Caffe 中所有的数据都是存放在Blob对象中，废话不多说，直接上代码，头文件
实际上blob是对SyncedMemory对象做了一次封装，意思是在内部，做了几个SyncedMemory类型变量指针，对于CPU和GPU内存管理最为核心的部分是在SyncedMemory对象中，后续将详细介绍这个部分。
（1）头文件
#ifndef CAFFE_BLOB_HPP_
#define CAFFE_BLOB_HPP_
#include <algorithm>
#include <string>
#include <vector>
#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/syncedmem.hpp"
const int kMaxBlobAxes = 32;
// 整个头文件定义了一个Blob类，类的名字首字母都是大写
namespace caffe {
/**
实际的内存空间在SyncedMemory对象中，该对象负责内存空间CPU,GPU位置的选择，
这里的blob.h在SyncedMemory对象基础上在进行了一次封装
* @brief A wrapper around SyncedMemory holders
serving as the basic computational unit through which Layer%s, Net%s, and Solver%s interact.
* TODO(dox): more thorough description.
*/
template <typename Dtype>
class Blob {
public:
Blob()
: data_(), diff_(), count_(0), capacity_(0) {}
/// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
// 显示构造函数，避免隐式数据类型转换
explicit Blob(const int num, const int channels, const int height,const int width);
explicit Blob(const vector<int>& shape);
/// @brief Deprecated; use <code>Reshape(const vector<int>& shape)</code>.
void Reshape(const int num, const int channels, const int height, const int width);
/**
* @brief Change the dimensions of the blob, allocating new memory if
* necessary. 在改变维度的同时，还需要分配所需的内存
* Reshape 函数可以用来创建新数据，还可以用来改变数据的维度
* This function can be called both to create an initial allocation
* of memory, and to adjust the dimensions of a top blob during Layer::Reshape
* or Layer::Forward. When changing the size of blob, memory will only be
* reallocated if sufficient memory does not already exist, and excess memory
* will never be freed.
*
* Note that reshaping an input blob and immediately calling Net::Backward is
* an error; either Net::Forward or Net::Reshape need to be called to
* propagate the new input shape to higher layers.
*/
void Reshape(const vector<int>& shape);
void Reshape(const BlobShape& shape);
void ReshapeLike(const Blob& other);
// 返回 shape的 string 类型
inline string shape_string() const {
ostringstream stream;
for (int i = 0; i < shape_.size(); ++i) {
stream << shape_[i] << " ";
}
// 这里如果shape[0]~shape[3]都没有值，那么count_为1，因为默认为1，也就是说blob正常情况下是一个张量，nxcxhxw,如果各个维度都没有值，那么为标量，只有一个值
// 就是说对于blob来说，虽然在内存上 blob只有一个值，a[0][0][0][0] = 1 和 blob 为标量只有一个值，count_ 都为1，但是一个是张量，一个是标量
stream << "(" << count_ << ")"; // num channels height width (count) 100 1 28 28 (78400)，
return stream.str();
}
// 返回Blob对象的形状 vector引用
inline const vector<int>& shape() const { return shape_; }
/**
* @brief Returns the dimension of the index-th axis (or the negative index-th
* axis from the end, if index is negative).
*
* @param index the axis index, which may be negative as it will be
* "canonicalized" using CanonicalAxisIndex.
* Dies on out of range index.
如果index是负数，那么调用CanonicalAxisIndex把index做一个变换
*/
// 返回blob指定维度的大小信息
inline int shape(int index) const {
return shape_[CanonicalAxisIndex(index)];
}
// 返回blob轴的个数，为4
inline int num_axes() const { return shape_.size(); }
// 返回整个blob内有效元素的个数
inline int count() const { return count_; }
/**
* @brief Compute the volume of a slice; i.e., the product of dimensions
* among a range of axes.
*
* @param start_axis The first axis to include in the slice.
*
* @param end_axis The first axis to exclude from the slice.
