常用的东西还是记下来，省的每次用的时候都要从头捋一遍。
本文是对用caffe做classification的inference流程的梳理。
做classification的主线：
int main(int argc, char** argv) {
if (argc != 6) {
std::cerr << "Usage: " << argv[0]
<< " deploy.prototxt network.caffemodel"
<< " mean.binaryproto labels.txt img.jpg" << std::endl;
return 1;
}
::google::InitGoogleLogging(argv[0]);
string model_file = argv[1];
string trained_file = argv[2];
string mean_file = argv[3];
string label_file = argv[4];
/* 1. 构建网络 */
Classifier classifier(model_file, trained_file, mean_file, label_file);
string file = argv[5];
std::cout << "---------- Prediction for "
<< file << " ----------" << std::endl;
/* 2. 读入图像 */
cv::Mat img = cv::imread(file, -1);
CHECK(!img.empty()) << "Unable to decode image " << file;
/* 3. Predict */
std::vector<Prediction> predictions = classifier.Classify(img);
/* 4. 输出结果 */
/* Print the top N predictions. */
for (size_t i = 0; i < predictions.size(); ++i) {
Prediction p = predictions[i];
std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
<< p.first << "\"" << std::endl;
}
}
从上面代码可看出做classification的主线是：构建网络→读入图像→Predict→输出结果。
做classification的类Classifier：
/* Pair (label, confidence) representing a prediction. */
/* 保存predict的结果：标签 + 概率得分 */
typedef std::pair<string, float> Prediction;
/* classification所用类，如果要做detection可以作为参考 */
class Classifier {
public:
Classifier(const string& model_file, /* develop.prototxt */
const string& trained_file, /* .caffemodel */
const string& mean_file, /* mean.binaryproto */
const string& label_file); /* label.txt */
/* 做classification的接口函数，输入图像和想要输出的topN的inference结果 */
std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
private:
/* 根据输入的均值文件binaryproto计算均值图cv::Mat mean_，
然后在Preprocess()中使用mean_ */
void SetMean(const string& mean_file);
/* 做classification的inference最主要的函数，
输入是图像，输出是所有类别的概率得分，这里的类别排序和train时的类别标签是一一对应的 */
std::vector<float> Predict(const cv::Mat& img);
/* 在WrapInputLayer()中将net_input_blobs_[0](即input_layer)的每个通道的data塞进
std::vector<cv::Mat>* input_channels，这样在Preprocess()中将const cv::Mat& img
的每个通道的data赋给std::vector<cv::Mat>* input_channels就等同于赋给了net_input_blobs_[0] */
void WrapInputLayer(std::vector<cv::Mat>* input_channels);
/* 在Preprocess()中完成了将图像送入网络前的所有工作，包括颜色通道转换、resize、减均值、分配通道顺序等。
例如，如果net_要求输入单通道灰度图，待inference的图像是彩色图，那么会将其转换为灰度图，反之亦然；
将输入的const cv::Mat& img进行通道分离(split)，将原始BRG,BGR,...,BGR排列方式
split成B,B,...,B，G,G,...,G，R,R,...R的形式塞入input_channels */
void Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels);
private:
/* 此处的net_是用来做inference的，因此去除了所有只在train中用的层 */
shared_ptr<Net<float> > net_;
/* 网络要求的图片输入宽高 */
cv::Size input_geometry_;
/* 网络要求的图片输入通道数目 */
int num_channels_;
/* 由输入的均值文件binaryproto换算得到的均值图 */
cv::Mat mean_;
/* 样本类别标签，顺序要和train时的类别编号一一对应，例如：
猫、狗、鱼三分类时训练的样本可能是：
fish1.jpg 1
cat1.jpg 0
dog1.jpg 2
dog2.jpg 2
fish2.jpg 1
cat2.jpg 0
那么label.txt的内容应该是：
cat
fish
dog */
std::vector<string> labels_;
};
Classifier构造函数：
Classifier::Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file) {
/* 计算模式选择，如果是为了熟悉Caffe使用CPU更便于调试跟踪数据流 */
#ifdef CPU_ONLY
Caffe::set_mode(Caffe::CPU);
#else
Caffe::set_mode(Caffe::GPU);
#endif
/* Load the network. */
/* 根据deploy.prototxt new一个inference的net赋给net_ */
net_.reset(new Net<float>(model_file, TEST));
/* 从.caffemodel中拷贝训练好的参数给net_ */
net_->CopyTrainedLayersFrom(trained_file);
CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
Blob<float>* input_layer = net_->input_blobs()[0];
num_channels_ = input_layer->channels();
CHECK(num_channels_ == 3 || num_channels_ == 1)
<< "Input layer should have 1 or 3 channels.";
input_geometry_ = cv::Size(input_layer->width(), input_layer->height());
/* Load the binaryproto mean file. */
SetMean(mean_file);
/* Load labels. */
std::ifstream labels(label_file.c_str());
CHECK(labels) << "Unable to open labels file " << label_file;
string line;
while (std::getline(labels, line))
labels_.push_back(string(line));
Blob<float>* output_layer = net_->output_blobs()[0];
CHECK_EQ(labels_.size(), output_layer->channels())
