前言

年后到现在，利用自己的业余时间断断续续将caffe的SSD源码看完了，虽然中间由于工作原因暂停了一段时间，但最终还算顺利完成了，SSD源码的阅读也是今年的年度计划中比较重要的一项内容，完成了还是很有成就感的。阅读完代码后，一个最大的体会就是之前论文中很多困惑我的细节现在豁然开朗了，哈哈。

在阅读代码期间，每次遇到困惑我的地方，我会反复思考，琢磨，利用走路，吃饭的时间思考，也常常会在宿舍里来回踱步，现在我对阅读代码有了一个新的体会。当你阅读一段对你来说很难的代码的时候，不要害怕，你只要静下心来，将一段很难的代码拆分成N个子块，然后针对每个子块各个击破，等你将所有子块都击破了，然后再将所有子块串联起来连接成一个整体，再从整体思考这段代码，会有更加深刻的理解。当然这个过程起初会很难，因为起初很多东西你都不懂，就像我阅读SSD代码的时候，起初很难，要了解很多细节，但是只要有耐心有毅力，慢慢你会发现，你对这些内容越来越熟悉，你也会感到越来越轻松，直到最后你豁然开朗，发现这段很难的代码也不过如此，那种感觉实在是太美妙了。

五一的第一天稍微整理了一下SSD源码的阅读笔记，写成博客，与大家一起分享交流，由于SSD源码比较复杂，加上时间精力有限，不可能对每个细节都有深入的理解，博客中有不足之处，希望大家能够提出宝贵的意见。

这篇博客是SSD源码解读系列的第1篇，对数据层进行解读。

SSD源码阅读的时候，我对SSD源码创建了QT工程，这样方便阅读，SSD源码的QT工程我上传到CSDN了，该工程用QT可以直接打开的，大家可以直接下载该QT工程阅读，提高阅读效率。
点击下载

