caffe代码阅读4：DataTransformer以及io的实现细节-2016.3.16

一、DataTransformer的作用简介

该类主要负责对数据进行预处理，将Datum、const vector<Datum>、cv::Mat&、vector<cv::Mat> 、Blob<Dtype>*类型的数据变换到目标大小的blob。

此外还负责根据参数中指定的预处理参数推断出处理后的数据的shape。

在正式介绍之前，先给个例子：

layer {  name: "jointimagedata"  type: "JointImage"  top: "jointimagedata"  top: "label"  include {    phase: TEST  }  transform_param {    mirror: true    crop_size: 227    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"  }  slidewindow_param {    root_folder: "D:/数据集/FLIC/FLIC-full"    filelistpath: "/imglist.txt"    batch_size: 300  }}

上述配置文件中就包含了transform_param这个参数，利用该参数可以实现crop，mirror，减去均值等功能。

该类用到了TransformationParameter。

其在caffe.proto的定义为

// Message that stores parameters used to apply transformation// to the data layer's datamessage 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];}

二、DataTransformer类的详细介绍

1）构造函数

  // 构造函数

  explicit DataTransformer(const TransformationParameter& param, Phase phase);  virtual ~DataTransformer() {}

2）成员变量

  // 变换所使用的参数  TransformationParameter param_;  // 随机数生成器的种子  shared_ptr<Caffe::RNG> rng_;  // 是训练还是测试？  Phase phase_;  // 数据均值 blob  Blob<Dtype> data_mean_;  // 数据均值blob的容器  vector<Dtype> mean_values_;

3）成员函数

 // 初始化随机数生成器，因为在对数据进行变换的时候有可能用到，比如说打乱数据的输入顺序  void InitRand();   // 对Datum的数据进行变换，放入到transformed_blob中  void Transform(const Datum& datum, Blob<Dtype>* transformed_blob);   // 对Datum容器的数据进行变换翻入到transformed_blob  void Transform(const vector<Datum> & datum_vector,                Blob<Dtype>* transformed_blob);   // 如果定义OpenCV还可能对mat容器数据类型的数据进行变换  void Transform(const vector<cv::Mat> & mat_vector,                Blob<Dtype>* transformed_blob);   // 将opencv读取的单个图像转换到blob中去  void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);   // 将输入的blob进行变换，可能是取出blob的中的一部分数据到新的blob  void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);   // 根据Datum获取blob的形状  vector<int> InferBlobShape(const Datum& datum);   // 根据Datum容器获取blob的形状  vector<int> InferBlobShape(const vector<Datum> & datum_vector);  // 根据Mat容器获取blob的形状  vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);  // 根据Mat获取blob的形状  vector<int> InferBlobShape(const cv::Mat& cv_img);// 生成从0到n-1的服从均匀分布的随机数,要求继承他的都必须实现如何生成随机数  virtual int Rand(int n);  // 将给定的Datum进行转换  void Transform(const Datum& datum, Dtype* transformed_data);

4）具体函数的实现：

首先是构造函数

在介绍构造函数之前不得不先贴出BlobShape和、BlobProto这两个结构体的在caffe.proto中的定义。message BlobShape {  repeated int64 dim = 1 [packed = true]; //blob的形状}message BlobProto {  optional BlobShape shape = 7;  repeated float data = 5 [packed = true]; // 前向传播的数据  repeated float diff = 6 [packed = true]; // 反向传播的数据  repeated double double_data = 8 [packed = true]; // double类型的前向传播的数据  repeated double double_diff = 9 [packed = true]; // 依次类推  // 4D dimensions -- deprecated.  Use "shape" instead.  // 下面是为了兼容  optional int32 num = 1 [default = 0];  optional int32 channels = 2 [default = 0];  optional int32 height = 3 [default = 0];  optional int32 width = 4 [default = 0];}template<typename Dtype>DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,    Phase phase)    : param_(param), phase_(phase) {  // check if we want to use mean_file  if (param_.has_mean_file()) {    CHECK_EQ(param_.mean_value_size(), 0) <<      "Cannot specify mean_file and mean_value at the same time";    const string& mean_file = param.mean_file();    if (Caffe::root_solver()) {      LOG(INFO) << "Loading mean file from: " << mean_file;    }    BlobProto blob_proto;    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);    data_mean_.FromProto(blob_proto);  }  // 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));    }  }}

