Caffe源码：blob 分析
目录
- 目录
- 简单介绍
- 源代码分析
- Reshape 函数
- Blob 构造函数
- data_数据操作函数
- 反向传播导数diff_ 操作函数
- ShareData 函数
- Updata 函数
- asum_data 函数
- asum_diff 函数
- sumsq_data 函数
- sumsq_diff函数
- scale_data 函数
- scale_diff函数
- ShapeEquals函数
- CopyFrom 函数
- FromProto 函数
- ToProto 函数
简单介绍
Blob 在caffe源码 blob.hpp中是一个模板类。
protected 的成员变量有：data_ , diff_ , shape_ , count_ , capacity_ ，其中data_ 和 diff_ 是共享SyncedMemory 类（在syncedmem的源码中定义）的智能指针，shape_是int型的vector，count_ 和capacity_ 是整型变量。
其成员函数主要有：Reshape 、ReshapeLike、SharedData、 Updata 等等。
blob.hpp 包含了caffe.pb.h ，说明caffe protobuf 会向blob 传递参数源代码分析
-
1.Reshape 函数：
templatevoid Blob ::Reshape(const int num, const int channels, const int height, const int width) { vector shape(4); shape[0] = num; shape[1] = channels; shape[2] = height; shape[3] = width; Reshape(shape); } //该函数将num,channels,height,width传递给vector shape_ template void Blob ::Reshape(const vector & 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 count_ *= shape[i]; shape_[i] = shape[i]; } if (count_ > capacity_) { capacity_ = count_; data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype))); } //由于count_超过了当前capacity_ 因此需要重新分配内存空间 } template // BlobShape 在caffe.proto 中定义 void Blob ::Reshape(const BlobShape& shape) { CHECK_LE(shape.dim_size(), kMaxBlobAxes); vector 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 } template void Blob ::ReshapeLike(const Blob & other) { Reshape(other.shape()); } //用已知的Blob的shape来对shape_ 进行reshape 2.Blob 构造函数：
//用构造函数的重载的方法定义2个构造函数，以便提供不同的初始化的方法。 templateBlob ::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num，channels，height， width 初始化 template Blob ::Blob(const vector & shape) // capacity_ must be initialized before calling Reshape : capacity_(0) { Reshape(shape); }//用shape 初始化 3.data_数据操作函数：
templateconst Dtype* Blob ::cpu_data() const { CHECK(data_); return (const Dtype*)data_->cpu_data(); } //返回cpu 中的数据 template void Blob ::set_cpu_data(Dtype* data) { CHECK(data); data_->set_cpu_data(data); }// 清空cpu 数据 template const Dtype* Blob ::gpu_data() const { CHECK(data_); return (const Dtype*)data_->gpu_data(); }//返回gpu 中的数据 4.反向传播导数diff_ 操作函数：
templateconst Dtype* Blob ::cpu_diff() const { CHECK(diff_); return (const Dtype*)diff_->cpu_data(); }//返回cpu 中的数据 template const Dtype* Blob ::gpu_diff() const { CHECK(diff_); return (const Dtype*)diff_->gpu_data(); }//返回gpu 中的数据 5.ShareData 函数：
templatevoid Blob ::ShareData(const Blob& other) { CHECK_EQ(count_, other.count()); data_ = other.data(); }//当前的blob 的data_ 指向已知blob的数据 template void Blob ::ShareDiff(const Blob& other) { CHECK_EQ(count_, other.count()); diff_ = other.diff(); }//当前的blob 的diff_ 指向已知blob的反向传播导数 6.Updata 函数：
该函数用于参数blob的更新（weight，bias 等减去对应的导数）
template
void Blob::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
caffe_axpy(count_, Dtype(-1),
static_cast(diff_->cpu_data()),
static_cast(data_->mutable_cpu_data())); //data_-diff_
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
caffe_gpu_axpy(count_, Dtype(-1),
static_cast(diff_->gpu_data()),
static_cast(data_->mutable_gpu_data()));
#else
NO_GPU;
#endif
break;
default:
LOG(FATAL) << "Syncedmem not initialized.";
}
} 7.asum_data 函数：
template
Dtype Blob::asum_data() const {
if (!data_) { return 0; }
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU: //数据在cpu上
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::asum_diff() const {
NOT_IMPLEMENTED;
return 0;
} // 返回data_ 中所有 element 的绝对值之和 8.asum_diff 函数：
template
Dtype Blob::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;
} // 返回diff_ 中所有 element 的绝对值之和 9.sumsq_data 函数：
template
Dtype Blob::sumsq_data() const {
Dtype sumsq;
const Dtype* data;
if (!data_) { return 0; }
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU: //数据在cpu上
data = cpu_data();
sumsq = caffe_cpu_dot(count_, data, data); //sumsq = sum(data[i]^2)
break;
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
data = gpu_data(); //数据在gpu上
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;
}//返回 data_ 中所有 element 的平方和
10.sumsq_diff函数：
template
Dtype Blob::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;
}//返回 diff_ 中所有 element 的平方和 11.scale_data 函数:
template
void Blob::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();
}
}// 给data乘以scale_factor 12.scale_diff函数
template
void Blob::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();
}
}// 给diff乘以scale_factor 13.ShapeEquals函数：
template
bool Blob::ShapeEquals(const BlobProto& other) {
//BlobProto 是定义在caffe.proto 中的一个message，其字段有 data， diff， shape， num， channels， height， width
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 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 14.CopyFrom 函数：
template
void Blob::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(diff_->mutable_gpu_data()));
} else {
caffe_copy(count_, source.gpu_data(),
static_cast(data_->mutable_gpu_data()));
}
break;
case Caffe::CPU:
if (copy_diff) {
caffe_copy(count_, source.cpu_diff(),
static_cast(diff_->mutable_cpu_data()));
} else {
caffe_copy(count_, source.cpu_data(),
static_cast(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 15.FromProto 函数：
template
void Blob::FromProto(const BlobProto& proto, bool reshape) {
if (reshape) {
vector 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
}
} 16.ToProto 函数：
template
void Blob::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;
template class Blob;
} // 本文转自：http://blog.csdn.net/seven_first/article/details/47398613#1reshape-函数