梳理caffe代码blob(三)

梳理caffe代码blob(三) 贯穿整个caffe的就是数据blob：

#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\" #include \"caffe/util/math_functions.hpp\"

const int kMaxBlobAxes = INT_MAX;

namespace caffe {

/**

* @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>.

//explicit关键字的作用是禁止单参数构造函数的隐式转换 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>. /*

Reshape函数将num,channels,height,width传递给vector shape_ */

void Reshape(const int num, const int channels, const int height,

const int width); /**

*Blob作为一个最基础的类，其中构造函数开辟一个内存空间来存储数据，Reshape函数在Layer中的

*reshape或者forward 操作中来adjust the dimensions of a top blob。同时在改变Blob大小时，

*内存将会被重新分配如果内存大小不够了，并且额外的内存将不会被释放。对input的blob进行reshape,

*如果立马调用Net::Backward是会出错的，因为reshape之后，要么Net::forward或者Net::Reshape就会

*被调用来将新的input shape 传播到高层 */

//根据shape来初始化shape_和shape_data_，以及为data_ 和diff_ 分配空间。

void Reshape(const vector<int>& shape); void Reshape(const BlobShape& shape); void ReshapeLike(const Blob& other);

//iniline主要是将代码进行复制，扩充，会使代码总量上升，好处就是可以节省调用的开销,以string形式获取shape_ inline string shape_string() const { ostringstream stream;

for (int i = 0; i < shape_.size(); ++i) { stream << shape_[i] << \" \"; }

stream << \"(\" << count_ << \")\"; return stream.str(); }

//获取shape_

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. */

//获取index维的大小

inline int shape(int index) const {

return shape_[CanonicalAxisIndex(index)]; }

//获取维的个数

inline int num_axes() const { return shape_.size(); } //获取当前data的大小

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. */

/*多个count()函数，主要还是为了统计Blob的容量（volume），或者是某一片（slice）， 从某个axis到具体某个axis的shape乘积。 */

//获取某几维数据的大小

inline int count(int start_axis, int end_axis) const { CHECK_LE(start_axis, end_axis); CHECK_GE(start_axis, 0); CHECK_GE(end_axis, 0);

CHECK_LE(start_axis, num_axes()); CHECK_LE(end_axis, num_axes()); int 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. */

//获取某一维到结束数据的大小 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 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. */

//Blob的Index是可以从负坐标开始读的,标准化索引，主要是对参数索引进行标准化，以满足要求

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; }

//Blob中的4个基本变量num,channel,height,width可以直接通过shape(0),shape(1),shape(2),shape(3)来访问 /// @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); } //data_维数不大于4时才能使用，功能同shape()类似。 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offset,offset计算的方式也支持两种方式，一种直接指定n,c,h,w或者放到一个vector中进行计算， //偏差是根据对应的n,c,h,w，返回的offset是((n*channels()+c)*height()+h)*width()+w

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; }

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 */

//一个blob中copy数据 ，通过开关控制是否copy_diff,如果是False则copy data。reshape控制是否需要reshape void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,

bool reshape = false);

/*这一部分函数主要通过给定的位置访问数据，根据位置计算与数据起始

的偏差offset，在通过cpu_data*指针获得地址 */

//获取某位置的data_数据

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)]; }

//获取某位置的diff_数据

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)]; } //获取data_

inline const shared_ptr<SyncedMemory>& data() const {

CHECK(data_); return data_; } //获取diff_

inline const shared_ptr<SyncedMemory>& diff() const {

CHECK(diff_); return diff_; }

//这里有data和diff两类数据，而这个diff就是我们所熟知的偏差，前者主要存储

//前向传递的数据，而后者存储的是反向传播中的梯度 const Dtype* cpu_data() const;//获取data_ cpu指针 void set_cpu_data(Dtype* data);//设置data_的cpu指针，只是修改了指针

const Dtype* gpu_data() const;//获取data_的gpu指针 const Dtype* cpu_diff() const;//获取diff_的cpu指针 const Dtype* gpu_diff() const;//获取diff_的gpu指针 Dtype* mutable_cpu_data();//见SyncedMemory的mutable_cpu_data();

Dtype* mutable_gpu_data();//见SyncedMemory的mutable_gpu_data();

Dtype* mutable_cpu_diff();//见SyncedMemory的mutable_cpu_data();

Dtype* mutable_gpu_diff();//见SyncedMemory的mutable_gpu_data();

//更新data_的数据,减去diff_的数据 void Update(); /*

其中用到math_functions.hpp中的函数caffe_axpy(),该函数封装了cblas_saxpy，实现的是Y=alpha*X+Y。

由此，知该函数的功能是data_=(data_-diff_)。另外，该函数只实现了对double和float型数据，

对于unsigned int和int由于该函数主要是在Net中被调用，只有Blob<float>和Blob<double>型式， 因此没有定义unsigned int和int。 */

void FromProto(const BlobProto& proto, bool reshape = true); /*

由BlobProto对Blob进行赋值操作。reshape代表是否允许修改shape_的大小。

需要注意的是再这里有double和float两种类型的数据 ，在代码中可以看到具体的体现 */

void ToProto(BlobProto* proto, bool write_diff = false) const;

/// @brief Compute the sum of absolute values (L1 norm) of

the data. /*

功能：计算L1范数

说明：其中用到了math_function.hpp中的函数

caffe_cpu_asum()和caffe_gpu_asum，实现的功能是对向量X求其每个元素绝对值的和，不同的是X分别在cpu和gpu中。 */

Dtype asum_data() const;

/// @brief Compute the sum of absolute values (L1 norm) of the diff.