数据层AnnotatedDataLayer源码解读

#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>
AnnotatedDataLayer<Dtype>::AnnotatedDataLayer(const LayerParameter& param): BasePrefetchingDataLayer<Dtype>(param),reader_(param) {
}template <typename Dtype>
AnnotatedDataLayer<Dtype>::~AnnotatedDataLayer() {this->StopInternalThread();
}template <typename Dtype>
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();const AnnotatedDataParameter& anno_data_param = this->layer_param_.annotated_data_param();// 读取所有数据增强采样参数for (int i = 0; i < anno_data_param.batch_sampler_size(); ++i) {batch_samplers_.push_back(anno_data_param.batch_sampler(i));}label_map_file_ = anno_data_param.label_map_file();// Make sure dimension is consistent within batch.const TransformationParameter& transform_param = 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.";}}// 读取一个数据，并读取数据的shape,初始化top的shape和prefetch的shape(比如数据大小为300x300)// AnnotatedDatum包含了数据和标注(标注包含了label和bounding box)// Read a data point, and use it to initialize the top blob.AnnotatedDatum& anno_datum = *(reader_.full().peek()); // reader_中读到的数据就是输入的数据(包括图像数据和boundingbox坐标)// 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 top[0] and prefetch_data according to the batch_size.top_shape[0] = batch_size;top[0]->Reshape(top_shape);// 预读线程中的图像数据for (int i = 0; i < this->PREFETCH_COUNT; ++i) {this->prefetch_[i].data_.Reshape(top_shape);}LOG(INFO) << "output data size: " << top[0]->num() << ","<< top[0]->channels() << "," << top[0]->height() << ","<< top[0]->width();// labelif (this->output_labels_) {// 生成数据的时候是有类型的 anno_datum.set_type(AnnotatedDatum_AnnotationType_BBOX);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;// 读取该图像的所有box数量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);// 预读线程中的label数据for (int i = 0; i < this->PREFETCH_COUNT; ++i) {this->prefetch_[i].label_.Reshape(label_shape);}}
}// This function is called on prefetch thread
template<typename Dtype>
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();// 初始化transformed_data_和 batch->data_的大小AnnotatedDatum& anno_datum = *(reader_.full().peek());vector<int> top_shape =this->data_transformer_->InferBlobShape(anno_datum.datum());// 3x300x300this->transformed_data_.Reshape(top_shape); // transformed_data_存储一幅图像，对于SSD300,transformed_data_大小为:[1,3,300,300]top_shape[0] = batch_size;batch->data_.Reshape(top_shape); // batch->data_存储batchsize个图像,对于SSD300，batch->data_大小为[batchsize,3,300,300]Dtype* top_data = batch->data_.mutable_cpu_data();Dtype* top_label = NULL;  // suppress warnings about uninitialized variablesif (this->output_labels_ && !has_anno_type_) {top_label = batch->label_.mutable_cpu_data();}// Store transformed annotation.map<int, vector<AnnotationGroup> > all_anno; // batchsize中每一幅图像以及对应的标注int num_bboxes = 0;for (int item_id = 0; item_id < batch_size; ++item_id) {timer.Start();// 获取一幅图像，并做相应的预处理(比如加入扰动)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){/* 1. 首先进行数据增强(对应论文2.2 Training部分的Data augmentation)* 对于batchsize中的每一幅图像，为每个采样器(batch_sampler)生成max_sample个boundingbox(候选框)* 每个采样器生成的boundingbox与目标的IOU=0.1,0.3,0.5,0.7,0.9，这个与论文的描述是一致的* 示例：batch_sampler{sampler{min_scale: 0.3max_scale: 1.0min_aspect_ratio: 0.5max_aspect_ratio: 2.0}sample_constraint{min_jaccard_overlap: 0.7}max_sample: 1max_trials: 50}*  对于该采样器，随机生成的满足条件的boundingbox与图像中任一目标的IOU>0.7*  注意：*    1. 生成的boundingbox坐标是归一化的坐标，这样不受resize的影响，目标检测的回归都是采用的这种形式(比如MTCNN)*    2. 随机生成boundingbox的时候，根据每个batch_sampler的参数：尺度，宽高比，每个采样器最多尝试max_trials次**/vector<NormalizedBBox> sampled_bboxes;// 生成的是归一化的坐标GenerateBatchSamples(*expand_datum, batch_samplers_, &sampled_bboxes);/*2. 从生成的所有bounding box中随机挑选一个bounding box* 裁剪出该bounding box对应的图像(大小就是sampled_bboxes[rand_idx]在原图中的大小)并计算该bounding box中所有目标的坐标以及类别* 注意：*    1. bounding box中目标的坐标=(原图中ground truth的坐标-该bounding box的坐标)/(bounding box的边长)*     这里groundtruth与boundingbox的坐标都相对于原图,在mtcnn中也是采用了该计算方式**/if (sampled_bboxes.size() > 0){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()) {// 不执行该部分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 timetransformed_anno_vec.clear();// AnnotatedDatum,Blob<float>,vector<AnnotationGroup>/* 3. 将crop出来的AnnotatedDatum转换为数据部分和标注部分*  数据部分会resize到数据层设置的大小(比如300x300)并保存到top[0]中*  标注是所有目标在图像中的坐标** 注意：*  1. 这里的图像并不一定是原始crop的图像，如果transform_param有crop_size这个参数，原来crop出来的图像会再次crop的*  2. 由于这里对crop出来的图像进行了一次resize,所以如果生成lmdb的时候，进行resize会导致数据层对原图进行两次resize，*     这样有可能会影响到目标的宽高比，所以在SFD(Single Shot Scale-invariant Face Detector)中，对此处做了一点改进，即在第一步*     生成boundingbox的时候，保证每个boundingbox都是正方形，这样resize到300x300的时候就不会改变目标的宽高比*/this->data_transformer_->Transform(*sampled_datum,&(this->transformed_data_),&transformed_anno_vec);if (anno_type_ == AnnotatedDatum_AnnotationType_BBOX) {// Count the number of bboxes.// 计算该随机生成的bounding box中有多少目标for (int g = 0; g < transformed_anno_vec.size(); ++g) {num_bboxes += transformed_anno_vec[g].annotation_size();}} else {LOG(FATAL) << "Unknown annotation type.";}// batchsize中第item_id个图像的标注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 memoryif (has_sampled) {delete sampled_datum;}if (transform_param.has_expand_param()) {delete expand_datum;}trans_time += timer.MicroSeconds();// 将读过的数据再放回去reader_.free().push(const_cast<AnnotatedDatum*>(&anno_datum));}// Store "rich" annotation if needed./*4. 最后将标注信息保存到top[1]中，top[1]的shape:[1,1,numberOfBoxes,8]*每一行格式：[item_id, group_label, instance_id, xmin, ymin, xmax, ymax, diff]*这个8维向量表示的含义：batchsize个图像中的第item_id幅图像中的第group_label个类别下的第instance_id个box的坐标为[xmin, ymin, xmax, ymax]**/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 {// num_bboxes就是前面crop出来的所有图像中所有目标的数量label_shape[2] = num_bboxes;batch->label_.Reshape(label_shape);top_label = batch->label_.mutable_cpu_data();int idx = 0;// 遍历bachsizes中每一幅图像的label信息for (int item_id = 0; item_id < batch_size; ++item_id) {// 第ite_id幅图像的label信息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