*/
// 统计从start_axis开始到 end_axis结束的维度的大小乘积 - 也即这些维度内的元素总个数
inline int count(int start_axis, int end_axis) const {
CHECK_LE(start_axis, end_axis); // 这个star和end可以相等
CHECK_GE(start_axis, 0);
CHECK_GE(end_axis, 0);
CHECK_LE(start_axis, num_axes());
CHECK_LE(end_axis, num_axes());
// 尼玛的，这个函数至少返回1，不会返回0，下面的循环不执行也返回1
int count = 1;
// 如果 start_axis = 0 end_axis = 0, 那么下面这句不会被执行，整个函数返回 count = 1
for (int i = start_axis; i < end_axis; ++i) {
count *= shape(i);
}
return count;
}
/**
* @brief Compute the volume of a slice spanning from a particular first
* axis to the final axis.
*
* @param start_axis The first axis to include in the slice.
*/
// 统计从start_axis开始到 结束的维度的大小乘积 - 也即这些维度内的元素总个数
inline int count(int start_axis) const {
return count(start_axis, num_axes());
}
/**
* @brief Returns the 'canonical' version of a (usually) user-specified axis,
* allowing for negative indexing (e.g., -1 for the last axis).
*
* @param axis_index the axis index.
* If 0 <= index < num_axes(), return index.
* If -num_axes <= index <= -1, return (num_axes() - (-index)),
* e.g., the last axis index (num_axes() - 1) if index == -1,
* the second to last if index == -2, etc.
* Dies on out of range index.
*/
// 将负数索引转换为实际的正数索引
// 负数索引表示从后向前访问，-1表示最后一个元素，普通索引值为N-1; 同理 -2 为 N - 2, -3 为 N - 3
inline int CanonicalAxisIndex(int axis_index) const {
CHECK_GE(axis_index, -num_axes())
<< "axis " << axis_index << " out of range for " << num_axes()
<< "-D Blob with shape " << shape_string();
CHECK_LT(axis_index, num_axes())
<< "axis " << axis_index << " out of range for " << num_axes()
<< "-D Blob with shape " << shape_string();
if (axis_index < 0) {
return axis_index + num_axes();
}
return axis_index;
}
// 返回blob的维度信息，这里的legacy shape不知道是什么意义
/// @brief Deprecated legacy shape accessor num: use shape(0) instead.
inline int num() const { return LegacyShape(0); }
/// @brief Deprecated legacy shape accessor channels: use shape(1) instead.
inline int channels() const { return LegacyShape(1); }
/// @brief Deprecated legacy shape accessor height: use shape(2) instead.
inline int height() const { return LegacyShape(2); }
/// @brief Deprecated legacy shape accessor width: use shape(3) instead.
inline int width() const { return LegacyShape(3); }
inline int LegacyShape(int index) const {
CHECK_LE(num_axes(), 4)
<< "Cannot use legacy accessors on Blobs with > 4 axes.";
CHECK_LT(index, 4);
CHECK_GE(index, -4);
if (index >= num_axes() || index < -num_axes()) {
// Axis is out of range, but still in [0, 3] (or [-4, -1] for reverse
// indexing) -- this special case simulates the one-padding used to fill
// extraneous axes of legacy blobs.
return 1;
}
return shape(index);
}
// 计算偏移，输入是(Num, Chanel, Height, Width) 返回的结果是 w + Width*(h + Height*(c + n*Num))
inline int offset(const int n, const int c = 0, const int h = 0,const int w = 0) const {
CHECK_GE(n, 0);
CHECK_LE(n, num());
CHECK_GE(channels(), 0);
CHECK_LE(c, channels());
CHECK_GE(height(), 0);
CHECK_LE(h, height());
CHECK_GE(width(), 0);
CHECK_LE(w, width());
return ((n * channels() + c) * height() + h) * width() + w;
// 理解这个多维数组的索引机制 其中
// channels() =3 height()=固定数 width()= 固定数
// n, c, h, w 为变动数字，分别为在各个维度上的索引
// 通常简单的做法是做一个四维数组取值时 a[n][c][h][w]即可，但是这个函数offset貌似是将内存视为1维，然后计算当前索引对于1维数据的偏置
}
/*
bottom_data += bottom[0]->offset(0, 1);
这里bottom[0]->offset(0, 1),是调用的上面的函数
offset(0, 1, 0, 0)
w + Width*(h + Height*(c + n*Num)) = 0 + Width*(0 + Height*(1 + 0*Num)) = Width*Height， 实际上是返回的一个Channel维度上，一个变化，对于的数据空间变化数据
*/
inline int offset(const vector<int>& indices) const {
CHECK_LE(indices.size(), num_axes());
int offset = 0;
for (int i = 0; i < num_axes(); ++i) {
offset *= shape(i);
if (indices.size() > i) {
CHECK_GE(indices[i], 0);
CHECK_LT(indices[i], shape(i));
offset += indices[i];
}
}
return offset;
}
/**
* @brief Copy from a source Blob.
