Caffe源码理解1:Blob存储结构与设计

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Blob作用

Caffe官方描述:

A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion.

Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization.

Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.

Blob是Caffe中的基础数据结构,主要作用如下:

  1. 存储和传输数据,对外提供统一的内存接口。在Caffe中,输入图像、每层的权重和反向传播时的梯度、每层的输入和输出等都以Blob形式管理
  2. 隐藏CPU和GPU之间数据同步的细节(通过SyncedMemory实现),用户使用时不需要自己管理CPU和GPU间的数据同步

在逻辑上,Blob是个Nd" role="presentation" style="position: relative;">NdNd维张量。当Nd=4" role="presentation" style="position: relative;">Nd=4Nd=4时,Blob的shape定义为N∗C∗H∗W" role="presentation" style="position: relative;">N∗C∗H∗WN∗C∗H∗W,即Num∗Channel∗Height∗Width" role="presentation" style="position: relative;">Num∗Channel∗Height∗WidthNum∗Channel∗Height∗Width,可以表示输入图像Batch、卷积层的kernel参数、卷积层的输入输出map等;当Nd=2" role="presentation" style="position: relative;">Nd=2Nd=2时,可以表示全连接层的权重,Nout∗Nin" role="presentation" style="position: relative;">Nout∗NinNout∗Nin;当Nd=1" role="presentation" style="position: relative;">Nd=1Nd=1时,可以表示卷积层和全连接层的bias参数。

具体地,

  • Nd=4" role="presentation" style="position: relative;">Nd=4Nd=4,Blob表示输入图像时,N" role="presentation" style="position: relative;">NN为当前批次的图片数量即MiniBatchNum,C" role="presentation" style="position: relative;">CC为图像的通道数,RGB图C=3" role="presentation" style="position: relative;">C=3C=3,H" role="presentation" style="position: relative;">HH和W" role="presentation" style="position: relative;">WW为图像的高和宽。
  • Nd=4" role="presentation" style="position: relative;">Nd=4Nd=4,Blob表示卷积层的输入输出时,N=1" role="presentation" style="position: relative;">N=1N=1,C" role="presentation" style="position: relative;">CC为特征图的数量,H" role="presentation" style="position: relative;">HH和W" role="presentation" style="position: relative;">WW为特征图的高和宽。
  • Nd=4" role="presentation" style="position: relative;">Nd=4Nd=4,Blob表示卷积层kernel参数时,N" role="presentation" style="position: relative;">NN为当前层输出特征图的数量,其与卷积核数量相同,C" role="presentation" style="position: relative;">CC为当前层输入特征图的数量,其与一个卷积核的层数相同,H" role="presentation" style="position: relative;">HH和W" role="presentation" style="position: relative;">WW为卷积核的高和宽,每个卷积是三维的即C∗H∗W" role="presentation" style="position: relative;">C∗H∗WC∗H∗W。
  • Nd=2" role="presentation" style="position: relative;">Nd=2Nd=2,Blob表示全连接层的权重时,shape为Nout∗Nin" role="presentation" style="position: relative;">Nout∗NinNout∗Nin的二维矩阵,Nout" role="presentation" style="position: relative;">NoutNout为输出数量,Nin" role="presentation" style="position: relative;">NinNin为输入数量。
  • Nd=1" role="presentation" style="position: relative;">Nd=1Nd=1,Blob为长度为N" role="presentation" style="position: relative;">NN的向量,表示卷积层bias参数时,N" role="presentation" style="position: relative;">NN为卷积核数量(与输出特征图数量相同),表示全连接层bias参数时,N" role="presentation" style="position: relative;">NN为输出数量(与上面的Nout" role="presentation" style="position: relative;">NoutNout相同)。

主要成员变量

shared_ptr<SyncedMemory> data_; // 数据,存储图像、参数、输入输出等
shared_ptr<SyncedMemory> diff_; // 反向传播时的梯度,训练阶段update时参数的更新量
shared_ptr<SyncedMemory> shape_data_; // GPU shape,与下面的shape是相同的
vector<int> shape_; // shape,data和diff相同
int count_; // 张量中的元素数量,比如 N*C*H*W
int capacity_; // 容量,当前分配内存的大小,当reshape时,可能需要扩容

Blob存储结构

Blobdata_diff_对应的数据区,在内存中均以行有先的方式存储(C语言风格)。行优先和列优先的存储方式如下图所示,9个数连续存储,表示同一个矩阵,但是存储顺序不同,图片来自WIKI

当输入图像为1张RGB图时,shape为1∗3∗4∗5" role="presentation" style="position: relative;">1∗3∗4∗51∗3∗4∗5,其存储顺序如下图所示,图片素材来自链接。channel维上,0为R,1为G、2为B,先在R上行有先存储,再在G上行有先存储,最后在B上行有先存储。这里仅作示意,在caffe中实际存储顺序为BGR。

当N=4" role="presentation" style="position: relative;">N=4N=4时,Num∗Channel∗Height∗Width" role="presentation" style="position: relative;">Num∗Channel∗Height∗WidthNum∗Channel∗Height∗Width,Blob在Width" role="presentation" style="position: relative;">WidthWidth维上连续存储,如下图所示:

理解了上图,再理解多维Blob的拼接、裁剪等操作就很容易了。

通过Bloboffset成员函数可以获得(n,c,h,w)" role="presentation" style="position: relative;">(n,c,h,w)(n,c,h,w)处的偏移量,偏移的计算方式与行优先存储是一致的,代码如下:

  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;
  }

CPU与GPU间的数据传递

const Dtype* cpu_data() const; // 不可修改数据,return (const Dtype*)data_->cpu_data();
const Dtype* gpu_data() const; // return (const Dtype*)data_->gpu_data();
Dtype* mutable_cpu_data(); // 可修改数据,return static_cast<Dtype*>(data_->mutable_cpu_data());
Dtype* mutable_gpu_data(); // static_cast<Dtype*>(data_->mutable_gpu_data());

Caffe中通过上述方式来获取CPU和GPU上的数据区指针,在调用函数时,SyncedMemory会自行判断是否需要同步数据(具体是如何判断的,在讲SyncedMemory时再详细说明),当访问CPU(GPU)侧数据时,如果GPU(CPU)侧数据(可能)更新过,则将数据同步至CPU(GPU)。可参考下面示例代码来理解何时会发生数据同步,示例代码来自Caffe官网。

// Assuming that data are on the CPU initially, and we have a blob.
const Dtype* foo;
Dtype* bar;
foo = blob.gpu_data(); // data copied cpu->gpu.
foo = blob.cpu_data(); // no data copied since both have up-to-date contents.
bar = blob.mutable_gpu_data(); // no data copied.
// ... some operations ...
bar = blob.mutable_gpu_data(); // no data copied when we are still on GPU.
foo = blob.cpu_data(); // data copied gpu->cpu, since the gpu side has modified the data
foo = blob.gpu_data(); // no data copied since both have up-to-date contents
bar = blob.mutable_cpu_data(); // still no data copied.
bar = blob.mutable_gpu_data(); // data copied cpu->gpu.
bar = blob.mutable_cpu_data(); // data copied gpu->cpu.

只要调用了mutable函数,即便没有实际修改数据,再调用另一侧的mutable函数,也会发生数据同步。因此,在明确不修改数据时,尽量调用const函数,只有在操纵数据时才调用mutable函数。

主要成员函数

Blob的主要成员函数有:

  1. 基本函数,包括构造函数、set和get类函数、逻辑判断等
  2. Reshape函数,用于设置Blobshape,分配内存
  3. Update函数,用于在网络训练时更新参数使用,data=data−diff" role="presentation" style="position: relative;">data=data−diffdata=data−diff
  4. Blob运算函数,用于切片统计、求L1范数、L2范数、数乘等
  5. 辅助函数,proto导入导出等

下面重点介绍其中主要的成员函数。

template <typename Dtype>
class Blob {
 public:
  Blob()
       : data_(), diff_(), count_(0), capacity_(0) {}

  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.
  explicit Blob(const int num, const int channels, const int height,
      const int width);
  explicit Blob(const vector<int>& shape);
// ......
}

Blob的构造函数中,会调用Reshape函数,给shape成员变量赋值以及分配初始内存。在Layer::Reshape或者Layer::Forward时,也会调用Reshape函数来设置输出Blob的维度,如果reshape了整个网络的输入Blob,则需要调用Net::Forward或者Net::Reshape来重新确定每一层相关Blob的shape(从bottom到top逐层推算得出)。当Blob size发生改变时,只有在内存不够才会再分配内存,具体代码如下

template <typename Dtype>
bool Blob<Dtype>::Reshape(const vector<int>& shape) {
  CHECK_LE(shape.size(), kMaxBlobAxes);
  count_ = 1;
  shape_.resize(shape.size());
  if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
    shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
  }
  int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
  for (int i = 0; i < shape.size(); ++i) {
    CHECK_GE(shape[i], 0);
    if (count_ != 0) {
      CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
    }
    count_ *= shape[i];
    shape_[i] = shape[i];
    shape_data[i] = shape[i];
  }
  // 不够时分配内存,原内存会释放(shared_ptr)
  if (count_ > capacity_) {
    capacity_ = count_;
    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    return true;
  }
  else {
    return false;
  }
}

在网络训练阶段,根据损失函数以及反向传播得到的梯度,获得每层参数的更新量diff_,会调用Update函数来更新参数,如下

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:
    // perform computation on CPU
    // data = data - diff, axpy: y = ax + y
    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
    // 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.";
  }
}

值得一提的是,Blob维度索引支持负数,-1表示最后一个维度,与Python相同,实现代码如下,在需要访问某个维度时,先使用CanonicalAxisIndex获得真正维度,比如CanonicalAxisIndex(-1)

// axis_index the axis index.
// If 0 <= index < num_axes(), return index.
// If -num_axes <= index <= -1, return (num_axes() - (-index))
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;
}

其他函数,只取代表。

// set get
// 省略基本的set和get函数,如上面提到的const和mutable函数
// 返回(n, c, h, w)处的数据,return cpu_data()[offset(n, c, h, w)]
inline Dtype data_at(const int n, const int c, const int h, const int w) const;
inline Dtype diff_at(const int n, const int c, const int h, const int w) const;
void ShareData(const Blob& other); // 与另一Blob共享data,类似浅拷贝
void ShareDiff(const Blob& other); // 与另一Blob共享diff
// 从另一Blob拷贝,类似深拷贝
void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape); 

// 切片元素数量统计,count *= shape(i)
inline int count(int start_axis, int end_axis) const; 

// proto序列化与反序列化
void FromProto(const BlobProto& proto, bool reshape = true); // 从proto导入
void ToProto(BlobProto* proto, bool write_diff = false) const; // 导出为proto

// 运算
Dtype asum_data() const; // data L1 norm
Dtype asum_diff() const; // diff L1 norm
Dtype sumsq_data() const; // data L2 norm
Dtype sumsq_diff() const; // diff L2 norm
void scale_data(Dtype scale_factor); // data 数乘,in place
void scale_diff(Dtype scale_factor); // diff 数乘,in place

// 逻辑判断
bool ShapeEquals(const BlobProto& other); // 判断shape是否相同

以上。

参考

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