caffe代码data_transform

//DataTransformer需要输入的是blob，所以需要看一下里面的参数，因此再把这一部分内容的proto贴出来，这是新版的caffe  /* // Specifies the shape (dimensions) of a Blob. message BlobShape {   repeated int64 dim = 1 [packed = true]; }  message BlobProto {   optional BlobShape shape = 7;   repeated float data = 5 [packed = true];   repeated float diff = 6 [packed = true];   repeated double double_data = 8 [packed = true];   repeated double double_diff = 9 [packed = true];    // 4D dimensions -- deprecated.  Use "shape" instead.   optional int32 num = 1 [default = 0];   optional int32 channels = 2 [default = 0];   optional int32 height = 3 [default = 0];   optional int32 width = 4 [default = 0]; } */  /////////////////TransformationParameter的caffe消息定义  /* // Message that stores parameters used to apply transformation // to the data layer's data message TransformationParameter {   // For data pre-processing, we can do simple scaling and subtracting the   // data mean, if provided. Note that the mean subtraction is always carried   // out before scaling.   optional float scale = 1 [default = 1];   // Specify if we want to randomly mirror data.   optional bool mirror = 2 [default = false];   // Specify if we would like to randomly crop an image.   optional uint32 crop_size = 3 [default = 0];   // mean_file and mean_value cannot be specified at the same time   optional string mean_file = 4;   // if specified can be repeated once (would substract it from all the channels)   // or can be repeated the same number of times as channels   // (would subtract them from the corresponding channel)   repeated float mean_value = 5;   // Force the decoded image to have 3 color channels.   optional bool force_color = 6 [default = false];   // Force the decoded image to have 1 color channels.   optional bool force_gray = 7 [default = false]; } */  #ifdef USE_OPENCV  #include <opencv2/core/core.hpp>  #endif  // USE_OPENCV    #include <string>  #include <vector>    #include "caffe/data_transformer.hpp"  #include "caffe/util/io.hpp"  #include "caffe/util/math_functions.hpp"  #include "caffe/util/rng.hpp"    namespace caffe {  // 构造函数  template<typename Dtype>  DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,      Phase phase)      : param_(param), phase_(phase) {    // check if we want to use mean_file    // 判断是否有平均值文件    if (param_.has_mean_file()) {      CHECK_EQ(param_.mean_value_size(), 0) <<        "Cannot specify mean_file and mean_value at the same time";      // 平均值文件的路径      const string& mean_file = param.mean_file();      if (Caffe::root_solver()) {        LOG(INFO) << "Loading mean file from: " << mean_file;      }      BlobProto blob_proto;// 调用google/protobuf?? ,用于加速运算的数据接口，有时间再详细了解其应用方法   //这个函数是实现了从二进制文件中读取数据到blob_proto中,猜测函数来自第3方库的google/protobuf模块       ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);      data_mean_.FromProto(blob_proto);// 调用Blob类的成员函数FromRroto从BlobProto中加载数据     }    // check if we want to use mean_value    if (param_.mean_value_size() > 0) {      CHECK(param_.has_mean_file() == false) <<        "Cannot specify mean_file and mean_value at the same time";      for (int c = 0; c < param_.mean_value_size(); ++c) {        mean_values_.push_back(param_.mean_value(c));//将元素param_.mean_value(c)加入到mean_values_容器的最后一位      }    }  }    /*提前先描述一下数据层的Datum， Datum数据结构,Caffe并不是把向量和矩阵直接放进数据库的，而是将数据通过caffe.proto里定义的一个datum类来封装。数据库里放的是一个个的datum序列化成的字符串。Datum的定义摘录如下： message Datum {   optional int32 channels = 1;   optional int32 height = 2;   optional int32 width = 3;   // the actual image data, in bytes   optional bytes data = 4;   optional int32 label = 5;   // Optionally, the datum could also hold float data.   repeated float float_data = 6;   // If true data contains an encoded image that need to be decoded   optional bool encoded = 7 [default = false]; } 一个Datum有三个维度，channels, height,和width，可以看做是少了num维度的Blob。