Dtype asum_diff() const;

/// @brief Compute the sum of squares (L2 norm squared) of the data. /*

功能：计算L2范数。

说明：用到了math_function.hpp中的

caffe_cpu_dot(),caffe_cpu_strided_dot(),caffe_gpu_dot(), caffe_gpu_strided_dot()。具体就是就向量X的平方和。 */

Dtype sumsq_data() const;

/// @brief Compute the sum of squares (L2 norm squared) of the diff.

Dtype sumsq_diff() const;

/// @brief Scale the blob data by a constant factor. /*

功能：正规化data_。

说明：用到math_function.hpp中的caffe_scal()和caffe_gpu_scal()函数，就是对向量X乘上一个因子。 */

void scale_data(Dtype scale_factor);

/// @brief Scale the blob diff by a constant factor. 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. */

void ShareData(const Blob& other);//本Blob共享other的data_ /**

* @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. */

void ShareDiff(const Blob& other);//本Blob共享other的diff_

bool ShapeEquals(const BlobProto& other);//判断other与本Blob形状是否相同。

protected:

//data_指针，指针类型是shared_ptr，属于boost库的一个智能指针，这一部分主要用来申请内存存储data，data主要是正向传播的时候用的

shared_ptr<SyncedMemory> data_; //diff_主要用来存储偏差，update data shared_ptr<SyncedMemory> diff_; //shape_存储Blob的形状 vector<int> shape_;

//count_表示Blob中的元素个数，也就是个数*通道数*高度*宽度 int count_;

//capacity表示当前的元素个数，因为Blob可能会reshape int capacity_;

DISABLE_COPY_AND_ASSIGN(Blob); }; // class Blob

} // namespace caffe

#endif // CAFFE_BLOB_HPP_

顺便将实现部分也贴出来，方便对照：

#include <climits> #include <vector>

#include \"caffe/blob.hpp\" #include \"caffe/common.hpp\" #include \"caffe/syncedmem.hpp\" #include \"caffe/util/math_functions.hpp\"

namespace caffe {

template <typename Dtype>

//该函数将num,channels,height,width传递给vector shape_ 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); }

template <typename Dtype> void Blob<Dtype>::Reshape(const vector<int>& shape) {

CHECK_LE(shape.size(), kMaxBlobAxes); count_ = 1;

shape_.resize(shape.size());//重新定义vector shape_ 的size for (int i = 0; i < shape.size(); ++i) {

CHECK_GE(shape[i], 0);//确保shape 每个元素为正数 CHECK_LE(shape[i], INT_MAX / count_) << \"blob size exceeds INT_MAX\"; count_ *= shape[i]; shape_[i] = shape[i]; }

//由于count_超过了当前capacity_ 因此需要重新分配内存空间

if (count_ > capacity_) { capacity_ = count_;

data_.reset(new SyncedMemory(capacity_ *

sizeof(Dtype)));

diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); } }

template <typename Dtype>// BlobShape 在caffe.proto 中定义

void Blob<Dtype>::Reshape(const BlobShape& shape) {

CHECK_LE(shape.dim_size(), kMaxBlobAxes); vector<int> shape_vec(shape.dim_size()); for (int i = 0; i < shape.dim_size(); ++i) {

shape_vec[i] = shape.dim(i);//dim 包含num，channels，height， width }

Reshape(shape_vec);//用protobuf传递来dim 对shape_ 进行reshape }

//用已知的Blob的shape来对shape_ 进行reshape template <typename Dtype> void Blob<Dtype>::ReshapeLike(const

Blob<Dtype>& other) { Reshape(other.shape()); }

//用num，channels，height， width 初始化 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); }

//用shape 初始化

template <typename Dtype>

Blob<Dtype>::Blob(const vector<int>& shape) // capacity_ must be initialized before calling Reshape : capacity_(0) { Reshape(shape); }