具体的实现如下：

#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;    ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);    data_mean_.FromProto(blob_proto);  }  // 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));    }  }}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;  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        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_OPENCVtemplate<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_OPENCVtemplate<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

三、与DataTransformer类相关类的介绍

（1）io的介绍

首先给出io中定义的各个函数的含义：

#ifndef CAFFE_UTIL_IO_H_#define CAFFE_UTIL_IO_H_#include <unistd.h>#include <string>#include "google/protobuf/message.h"#include "caffe/blob.hpp"#include "caffe/common.hpp"#include "caffe/proto/caffe.pb.h"namespace caffe {using ::google::protobuf::Message;// 内联函数，创建临时文件inline void MakeTempFilename(string* temp_filename) {  temp_filename->clear();  *temp_filename = "/tmp/caffe_test.XXXXXX";  char* temp_filename_cstr = new char[temp_filename->size() + 1];  // NOLINT_NEXT_LINE(runtime/printf)  strcpy(temp_filename_cstr, temp_filename->c_str());  int fd = mkstemp(temp_filename_cstr);  CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;  close(fd);  *temp_filename = temp_filename_cstr;  delete[] temp_filename_cstr;}// 内联函数，创建临时目录inline void MakeTempDir(string* temp_dirname) {  temp_dirname->clear();  *temp_dirname = "/tmp/caffe_test.XXXXXX";  char* temp_dirname_cstr = new char[temp_dirname->size() + 1];  // NOLINT_NEXT_LINE(runtime/printf)  strcpy(temp_dirname_cstr, temp_dirname->c_str());  char* mkdtemp_result = mkdtemp(temp_dirname_cstr);  CHECK(mkdtemp_result != NULL)      << "Failed to create a temporary directory at: " << *temp_dirname;  *temp_dirname = temp_dirname_cstr;  delete[] temp_dirname_cstr;}// 从txt读取proto的定义bool ReadProtoFromTextFile(const char* filename, Message* proto);// 从text读取proto的定义inline bool ReadProtoFromTextFile(const string& filename, Message* proto) {  return ReadProtoFromTextFile(filename.c_str(), proto);}// 从text读取proto的定义,只是增加了检查而已inline void ReadProtoFromTextFileOrDie(const char* filename, Message* proto) {  CHECK(ReadProtoFromTextFile(filename, proto));}// 从text读取proto的定义,只是增加了检查而已inline void ReadProtoFromTextFileOrDie(const string& filename, Message* proto) {  ReadProtoFromTextFileOrDie(filename.c_str(), proto);}// 将proto写入到txt文件void WriteProtoToTextFile(const Message& proto, const char* filename);inline void WriteProtoToTextFile(const Message& proto, const string& filename) {  WriteProtoToTextFile(proto, filename.c_str());}// 从bin读取proto的定义bool ReadProtoFromBinaryFile(const char* filename, Message* proto);// 从bin读取proto的定义inline bool ReadProtoFromBinaryFile(const string& filename, Message* proto) {  return ReadProtoFromBinaryFile(filename.c_str(), proto);}// 从bin读取proto的定义,只是增加了检查而已inline void ReadProtoFromBinaryFileOrDie(const