*
* @param source the Blob to copy from
* @param copy_diff if false, copy the data; if true, copy the diff
* @param reshape if false, require this Blob to be pre-shaped to the shape
* of other (and die otherwise); if true, Reshape this Blob to other's
* shape if necessary
*/
// 按值拷贝source blob 到当前的blob
void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false, bool reshape = false);
// getter and setter, 取出在(n, c, h, w)位置的值
// 取(n, c, h, w)位置的cpu_data()所指向的值，从而可以设置或者读取
inline Dtype data_at(const int n, const int c, const int h, const int w) const {
return cpu_data()[offset(n, c, h, w)];
}
// 取(n, c, h, w)位置的cpu_diff()，从而可以设置或者读取
inline Dtype diff_at(const int n, const int c, const int h,const int w) const {
return cpu_diff()[offset(n, c, h, w)];
}
inline Dtype data_at(const vector<int>& index) const {
return cpu_data()[offset(index)];
}
inline Dtype diff_at(const vector<int>& index) const {
return cpu_diff()[offset(index)];
}
// 返回SyncedMemory对象 data_ 引用
inline const shared_ptr<SyncedMemory>& data() const {
CHECK(data_);
return data_;
}
// 返回SyncedMemory对象 diff_ 引用
inline const shared_ptr<SyncedMemory>& diff() const {
CHECK(diff_);
return diff_;
}
// 下面这些函数，都是返回的数据指针值，即数据所在地址
// 实际的指针是在SyncedMemory对象中
// 只读访问 cpu data
const Dtype* cpu_data() const;
// 设置cpu data
void set_cpu_data(Dtype* data);
const int* gpu_shape() const;
// 只读访问 cpu data
const Dtype* gpu_data() const;
const Dtype* cpu_diff() const;
const Dtype* gpu_diff() const;
// 读写访问 cpu data 和 gpu data
Dtype* mutable_cpu_data();
Dtype* mutable_gpu_data();
Dtype* mutable_cpu_diff();
Dtype* mutable_gpu_diff();
// data = data - diff
void Update();
// 反序列化函数，从BlobProto中恢复一个Blob对象
void FromProto(const BlobProto& proto, bool reshape = true);
// 序列化函数，将内存中的Blob对象保存到BlobProto中
void ToProto(BlobProto* proto, bool write_diff = false) const;
/// @brief Compute the sum of absolute values (L1 norm) of the data. 计算data的L1范数
Dtype asum_data() const;
/// @brief Compute the sum of absolute values (L1 norm) of the diff. 计算diff的L1范数
Dtype asum_diff() const;
/// @brief Compute the sum of squares (L2 norm squared) of the data. 计算data的L2范数
Dtype sumsq_data() const;
/// @brief Compute the sum of squares (L2 norm squared) of the diff. 计算diff的L2范数
Dtype sumsq_diff() const;
/// @brief Scale the blob data by a constant factor.
// data 数据乘以一个标量
void scale_data(Dtype scale_factor);
/// @brief Scale the blob diff by a constant factor.
// diff 数据乘以一个标量
void scale_diff(Dtype scale_factor);
/**
* @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
* data_ of Blob other -- useful in Layer%s which simply perform a copy
* in their Forward pass.
*
* This deallocates the SyncedMemory holding this Blob's data_, as
* shared_ptr calls its destructor when reset with the "=" operator.
*/
// 共享另外一个Blob的 Data_
void ShareData(const Blob& other);
/**
* @brief Set the diff_ shared_ptr to point to the SyncedMemory holding the
* diff_ of Blob other -- useful in Layer%s which simply perform a copy
* in their Forward pass.
*
* This deallocates the SyncedMemory holding this Blob's diff_, as
* shared_ptr calls its destructor when reset with the "=" operator.