存放数据的地方有两个：byte_data和float_data，分别存放整数型和浮点型数据。图像数据一般是整形，放在byte_data里，特征向量一般是浮点型，放在float_data里。label存放数据的类别标签，是整数型。encoded标识数据是否需要被解码（里面有可能放的是JPEG或者PNG之类经过编码的数据）。Datum这个数据结构将数据和标签封装在一起，兼容整形和浮点型数据。经过Protobuf编译后，可以在Python和C++中都提供高效的访问。同时Protubuf还为它提供了序列化与反序列化的功能。存放进LMDB的就是Datum序列化生成的字符串。 Caffe中关于LMDB的代码有三类：生成数据集、读取数据集、生成特征向量。接下来就分别针对三者进行分析。 生成数据集: 生成数据集的代码在examples，随数据集提供，比如MNIST。 首先，创建访问LMDB所需的一些变量： MDB_env *mdb_env; MDB_dbi mdb_dbi; MDB_val mdb_key, mdb_data; MDB_txn *mdb_txn; ... mdb_env是整个数据库环境的句柄，mdb_dbi是环境中一个数据库的句柄，mdb_key和mdb_data用来存放向数据库中输入数据的“值”。mdb_txn是数据库事物操作的句柄，”txn”是”transaction”的缩写。 然后，创建数据库环境，创建并打开数据库： if (db_backend == "lmdb") {  // lmdb   LOG(INFO) << "Opening lmdb " << db_path;   CHECK_EQ(mkdir(db_path, 0744), 0)       << "mkdir " << db_path << "failed";   CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";   CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB       << "mdb_env_set_mapsize failed";   CHECK_EQ(mdb_env_open(mdb_env, db_path, 0, 0664), MDB_SUCCESS)       << "mdb_env_open failed";   CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)       << "mdb_txn_begin failed";   CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)       << "mdb_open failed. Does the lmdb already exist? "; } else {   LOG(FATAL) << "Unknown db backend " << db_backend; } mkdir(db_path, 0744)为数据库创建文件夹，如果文件夹已经存在，程序会报错退出。也就是说，程序不会覆盖已有的数据库。已有的数据库如果不要了，需要手动删除。需要注意的是，LMDB的一个环境中是可以有多个数据库的，数据库之间以名字区分。mdb_open()的第二个参数实际上就是数据库的名称(char *)。当一个环境中只有一个数据库的时候，这个参数可以给NULL。最后，为每一个图像创建Datum对象，向对象内写入数据，然后将其序列化成字符串，将字符串放入数据库中： Datum datum; datum.set_channels(1); datum.set_height(rows); datum.set_width(cols); for (int item_id = 0; item_id < num_items; ++item_id) {   image_file.read(pixels, rows * cols);   label_file.read(&label, 1);   datum.set_data(pixels, rows*cols);   datum.set_label(label);   snprintf(key_cstr, kMaxKeyLength, "%08d", item_id);   datum.SerializeToString(&value);   string keystr(key_cstr);    // Put in db   if (db_backend == "lmdb") {  // lmdb     mdb_data.mv_size = value.size();     mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);     mdb_key.mv_size = keystr.size();     mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);     CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)         << "mdb_put failed";   } else {     LOG(FATAL) << "Unknown db backend " << db_backend;   }    if (++count % 1000 == 0) {     // Commit txn     if (db_backend == "lmdb") {  // lmdb       CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)           << "mdb_txn_commit failed";       CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)           << "mdb_txn_begin failed";     } else {       LOG(FATAL) << "Unknown db backend " << db_backend;     }   } } 放入数据的Key是图像的编号，前面补0至8位。MDB_val类型的mdb_data和mdb_key中存放的是数据来源的指针，以及数据的长度。mdb_put()函数将数据存入数据库。每隔1000个图像commit一次数据库。只有commit之后，数据才真正写入磁盘。 读取数据集: Caffe中读取LMDB数据集的代码是DataLayer，用在网络的最下层，提供数据。DataLayer采用顺序遍历的方式读取数据，不支持打乱数据顺序，只能随机跳过前若干个数据。 首先，在DataLayer的DataLayerSetUp方法中，打开数据库，并获取迭代器cursor_： db_.reset(db::GetDB(this->layer_param_.data_param().backend())); db_->Open(this->layer_param_.data_param().source(), db::READ); cursor_.reset(db_->NewCursor()); 然后，在每一次的数据预取时，InternalThreadEntry()方法中，从数据库中读取字符串，反序列化为Datum对象，再从Datum对象中取出数据： Datum datum; datum.ParseFromString(cursor_->value()); 其中，cursor_->value()获取序列化后的字符串。datum.ParseFromString()方法对字符串进行反序列化。 最后，要将cursor_向前推进： cursor_->Next(); if (!cursor_->valid()) {   DLOG(INFO) << "Restarting data prefetching from start."       cursor_->SeekToFirst(); } 如果cursor->valid()返回false，说明数据库已经遍历到头，这时需要将cursor_重置回数据库开头。不支持样本随机排序应该是DataLayer的致命弱点。如果数据库的key能够统一，其实可以通过对key随机枚举的方式实现。所以caffe定义了一个随机生成器RNG。 */  template<typename Dtype>  void DataTransformer<Dtype>::Transform(const Datum& datum,                                         Dtype* transformed_data) {    // 参考TransformationParameter的定义    const string& data = datum.data();    const int datum_channels = datum.channels();//数据的channel    const int datum_height = datum.height();//数据的行数    const int datum_width = datum.width();// 数据的列数      const int crop_size = param_.crop_size();// crop大小    const Dtype scale = param_.scale();// 缩放比例    const bool do_mirror = param_.mirror() && Rand(2);// 该参数用于在镜像位置对数据处理    const bool has_mean_file = param_.has_mean_file();// 是否有均值文件    const bool has_uint8 = data.size() > 0;// 数据是否为uint8还是float类型的    const bool has_mean_values = mean_values_.size() > 0;// 是否有每个channel的均值      // 检查合法性    CHECK_GT(datum_channels, 0);    CHECK_GE(datum_height, crop_size);    CHECK_GE(datum_width, crop_size);      Dtype* mean = NULL;  /* 前面有介绍这一部分CHECK内容，glog提供了多个便利的宏来处理特定关系的判定。具体有： 1,判定大小关系 CHECK_EQ, CHECK_NE, CHECK_LE, CHECK_LT, CHECK_GE, CHECK_GT，使用这些宏需要注意类型一致，如果出现类型不一致的，可使用static_cast转换。 2,判定指针是否为空 CHECK_NOTNULL（some_ptr），可用于对象初始化的时候。 3，判定字符串是否相等 CHECK_STREQ, CHECK_STRNE, CHECK_STRCASEEQ,CHECK_STRCASENE。可进行大小写敏感或不敏感字符串来分别判定。 4， 判定浮点是否相等或相近 CHECK_DOUBLE_EQ，CHECK_NEAR。这两个宏都需要指定一个可容忍的偏差上限。 */    if (has_mean_file) {// 检查mean_file是否与数据的参数一致      CHECK_EQ(datum_channels, data_mean_.channels());      CHECK_EQ(datum_height, data_mean_.height());      CHECK_EQ(datum_width, data_mean_.width());      mean = data_mean_.mutable_cpu_data();    }    if (has_mean_values) {      CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) <<       "Specify either 1 mean_value or as many as channels: " << datum_channels;      if (datum_channels > 1 && mean_values_.size() == 1) {        // Replicate the mean_value for simplicity        for (int c = 1; c < datum_channels; ++c) {          mean_values_.push_back(mean_values_[0]);        }      }    }      int height = datum_height;    int width = datum_width;      // 根据是否需要crop来生成h_off和w_off    int h_off = 0;    int w_off = 0;    if (crop_size) {// 如果crop_size不为0      height = crop_size;      width = crop_size;      // We only do random crop when we do training.      // 在训练的时候随机crop图像块,这里需要自己实现Rand这个函数来确定是如何随机的      if (phase_ == TRAIN) {        h_off = Rand(datum_height - crop_size + 1);// 产生从0到datum_height - crop_size的随机数        w_off = Rand(datum_width - crop_size + 1);      } else {// 测试的时候不用随机，取图像的中心        h_off = (datum_height - crop_size) / 2;        w_off = (datum_width - crop_size) / 2;      }    }      // 对数据进行变换，主要是将原来的像素值减去均值，然后乘以scale这么一个操作    // 如果需要crop则最终转换的Blob的大小即为crop*crop    // 如果不是，则最终的Blob大小即为datum_height*datum_width    Dtype datum_element;    int top_index, data_index;    for (int c = 0; c < datum_channels; ++c) {      for (int h = 0; h < height; ++h) {        for (int w = 0; w < width; ++w) {          data_index = (c * datum_height + h_off + h) * datum_width + w_off + w;// 获取数据的索引，我不是很明白怎么计算的？          if (do_mirror) {// 是否需要在镜像位置转换            top_index = (c * height + h) * width + (width - 1 - w);//在宽这个坐标上做文章，来实现镜像          } else {//            top_index = (c * height + h) * width + w;          }          if (has_uint8) {// 数据如果是uint8则进行转换            datum_element =              static_cast<Dtype>(static_cast<uint8_t>(data[data_index]));          } else {// 否则就是float            datum_element = datum.float_data(data_index);          }          if (has_mean_file) {// 如果有mean_file,则原来的像素值减去均值，然后乘以scale            transformed_data[top_index] =              (datum_element - mean[data_index]) * scale;          } else {            if (has_mean_values) {// 否则减去该channel的均值（每个channel有其一个均值），然后乘以scale              transformed_data[top_index] =                (datum_element - mean_values_[c]) * scale;            } else {// 否则如果没有均值那么就直接乘以scale即可              transformed_data[top_index] = datum_element * scale;            }          }        }      }    }  }      template<typename Dtype>  void DataTransformer<Dtype>::Transform(const Datum& datum,                                         Blob<Dtype>* transformed_blob) {    // If datum is encoded, decoded and transform the cv::image.    