【caffe Blob】caffe中与Blob相关的代码注释、使用举例

首先，Blob使用的小例子（通过运行结果即可知道相关功能）：

#include <vector>
#include <caffe/blob.hpp>
#include <caffe/util/io.hpp>//磁盘读写
#include <iostream>

using namespace std;
using namespace caffe;

int  main()
{
    Blob<float> a;
    cout<<"Size: "<<a.shape_string()<<endl;
    a.Reshape(1,2,3,4);
    cout<<"Size: "<<a.shape_string()<<endl;
    a.Reshape(1,1,1,4);
    cout<<"Size: "<<a.shape_string()<<endl;
    
    float* p=a.mutable_cpu_data();
    float* q=a.mutable_cpu_diff();
    for(int i=0;i<a.count();i++)
    {
        p[i]=i;
        q[i]=a.count()-1-i;
    }
    cout<<"L1: "<<a.asum_data()<<endl;
    cout<<"L2: "<<a.sumsq_data()<<endl;
    //a.Update();

    //磁盘读写
    BlobProto bp;
    a.ToProto(&bp,true);//a序列化，连带diff（默认不带）
    WriteProtoToBinaryFile(bp,"a.blob");
    BlobProto bp2;
    ReadProtoFromBinaryFileOrDie("a.blob",&bp2);
    Blob<float> b;
    b.FromProto(bp2,true);//从序列化对象中克隆b（连同形状）
    b.Update();
    cout<<"L1: "<<b.asum_data()<<endl;
    cout<<"L2: "<<b.sumsq_data()<<endl;
    
    return 0;
}

编译：

export LD_LIBRARY_PATH=./build/lib/:$LD_LIBRARY_PATH

g++ -o app ./bambootry/try.cpp -I ./include/ -D CPU_ONLY \
-I ./.build_release/src/ \
-L ./.build_release/lib/ -lcaffe -lglog -lboost_system

运行结果：

进入正题，代码注释

src/caffe/proto/caffe.proto中Blob部分

// Specifies the shape (dimensions) of a Blob.表示Blob每个维度的大小
message BlobShape {
  repeated int64 dim = 1 [packed = true];//packed表示这些值在内存中紧密排布没有空洞
}
//该结构描述Blob在磁盘中序列化的形态
message BlobProto {
  optional BlobShape shape = 7; //可选，包括一个BlobShape对象
  repeated float data = 5 [packed = true]; //包括若干浮点元素，存储数据或权重，元素数目由shape或（num，channels，height，weight）决定
  repeated float diff = 6 [packed = true]; //包括若干浮点元素，用于存储增量信息，维度与data数组一致
  repeated double double_data = 8 [packed = true]; //data 类型double
  repeated double double_diff = 9 [packed = true]; //diff 类型double

  // 4D dimensions -- deprecated.  Use "shape" instead.可选，维度信息。新版本推荐使用shape
  optional int32 num = 1 [default = 0];
  optional int32 channels = 2 [default = 0];
  optional int32 height = 3 [default = 0];
  optional int32 width = 4 [default = 0];
}

include/caffe/blob.hpp （其中使用了SyncedMemory类，具体有关该类的内容在其他文件中）作为基本计算单元服务 Layer Net Solver等