char* filename, Message* proto) {  CHECK(ReadProtoFromBinaryFile(filename, proto));}// 从bin读取proto的定义,只是增加了检查而已inline void ReadProtoFromBinaryFileOrDie(const string& filename,                                         Message* proto) {  ReadProtoFromBinaryFileOrDie(filename.c_str(), proto);}// 将proto写入到bin文件void WriteProtoToBinaryFile(const Message& proto, const char* filename);// 内联函数，将proto写入到bin文件inline void WriteProtoToBinaryFile(    const Message& proto, const string& filename) {  WriteProtoToBinaryFile(proto, filename.c_str());}// 从文件读取数据到Datumbool ReadFileToDatum(const string& filename, const int label, Datum* datum);// 内联函数，从文件读取数据到Datuminline bool ReadFileToDatum(const string& filename, Datum* datum) {  return ReadFileToDatum(filename, -1, datum);}// 从图像文件读取数据到Datumbool ReadImageToDatum(const string& filename, const int label,    const int height, const int width, const bool is_color,    const std::string & encoding, Datum* datum);// 内联函数，从图像文件（彩色还是黑白？）读取数据到Datum，指定图像大小inline bool ReadImageToDatum(const string& filename, const int label,    const int height, const int width, const bool is_color, Datum* datum) {  return ReadImageToDatum(filename, label, height, width, is_color,                          "", datum);}// 内联函数，从彩色图像文件读取数据到Datum，指定图像大小inline bool ReadImageToDatum(const string& filename, const int label,    const int height, const int width, Datum* datum) {  return ReadImageToDatum(filename, label, height, width, true, datum);}// 内联函数，从图像文件（彩色还是黑白？）读取数据到Datum，自动获取图像大小inline bool ReadImageToDatum(const string& filename, const int label,    const bool is_color, Datum* datum) {  return ReadImageToDatum(filename, label, 0, 0, is_color, datum);}// 内联函数，从彩色图像文件读取数据到Datum，自动获取图像大小inline bool ReadImageToDatum(const string& filename, const int label,    Datum* datum) {  return ReadImageToDatum(filename, label, 0, 0, true, datum);}// 内联函数，从彩色图像文件读取数据到Datum，自动获取图像大小，指定编码格式inline bool ReadImageToDatum(const string& filename, const int label,    const std::string & encoding, Datum* datum) {  return ReadImageToDatum(filename, label, 0, 0, true, encoding, datum);}// 对Datum进行解码bool DecodeDatumNative(Datum* datum);// 对彩色图像的Datum进行解码bool DecodeDatum(Datum* datum, bool is_color);#ifdef USE_OPENCV// 将图像读取到CVMat，指定图像大小，是否彩色cv::Mat ReadImageToCVMat(const string& filename,    const int height, const int width, const bool is_color);// 将图像读取到CVMat，指定图像大小cv::Mat ReadImageToCVMat(const string& filename,    const int height, const int width);// 将图像读取到CVMat，指定是否彩色cv::Mat ReadImageToCVMat(const string& filename,    const bool is_color);// 将图像读取到CVMatcv::Mat ReadImageToCVMat(const string& filename);// 将Datum解码为为CVMatcv::Mat DecodeDatumToCVMatNative(const Datum& datum);// 将彩色图像的Datum解码为为CVMatcv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);// 将CVMat转换为Datumvoid CVMatToDatum(const cv::Mat& cv_img, Datum* datum);#endif  // USE_OPENCV}  // namespace caffe#endif   // CAFFE_UTIL_IO_H_