if (datum.encoded()) {//  检查是否编码了，如果是则解码  #ifdef USE_OPENCV      // 先检查是不是两个属性都设置， 如果是则说明参数设置有误      CHECK(!(param_.force_color() && param_.force_gray()))          << "cannot set both force_color and force_gray";      cv::Mat cv_img;      if (param_.force_color() || param_.force_gray()) {          // 如果强制彩色或者强制灰度图像一个成立则使用DecodeDatumToCVMat解码      // If force_color then decode in color otherwise decode in gray.        cv_img = DecodeDatumToCVMat(datum, param_.force_color());      } else {// 否则使用DecodeDatumToCVMatNative解码        cv_img = DecodeDatumToCVMatNative(datum);      }      // Transform the cv::image into blob.      // 变换      return Transform(cv_img, transformed_blob);  #else      LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";  #endif  // USE_OPENCV    } else {// 如果没有编码则，检查force_color和force_gray是否设置，如果设置则不合法，因为该选项只适合于编码后的数据      if (param_.force_color() || param_.force_gray()) {        LOG(ERROR) << "force_color and force_gray only for encoded datum";      }    }      const int crop_size = param_.crop_size();    const int datum_channels = datum.channels();    const int datum_height = datum.height();    const int datum_width = datum.width();      // Check dimensions.    const int channels = transformed_blob->channels();    const int height = transformed_blob->height();    const int width = transformed_blob->width();    const int num = transformed_blob->num();      CHECK_EQ(channels, datum_channels);    CHECK_LE(height, datum_height);    CHECK_LE(width, datum_width);    CHECK_GE(num, 1);      if (crop_size) {      CHECK_EQ(crop_size, height);      CHECK_EQ(crop_size, width);    } else {      CHECK_EQ(datum_height, height);      CHECK_EQ(datum_width, width);    }    // 继续变换数据    Dtype* transformed_data = transformed_blob->mutable_cpu_data();    Transform(datum, transformed_data);  }    template<typename Dtype>  void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,                                         Blob<Dtype>* transformed_blob) {    const int datum_num = datum_vector.size();    // 变换到的目标blob的形状    const int num = transformed_blob->num();    const int channels = transformed_blob->channels();    const int height = transformed_blob->height();    const int width = transformed_blob->width();      CHECK_GT(datum_num, 0) << "There is no datum to add";    CHECK_LE(datum_num, num) <<      "The size of datum_vector must be no greater than transformed_blob->num()";    // 新建一个uni_blob,里面只有一个batch    Blob<Dtype> uni_blob(1, channels, height, width);    for (int item_id = 0; item_id < datum_num; ++item_id) {      int offset = transformed_blob->offset(item_id);      uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);      Transform(datum_vector[item_id], &uni_blob);    }  }    #ifdef USE_OPENCV  template<typename Dtype>  void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,                                         Blob<Dtype>* transformed_blob) {    // 获取mat的参数    const int mat_num = mat_vector.size();    const int num = transformed_blob->num();    const int channels = transformed_blob->channels();    const int height = transformed_blob->height();    const int width = transformed_blob->width();      CHECK_GT(mat_num, 0) << "There is no MAT to add";    CHECK_EQ(mat_num, num) <<      "The size of mat_vector must be equals to transformed_blob->num()";    //  同上    Blob<Dtype> uni_blob(1, channels, height, width);    for (int item_id = 0; item_id < mat_num; ++item_id) {      int offset = transformed_blob->offset(item_id);      uni_blob.set_cpu_data(transformed_blob->mutable_cpu_data() + offset);      Transform(mat_vector[item_id], &uni_blob);    }  }    // 如果是图像的话，需要减去均值乘以scale，判断是不是需要做镜像处理  // 逻辑与前面类似  template<typename Dtype>  void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,                                         Blob<Dtype>* transformed_blob) {    const int crop_size = param_.crop_size();    const int img_channels = cv_img.channels();    const int img_height = cv_img.rows;    const int img_width = cv_img.cols;      // Check dimensions.    