<< "Number of labels is different from the output layer dimension.";
}
net_.reset(new Net<float>(model_file, TEST))
创建网络主要是Net的构造函数实现的，如下：
template <typename Dtype>
Net<Dtype>::Net(const string& param_file, Phase phase,
const int level, const vector<string>* stages,
const Net* root_net)
: root_net_(root_net) {
NetParameter param;
/* 从param_file(develop.prototxt)中读取各个layer到param中 */
ReadNetParamsFromTextFileOrDie(param_file, ¶m);
// Set phase, stages and level
param.mutable_state()->set_phase(phase);
if (stages != NULL) {
for (int i = 0; i < stages->size(); i++) {
param.mutable_state()->add_stage((*stages)[i]);
}
}
param.mutable_state()->set_level(level);
/* 在Init()中根据param中的内容给new出来的net中的参数赋值，
因此构造函数中最重要的是成员函数Init()的实现 */
Init(param);
}
void Net<Dtype>::Init(const NetParameter& in_param)
在Init()中完成了整个网络的构造。
template <typename Dtype>
/* 将从develop.prototxt中读取到in_param中的参数一一赋给net的成员 */
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";
// Set phase from the state.
phase_ = in_param.state().phase();
// Filter layers based on their include/exclude rules and
// the current NetState.
NetParameter filtered_param;
/* 将.prototxt中对inference无用的层去掉，例如只在train中使用到的层 */
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;
/* 网络中的数据层的输出label对应accuracy层和loss层，则需要在数据层再插入一层 */
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());
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 (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));
}
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
for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
++bottom_id) {
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) {
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];
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 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);
}
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;
}
}
}
// 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;
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 (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;
}
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 (!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);
}
}
}
// 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;
}
ShareWeights();
debug_info_ = param.debug_info();
LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
}
net_->CopyTrainedLayersFrom(trained_file)
该函数主要调用CopyTrainedLayersFrom()：
template <typename Dtype>
void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
const string trained_filename) {
NetParameter param;
ReadNetParamsFromBinaryFileOrDie(trained_filename, ¶m);
CopyTrainedLayersFrom(param);
}
在CopyTrainedLayersFrom中实现了参数的拷贝：
template <typename Dtype>
void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
/* net_的层数，一般会比develop.prototxt中的网络层数要多，
因为经过net.cpp中InsertSplits()中处理之后层数会变多 */
int num_source_layers = param.layer_size();
/* for循环中遍历.caffemodel的每层进行参数拷贝，具体地，
将develop.prototxt中的每层名字与.caffemodel中的每层名字进行比对，
比对上了就将.caffemodel中对应层的参数拷贝过来 */
for (int i = 0; i < num_source_layers; ++i) {
/* source_layer是指.caffemodel中的layer */
const LayerParameter& source_layer = param.layer(i);
const string& source_layer_name = source_layer.name();
int target_layer_id = 0;
/* 由于.caffemodel是训练时根据train.prototxt得到的，因此这里需要跳过train时专用的层 */
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;
}
LOG(INFO) << "Copying source layer " << source_layer_name;
/* target_blobs是指develop.prototxt中含有参数的某层的blob */
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;
/* 有参数的层在下面的for循环中对当前层进行参数拷贝，
没有参数的层target_blobs.size()==0不会进入for循环。
对于conv层其size为2，表示有两类参数：w、b，for循环中j=0是对w进行赋值，j=1是对b进行赋值 */
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;
/* 对每个参数逐一赋值。
例如，如果输入channel为3、conv核大小为3*3、输出通道为64，
则conv层的w共3*3*3*64=1728个参数，那么在j=0时FromProto()中的for循环中会有1728次循环逐一对1728个参数进行赋值；
另外，conv层还有64个bias，那么在j=1时FromProto()中的for循环中会有64次循环逐一对64个参数进行赋值 */
target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
}
}
}
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