数据层中有几个比较重要的函数GenerateBatchSamples(),this->data_transformer_->CropImage(),this->data_transformer_->Transform()，下面对他们详细解读一下

GenerateBatchSamples

void GenerateBatchSamples(const AnnotatedDatum& anno_datum,const vector<BatchSampler>& batch_samplers,vector<NormalizedBBox>* sampled_bboxes)
{sampled_bboxes->clear();// 获取groundtruth boxvector<NormalizedBBox> object_bboxes;GroupObjectBBoxes(anno_datum, &object_bboxes); // 对于每个采样器生成多个boxfor (int i = 0; i < batch_samplers.size(); ++i) {// Use original image as the source for sampling.if (batch_samplers[i].use_original_image()) {NormalizedBBox unit_bbox;unit_bbox.set_xmin(0);unit_bbox.set_ymin(0);unit_bbox.set_xmax(1);unit_bbox.set_ymax(1);GenerateSamples(unit_bbox,  // 单位boxobject_bboxes,// ground truth boxbatch_samplers[i], // 采样器sampled_bboxes);}}
}void GenerateSamples(const NormalizedBBox& source_bbox, // 单位boxconst vector<NormalizedBBox>& object_bboxes, // object_bboxes就是该图像中所有的ground truth boxesconst BatchSampler& batch_sampler, // 采样器vector<NormalizedBBox>* sampled_bboxes)
{int found = 0;// 每个采样器batch_sampler都要尝试max_trials次for (int i = 0; i < batch_sampler.max_trials(); ++i) {// 每个batch_sampler生成的boundingbox个数大于等于max_sample了，就跳出if (batch_sampler.has_max_sample() && found >= batch_sampler.max_sample()) {break;}// Generate sampled_bbox in the normalized space [0, 1].// 随机生成一个boxNormalizedBBox sampled_bbox;SampleBBox(batch_sampler.sampler(), &sampled_bbox);// Transform the sampled_bbox w.r.t. source_bbox.// 转换为在单位box中的坐标，由于都是单位box，所以转换后还是自己LocateBBox(source_bbox, sampled_bbox, &sampled_bbox);// Determine if the sampled bbox is positive or negative by the constraint.// 所有的ground truth 与生成的boundingbox计算IOU,是否满足条件if (SatisfySampleConstraint(sampled_bbox, object_bboxes,batch_sampler.sample_constraint())) {++found;sampled_bboxes->push_back(sampled_bbox);}}
}