const int channels = transformed_blob->channels();    const int height = transformed_blob->height();    const int width = transformed_blob->width();    const int num = transformed_blob->num();      CHECK_EQ(channels, img_channels);    CHECK_LE(height, img_height);    CHECK_LE(width, img_width);    CHECK_GE(num, 1);      CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";      const Dtype scale = param_.scale();    const bool do_mirror = param_.mirror() && Rand(2);    const bool has_mean_file = param_.has_mean_file();    const bool has_mean_values = mean_values_.size() > 0;      CHECK_GT(img_channels, 0);    CHECK_GE(img_height, crop_size);    CHECK_GE(img_width, crop_size);      Dtype* mean = NULL;    if (has_mean_file) {      CHECK_EQ(img_channels, data_mean_.channels());      CHECK_EQ(img_height, data_mean_.height());      CHECK_EQ(img_width, data_mean_.width());      mean = data_mean_.mutable_cpu_data();    }    if (has_mean_values) {      CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) <<       "Specify either 1 mean_value or as many as channels: " << img_channels;      if (img_channels > 1 && mean_values_.size() == 1) {        // Replicate the mean_value for simplicity        for (int c = 1; c < img_channels; ++c) {          mean_values_.push_back(mean_values_[0]);        }      }    }      int h_off = 0;    int w_off = 0;    cv::Mat cv_cropped_img = cv_img;    if (crop_size) {      CHECK_EQ(crop_size, height);      CHECK_EQ(crop_size, width);      // We only do random crop when we do training.      if (phase_ == TRAIN) {        h_off = Rand(img_height - crop_size + 1);        w_off = Rand(img_width - crop_size + 1);      } else {        h_off = (img_height - crop_size) / 2;        w_off = (img_width - crop_size) / 2;      }      cv::Rect roi(w_off, h_off, crop_size, crop_size);      cv_cropped_img = cv_img(roi);    } else {      CHECK_EQ(img_height, height);      CHECK_EQ(img_width, width);    }      CHECK(cv_cropped_img.data);      Dtype* transformed_data = transformed_blob->mutable_cpu_data();    int top_index;    for (int h = 0; h < height; ++h) {      const uchar* ptr = cv_cropped_img.ptr<uchar>(h);      int img_index = 0;      for (int w = 0; w < width; ++w) {        for (int c = 0; c < img_channels; ++c) {          if (do_mirror) {            top_index = (c * height + h) * width + (width - 1 - w);          } else {            top_index = (c * height + h) * width + w;          }          // int top_index = (c * height + h) * width + w;          Dtype pixel = static_cast<Dtype>(ptr[img_index++]);          if (has_mean_file) {            int mean_index = (c * img_height + h_off + h) * img_width + w_off + w;            transformed_data[top_index] =              (pixel - mean[mean_index]) * scale;          } else {            if (has_mean_values) {              transformed_data[top_index] =                (pixel - mean_values_[c]) * scale;            } else {              transformed_data[top_index] = pixel * scale;            }          }        }      }    }  }  #endif  // USE_OPENCV    template<typename Dtype>  void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,                                         Blob<Dtype>* transformed_blob) {    const int crop_size = param_.crop_size();    const int input_num = input_blob->num();    const int input_channels = input_blob->channels();    const int input_height = input_blob->height();    const int input_width = input_blob->width();      if (transformed_blob->count() == 0) {      // Initialize transformed_blob with the right shape.      