  1 #ifndef CAFFE_BLOB_HPP_
  2 #define CAFFE_BLOB_HPP_
  3 
  4 #include <algorithm>
  5 #include <string>
  6 #include <vector>
  7 
  8 #include "caffe/common.hpp"
  9 #include "caffe/proto/caffe.pb.h" //由protoc生成的头文件，声明了BlobProto、BlobShape等遵循caffe.proto协议的数据结构
 10 #include "caffe/syncedmem.hpp" //CPU和GPU共享内存类，用于数据同步
 11 
 12 const int kMaxBlobAxes = 32; //Blob最大维数
 13 
 14 namespace caffe {
 15 
 16 /**
 17  * @brief A wrapper around SyncedMemory holders serving as the basic
 18  *        computational unit through which Layer%s, Net%s, and Solver%s
 19  *        interact.
 20  *
 21  * TODO(dox): more thorough description.
 22  */
 23 template <typename Dtype>
 24 class Blob {
 25  public:
 26   Blob()
 27        : data_(), diff_(), count_(0), capacity_(0) {} //默认构造函数
 28 
 29   /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
 30   explicit Blob(const int num, const int channels, const int height,
 31       const int width); //显式构造函数，避免隐式数据类型转换
 32   explicit Blob(const vector<int>& shape); 
 33 
 34   /// @brief Deprecated; use <code>Reshape(const vector<int>& shape)</code>.
 35   // 变形函数，根据输入参数重新设置当前Blob形状，必要时重新分配内存
 36   void Reshape(const int num, const int channels, const int height,
 37       const int width);
 38   /**
 39    * @brief Change the dimensions of the blob, allocating new memory if
 40    *        necessary.
 41    *
 42    * This function can be called both to create an initial allocation
 43    * of memory, and to adjust the dimensions of a top blob during Layer::Reshape
 44    * or Layer::Forward. When changing the size of blob, memory will only be
 45    * reallocated if sufficient memory does not already exist, and excess memory
 46    * will never be freed.
 47    *
 48    * Note that reshaping an input blob and immediately calling Net::Backward is
 49    * an error; either Net::Forward or Net::Reshape need to be called to
 50    * propagate the new input shape to higher layers.
 51    */
 52   void Reshape(const vector<int>& shape);
 53   void Reshape(const BlobShape& shape);
 54   void ReshapeLike(const Blob& other);
 55   //得到Blob形状字符串，用于打印log，见Caffe运行log
 56   //例如： Top Shape: 100 1 28 28 (78400)
 57   inline string shape_string() const {
 58     ostringstream stream;
 59     for (int i = 0; i < shape_.size(); ++i) {
 60       stream << shape_[i] << " ";
 61     }
 62     stream << "(" << count_ << ")";
 63     return stream.str();
 64   }
 65   //返回blob形状
 66   inline const vector<int>& shape() const { return shape_; }
 67   /**
 68    * @brief Returns the dimension of the index-th axis (or the negative index-th
 69    *        axis from the end, if index is negative).
 70    *
 71    * @param index the axis index, which may be negative as it will be
 72    *        "canonicalized" using CanonicalAxisIndex.
 73    *        Dies on out of range index.
 74    */
 75    //返回某一维度尺寸
 76   inline int shape(int index) const {
 77     return shape_[CanonicalAxisIndex(index)];
 78   }
 79   //返回维度数目
 80   inline int num_axes() const { return shape_.size(); }
 81   //返回Blob中元素总数
 82   inline int count() const { return count_; }
 83 
 84   /**
 85    * @brief Compute the volume of a slice; i.e., the product of dimensions
 86    *        among a range of axes.
 87    *
 88    * @param start_axis The first axis to include in the slice.
 89    *
 90    * @param end_axis The first axis to exclude from the slice.
 91    */
 92    //返回Blob中某几个维度子集的元素总数
 93   inline int count(int start_axis, int end_axis) const {
 94     CHECK_LE(start_axis, end_axis);//保证start_sxis<=end_axis
 95     CHECK_GE(start_axis, 0);//保证start_sxis>=0
 96     CHECK_GE(end_axis, 0);//保证end_axis>=0
 97     CHECK_LE(start_axis, num_axes());//保证start_sxis小于总维度数目
 98     CHECK_LE(end_axis, num_axes());//保证end_axis小于总维度数目
 99     int count = 1;
100     for (int i = start_axis; i < end_axis; ++i) {
101       count *= shape(i);
102     }
103     return count;
104   }
105   /**
106    * @brief Compute the volume of a slice spanning from a particular first
107    *        axis to the final axis.
108    *
109    * @param start_axis The first axis to include in the slice.
110    */
111    //计算从某一个维度开始的元素总数
112   inline int count(int start_axis) const {
113     return count(start_axis, num_axes());
114   }
115 
116   /**
117    * @brief Returns the 'canonical' version of a (usually) user-specified axis,
118    *        allowing for negative indexing (e.g., -1 for the last axis).
119    *
120    * @param axis_index the axis index.
121    *        If 0 <= index < num_axes(), return index.
122    *        If -num_axes <= index <= -1, return (num_axes() - (-index)),
123    *        e.g., the last axis index (num_axes() - 1) if index == -1,
124    *        the second to last if index == -2, etc.
125    *        Dies on out of range index.
126    */
127    //转换坐标轴索引
128   inline int CanonicalAxisIndex(int axis_index) const {
129     CHECK_GE(axis_index, -num_axes())//保证axis_index>=-num_axes()
130         << "axis " << axis_index << " out of range for " << num_axes()
131         << "-D Blob with shape " << shape_string();
132     CHECK_LT(axis_index, num_axes())//保证axis_index<=num_axes()
133         << "axis " << axis_index << " out of range for " << num_axes()
134         << "-D Blob with shape " << shape_string();
135     if (axis_index < 0) {
136       return axis_index + num_axes();//负索引号表示从后向前索引，例如-1为最后一个，即正索引号的N-1
137     }
138     return axis_index;
139   }
140 
141   /// @brief Deprecated legacy shape accessor num: use shape(0) instead.
142   inline int num() const { return LegacyShape(0); }
143   /// @brief Deprecated legacy shape accessor channels: use shape(1) instead.
144   inline int channels() const { return LegacyShape(1); }
