21天实战caffe数据结构

Posted 2020-10-06

caffe中一个CNN模型由Net表示，Net由多个Layer堆叠而成。

caffe的万丈高楼(Net)是由图纸（prototxt），用blob这些砖块筑成一层层（Layer），最后通过SGD方法（Solver）进行简装修（train）、精装修（finetune）实现的。

 

Blob

Blob在内存中表示为4维数组，维度从低到高为（width_,height_,channels_,num_）。用于存储数据（data）或权值增量（diff）。

 Blob中封装里SyncedMemory类。Bolob作为基本单元服务Layer、Net、Solver等。

接下来看看blob.hpp:

 

#include "caffe/proto/caffe.pb.h"

#include "caffe/syncedmem.hpp"

 

const int kMaxBlobAxes = 32;

 

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 axis_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 int* gpu_shape() const;

  const Dtype* gpu_data() const;

  void set_gpu_data(Dtype* data);

  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_;

  shared_ptr<SyncedMemory> shape_data_;

  vector<int> shape_;

  int count_;

  int capacity_;

 

  DISABLE_COPY_AND_ASSIGN(Blob);

};  // class Blob