if (crop_size) {        transformed_blob->Reshape(input_num, input_channels,                                  crop_size, crop_size);      } else {        transformed_blob->Reshape(input_num, input_channels,                                  input_height, input_width);      }    }      const int num = transformed_blob->num();    const int channels = transformed_blob->channels();    const int height = transformed_blob->height();    const int width = transformed_blob->width();    const int size = transformed_blob->count();      CHECK_LE(input_num, num);    CHECK_EQ(input_channels, channels);    CHECK_GE(input_height, height);    CHECK_GE(input_width, width);        const Dtype scale = param_.scale();    const bool do_mirror = param_.mirror() && Rand(2);    const bool has_mean_file = param_.has_mean_file();    const bool has_mean_values = mean_values_.size() > 0;      int h_off = 0;    int w_off = 0;    if (crop_size) {      CHECK_EQ(crop_size, height);      CHECK_EQ(crop_size, width);      // We only do random crop when we do training.      if (phase_ == TRAIN) {        h_off = Rand(input_height - crop_size + 1);        w_off = Rand(input_width - crop_size + 1);      } else {        h_off = (input_height - crop_size) / 2;        w_off = (input_width - crop_size) / 2;      }    } else {      CHECK_EQ(input_height, height);      CHECK_EQ(input_width, width);    }      // 如果有均值文件则    Dtype* input_data = input_blob->mutable_cpu_data();    if (has_mean_file) {      CHECK_EQ(input_channels, data_mean_.channels());      CHECK_EQ(input_height, data_mean_.height());      CHECK_EQ(input_width, data_mean_.width());      for (int n = 0; n < input_num; ++n) {        int offset = input_blob->offset(n);        /*          template <typename Dtype>        void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);        math_function中定义的caffe_sub目的是矩阵相减input_data（以offset开始的矩阵） = input_data（以offset开始的矩阵） - data_mean_     */        caffe_sub(data_mean_.count(), input_data + offset,              data_mean_.cpu_data(), input_data + offset);      }    }    // 如果每个channel有均值则    if (has_mean_values) {      CHECK(mean_values_.size() == 1 || mean_values_.size() == input_channels) <<       "Specify either 1 mean_value or as many as channels: " << input_channels;      if (mean_values_.size() == 1) {        caffe_add_scalar(input_blob->count(), -(mean_values_[0]), input_data);      } else {        for (int n = 0; n < input_num; ++n) {          for (int c = 0; c < input_channels; ++c) {            int offset = input_blob->offset(n, c);            // 给nput_data[offset]地址开始的每一个元素加上一个-mean_values_[c]            caffe_add_scalar(input_height * input_width, -(mean_values_[c]),              input_data + offset);          }        }      }    }      // 如果啥均值都没有则直接复制    Dtype* transformed_data = transformed_blob->mutable_cpu_data();      for (int n = 0; n < input_num; ++n) {      int top_index_n = n * channels;      int data_index_n = n * channels;      for (int c = 0; c < channels; ++c) {        int top_index_c = (top_index_n + c) * height;        int data_index_c = (data_index_n + c) * input_height + h_off;        for (int h = 0; h < height; ++h) {          int top_index_h = (top_index_c + h) * width;          int data_index_h = (data_index_c + h) * input_width + w_off;          if (do_mirror) {            int top_index_w = top_index_h + width - 1;            for (int w = 0; w < width; ++w) {              transformed_data[top_index_w-w] = input_data[data_index_h + w];            }          } else {            for (int w = 0; w < width; ++w) {              transformed_data[top_index_h + w] = input_data[data_index_h + w];            }          }        }      }    }    if (scale != Dtype(1)) {      DLOG(INFO) << "Scale: " << scale;      caffe_scal(size, scale, transformed_data);    }  }    template<typename Dtype>  vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {    if (datum.encoded()) {  #ifdef USE_OPENCV // 如果使用OpenCV则可以用先转换为CVMat，然后在推断blob的形状      CHECK(!