*/
// 共享另外一个Blob的 diff_
void ShareDiff(const Blob& other);
bool ShapeEquals(const BlobProto& other);
protected:
// data_ 为一个SyncedMemory对象指针，
// 实际存放数据空间为对象中的 cpu_ptr_ 指针地址值 和 gpu_ptr_
shared_ptr<SyncedMemory> data_;
shared_ptr<SyncedMemory> diff_; //
shared_ptr<SyncedMemory> shape_data_; //
vector<int> shape_; // 存放shape 信息的vector
// 这里 shape_data_ 与 shape_ 存放的数据一样
int count_; // 存放 有效元素数目的变量
int capacity_; // 存放 Blob 容器的容量信息
//DISABLE_COPY_AND_ASSIGN(Blob); // 禁止拷贝构造很熟，赋值运算符重载
}; // class Blob
} // namespace caffe
#endif // CAFFE_BLOB_HPP_
/**
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];}
*/(2) cpp文件
#include <climits>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"
/*----------------------------------------------------------------------
(1) Blob.cpp 与 syncedmen.cpp 文件关系
Blob.cpp 文件一定要与syncedmen.cpp文件结合起来一起看
Blob是比syncedmen大的概念
Blob内包含了 <SyncedMemory> data_, diff_, shape_data_ 三个 SyncedMemory 对象
而每个对象其实是代表了一段内存或者数据区域，用SyncedMemory来控制数据在CPU还是GPU上
SyncedMemory 对象中包含了cpu_ptr_，gpu_ptr_ 具体的CPU和GPU数据指针
(2) Blob 对象与BlobProto对象，Datum 对象 数据位数关系
--1：对于Blob 对象的本身可以存储任意类型的数据 (new SyncedMemory(capacity_ * sizeof(Dtype))
所以Blob对象的FromProto 函数可以接受任何类型数据Dtype
Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape)
Blob对象的ToProto 函数只接受double 和 float
void Blob<double>::ToProto(BlobProto* proto, bool write_diff)
void Blob<float>::ToProto(BlobProto* proto, bool write_diff)
--2：对于BlobProto中只能有float 和 double类型数据
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];
--3：对于Datum对象，主要用来读一张图像的数据和标记，只有float和byte类型
optional bytes data = 4;
optional int32 label = 5;
repeated float float_data = 6;
所有通过Datum对象，读入数据以后，需要根据读入是float还是byte类型，写入到BlobProto里面
Blob内的 count_值 = (各个维度的乘积)， 那么data_, diff_ 这2个数据集和具有同样的count_值
两个类最直接的结合点是:
1: data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
2: diff_->cpu_data()
有很多关键函数是定义在Blob.h文件中， 如果要访问具体位置的数据，方法是 cpu_data()[offset(n, c, h, w)];
-*/
namespace caffe {
// 只针对4个维度的Blob，专门针对图像应用
template <typename Dtype>
void Blob<Dtype>::Reshape(const int num, const int channels, const int height,const int width) {
vector<int> shape(4);
shape[0] = num;
shape[1] = channels;
shape[2] = height;
shape[3] = width;
Reshape(shape);
}
// 真正的变维函数
// 这个Reshape是Blob初始化的核心函数，内存相关的部分都在这里
// 针对任意维度数据，4个维度Blob是一种特殊情况
template <typename Dtype>
void Blob<Dtype>::Reshape(const vector<int>& shape)
// shape 是传入参数 为一个int类型的 vector
// shape_ 是当前Blob对象的成员，也是int类型的vector
// shape_data_ 是当前Blob对象的成员，是SyncedMemory对象指针
{
CHECK_LE(shape.size(), kMaxBlobAxes);
count_ = 1;// 用于计算元素总数 = num * channels * height * width， 这里上来就把这个值直接设置成1，表示blob至少是一个标量
shape_.resize(shape.size()); // 成员变量的维度也被重置
// 搞不懂为什么这里的shape本身就很小，为什么还要弄一个SyncedMemory对象 对于图像 shape.size()应该为四, 这里的shape_data_就存放了4个整形数据，可能是怕数据维数太大，非图像数据？
// 这个shape本身是一维数据，存放了各个维度的尺寸大小值，并不存放各个维度的具体数据值
// 而且这回shape_data_与shape_两个数组，存的内容一样。唯一的区别是shape_data_可以定位到GPU上
if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
// 如果当前Blob对象的 shape_data_值为空，或者 shape_data_所指向空间的大小小于所需要空间的大小时
shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
}
// 获得形状信息数据地址指针
int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
// 多所有axes进行遍历，在遍历的时候累成，获得需要总的数据大小
for (int i = 0; i < shape.size(); ++i) {
CHECK_GE(shape[i], 0);// 保证每个axes尺寸都大于等于0
if (count_ != 0) { // 保证count_不溢出
CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX"; // 这里count_ 初始值为1// 当前axei的数据尺寸，要小于 INT_MAX / count_,
}
count_ *= shape[i]; // count_ 累乘 在图像里面相当于总的数据量是 chanel数 x height数 x width数
// 这两个值一样
shape_[i] = shape[i]; // 对Blob对象的shape_成员赋值
shape_data[i] = shape[i]; // Blob对象中 shape_data_指针所指地址赋值