145   /// @brief Deprecated legacy shape accessor height: use shape(2) instead.
146   inline int height() const { return LegacyShape(2); }
147   /// @brief Deprecated legacy shape accessor width: use shape(3) instead.
148   inline int width() const { return LegacyShape(3); }
149   inline int LegacyShape(int index) const {
150     CHECK_LE(num_axes(), 4)
151         << "Cannot use legacy accessors on Blobs with > 4 axes.";
152     CHECK_LT(index, 4);
153     CHECK_GE(index, -4);
154     if (index >= num_axes() || index < -num_axes()) {
155       // Axis is out of range, but still in [0, 3] (or [-4, -1] for reverse
156       // indexing) -- this special case simulates the one-padding used to fill
157       // extraneous axes of legacy blobs.
158       return 1;
159     }
160     return shape(index);
161   }
162   //offset函数用于计算偏移量
163   inline int offset(const int n, const int c = 0, const int h = 0,
164       const int w = 0) const {
165     CHECK_GE(n, 0);
166     CHECK_LE(n, num());
167     CHECK_GE(channels(), 0);
168     CHECK_LE(c, channels());
169     CHECK_GE(height(), 0);
170     CHECK_LE(h, height());
171     CHECK_GE(width(), 0);
172     CHECK_LE(w, width());
173     return ((n * channels() + c) * height() + h) * width() + w;
174   }
175 
176   inline int offset(const vector<int>& indices) const {
177     CHECK_LE(indices.size(), num_axes());
178     int offset = 0;
179     for (int i = 0; i < num_axes(); ++i) {
180       offset *= shape(i);
181       if (indices.size() > i) {
182         CHECK_GE(indices[i], 0);
183         CHECK_LT(indices[i], shape(i));
184         offset += indices[i];
185       }
186     }
187     return offset;
188   }
189   /**
190    * @brief Copy from a source Blob.
191    *
192    * @param source the Blob to copy from
193    * @param copy_diff if false, copy the data; if true, copy the diff
194    * @param reshape if false, require this Blob to be pre-shaped to the shape
195    *        of other (and die otherwise); if true, Reshape this Blob to other's
196    *        shape if necessary
197    */
198    //拷贝Blob到当前Blob
199   void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
200       bool reshape = false);
201   //下面为一系列存取器
202   inline Dtype data_at(const int n, const int c, const int h,
203       const int w) const {
204     return cpu_data()[offset(n, c, h, w)];
205   }
206 
207   inline Dtype diff_at(const int n, const int c, const int h,
208       const int w) const {
209     return cpu_diff()[offset(n, c, h, w)];
210   }
211 
212   inline Dtype data_at(const vector<int>& index) const {
213     return cpu_data()[offset(index)];
214   }
215 
216   inline Dtype diff_at(const vector<int>& index) const {
217     return cpu_diff()[offset(index)];
218   }
219 
220   inline const shared_ptr<SyncedMemory>& data() const {
221     CHECK(data_);
222     return data_;
223   }
224 
225   inline const shared_ptr<SyncedMemory>& diff() const {
226     CHECK(diff_);
227     return diff_;
228   }
229 
230   const Dtype* cpu_data() const;//只读访问cpu data
231   void set_cpu_data(Dtype* data);//设置cpu data
232   const int* gpu_shape() const;
233   const Dtype* gpu_data() const;//只读访问gpu data
234   void set_gpu_data(Dtype* data);//设置gpu data
235   const Dtype* cpu_diff() const;//只读访问cpu diff
236   const Dtype* gpu_diff() const;//只读访问gpu diff
237   Dtype* mutable_cpu_data();//读写访问
238   Dtype* mutable_gpu_data();//读写访问
239   Dtype* mutable_cpu_diff();//读写访问
240   Dtype* mutable_gpu_diff();//读写访问
241   void Update();//Blob更新运算
242   void FromProto(const BlobProto& proto, bool reshape = true);//反序列化函数，从BlobProto中恢复一个Blob对象
243   void ToProto(BlobProto* proto, bool write_diff = false) const;//序列化函数，将内存中的Blob对象保存到BlobProto中
244 
245   /// @brief Compute the sum of absolute values (L1 norm) of the data.
246   Dtype asum_data() const;//计算data的L1范数，即绝对值求和
247   /// @brief Compute the sum of absolute values (L1 norm) of the diff.
248   Dtype asum_diff() const;//计算diff的L1范数，即绝对值求和
249   /// @brief Compute the sum of squares (L2 norm squared) of the data.
250   Dtype sumsq_data() const;//计算data的平方和，用于L2范数
251   /// @brief Compute the sum of squares (L2 norm squared) of the diff.
252   Dtype sumsq_diff() const;//计算diff的平方和，用于L2范数
253 
254   /// @brief Scale the blob data by a constant factor.
255   void scale_data(Dtype scale_factor);//data乘一个标量
256   /// @brief Scale the blob diff by a constant factor.
257   void scale_diff(Dtype scale_factor);//diff乘一个标量
258 
259   /**
260    * @brief Set the data_ shared_ptr to point to the SyncedMemory holding the
261    *        data_ of Blob other -- useful in Layer%s which simply perform a copy
262    *        in their Forward pass.
263    *
264    * This deallocates the SyncedMemory holding this Blob's data_, as
265    * shared_ptr calls its destructor when reset with the "=" operator.
266    */
267   void ShareData(const Blob& other);
268   /**
269    * @brief Set the diff_ shared_ptr to point to the SyncedMemory holding the
270    *        diff_ of Blob other -- useful in Layer%s which simply perform a copy
271    *        in their Forward pass.
272    *
273    * This deallocates the SyncedMemory holding this Blob's diff_, as
274    * shared_ptr calls its destructor when reset with the "=" operator.
275    */
276   void ShareDiff(const Blob& other);//共享另一个Blob的diff_
277 
278   bool ShapeEquals(const BlobProto& other);
279 
280  protected:
281   shared_ptr<SyncedMemory> data_;//存放指向data的指针
282   shared_ptr<SyncedMemory> diff_;//存放指向diff的指针
283   shared_ptr<SyncedMemory> shape_data_;
284   vector<int> shape_;
285   int count_;//存放有效元素数目信息
286   int capacity_;//存放Blob容器的容量信息
287 
288   DISABLE_COPY_AND_ASSIGN(Blob);//禁止拷贝构造函数、赋值运算符重载
289 };  // class Blob
290 
291 }  // namespace caffe
292 
293 #endif  // CAFFE_BLOB_HPP_

