本文详细介绍了Caffe框架中的数据预处理流程，包括数据层的Datum定义、数据的读取与写入、数据预处理类DataTransformer的具体实现，以及如何进行数据变换如缩放、裁剪和镜像。
//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
caffe代码data_transform
最新推荐文章于 2024-06-25 16:48:00 发布
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