SSD：Single Shot MultiBox Detector代码解读（二）
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SSD：Single Shot MultiBox Detector代码解读（一）： http://blog.csdn.net/u011956147/article/details/73028773
SSD：Single Shot MultiBox Detector代码解读（二）： http://blog.csdn.net/u011956147/article/details/73030116
SSD：Single Shot MultiBox Detector代码解读（三）： http://blog.csdn.net/u011956147/article/details/73032867
SSD：Single Shot MultiBox Detector代码解读（四）： http://blog.csdn.net/u011956147/article/details/73033170
SSD：Single Shot MultiBox Detector代码解读（五）： http://blog.csdn.net/u011956147/article/details/73033282
看网上的说法，SSD代码有过更新，在这里，我采用的是最新版本的（2017/6/7）
主要粗略的分析下caffe版本的SSD代码，还有很多细节没有去仔细研究，希望辩证的看待，如果有什么问题和不同的见解可以提出来，大家一起进步，当然SSD还有比如TensorFlow版本的，网上也有教程，这里就不在说明了。
原作者的代码分散在很多地方，主要是include/caffe/layer，src/caffe/layer和src/caffe/utlis/目录下面。包括annotated_data_layer.hpp、detection_evaluate_layer.hpp、detection_output_layer.hpp、multibox_loss_layer.hpp、prior_box_layer.hpp和与之对应的.cpp文件和.cu文件，这里也只是分析.cpp文件。
跟训练有关的是annotated_data_layer、multibox_loss_layer、prior_box_layer以及 bbox_util。detection_evaluate_layer是验证模型效果用的、detection_output_layer是输出检测结果用的，在后续再继续补充。
首先，annotated_data_layer：主要就是把图片读进来，同时做一些数据增广（data augmentation），同时把gt box取出来保存。
代码如下（其中有相应注释）：
#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#endif // USE_OPENCV#include <stdint.h>#include <algorithm>#include <map>#include <vector>#include "caffe/data_transformer.hpp"#include "caffe/layers/annotated_data_layer.hpp"#include "caffe/util/benchmark.hpp"#include "caffe/util/sampler.hpp"namespace caffe {template <typename Dtype> // 构造函数AnnotatedDataLayer<Dtype>::AnnotatedDataLayer(const LayerParameter& param) : BasePrefetchingDataLayer<Dtype>(param), reader_(param) {}template <typename Dtype> // 析构AnnotatedDataLayer<Dtype>::~AnnotatedDataLayer() { this->StopInternalThread();}template <typename Dtype> // 该函数主要是参数设置void AnnotatedDataLayer<Dtype>::DataLayerSetUp( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { const int batch_size = this->layer_param_.data_param().batch_size(); //设置batch_size,一般默认是32,在网络结构的代码中修改 const AnnotatedDataParameter& anno_data_param = this->layer_param_.annotated_data_param(); // 为数据增广设置的采样batch for (int i = 0; i < anno_data_param.batch_sampler_size(); ++i) { // 依次使用第i种采样方式获取图片的样本数据,存入batch_samplers中 batch_samplers_.push_back(anno_data_param.batch_sampler(i)); } label_map_file_ = anno_data_param.label_map_file(); // 每一类的物体分类标签的文件 // Make sure dimension is consistent within batch. const TransformationParameter& transform_param = // 待考虑。。。 this->layer_param_.transform_param(); if (transform_param.has_resize_param()) { if (transform_param.resize_param().resize_mode() == ResizeParameter_Resize_mode_FIT_SMALL_SIZE) { CHECK_EQ(batch_size, 1) << "Only support batch size of 1 for FIT_SMALL_SIZE."; } } // Read a data point, and use it to initialize the top blob. AnnotatedDatum& anno_datum = *(reader_.full().peek()); // 待考虑。。。 // Use data_transformer to infer the expected blob shape from anno_datum. vector<int> top_shape = this->data_transformer_->InferBlobShape(anno_datum.datum()); // 利用transform_param确定top shape this->transformed_data_.Reshape(top_shape); // Reshape top[0] and prefetch_data according to the batch_size. top_shape[0] = batch_size; // N = batch_size top[0]->Reshape(top_shape); // 根据刚才确定的值设定top的大小 for (int i = 0; i < this->PREFETCH_COUNT; ++i) { // this->prefetch_[i].data_.Reshape(top_shape); // 根据top shape把相应大小的batch提前拿出 } LOG(INFO) << "output data size: " << top[0]->num() << "," // 打印结果 << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width(); // label if (this->output_labels_) { // 读取标签文件 has_anno_type_ = anno_datum.has_type() || anno_data_param.has_anno_type(); vector<int> label_shape(4, 1); if (has_anno_type_) { anno_type_ = anno_datum.type(); if (anno_data_param.has_anno_type()) { // If anno_type is provided in AnnotatedDataParameter, replace // the type stored in each individual AnnotatedDatum. LOG(WARNING) << "type stored in AnnotatedDatum is shadowed."; anno_type_ = anno_data_param.anno_type(); } // Infer the label shape from anno_datum.AnnotationGroup(). int num_bboxes = 0; if (anno_type_ == AnnotatedDatum_AnnotationType_BBOX) { // Since the number of bboxes can be different for each image, // we store the bbox information in a specific format. In specific: // All bboxes are stored in one spatial plane (num and channels are 1) // And each row contains one and only one box in the following format: // [item_id, group_label, instance_id, xmin, ymin, xmax, ymax, diff] // Note: Refer to caffe.proto for details about group_label and // instance_id. for (int g = 0; g < anno_datum.annotation_group_size(); ++g) { num_bboxes += anno_datum.annotation_group(g).annotation_size(); } label_shape[0] = 1; label_shape[1] = 1; // BasePrefetchingDataLayer<Dtype>::LayerSetUp() requires to call // cpu_data and gpu_data for consistent prefetch thread. Thus we make // sure there is at least one bbox. label_shape[2] = std::max(num_bboxes, 1); label_shape[3] = 8; } else { LOG(FATAL) << "Unknown annotation type."; } } else { label_shape[0] = batch_size; } top[1]->Reshape(label_shape); for (int i = 0; i < this->PREFETCH_COUNT; ++i) { this->prefetch_[i].label_.Reshape(label_shape); } }}// This function is called on prefetch threadtemplate<typename Dtype> // 提前从内存中取出数据void AnnotatedDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) { CPUTimer batch_timer; batch_timer.Start(); double read_time = 0; double trans_time = 0; CPUTimer timer; CHECK(batch->data_.count()); CHECK(this->transformed_data_.count()); // Reshape according to the first anno_datum of each batch // on single input batches allows for inputs of varying dimension. const int batch_size = this->layer_param_.data_param().batch_size(); const AnnotatedDataParameter& anno_data_param = this->layer_param_.annotated_data_param(); const TransformationParameter& transform_param = this->layer_param_.transform_param(); AnnotatedDatum& anno_datum = *(reader_.full().peek()); // Use data_transformer to infer the expected blob shape from anno_datum. vector<int> top_shape = this->data_transformer_->InferBlobShape(anno_datum.datum()); this->transformed_data_.Reshape(top_shape); // Reshape batch according to the batch_size. top_shape[0] = batch_size; batch->data_.Reshape(top_shape); Dtype* top_data = batch->data_.mutable_cpu_data(); Dtype* top_label = NULL; // suppress warnings about uninitialized variables if (this->output_labels_ && !has_anno_type_) { top_label = batch->label_.mutable_cpu_data(); } // Store transformed annotation. map<int, vector<AnnotationGroup> > all_anno; int num_bboxes = 0; for (int item_id = 0; item_id < batch_size; ++item_id) { timer.Start(); // get a anno_datum AnnotatedDatum& anno_datum = *(reader_.full().pop("Waiting for data")); read_time += timer.MicroSeconds(); timer.Start(); AnnotatedDatum distort_datum; AnnotatedDatum* expand_datum = NULL; if (transform_param.has_distort_param()) { distort_datum.CopyFrom(anno_datum); this->data_transformer_->DistortImage(anno_datum.datum(), distort_datum.mutable_datum()); if (transform_param.has_expand_param()) { expand_datum = new AnnotatedDatum(); this->data_transformer_->ExpandImage(distort_datum, expand_datum); } else { expand_datum = &distort_datum; } } else { if (transform_param.has_expand_param()) { expand_datum = new AnnotatedDatum(); this->data_transformer_->ExpandImage(anno_datum, expand_datum); } else { expand_datum = &anno_datum; } } AnnotatedDatum* sampled_datum = NULL; bool has_sampled = false; if (batch_samplers_.size() > 0) { // Generate sampled bboxes from expand_datum. vector<NormalizedBBox> sampled_bboxes; GenerateBatchSamples(*expand_datum, batch_samplers_, &sampled_bboxes); if (sampled_bboxes.size() > 0) { // Randomly pick a sampled bbox and crop the expand_datum. int rand_idx = caffe_rng_rand() % sampled_bboxes.size(); sampled_datum = new AnnotatedDatum(); this->data_transformer_->CropImage(*expand_datum, sampled_bboxes[rand_idx], sampled_datum); has_sampled = true; } else { sampled_datum = expand_datum; } } else { sampled_datum = expand_datum; } CHECK(sampled_datum != NULL); timer.Start(); vector<int> shape = this->data_transformer_->InferBlobShape(sampled_datum->datum()); if (transform_param.has_resize_param()) { if (transform_param.resize_param().resize_mode() == ResizeParameter_Resize_mode_FIT_SMALL_SIZE) { this->transformed_data_.Reshape(shape); batch->data_.Reshape(shape); top_data = batch->data_.mutable_cpu_data(); } else { CHECK(std::equal(top_shape.begin() + 1, top_shape.begin() + 4, shape.begin() + 1)); } } else { CHECK(std::equal(top_shape.begin() + 1, top_shape.begin() + 4, shape.begin() + 1)); } // Apply data transformations (mirror, scale, crop...) int offset = batch->data_.offset(item_id); this->transformed_data_.set_cpu_data(top_data + offset); vector<AnnotationGroup> transformed_anno_vec; if (this->output_labels_) { if (has_anno_type_) { // Make sure all data have same annotation type. CHECK(sampled_datum->has_type()) << "Some datum misses AnnotationType."; if (anno_data_param.has_anno_type()) { sampled_datum->set_type(anno_type_); } else { CHECK_EQ(anno_type_, sampled_datum->type()) << "Different AnnotationType."; } // Transform datum and annotation_group at the same time transformed_anno_vec.clear(); this->data_transformer_->Transform(*sampled_datum, &(this->transformed_data_), &transformed_anno_vec); if (anno_type_ == AnnotatedDatum_AnnotationType_BBOX) { // Count the number of bboxes. for (int g = 0; g < transformed_anno_vec.size(); ++g) { num_bboxes += transformed_anno_vec[g].annotation_size(); } } else { LOG(FATAL) << "Unknown annotation type."; } all_anno[item_id] = transformed_anno_vec; } else { this->data_transformer_->Transform(sampled_datum->datum(), &(this->transformed_data_)); // Otherwise, store the label from datum. CHECK(sampled_datum->datum().has_label()) << "Cannot find any label."; top_label[item_id] = sampled_datum->datum().label(); } } else { this->data_transformer_->Transform(sampled_datum->datum(), &(this->transformed_data_)); } // clear memory if (has_sampled) { delete sampled_datum; } if (transform_param.has_expand_param()) { delete expand_datum; } trans_time += timer.MicroSeconds(); reader_.free().push(const_cast<AnnotatedDatum*>(&anno_datum)); } // Store "rich" annotation if needed. if (this->output_labels_ && has_anno_type_) { vector<int> label_shape(4); if (anno_type_ == AnnotatedDatum_AnnotationType_BBOX) { label_shape[0] = 1; label_shape[1] = 1; label_shape[3] = 8; if (num_bboxes == 0) { // Store all -1 in the label. label_shape[2] = 1; batch->label_.Reshape(label_shape); caffe_set<Dtype>(8, -1, batch->label_.mutable_cpu_data()); } else { // Reshape the label and store the annotation. label_shape[2] = num_bboxes; batch->label_.Reshape(label_shape); top_label = batch->label_.mutable_cpu_data(); int idx = 0; for (int item_id = 0; item_id < batch_size; ++item_id) { const vector<AnnotationGroup>& anno_vec = all_anno[item_id]; for (int g = 0; g < anno_vec.size(); ++g) { const AnnotationGroup& anno_group = anno_vec[g]; for (int a = 0; a < anno_group.annotation_size(); ++a) { const Annotation& anno = anno_group.annotation(a); const NormalizedBBox& bbox = anno.bbox(); top_label[idx++] = item_id; top_label[idx++] = anno_group.group_label(); top_label[idx++] = anno.instance_id(); top_label[idx++] = bbox.xmin(); top_label[idx++] = bbox.ymin(); top_label[idx++] = bbox.xmax(); top_label[idx++] = bbox.ymax(); top_label[idx++] = bbox.difficult(); } } } } } else { LOG(FATAL) << "Unknown annotation type."; } } timer.Stop(); batch_timer.Stop(); DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms."; DLOG(INFO) << " Read time: " << read_time / 1000 << " ms."; DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";}INSTANTIATE_CLASS(AnnotatedDataLayer);REGISTER_LAYER_CLASS(AnnotatedData);} // namespace caffe本文链接：http://blog.csdn.net/u011956147/article/details/73030116
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