include/caffe/syncedmem.hpp

 1 #ifndef CAFFE_SYNCEDMEM_HPP_
 2 #define CAFFE_SYNCEDMEM_HPP_
 3 
 4 #include <cstdlib>
 5 
 6 #ifdef USE_MKL
 7   #include "mkl.h"
 8 #endif
 9 
10 #include "caffe/common.hpp"
11 
12 namespace caffe {
13 
14 // If CUDA is available and in GPU mode, host memory will be allocated pinned,
15 // using cudaMallocHost. It avoids dynamic pinning for transfers (DMA).
16 // The improvement in performance seems negligible in the single GPU case,
17 // but might be more significant for parallel training. Most importantly,
18 // it improved stability for large models on many GPUs.
19 // 如果是GPU模式且CUDA使能，则主机内存会以页锁定内存方式分配（使用cudaMallocHost（）函数）
20 // 对单GPU提升并不明显，对多GPU提升非常明显
21 inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
22 #ifndef CPU_ONLY
23   if (Caffe::mode() == Caffe::GPU) {
24     CUDA_CHECK(cudaMallocHost(ptr, size));
25     *use_cuda = true;
26     return;
27   }
28 #endif
29 #ifdef USE_MKL
30   *ptr = mkl_malloc(size ? size:1, 64);
31 #else
32   *ptr = malloc(size);
33 #endif
34   *use_cuda = false;
35   CHECK(*ptr) << "host allocation of size " << size << " failed";
36 }
37 // 与CaffeMallocHost对应
38 inline void CaffeFreeHost(void* ptr, bool use_cuda) {
39 #ifndef CPU_ONLY
40   if (use_cuda) {
41     CUDA_CHECK(cudaFreeHost(ptr));
42     return;
43   }
44 #endif
45 #ifdef USE_MKL
46   mkl_free(ptr);
47 #else
48   free(ptr);
49 #endif
50 }
51 
52 
53 /**
54  * @brief Manages memory allocation and synchronization between the host (CPU)
55  *        and device (GPU).
56  *
57  * TODO(dox): more thorough description.
58  */
59  // 该类负责存储分配以及主机设备间同步
60 class SyncedMemory {
61  public:
62   SyncedMemory();
63   explicit SyncedMemory(size_t size);
64   ~SyncedMemory();
65   const void* cpu_data();       //只读
66   void set_cpu_data(void* data);//设置
67   const void* gpu_data();       //只读
68   void set_gpu_data(void* data);//设置
69   void* mutable_cpu_data();     //读写
70   void* mutable_gpu_data();     //读写
71   //状态机变量，4种状态：未初始化 CPU数据有效 GPU数据有效 已同步
72   enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED }；
73   SyncedHead head() { return head_; }//获取当前状态机变量值
74   size_t size() { return size_; }    //获取当前存储空间尺寸
75 
76 #ifndef CPU_ONLY
77   void async_gpu_push(const cudaStream_t& stream);
78 #endif
79 
80  private:
81   void check_device();
82 
83   void to_cpu();  //数据同步到CPU
84   void to_gpu();  //数据同步到GPU
85   void* cpu_ptr_; //位于CPU的数据指针
86   void* gpu_ptr_; //位于GPU的数据指针
87   size_t size_;   //存储空间大小
88   SyncedHead head_;  //状态机变量
89   bool own_cpu_data_;//标志是否拥有CPU数据权（否，即从别的对象共享）
90   bool cpu_malloc_use_cuda_;
91   bool own_gpu_data_;//标志是否拥有GPU数据权
92   int device_;       
93 
94   DISABLE_COPY_AND_ASSIGN(SyncedMemory);
95 };  // class SyncedMemory
96 
97 }  // namespace caffe
98 
99 #endif  // CAFFE_SYNCEDMEM_HPP_

