caffe源码之blob.cpp

caffe环境搭建好了，也测试成功了，接下来就是caffe源码的阅读了。看了很多，遗憾的是自己对java语言比较熟悉吧，因为一直在使用，对c++语言，早就还回去了。可怜自己呀。遇到了很多问题。不多少了，blob.cpp很容易找到的，之所以首先看这个，是觉得它是存储结构吧，存储解决了。那么对于后面的源代码阅读就会简单很多吧。它的存在目录是/src/caffe/blob.cpp

Blob是一个思维的结构体、在源代码中，它是通过shape属性的以计算的成员变量。仔细看它的成员变量，我们会发现有：data_,(存放数据)diff_,(存放梯度)shape_,(存放形状)count_,(数据个数)capacity(数据容量),

它的成员函数有

const Dtype* cpu_data() const; //cpu使用的数据
void set_cpu_data(Dtype* data);/span>//用数据块的值来设置cpu里面的data。
const Dtype* gpu_data() const;//返回不可更改的指针，下同
const Dtype* cpu_diff() const;//返回不可更改的地图指针
const Dtype* gpu_diff() const;
Dtype* mutable_cpu_data();//返回可更改数据的指针，下同
Dtype* mutable_gpu_data();//返回可以更改的梯度指针
Dtype* mutable_cpu_diff();
Dtype* mutable_gpu_diff();

在这里我们会看到mutable这个关键字，对的。这是c++一个很神奇的关键字，它只能用于修饰一个类的非静态数据成员。不像普通的数据成员，const成员函数可以修改mutable数据成员。</span></span></code><span style="background-color: rgb(240, 240, 240);">  </span>

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 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.
*
* 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);
inline string shape_string() const {
ostringstream stream;
for (int i = 0; i < shape_.size(); ++i) {
stream << shape_[i] << " ";
}
stream << "(" << count_ << ")";
return stream.str();
}
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.
*/
inline int shape(int index) const {
return shape_[CanonicalAxisIndex(index)];
}
inline int num_axes() const { return shape_.size(); }
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.
*/
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.
*/
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.
*/
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;
}

/// @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);
}

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
*/
void CopyFrom(const Blob<Dtype>& source, bool copy_diff = false,
bool reshape = false);

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)];
}

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)];
}

inline const shared_ptr<SyncedMemory>& data() const {
CHECK(data_);
return data_;
}

inline const shared_ptr<SyncedMemory>& diff() const {
CHECK(diff_);
return diff_;
}

const Dtype* cpu_data() const;
void set_cpu_data(Dtype* data);
const Dtype* gpu_data() const;
const Dtype* cpu_diff() const;
const Dtype* gpu_diff() const;
Dtype* mutable_cpu_data();
Dtype* mutable_gpu_data();
Dtype* mutable_cpu_diff();
Dtype* mutable_gpu_diff();
void Update();
void FromProto(const BlobProto& proto, bool reshape = true);
void ToProto(BlobProto* proto, bool write_diff = false) const;

/// @brief Compute the sum of absolute values (L1 norm) of the data.
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.
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.
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);
/**
* @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);

bool ShapeEquals(const BlobProto& other);

protected:
shared_ptr<SyncedMemory> data_;
shared_ptr<SyncedMemory> diff_;
vector<int> shape_;
int count_;
int capacity_;

DISABLE_COPY_AND_ASSIGN(Blob);
};  // class Blob

}  // namespace caffe

至此，blob的源码阅读就这样了，一天一个他的源码学习。希望自己可以提高吧。还有要加快c++语言的学习了。