} // for (int i = 0; i < shape.size(); ++i) {
// capacity_ 在Blob对象创建时就已经初始化为0了
if (count_ > capacity_) { //如果新的count_大于当前已分配空间容量，扩容，重新分配data_和diff_的空间
capacity_ = count_; //
data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); // 这里reset是对这个指针重新赋值，可以发现SyncedMemory的数据空间大小不是随便设置，而是需要多少就严格申请多少
diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); // 这里发现，如果Blob是float类型的数据，那么这里就申请float型大小的空间，double也一样，对于char呢？
}
}
template <typename Dtype>
void Blob<Dtype>::Reshape(const BlobShape& shape) {
CHECK_LE(shape.dim_size(), kMaxBlobAxes);
vector<int> shape_vec(shape.dim_size());
// 将数据的各个维度尺寸信息读入shape_vec
for (int i = 0; i < shape.dim_size(); ++i) {
shape_vec[i] = shape.dim(i);
}
Reshape(shape_vec);
}
template <typename Dtype>
void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
Reshape(other.shape());
}
// 在调用Reshape之前，必须初始化capacity_，否则会导致不可 预知的结果
template <typename Dtype>
Blob<Dtype>::Blob(const int num, const int channels, const int height, const int width)
// capacity_ must be initialized before calling Reshape
: capacity_(0) {
Reshape(num, channels, height, width);
}
template <typename Dtype>
Blob<Dtype>::Blob(const vector<int>& shape)
// capacity_ must be initialized before calling Reshape
: capacity_(0) {
Reshape(shape);
}
template <typename Dtype>
const int* Blob<Dtype>::gpu_shape() const {
CHECK(shape_data_);
return (const int*)shape_data_->gpu_data();
}
// 只读获得cpu data指针
template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_data() const {
CHECK(data_);
return (const Dtype*)data_->cpu_data(); // 返回data_对象的 cpu指针值
}
// 修改cpu data指针
template <typename Dtype>
void Blob<Dtype>::set_cpu_data(Dtype* data) {
CHECK(data);
data_->set_cpu_data(data); //返回data_对象的 cpu指针值
}
// 只读获得gpu data指针
template <typename Dtype>
const Dtype* Blob<Dtype>::gpu_data() const {
CHECK(data_);
return (const Dtype*)data_->gpu_data(); // 返回data_对象的 gpu指针值
}
// 只读获得cpu diff指针
template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_diff() const {
CHECK(diff_);
return (const Dtype*)diff_->cpu_data(); // 返回diff_对象的 cpu指针值
}
// 只读获得gpu diff指针
template <typename Dtype>
const Dtype* Blob<Dtype>::gpu_diff() const {
CHECK(diff_);
return (const Dtype*)diff_->gpu_data();
}
// static_cast < type-id > ( expression ) 该运算符把expression转换为type-id类型，但没有运行时类型检查来保证转换的安全性。
// 读写访问cpu data 指针
// 这里在做指针类型的转换，本来是void类型的指针，转成特定类型Dtype类型的指针
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_cpu_data() {
CHECK(data_);
return static_cast<Dtype*>(data_->mutable_cpu_data());
}
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_gpu_data() {
CHECK(data_);
return static_cast<Dtype*>(data_->mutable_gpu_data());
}
// 读写访问cpu diff 指针
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_cpu_diff() {
CHECK(diff_);
return static_cast<Dtype*>(diff_->mutable_cpu_data());
}
// 读写访问gpu diff 指针
template <typename Dtype>
Dtype* Blob<Dtype>::mutable_gpu_diff() {
CHECK(diff_);
return static_cast<Dtype*>(diff_->mutable_gpu_data());
}
// 共享另外一个Blob的data指针
template <typename Dtype>
void Blob<Dtype>::ShareData(const Blob& other) {
CHECK_EQ(count_, other.count());
data_ = other.data();
}
// 共享另外一个Blob的diff指针，
template <typename Dtype>
void Blob<Dtype>::ShareDiff(const Blob& other) {
CHECK_EQ(count_, other.count());
diff_ = other.diff();//智能指针直接赋值--这里需要查看
}
//Update() 函数用于网络参数Blob的更新，其中int 和 unsigned int 类型处理并未实现，因为Update函数是用来更新参数的
// The "update" method is used for parameter blobs in a Net, which are stored
// as Blob<float> or Blob<double> -- hence we do not define it for
// Blob<int> or Blob<unsigned int>.