src/caffe/blob.cpp

  1 #include <climits>
  2 #include <vector>
  3 
  4 #include "caffe/blob.hpp"
  5 #include "caffe/common.hpp"
  6 #include "caffe/syncedmem.hpp"
  7 #include "caffe/util/math_functions.hpp"
  8 
  9 namespace caffe {
 10 //变维函数，将（num,channels,height,width）转为vector<int>并调用重载函数
 11 template <typename Dtype>
 12 void Blob<Dtype>::Reshape(const int num, const int channels, const int height,
 13     const int width) {
 14   vector<int> shape(4);
 15   shape[0] = num;
 16   shape[1] = channels;
 17   shape[2] = height;
 18   shape[3] = width;
 19   Reshape(shape);
 20 }
 21 //真正的变维函数
 22 template <typename Dtype>
 23 void Blob<Dtype>::Reshape(const vector<int>& shape) {
 24   CHECK_LE(shape.size(), kMaxBlobAxes);//保证vector维度小于kMaxBlobAxes
 25   count_ = 1;//用于计算元素总数=num*channels*height*width
 26   shape_.resize(shape.size());//成员变量维度重置
 27   if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
 28     shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
 29   }
 30   int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
 31   for (int i = 0; i < shape.size(); ++i) {
 32     CHECK_GE(shape[i], 0);
 33     if (count_ != 0) {//保证count不溢出
 34       CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
 35     }
 36     count_ *= shape[i];
 37     shape_[i] = shape[i];//成员变量赋值
 38     shape_data[i] = shape[i];
 39   }
 40   if (count_ > capacity_) {//如果新的count_大于当前分配的空间容量
 41     capacity_ = count_;    //扩容，重新分配data_和diff_空间
 42     data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
 43     diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
 44   }
 45 }
 46 
 47 template <typename Dtype>
 48 void Blob<Dtype>::Reshape(const BlobShape& shape) {
 49   CHECK_LE(shape.dim_size(), kMaxBlobAxes);
 50   vector<int> shape_vec(shape.dim_size());
 51   for (int i = 0; i < shape.dim_size(); ++i) {
 52     shape_vec[i] = shape.dim(i);
 53   }
 54   Reshape(shape_vec);
 55 }
 56 
 57 template <typename Dtype>
 58 void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
 59   Reshape(other.shape());
 60 }
 61 //构造函数
 62 template <typename Dtype>
 63 Blob<Dtype>::Blob(const int num, const int channels, const int height,
 64     const int width)
 65   // capacity_ must be initialized before calling Reshape
 66   : capacity_(0) {
 67   //调用Reshape之前必须初始化capacity，否则会导致不可预期的后果
 68   Reshape(num, channels, height, width);
 69 }
 70 
 71 template <typename Dtype>
 72 Blob<Dtype>::Blob(const vector<int>& shape)
 73   // capacity_ must be initialized before calling Reshape
 74   : capacity_(0) {
 75   Reshape(shape);
 76 }
 77 
 78 template <typename Dtype>
 79 const int* Blob<Dtype>::gpu_shape() const {
 80   CHECK(shape_data_);
 81   return (const int*)shape_data_->gpu_data();
 82 }
 83 //只读获得cpu data指针
 84 template <typename Dtype>
 85 const Dtype* Blob<Dtype>::cpu_data() const {
 86   CHECK(data_);//保证data_不为空
 87   return (const Dtype*)data_->cpu_data();
 88 }
 89 //修改cpu data指针
 90 template <typename Dtype>
 91 void Blob<Dtype>::set_cpu_data(Dtype* data) {
 92   CHECK(data);
 93   // Make sure CPU and GPU sizes remain equal
 94   size_t size = count_ * sizeof(Dtype);
 95   if (data_->size() != size) {
 96     data_.reset(new SyncedMemory(size));
 97     diff_.reset(new SyncedMemory(size));
 98   }
 99   data_->set_cpu_data(data);//设置成员变量值为传入参数值
100 }
101 //只读获得gpu data指针
102 template <typename Dtype>
103 const Dtype* Blob<Dtype>::gpu_data() const {
104   CHECK(data_);//保证不为空
105   return (const Dtype*)data_->gpu_data();
106 }
107 //修改gpu data指针
108 template <typename Dtype>
109 void Blob<Dtype>::set_gpu_data(Dtype* data) {
110   CHECK(data);
111   // Make sure CPU and GPU sizes remain equal
112   size_t size = count_ * sizeof(Dtype);
113   if (data_->size() != size) {
114     data_.reset(new SyncedMemory(size));
115     diff_.reset(new SyncedMemory(size));
116   }
117   data_->set_gpu_data(data);
118 }
119 //只读获得cpu diff指针
120 template <typename Dtype>
121 const Dtype* Blob<Dtype>::cpu_diff() const {
122   CHECK(diff_);
123   return (const Dtype*)diff_->cpu_data();
124 }
125 //只读获得gpu diff指针
126 template <typename Dtype>
127 const Dtype* Blob<Dtype>::gpu_diff() const {
128   CHECK(diff_);
129   return (const Dtype*)diff_->gpu_data();
130 }
131 //读写访问cpu_data
132 template <typename Dtype>
133 Dtype* Blob<Dtype>::mutable_cpu_data() {
134   CHECK(data_);
135   return static_cast<Dtype*>(data_->mutable_cpu_data());
136 }
137 //读写访问gpu_data
138 template <typename Dtype>