DataTransformer::CropImage

template<typename Dtype>
void DataTransformer<Dtype>::CropImage(const AnnotatedDatum& anno_datum,const NormalizedBBox& bbox,AnnotatedDatum* cropped_anno_datum)
{// 首先crop数据：将bbox映射到原图的像素坐标并crop出该区域对应的图像，然后将crop出来的图像保存到cropped_anno_datumCropImage(anno_datum.datum(), bbox, cropped_anno_datum->mutable_datum());cropped_anno_datum->set_type(anno_datum.type());// 根据crop_bbox 转换 annotation// cropped_anno_datum保持的就是该图像中每个类别下所有bbox与该crop_bbox的偏移(用(ground truth-crop_bbox)/crop_bbox)// Transform the annotation according to crop_bbox.const bool do_resize = false;const bool do_mirror = false;NormalizedBBox crop_bbox;ClipBBox(bbox, &crop_bbox); // 边界判断TransformAnnotation(anno_datum, do_resize, crop_bbox, do_mirror,cropped_anno_datum->mutable_annotation_group());
}template<typename Dtype>
void DataTransformer<Dtype>::TransformAnnotation(const AnnotatedDatum& anno_datum, const bool do_resize,const NormalizedBBox& crop_bbox, const bool do_mirror,RepeatedPtrField<AnnotationGroup>* transformed_anno_group_all)
{const int img_height = anno_datum.datum().height();const int img_width = anno_datum.datum().width();if (anno_datum.type() == AnnotatedDatum_AnnotationType_BBOX) {// Go through each AnnotationGroup.// 计算每个类别下所有bbox与该crop_bbox的偏移,其实就算计算随机生成的box中所有目标的坐标for (int g = 0; g < anno_datum.annotation_group_size(); ++g) {const AnnotationGroup& anno_group = anno_datum.annotation_group(g);AnnotationGroup transformed_anno_group;bool has_valid_annotation = false;// 每个类别的所有Annotationfor (int a = 0; a < anno_group.annotation_size(); ++a) {const Annotation& anno = anno_group.annotation(a);const NormalizedBBox& bbox = anno.bbox();// Adjust bounding box annotation.NormalizedBBox resize_bbox = bbox;// 这里do_resize和do_mirror都是falseif (do_resize && param_.has_resize_param()) {CHECK_GT(img_height, 0);CHECK_GT(img_width, 0);UpdateBBoxByResizePolicy(param_.resize_param(), img_width, img_height,&resize_bbox);}if (param_.has_emit_constraint() &&!MeetEmitConstraint(crop_bbox, resize_bbox,param_.emit_constraint())) {continue;}// ProjectBBox计算ground truth与随机生成的bbox的偏移(只计算有交集的)NormalizedBBox proj_bbox; // proj_bbox 就是偏移，就是做回归用的if (ProjectBBox(crop_bbox, resize_bbox, &proj_bbox)){has_valid_annotation = true;Annotation* transformed_anno =transformed_anno_group.add_annotation();transformed_anno->set_instance_id(anno.instance_id());NormalizedBBox* transformed_bbox = transformed_anno->mutable_bbox();transformed_bbox->CopyFrom(proj_bbox);if (do_mirror) {Dtype temp = transformed_bbox->xmin();transformed_bbox->set_xmin(1 - transformed_bbox->xmax());transformed_bbox->set_xmax(1 - temp);}if (do_resize && param_.has_resize_param()) {ExtrapolateBBox(param_.resize_param(), img_height, img_width,crop_bbox, transformed_bbox);}}}// Save for output.if (has_valid_annotation){// 遍历完该类别下所有ground truth,设置labeltransformed_anno_group.set_group_label(anno_group.group_label());transformed_anno_group_all->Add()->CopyFrom(transformed_anno_group);}}}else{LOG(FATAL) << "Unknown annotation type.";}
}

DataTransformer::Transform

template<typename Dtype>
void DataTransformer<Dtype>::Transform(const AnnotatedDatum& anno_datum, Blob<Dtype>* transformed_blob,vector<AnnotationGroup>* transformed_anno_vec)
{bool do_mirror;Transform(anno_datum, transformed_blob, transformed_anno_vec, &do_mirror);
}template<typename Dtype>
void DataTransformer<Dtype>::Transform(const AnnotatedDatum& anno_datum, Blob<Dtype>* transformed_blob,vector<AnnotationGroup>* transformed_anno_vec, bool* do_mirror) {RepeatedPtrField<AnnotationGroup> transformed_anno_group_all;Transform(anno_datum, transformed_blob, &transformed_anno_group_all,do_mirror);for (int g = 0; g < transformed_anno_group_all.size(); ++g) {transformed_anno_vec->push_back(transformed_anno_group_all.Get(g));}
}template<typename Dtype>
void DataTransformer<Dtype>::Transform(const AnnotatedDatum& anno_datum, Blob<Dtype>* transformed_blob,RepeatedPtrField<AnnotationGroup>* transformed_anno_group_all,bool* do_mirror)
{// Transform datum./* 转换数据* 如果DataTransformer参数中没有crop_size,则crop_box还是原图大小(归一化大小，这里就是(0,0,1,1))* 如果数据层有resize参数，就会缩放**/const Datum& datum = anno_datum.datum();NormalizedBBox crop_bbox;Transform(datum, transformed_blob, &crop_bbox, do_mirror);// Transform annotation./* 转换标注* 计算最后cropped出来的图像中所有目标的坐标(transform_param如果有crop_size,会crop出一块区域)*/const bool do_resize = true;TransformAnnotation(anno_datum, do_resize, crop_bbox, *do_mirror,transformed_anno_group_all);
}

数据层的源码大概就是这样，大家有什么疑问的，可以留言一起讨论。

2018-4-29 22:44:01
Last updated: 2018-5-1 10:53:20

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SSD源码解读1-数据层AnnotatedDataLayer

前言

数据层AnnotatedDataLayer源码解读

GenerateBatchSamples

DataTransformer::CropImage

DataTransformer::Transform

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