接下来给出io中的具体的实现的注释

#include <fcntl.h>#include <google/protobuf/io/coded_stream.h>#include <google/protobuf/io/zero_copy_stream_impl.h>#include <google/protobuf/text_format.h>#include <opencv2/core/core.hpp>#ifdef USE_OPENCV#include <opencv2/highgui/highgui.hpp>#include <opencv2/highgui/highgui_c.h>#include <opencv2/imgproc/imgproc.hpp>#endif  // USE_OPENCV#include <stdint.h>#include <algorithm>#include <fstream>  // NOLINT(readability/streams)#include <string>#include <vector>#include "caffe/common.hpp"#include "caffe/proto/caffe.pb.h"#include "caffe/util/io.hpp"const int kProtoReadBytesLimit = INT_MAX;  // Max size of 2 GB minus 1 byte.namespace caffe {using google::protobuf::io::FileInputStream;using google::protobuf::io::FileOutputStream;using google::protobuf::io::ZeroCopyInputStream;using google::protobuf::io::CodedInputStream;using google::protobuf::io::ZeroCopyOutputStream;using google::protobuf::io::CodedOutputStream;using google::protobuf::Message;// 从文件读取Proto的txt文件bool ReadProtoFromTextFile(const char* filename, Message* proto) {  int fd = open(filename, O_RDONLY);  CHECK_NE(fd, -1) << "File not found: " << filename;  FileInputStream* input = new FileInputStream(fd);  // 注意如何使用protobuf去读取  bool success = google::protobuf::TextFormat::Parse(input, proto);  delete input;  close(fd);  return success;}// 将proto写入到txt文件void WriteProtoToTextFile(const Message& proto, const char* filename) {  int fd = open(filename, O_WRONLY | O_CREAT | O_TRUNC, 0644);  FileOutputStream* output = new FileOutputStream(fd);  // 注意如何写入  CHECK(google::protobuf::TextFormat::Print(proto, output));  delete output;  close(fd);}// 从bin读取proto的定义bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {  int fd = open(filename, O_RDONLY);  CHECK_NE(fd, -1) << "File not found: " << filename;  ZeroCopyInputStream* raw_input = new FileInputStream(fd);  //  解码流com.google.protobuf.CodedInputStream  CodedInputStream* coded_input = new CodedInputStream(raw_input);  coded_input->SetTotalBytesLimit(kProtoReadBytesLimit, 536870912);  bool success = proto->ParseFromCodedStream(coded_input);  delete coded_input;  delete raw_input;  close(fd);  return success;}// 将proto写入到bin文件void WriteProtoToBinaryFile(const Message& proto, const char* filename) {  fstream output(filename, ios::out | ios::trunc | ios::binary);  CHECK(proto.SerializeToOstream(&output));}#ifdef USE_OPENCV// 将图像读取到CVMat，指定图像大小，是否彩色cv::Mat ReadImageToCVMat(const string& filename,    const int height, const int width, const bool is_color) {  cv::Mat cv_img;  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :    CV_LOAD_IMAGE_GRAYSCALE);  cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);  if (!cv_img_origin.data) {    LOG(ERROR) << "Could not open or find file " << filename;    return cv_img_origin;  }  if (height > 0 && width > 0) {    cv::resize(cv_img_origin, cv_img, cv::Size(width, height));  } else {    cv_img = cv_img_origin;  }  return cv_img;}cv::Mat ReadImageToCVMat(const string& filename,    const int height, const int width) {  return ReadImageToCVMat(filename, height, width, true);}cv::Mat ReadImageToCVMat(const string& filename,    const bool is_color) {  return ReadImageToCVMat(filename, 0, 0, is_color);}cv::Mat ReadImageToCVMat(const string& filename) {  return ReadImageToCVMat(filename, 0, 0, true);}// Do the file extension and encoding match?// 看看是不是jpg还是jpeg的图像static bool matchExt(const std::string & fn,                     std::string en) {  size_t p = fn.rfind('.');  std::string ext = p != fn.npos ? fn.substr(p) : fn;  std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);  std::transform(en.begin(), en.end(), en.begin(), ::tolower);  if ( ext == en )    return true;  if ( en == "jpg" && ext == "jpeg" )    return true;  return false;}// 从图像文件读取数据到Datumbool ReadImageToDatum(const string& filename, const int label,    const int height, const int width, const bool is_color,    const std::string & encoding, Datum* datum) {  cv::Mat cv_img = ReadImageToCVMat(filename, height, width, is_color);  if (cv_img.data) {    if (encoding.size()) {      if ( (cv_img.channels() == 3) == is_color && !height && !width &&          matchExt(filename, encoding) )        return ReadFileToDatum(filename, label, datum);      std::vector<uchar> buf;      // 对数据解码      cv::imencode("."