139 Dtype* Blob<Dtype>::mutable_gpu_data() {
140   CHECK(data_);
141   return static_cast<Dtype*>(data_->mutable_gpu_data());
142 }
143 //读写访问cpu_diff
144 template <typename Dtype>
145 Dtype* Blob<Dtype>::mutable_cpu_diff() {
146   CHECK(diff_);
147   return static_cast<Dtype*>(diff_->mutable_cpu_data());
148 }
149 //读写访问gpu_diff
150 template <typename Dtype>
151 Dtype* Blob<Dtype>::mutable_gpu_diff() {
152   CHECK(diff_);
153   return static_cast<Dtype*>(diff_->mutable_gpu_data());
154 }
155 //共享另一个Blob的data指针
156 template <typename Dtype>
157 void Blob<Dtype>::ShareData(const Blob& other) {
158   CHECK_EQ(count_, other.count());
159   data_ = other.data();
160 }
161 //共享另一个Blob的diff指针
162 template <typename Dtype>
163 void Blob<Dtype>::ShareDiff(const Blob& other) {
164   CHECK_EQ(count_, other.count());
165   diff_ = other.diff();
166 }
167 
168 // The "update" method is used for parameter blobs in a Net, which are stored
169 // as Blob<float> or Blob<double> -- hence we do not define it for
170 // Blob<int> or Blob<unsigned int>.
171 //更新函数用于网络参数Blob的更新。其中int和unsigned int类型的处理并未实现
172 //实现的类型为Blob<float>和Blob<double>
173 template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
174 template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }
175 
176 template <typename Dtype>
177 void Blob<Dtype>::Update() {
178   // We will perform update based on where the data is located.
179   //data在哪儿就在哪儿更新
180   switch (data_->head()) {
181   case SyncedMemory::HEAD_AT_CPU:
182     // perform computation on CPU
183     //执行CPU运算，data[i]=data[i]-diff[i]，i=0~count-1
184     caffe_axpy<Dtype>(count_, Dtype(-1),
185         static_cast<const Dtype*>(diff_->cpu_data()),
186         static_cast<Dtype*>(data_->mutable_cpu_data()));
187     break;
188   case SyncedMemory::HEAD_AT_GPU://data位于GPU或CPU/GPU已同步
189   case SyncedMemory::SYNCED:
190 #ifndef CPU_ONLY
191     // perform computation on GPU
192     //执行GPU运算,data[i]=data[i]-diff[i]，i=0~count-1
193     caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
194         static_cast<const Dtype*>(diff_->gpu_data()),
195         static_cast<Dtype*>(data_->mutable_gpu_data()));
196 #else
197     NO_GPU;//编译时如果打开CPU_ONLY，则GPU禁用
198 #endif
199     break;
200   default:
201     LOG(FATAL) << "Syncedmem not initialized.";
202   }
203 }
204 
205 template <> unsigned int Blob<unsigned int>::asum_data() const {
206   NOT_IMPLEMENTED;
207   return 0;
208 }
209 
210 template <> int Blob<int>::asum_data() const {
211   NOT_IMPLEMENTED;
212   return 0;
213 }
214 //计算L1范数，即绝对值和
215 template <typename Dtype>
216 Dtype Blob<Dtype>::asum_data() const {
217   if (!data_) { return 0; }
218   switch (data_->head()) {
219   case SyncedMemory::HEAD_AT_CPU:
220     return caffe_cpu_asum(count_, cpu_data());//执行CPU上的asum计算
221   case SyncedMemory::HEAD_AT_GPU:
222   case SyncedMemory::SYNCED:
223 #ifndef CPU_ONLY
224   {
225     Dtype asum;
226     caffe_gpu_asum(count_, gpu_data(), &asum);//执行GPU上的asum计算
227     return asum;
228   }
229 #else
230     NO_GPU;
231 #endif
232   case SyncedMemory::UNINITIALIZED:
233     return 0;
234   default:
235     LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
236   }
237   return 0;
238 }
239 
240 template <> unsigned int Blob<unsigned int>::asum_diff() const {
241   NOT_IMPLEMENTED;
242   return 0;
243 }
244 
245 template <> int Blob<int>::asum_diff() const {
246   NOT_IMPLEMENTED;
247   return 0;
248 }
249 
250 template <typename Dtype>
251 Dtype Blob<Dtype>::asum_diff() const {
252   if (!diff_) { return 0; }
253   switch (diff_->head()) {
254   case SyncedMemory::HEAD_AT_CPU:
255     return caffe_cpu_asum(count_, cpu_diff());
256   case SyncedMemory::HEAD_AT_GPU:
257   case SyncedMemory::SYNCED:
258 #ifndef CPU_ONLY
259   {
260     Dtype asum;
261     caffe_gpu_asum(count_, gpu_diff(), &asum);
262     return asum;
263   }
264 #else
265     NO_GPU;
266 #endif
267   case SyncedMemory::UNINITIALIZED:
268     return 0;
269   default:
270     LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
271   }
272   return 0;
273 }
274 
275 template <> unsigned int Blob<unsigned int>::sumsq_data() const {
276   NOT_IMPLEMENTED;
277   return 0;
278 }
279 
280 template <> int Blob<int>::sumsq_data() const {
281   NOT_IMPLEMENTED;
282   return 0;
283 }
284 //用于L2范数，平方和
285 template <typename Dtype>
286 Dtype Blob<Dtype>::sumsq_data() const {
287   Dtype sumsq;
288   const Dtype* data;