template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }
template <typename Dtype>
void Blob<Dtype>::Update() {
// We will perform update based on where the data is located.
// data 在哪里，就在哪里更新
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU: // data 在 CPU端
// perform computation on CPU // data_->mutable_cpu_data() = -1 * diff_->cpu_data() + data_->mutable_cpu_data()
caffe_axpy<Dtype>(count_, Dtype(-1), static_cast<const Dtype*>(diff_->cpu_data()), static_cast<Dtype*>(data_->mutable_cpu_data()));
break;
case SyncedMemory::HEAD_AT_GPU: // data 位于 GPU端，或者CPU/GPU 已同步
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
// perform computation on GPU
caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
static_cast<const Dtype*>(diff_->gpu_data()),
static_cast<Dtype*>(data_->mutable_gpu_data()));
#else
NO_GPU;
#endif
break;
default:
LOG(FATAL) << "Syncedmem not initialized.";
}
}
template <> unsigned int Blob<unsigned int>::asum_data() const {
NOT_IMPLEMENTED;
return 0;
}
template <> int Blob<int>::asum_data() const {
NOT_IMPLEMENTED;
return 0;
}
// 计算 data_ 的 L1 范数
template <typename Dtype>
Dtype Blob<Dtype>::asum_data() const {
if (!data_) { return 0; }
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU:
return caffe_cpu_asum(count_, cpu_data()); // 执行 CPU上的 asum计算
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
{
Dtype asum;
caffe_gpu_asum(count_, gpu_data(), &asum); // 执行 GPU上的 asum计算
return asum;
}
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return 0;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
}
return 0;
}
template <> unsigned int Blob<unsigned int>::asum_diff() const {
NOT_IMPLEMENTED;
return 0;
}
template <> int Blob<int>::asum_diff() const {
NOT_IMPLEMENTED;
return 0;
}
// 计算 diff_ 的 L1 范数
template <typename Dtype>
Dtype Blob<Dtype>::asum_diff() const {
if (!diff_) { return 0; }
switch (diff_->head()) {
case SyncedMemory::HEAD_AT_CPU:
return caffe_cpu_asum(count_, cpu_diff());
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
{
Dtype asum;
caffe_gpu_asum(count_, gpu_diff(), &asum);
return asum;
}
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return 0;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
}
return 0;
}
template <> unsigned int Blob<unsigned int>::sumsq_data() const {
NOT_IMPLEMENTED;
return 0;
}
template <> int Blob<int>::sumsq_data() const {
NOT_IMPLEMENTED;
return 0;
}
// 计算 data_ 的 L2 范数
template <typename Dtype>
Dtype Blob<Dtype>::sumsq_data() const {
Dtype sumsq;
const Dtype* data;
if (!data_) { return 0; }
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU:
data = cpu_data();
sumsq = caffe_cpu_dot(count_, data, data); // 执行 CPU上的 dot 计算
break;
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
data = gpu_data();
caffe_gpu_dot(count_, data, data, &sumsq); // 执行 GPU上的 dot 计算
#else
NO_GPU;
#endif
break;
case SyncedMemory::UNINITIALIZED:
return 0;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
}
return sumsq;
}
template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
NOT_IMPLEMENTED;
return 0;
}
template <> int Blob<int>::sumsq_diff() const {
NOT_IMPLEMENTED;
return 0;
}
// 计算 diff_ 的 L2 范数
template <typename Dtype>
Dtype Blob<Dtype>::sumsq_diff() const {
Dtype sumsq;
const Dtype* diff;
if (!diff_) { return 0; }
switch (diff_->head()) {
case SyncedMemory::HEAD_AT_CPU:
diff = cpu_diff();
sumsq = caffe_cpu_dot(count_, diff, diff);
break;
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
diff = gpu_diff();
caffe_gpu_dot(count_, diff, diff, &sumsq);
break;
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return 0;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
}
return sumsq;
}
