
debug_info_ = param.debug_info();
LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
}
// Helpers for Init.
/**
* @brief Remove layers that the user specified should be excluded given the current
* phase, level, and stage.
*/
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);
}
}
}
/// @brief return whether NetState state meets NetStateRule rule
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;
}

// Helper for Net::Init: add a new top blob to the net.
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) {
shared_ptr<LayerParameter> layer_param(
new LayerParameter(param.layer(layer_id)));
const string& blob_name = (layer_param->top_size() > top_id) ?
layer_param->top(top_id) : "(automatic)";
// Check if we are doing in-place computation
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)";
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;
}
shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
const int blob_id = blobs_.size();
blobs_.push_back(blob_pointer);
blob_names_.push_back(blob_name);
blob_need_backward_.push_back(false);
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); }
}

// Helper for Net::Init: add a new bottom blob to the net.
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) {
const LayerParameter& layer_param = param.layer(layer_id);
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 << ")";
}
const int blob_id = (*blob_name_to_idx)[blob_name];
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();
params_.push_back(layers_[layer_id]->blobs()[param_id]);
param_id_vecs_[layer_id].push_back(net_param_id);
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();
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());
} 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();
}
}
}
}

其余的函数不在做详尽解释了
/**
* @brief Run Forward and return the result.
*
*/
const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
/// @brief DEPRECATED; use Forward() instead.
const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
<< "will be removed in a future version. Use Forward().";
return Forward(loss);
}

/**
* 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.
*/
// forword function from a layer and end in another, the start and end specify layer id.
// if start is not specified, it will start at the beginning of network, usually data layer.
// if end is not specified, it will end at the last layer, usually loss layer.
Dtype ForwardFromTo(int start, int end);
Dtype ForwardFrom(int start);
Dtype ForwardTo(int end);
/// @brief DEPRECATED; set input blobs then use Forward() instead.
const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
Dtype* loss = NULL);

/**
* @brief Zeroes out the diffs of all net parameters.
* Should be run before Backward.
*/
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.
*/
//generally like forward, and the start and end have same meaning.
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.
*/
void Reshape();
// forward and then backward. it will return loss.
Dtype ForwardBackward() {
Dtype loss;
Forward(&loss);
Backward();
return loss;
}

/// @brief Updates the network weights based on the diff values computed.
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.
*/
void ShareWeights();

/**
* @brief For an already initialized net, implicitly copies (i.e., using no
* additional memory) the pre-trained layers from another Net.
*/
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.
*/
void CopyTrainedLayersFrom(const NetParameter& param);
// the following methods load trained net from special file type.
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.
void ToProto(NetParameter* param, bool write_diff = false) const;
/// @brief Writes the net to an HDF5 file.
void ToHDF5(const string& filename, bool write_diff = false) const;