289   if (!data_) { return 0; }
290   switch (data_->head()) {
291   case SyncedMemory::HEAD_AT_CPU:
292     data = cpu_data();
293     sumsq = caffe_cpu_dot(count_, data, data);
294     break;
295   case SyncedMemory::HEAD_AT_GPU:
296   case SyncedMemory::SYNCED:
297 #ifndef CPU_ONLY
298     data = gpu_data();
299     caffe_gpu_dot(count_, data, data, &sumsq);
300 #else
301     NO_GPU;
302 #endif
303     break;
304   case SyncedMemory::UNINITIALIZED:
305     return 0;
306   default:
307     LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
308   }
309   return sumsq;
310 }
311 
312 template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
313   NOT_IMPLEMENTED;
314   return 0;
315 }
316 
317 template <> int Blob<int>::sumsq_diff() const {
318   NOT_IMPLEMENTED;
319   return 0;
320 }
321 
322 template <typename Dtype>
323 Dtype Blob<Dtype>::sumsq_diff() const {
324   Dtype sumsq;
325   const Dtype* diff;
326   if (!diff_) { return 0; }
327   switch (diff_->head()) {
328   case SyncedMemory::HEAD_AT_CPU:
329     diff = cpu_diff();
330     sumsq = caffe_cpu_dot(count_, diff, diff);
331     break;
332   case SyncedMemory::HEAD_AT_GPU:
333   case SyncedMemory::SYNCED:
334 #ifndef CPU_ONLY
335     diff = gpu_diff();
336     caffe_gpu_dot(count_, diff, diff, &sumsq);
337     break;
338 #else
339     NO_GPU;
340 #endif
341   case SyncedMemory::UNINITIALIZED:
342     return 0;
343   default:
344     LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
345   }
346   return sumsq;
347 }
348 
349 template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
350   NOT_IMPLEMENTED;
351 }
352 
353 template <> void Blob<int>::scale_data(int scale_factor) {
354   NOT_IMPLEMENTED;
355 }
356 //对data_进行幅度缩放
357 template <typename Dtype>
358 void Blob<Dtype>::scale_data(Dtype scale_factor) {
359   Dtype* data;
360   if (!data_) { return; }
361   switch (data_->head()) {
362   case SyncedMemory::HEAD_AT_CPU:
363     data = mutable_cpu_data();
364     caffe_scal(count_, scale_factor, data);
365     return;
366   case SyncedMemory::HEAD_AT_GPU:
367   case SyncedMemory::SYNCED:
368 #ifndef CPU_ONLY
369     data = mutable_gpu_data();
370     caffe_gpu_scal(count_, scale_factor, data);
371     return;
372 #else
373     NO_GPU;
374 #endif
375   case SyncedMemory::UNINITIALIZED:
376     return;
377   default:
378     LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
379   }
380 }
381 
382 template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
383   NOT_IMPLEMENTED;
384 }
385 
386 template <> void Blob<int>::scale_diff(int scale_factor) {
387   NOT_IMPLEMENTED;
388 }
389 
390 template <typename Dtype>
391 void Blob<Dtype>::scale_diff(Dtype scale_factor) {
392   Dtype* diff;
393   if (!diff_) { return; }
394   switch (diff_->head()) {
395   case SyncedMemory::HEAD_AT_CPU:
396     diff = mutable_cpu_diff();
397     caffe_scal(count_, scale_factor, diff);
398     return;
399   case SyncedMemory::HEAD_AT_GPU:
400   case SyncedMemory::SYNCED:
401 #ifndef CPU_ONLY
402     diff = mutable_gpu_diff();
403     caffe_gpu_scal(count_, scale_factor, diff);
404     return;
405 #else
406     NO_GPU;
407 #endif
408   case SyncedMemory::UNINITIALIZED:
409     return;
410   default:
411     LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
412   }
413 }
414 //判断形状是否相同
415 template <typename Dtype>
416 bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
417   if (other.has_num() || other.has_channels() ||
418       other.has_height() || other.has_width()) {
419     // Using deprecated 4D Blob dimensions --每个维度对比
420     // shape is (num, channels, height, width).
421     // Note: we do not use the normal Blob::num(), Blob::channels(), etc.
422     // methods as these index from the beginning of the blob shape, where legacy
423     // parameter blobs were indexed from the end of the blob shape (e.g., bias
424     // Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
425     return shape_.size() <= 4 &&
426            LegacyShape(-4) == other.num() &&
427            LegacyShape(-3) == other.channels() &&
428            LegacyShape(-2) == other.height() &&
429            LegacyShape(-1) == other.width();
430   }
431   //直接对比
432   vector<int> other_shape(other.shape().dim_size());
433   for (int i = 0; i < other.shape().dim_size(); ++i) {
434     other_shape[i] = other.shape().dim(i);
435   }
436   return shape_ == other_shape;
437 }
438 //从另一个Blob拷贝data（可选diff），必要时进行变维
439 template <typename Dtype>