(param_.force_color() && param_.force_gray()))          << "cannot set both force_color and force_gray";      cv::Mat cv_img;      if (param_.force_color() || param_.force_gray()) {      // If force_color then decode in color otherwise decode in gray.        cv_img = DecodeDatumToCVMat(datum, param_.force_color());      } else {        cv_img = DecodeDatumToCVMatNative(datum);      }      // InferBlobShape using the cv::image.      return InferBlobShape(cv_img);  #else      LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";  #endif  // USE_OPENCV    }      // 否则直接粗暴地从datum里面获取形状的数据    const int crop_size = param_.crop_size();    const int datum_channels = datum.channels();    const int datum_height = datum.height();    const int datum_width = datum.width();    // Check dimensions.    CHECK_GT(datum_channels, 0);    CHECK_GE(datum_height, crop_size);    CHECK_GE(datum_width, crop_size);    // Build BlobShape.    vector<int> shape(4);    shape[0] = 1;    shape[1] = datum_channels;    shape[2] = (crop_size)? crop_size: datum_height;    shape[3] = (crop_size)? crop_size: datum_width;    return shape;  }    template<typename Dtype>  vector<int> DataTransformer<Dtype>::InferBlobShape(      const vector<Datum> & datum_vector) {    const int num = datum_vector.size();    CHECK_GT(num, 0) << "There is no datum to in the vector";    // Use first datum in the vector to InferBlobShape.    // 使用第一个来进行推断    vector<int> shape = InferBlobShape(datum_vector[0]);    // Adjust num to the size of the vector.    shape[0] = num;    return shape;  }    #ifdef USE_OPENCV  // 如果使用OpenCV  // 使用CVMat中的信息来推断形状  template<typename Dtype>  vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {    const int crop_size = param_.crop_size();    const int img_channels = cv_img.channels();    const int img_height = cv_img.rows;    const int img_width = cv_img.cols;    // Check dimensions.    CHECK_GT(img_channels, 0);    CHECK_GE(img_height, crop_size);    CHECK_GE(img_width, crop_size);    // Build BlobShape.    vector<int> shape(4);    shape[0] = 1;    shape[1] = img_channels;    shape[2] = (crop_size)? crop_size: img_height;    shape[3] = (crop_size)? crop_size: img_width;    return shape;  }    template<typename Dtype>  vector<int> DataTransformer<Dtype>::InferBlobShape(      const vector<cv::Mat> & mat_vector) {    const int num = mat_vector.size();    CHECK_GT(num, 0) << "There is no cv_img to in the vector";    // Use first cv_img in the vector to InferBlobShape.    // 使用第一个来推断    vector<int> shape = InferBlobShape(mat_vector[0]);    // Adjust num to the size of the vector.    shape[0] = num;    return shape;  }  #endif  // USE_OPENCV    // 初始化随机数种子  template <typename Dtype>  void DataTransformer<Dtype>::InitRand() {    // 要么需要镜像要么训练阶段和需要crop同时满足的情况下才初始化随机数种子    const bool needs_rand = param_.mirror() ||        (phase_ == TRAIN && param_.crop_size());    if (needs_rand) {      const unsigned int rng_seed = caffe_rng_rand();// 获得随机数种子（通过熵池或者时间生成种子）      rng_.reset(new Caffe::RNG(rng_seed));//初始化随机数种子并实例化随机数生成器    } else {      rng_.reset();//否则随机数生成器设置为空    }  }    // 产生从0到n的随机数  template <typename Dtype>  int DataTransformer<Dtype>::Rand(int n) {    CHECK(rng_);    CHECK_GT(n, 0);    caffe::rng_t* rng =        static_cast<caffe::rng_t*>(rng_->generator());    return ((*rng)() % n);  }    INSTANTIATE_CLASS(DataTransformer);  /* 初始化类的宏定义是这样的，前面有讲过，这里再给出来 #define INSTANTIATE_CLASS(classname) \   char gInstantiationGuard##classname; \   template class classname<float>; \   template class classname<double> */    }  // namespace caffe