template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
NOT_IMPLEMENTED;
}
template <> void Blob<int>::scale_data(int scale_factor) {
NOT_IMPLEMENTED;
}
// 对 data_ 进行幅度缩放
template <typename Dtype>
void Blob<Dtype>::scale_data(Dtype scale_factor) {
Dtype* data;
if (!data_) { return; }
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU:
data = mutable_cpu_data();
caffe_scal(count_, scale_factor, data);
return;
case SyncedMemory::HEAD_AT_GPU:
//由此可见在数据是同步的情况下，是只需要计算GPU的部分，对于CPU的部分，是不用管的
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
data = mutable_gpu_data();
caffe_gpu_scal(count_, scale_factor, data);
return;
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
}
}
template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
NOT_IMPLEMENTED;
}
template <> void Blob<int>::scale_diff(int scale_factor) {
NOT_IMPLEMENTED;
}
// 对 diff_ 进行幅度缩放
template <typename Dtype>
void Blob<Dtype>::scale_diff(Dtype scale_factor) {
Dtype* diff;
if (!diff_) { return; }
switch (diff_->head()) {
case SyncedMemory::HEAD_AT_CPU:
diff = mutable_cpu_diff();
caffe_scal(count_, scale_factor, diff);
return;
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
diff = mutable_gpu_diff();
caffe_gpu_scal(count_, scale_factor, diff);
return;
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
}
}
// 判断形状是否相同
template <typename Dtype>
bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
if (other.has_num() || other.has_channels() || other.has_height() || other.has_width()) {
// Using deprecated 4D Blob dimensions --
// shape is (num, channels, height, width).
// Note: we do not use the normal Blob::num(), Blob::channels(), etc.
// methods as these index from the beginning of the blob shape, where legacy
// parameter blobs were indexed from the end of the blob shape (e.g., bias
// Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
// 输入的维度若使用了过时的维度信息(num, channels, height, width)，则需要转换为新的vector参数，代码使用了C++中的 懒 逻辑
return shape_.size() <= 4 &&
LegacyShape(-4) == other.num() &&
LegacyShape(-3) == other.channels() &&
LegacyShape(-2) == other.height() &&
LegacyShape(-1) == other.width();
}
// 创建一个新的shape_数据，然后直接对比
vector<int> other_shape(other.shape().dim_size());
for (int i = 0; i < other.shape().dim_size(); ++i) {
other_shape[i] = other.shape().dim(i);
}
return shape_ == other_shape;
}
// 从另外一个Blob对象拷贝data (可选diff),必要时进行变维
template <typename Dtype>
void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
if (source.count() != count_ || source.shape() != shape_) {
if (reshape) {
ReshapeLike(source); // 要求变维，就照做
} else { // 不然因为两个Blob的形状不同，硬要拷贝，报错
LOG(FATAL) << "Trying to copy blobs of different sizes.";
}
}
// 拷贝数据
switch (Caffe::mode()) {
case Caffe::GPU: // GPU模式
if (copy_diff) {
caffe_copy(count_, source.gpu_diff(), static_cast<Dtype*>(diff_->mutable_gpu_data())); // diff - > diff
} else {
caffe_copy(count_, source.gpu_data(), static_cast<Dtype*>(data_->mutable_gpu_data())); // data -> data
}
break;
case Caffe::CPU: // CPU模式
if (copy_diff) {
caffe_copy(count_, source.cpu_diff(), static_cast<Dtype*>(diff_->mutable_cpu_data()));
} else {
caffe_copy(count_, source.cpu_data(), static_cast<Dtype*>(data_->mutable_cpu_data()));
}
break;
default:
LOG(FATAL) << "Unknown caffe mode.";
}
}
// 从BlobProto 中加载一个Blob, 适用于从磁盘载入之前导出的Blob
template <typename Dtype>
void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
// 从BlobProto对象中获取所需各个维度信息
if (reshape) { // 如果reshape flage = true
vector<int> shape;
if (proto.has_num() || proto.has_channels() || // 旧版的维度信息，只能有4个维度的固定输入，现在不推荐这样做
proto.has_height() || proto.has_width()) {
// Using deprecated 4D Blob dimensions -- deprecated// 不赞成的
// shape is (num, channels, height, width).