+encoding, cv_img, buf);      datum->set_data(std::string(reinterpret_cast<char*>(&buf[0]),                      buf.size()));      // 数据标签      datum->set_label(label);      // 是否被编码      datum->set_encoded(true);      return true;    }    CVMatToDatum(cv_img, datum);    datum->set_label(label);    return true;  } else {    return false;  }}#endif  // USE_OPENCV// 从文件读取数据到Datumbool ReadFileToDatum(const string& filename, const int label,    Datum* datum) {  std::streampos size;  fstream file(filename.c_str(), ios::in|ios::binary|ios::ate);  if (file.is_open()) {    size = file.tellg();    std::string buffer(size, ' ');    file.seekg(0, ios::beg);    file.read(&buffer[0], size);    file.close();    datum->set_data(buffer);    datum->set_label(label);    datum->set_encoded(true);    return true;  } else {    return false;  }}#ifdef USE_OPENCV// 直接编码数据的Datum到CVMatcv::Mat DecodeDatumToCVMatNative(const Datum& datum) {  cv::Mat cv_img;  CHECK(datum.encoded()) << "Datum not encoded";  const string& data = datum.data();  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());  cv_img = cv::imdecode(vec_data, -1);//flag=-1  if (!cv_img.data) {    LOG(ERROR) << "Could not decode datum ";  }  return cv_img;}// 直接编码彩色或者非彩色Datum到CVMatcv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color) {  cv::Mat cv_img;  CHECK(datum.encoded()) << "Datum not encoded";  const string& data = datum.data();  std::vector<char> vec_data(data.c_str(), data.c_str() + data.size());  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :    CV_LOAD_IMAGE_GRAYSCALE);  cv_img = cv::imdecode(vec_data, cv_read_flag);// flag为用户指定的  if (!cv_img.data) {    LOG(ERROR) << "Could not decode datum ";  }  return cv_img;}// If Datum is encoded will decoded using DecodeDatumToCVMat and CVMatToDatum// If Datum is not encoded will do nothingbool DecodeDatumNative(Datum* datum) {  if (datum->encoded()) {    cv::Mat cv_img = DecodeDatumToCVMatNative((*datum));    CVMatToDatum(cv_img, datum);    return true;  } else {    return false;  }}// 将Datum进行解码bool DecodeDatum(Datum* datum, bool is_color) {  if (datum->encoded()) {    cv::Mat cv_img = DecodeDatumToCVMat((*datum), is_color);    CVMatToDatum(cv_img, datum);    return true;  } else {    return false;  }}// 将CVMat转换到Datumvoid CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {  CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";  datum->set_channels(cv_img.channels());  datum->set_height(cv_img.rows);  datum->set_width(cv_img.cols);  datum->clear_data();  datum->clear_float_data();  datum->set_encoded(false);  int datum_channels = datum->channels();  int datum_height = datum->height();  int datum_width = datum->width();  int datum_size = datum_channels * datum_height * datum_width;  std::string buffer(datum_size, ' ');  for (int h = 0; h < datum_height; ++h) {    const uchar* ptr = cv_img.ptr<uchar>(h);    int img_index = 0;    for (int w = 0; w < datum_width; ++w) {      for (int c = 0; c < datum_channels; ++c) {        int datum_index = (c * datum_height + h) * datum_width + w;        buffer[datum_index] = static_cast<char>(ptr[img_index++]);      }    }  }  datum->set_data(buffer);}#endif  // USE_OPENCV}  // namespace caffe

四、总结

总结起来就是，DataTransformer所作的工作实际上就是crop数据，让数据减去均值，以及缩放数据。

然后就是根据数据来推断形状。此外还介绍了io的内容，里面包含了创建临时文件临时目录操作，以及从txt文件以及bin文件读取proto数据或者写入proto的数据到txt或者bin文件。

参考：

[1]你可能需要了解关于cv::imencode和 cv::imdecode函数的flag的含义

http://docs.opencv.org/2.4/modules/highgui/doc/reading_and_writing_images_and_video.html