//返回cpu 中的数据

template <typename Dtype>

const Dtype* Blob<Dtype>::cpu_data() const {

CHECK(data_);

return (const Dtype*)data_->cpu_data(); }

// 清空cpu 数据

template <typename Dtype>

void Blob<Dtype>::set_cpu_data(Dtype* data) { CHECK(data);

data_->set_cpu_data(data); }

//返回gpu 中的数据

template <typename Dtype>

const Dtype* Blob<Dtype>::gpu_data() const { CHECK(data_);

return (const Dtype*)data_->gpu_data(); }

//反向传播导数diff_ 操作函数,返回cpu 中的数据 template <typename Dtype>

const Dtype* Blob<Dtype>::cpu_diff() const { CHECK(diff_);

return (const Dtype*)diff_->cpu_data(); }

//返回gpu 中的数据

template <typename Dtype>

const Dtype* Blob<Dtype>::gpu_diff() const { CHECK(diff_);

return (const Dtype*)diff_->gpu_data(); }

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()); }

template <typename Dtype>

Dtype* Blob<Dtype>::mutable_cpu_diff() {

CHECK(diff_); return

static_cast<Dtype*>(diff_->mutable_cpu_data()); }

template <typename Dtype>

Dtype* Blob<Dtype>::mutable_gpu_diff() { CHECK(diff_); return

static_cast<Dtype*>(diff_->mutable_gpu_data()); }

//当前的blob 的data_ 指向已知blob的数据 template <typename Dtype>

void Blob<Dtype>::ShareData(const Blob& other) { CHECK_EQ(count_, other.count()); data_ = other.data(); }

//当前的blob 的diff_ 指向已知blob的反向传播导数 template <typename Dtype>

void Blob<Dtype>::ShareDiff(const Blob& other) { CHECK_EQ(count_, other.count()); diff_ = other.diff();

}

// 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; }

//Updata函数用于参数blob的更新（weight，bias 等减去对应的导数）

template <typename Dtype> void Blob<Dtype>::Update() {

// We will perform update based on where the data is located. switch (data_->head()) {

case SyncedMemory::HEAD_AT_CPU://数据在cpu上，则在cpu上进行计算

// perform computation on CPU

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: case SyncedMemory::SYNCED:

#ifndef CPU_ONLY//如果没有定义CPU_ONLY，且数据在gpu上，则在gpu上进行计算 // 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; }

//返回data_ 中所有 element 的绝对值之和 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()); case SyncedMemory::HEAD_AT_GPU: case SyncedMemory::SYNCED: #ifndef CPU_ONLY {

Dtype asum;

caffe_gpu_asum(count_, gpu_data(), &asum); 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; }

//返回diff_ 中所有 element 的绝对值之和 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; }

//返回 data_ 中所有 element 的平方和 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); break;

case SyncedMemory::HEAD_AT_GPU: case SyncedMemory::SYNCED: #ifndef CPU_ONLY data = gpu_data();

caffe_gpu_dot(count_, data, data, &sumsq); #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; }

//返回 diff_ 中所有 element 的平方和 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; }

// 给data乘以scale_factor 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: 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; }

// 给diff乘以scale_factor 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(); } }

//BlobProto 是定义在caffe.proto 中的一个message，其字段有 data,diff,shape,num,channels,height,width 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)).

return shape_.size() <= 4 &&

LegacyShape(-4) == other.num() && LegacyShape(-3) == other.channels() &&

LegacyShape(-2) == other.height() && LegacyShape(-1) == other.width(); }

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;

}//检查当前的blob和已知的 other 的 shape 是否相同，相同返回true

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); } else {

LOG(FATAL) << \"Trying to copy blobs of different

sizes.\"; } }

switch (Caffe::mode()) { case Caffe::GPU: if (copy_diff) {

caffe_copy(count_, source.gpu_diff(),

static_cast<Dtype*>(diff_->mutable_gpu_data())); } else {

caffe_copy(count_, source.gpu_data(),

static_cast<Dtype*>(data_->mutable_gpu_data())); } break; case Caffe::CPU: 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source 拷贝数据,copy_diff控制是拷贝diff还是data

template <typename Dtype>

void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) { if (reshape) {

vector<int> shape;

if (proto.has_num() || proto.has_channels() || proto.has_height() || proto.has_width()) { // Using deprecated 4D Blob dimensions -- // shape is (num, channels, height, width). 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);

} else {//如果不做reshape要求当前的blob的shape和proto传入的shape相同

CHECK(ShapeEquals(proto)) << \"shape mismatch (reshape not set)\"; }

// copy data

Dtype* data_vec = mutable_cpu_data(); for (int i = 0; i < count_; ++i) { data_vec[i] = proto.data(i);

}//将proto传入的data拷贝到cpu数据 if (proto.diff_size() > 0) {

Dtype* diff_vec = mutable_cpu_diff(); for (int i = 0; i < count_; ++i) { diff_vec[i] = proto.diff(i);

}//将proto传入的diff 拷贝到cpu数据 } }

template <typename Dtype>

void Blob<Dtype>::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 Dtype* data_vec = cpu_data(); for (int i = 0; i < count_; ++i) {

proto->add_data(data_vec[i]);//将data写入proto }

if (write_diff) {

const Dtype* diff_vec = cpu_diff(); for (int i = 0; i < count_; ++i) {

proto->add_diff(diff_vec[i]);//将diff写入proto }

} }

INSTANTIATE_CLASS(Blob); template class Blob<int>; template class Blob<unsigned int>;

} // namespace caffe