440 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
441   if (source.count() != count_ || source.shape() != shape_) {
442     if (reshape) {
443       ReshapeLike(source);
444     } else {
445       LOG(FATAL) << "Trying to copy blobs of different sizes.";
446     }
447   }
448   switch (Caffe::mode()) {
449   case Caffe::GPU:
450     if (copy_diff) {
451       caffe_copy(count_, source.gpu_diff(),
452           static_cast<Dtype*>(diff_->mutable_gpu_data()));
453     } else {
454       caffe_copy(count_, source.gpu_data(),
455           static_cast<Dtype*>(data_->mutable_gpu_data()));
456     }
457     break;
458   case Caffe::CPU:
459     if (copy_diff) {
460       caffe_copy(count_, source.cpu_diff(),
461           static_cast<Dtype*>(diff_->mutable_cpu_data()));
462     } else {
463       caffe_copy(count_, source.cpu_data(),
464           static_cast<Dtype*>(data_->mutable_cpu_data()));
465     }
466     break;
467   default:
468     LOG(FATAL) << "Unknown caffe mode.";
469   }
470 }
471 //从BlobProto中加载一个Blob
472 template <typename Dtype>
473 void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
474   if (reshape) {//获取维度信息
475     vector<int> shape;
476     if (proto.has_num() || proto.has_channels() ||
477         proto.has_height() || proto.has_width()) {
478       // Using deprecated 4D Blob dimensions --
479       // shape is (num, channels, height, width).
480       shape.resize(4);
481       shape[0] = proto.num();
482       shape[1] = proto.channels();
483       shape[2] = proto.height();
484       shape[3] = proto.width();
485     } else {
486       shape.resize(proto.shape().dim_size());
487       for (int i = 0; i < proto.shape().dim_size(); ++i) {
488         shape[i] = proto.shape().dim(i);
489       }
490     }
491     Reshape(shape);//变维
492   } else {
493     CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
494   }
495   // copy data 加载数据
496   Dtype* data_vec = mutable_cpu_data();
497   if (proto.double_data_size() > 0) {//若之前保存的是double类型的data
498     CHECK_EQ(count_, proto.double_data_size());
499     for (int i = 0; i < count_; ++i) {
500       data_vec[i] = proto.double_data(i);//加载double data
501     }
502   } else {
503     CHECK_EQ(count_, proto.data_size());
504     for (int i = 0; i < count_; ++i) {
505       data_vec[i] = proto.data(i);//否则加载float data
506     }
507   }
508   if (proto.double_diff_size() > 0) {//若之前保存的是double类型的diff
509     CHECK_EQ(count_, proto.double_diff_size());
510     Dtype* diff_vec = mutable_cpu_diff();
511     for (int i = 0; i < count_; ++i) {
512       diff_vec[i] = proto.double_diff(i);//加载double diff
513     }
514   } else if (proto.diff_size() > 0) {
515     CHECK_EQ(count_, proto.diff_size());
516     Dtype* diff_vec = mutable_cpu_diff();
517     for (int i = 0; i < count_; ++i) {
518       diff_vec[i] = proto.diff(i);//否则加载float diff
519     }
520   }
521 }
522 
523 template <>
524 void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
525   proto->clear_shape();
526   for (int i = 0; i < shape_.size(); ++i) {
527     proto->mutable_shape()->add_dim(shape_[i]);
528   }
529   proto->clear_double_data();
530   proto->clear_double_diff();
531   const double* data_vec = cpu_data();
532   for (int i = 0; i < count_; ++i) {
533     proto->add_double_data(data_vec[i]);
534   }
535   if (write_diff) {
536     const double* diff_vec = cpu_diff();
537     for (int i = 0; i < count_; ++i) {
538       proto->add_double_diff(diff_vec[i]);
539     }
540   }
541 }
542 //将Blob中的data（可选diff）导出到BlobProto结构体，便于存储到磁盘文件中
543 template <>
544 void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
545   proto->clear_shape();//重置proto维度，保证与Blob相同
546   for (int i = 0; i < shape_.size(); ++i) {
547     proto->mutable_shape()->add_dim(shape_[i]);
548   }
549   proto->clear_data();//清楚data
550   proto->clear_diff();//清楚diff
551   const float* data_vec = cpu_data();//将data导出到proto
552   for (int i = 0; i < count_; ++i) {
553     proto->add_data(data_vec[i]);
554   }
555   if (write_diff) {//如果要求导出diff
556     const float* diff_vec = cpu_diff();//导出diff到proto
557     for (int i = 0; i < count_; ++i) {
558       proto->add_diff(diff_vec[i]);
559     }
560   }
561 }
562 
563 INSTANTIATE_CLASS(Blob);//实例化Blob类模板（float,double ）
564 template class Blob<int>;
565 template class Blob<unsigned int>;
566 
567 }  // namespace caffe

初学者，慢慢看，待更新……

摘抄参考赵永科《深度学习 21天实战caffe》

posted on 2017-06-29 16:02 想飞的萌猪阅读(2365) 评论(0) 编辑收藏举报