// 过时的维度信息
shape.resize(4);
shape[0] = proto.num();
shape[1] = proto.channels();
shape[2] = proto.height();
shape[3] = proto.width();
} else {
shape.resize(proto.shape().dim_size());
for (int i = 0; i < proto.shape().dim_size(); ++i) {
shape[i] = proto.shape().dim(i);
}
}
Reshape(shape); // Blob 按照维度信息进行变换
} else {
CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
}
// 这里 data_和 diff_具有同样的数据空间大小
Dtype* data_vec = mutable_cpu_data();
if (proto.double_data_size() > 0) { // 如果之前保存的是 double 类型的data // 是不是proto内只要是repeated的成员，这里就能直接用double_data_size()，固定格式去套
CHECK_EQ(count_, proto.double_data_size()); // 确定事先申请的当前Blob对象数据总空间总大小是
for (int i = 0; i < count_; ++i) {
data_vec[i] = proto.double_data(i); // 加载double data
}
} else {
CHECK_EQ(count_, proto.data_size());
for (int i = 0; i < count_; ++i) { // 否则加载float data
data_vec[i] = proto.data(i);
}
}
if (proto.double_diff_size() > 0) { // 如果之前保存的是 double 类型的diff
CHECK_EQ(count_, proto.double_diff_size());
Dtype* diff_vec = mutable_cpu_diff();
for (int i = 0; i < count_; ++i) {
diff_vec[i] = proto.double_diff(i); // 加载double diff
}
} else if (proto.diff_size() > 0) {
CHECK_EQ(count_, proto.diff_size());
Dtype* diff_vec = mutable_cpu_diff();
for (int i = 0; i < count_; ++i) { // 否则加载float diff
diff_vec[i] = proto.diff(i);
}
}
}
// 将Blob 中的 data（可选 diff）导出到BlobProto的结构体中，便于存储到磁盘文件中
template <>
void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
proto->clear_shape(); //重置proto的维度，保证 与Blob相同，
for (int i = 0; i < shape_.size(); ++i) {
proto->mutable_shape()->add_dim(shape_[i]);
}
proto->clear_double_data(); // 清除 data
proto->clear_double_diff(); // 清除 diff
const double* data_vec = cpu_data(); // 将data 导出到 proto
for (int i = 0; i < count_; ++i) {
proto->add_double_data(data_vec[i]);
}
if (write_diff) { // 若有write_diff的需求
const double* diff_vec = cpu_diff(); // 将diff导出到proto
for (int i = 0; i < count_; ++i) {
proto->add_double_diff(diff_vec[i]);
}
}
}
template <>
void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
proto->clear_shape();
for (int i = 0; i < shape_.size(); ++i) {
proto->mutable_shape()->add_dim(shape_[i]);
}
proto->clear_data();
proto->clear_diff();
const float* data_vec = cpu_data();
for (int i = 0; i < count_; ++i) {
proto->add_data(data_vec[i]);
}
if (write_diff) {
const float* diff_vec = cpu_diff();
for (int i = 0; i < count_; ++i) {
proto->add_diff(diff_vec[i]);
}
}
}
INSTANTIATE_CLASS(Blob);
template class Blob<int>;
template class Blob<unsigned int>;
} // namespace caffe
/**
message BlobShape {
repeated int64 dim = 1 [packed = true];
}
message BlobProto{
optional BlobShape shape = 7; // 是否是所有optional的项，往外输出数据都是 proto->mutable_shape()->add_dim(shape_[i]);
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. 下面这种做法现在不推荐了
optional int32 num = 1[default = 0];
optional int32 channels = 2[default = 0];
optional int32 height = 3[default = 0];
optional int32 width = 4[default = 0];}
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];
}
*/