Caffe框架源码剖析(7)—全连接层InnerProductLayer

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    全连接层InnerProductLayer的原理很简单，说白了就是矩阵乘法运算。正向传导时输出数据等于输入数据乘上权重，如果有偏置项就再加上偏置项。写成公式就是：

    矩阵乘法在CPU端采用OpenBLAS实现，在GPU端则采用NVIDIA cuBLAS实现加速。

    inner_product_layer.hpp定义如下，

template <typename Dtype>
class InnerProductLayer : public Layer<Dtype> {
 public:
  explicit InnerProductLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline const char* type() const { return "InnerProduct"; }
  virtual inline int ExactNumBottomBlobs() const { return 1; }
  virtual inline int ExactNumTopBlobs() const { return 1; }

 protected:
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

  // 矩阵乘法参数(M, K) * (K, N) = (M, N)
  int M_;  // 输入数据的数量, 即patch_size
  int K_;  // 单个输入数据包含的元素个数
  int N_;  // 输出层的神经元个数
  // 是否包含偏置项
  bool bias_term_;
  // 偏置项乘子
  Blob<Dtype> bias_multiplier_;
  // 是否转置
  bool transpose_;  ///< if true, assume transposed weights
};

inner_product_layer.cpp定义如下，

template <typename Dtype>
void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  const int num_output = this->layer_param_.inner_product_param().num_output();
  bias_term_ = this->layer_param_.inner_product_param().bias_term();
  transpose_ = this->layer_param_.inner_product_param().transpose();
  N_ = num_output;
  const int axis = bottom[0]->CanonicalAxisIndex(
      this->layer_param_.inner_product_param().axis());
  // 如果输入图像的维度是(N, C, H, W)，则K_ = C * H * W
  K_ = bottom[0]->count(axis);
  // 确认是否需要初始化权重
  if (this->blobs_.size() > 0) {
    LOG(INFO) << "Skipping parameter initialization";
  } else {
    if (bias_term_) {
      // 如果包含偏置项，则将blobs_的size设为2
      this->blobs_.resize(2);
    } else {
      this->blobs_.resize(1);
    }
    // 设置权重的维度
    vector<int> weight_shape(2);
    if (transpose_) {
      weight_shape[0] = K_;
      weight_shape[1] = N_;
    } else {
      weight_shape[0] = N_;
      weight_shape[1] = K_;
    }
    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
    // 初始化权重（使用xavier初始化方法）
    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
        this->layer_param_.inner_product_param().weight_filler()));
    weight_filler->Fill(this->blobs_[0].get());
    // 如果包含偏置项，则将偏置项初始化为0
    if (bias_term_) {
      vector<int> bias_shape(1, N_);
      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
          this->layer_param_.inner_product_param().bias_filler()));
      bias_filler->Fill(this->blobs_[1].get());
    }
  }  // parameter initialization
  this->param_propagate_down_.resize(this->blobs_.size(), true);
}

template <typename Dtype>
void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  // 确认维度
  const int axis = bottom[0]->CanonicalAxisIndex(
      this->layer_param_.inner_product_param().axis());
  const int new_K = bottom[0]->count(axis);
  CHECK_EQ(K_, new_K)
      << "Input size incompatible with inner product parameters.";
  // M_等于patch_size
  M_ = bottom[0]->count(0, axis);
  // 将输出项top[0]的维度置为(patch_size, N_)
  vector<int> top_shape = bottom[0]->shape();
  top_shape.resize(axis + 1);
  top_shape[axis] = N_;
  top[0]->Reshape(top_shape);
  // 设置偏置项乘子
  if (bias_term_) {
    vector<int> bias_shape(1, M_);
    bias_multiplier_.Reshape(bias_shape);
    // 将偏置项乘子bias_multiplier_初始化为1
    caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
  }
}

// CPU正向传导
template <typename Dtype>
void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const Dtype* bottom_data = bottom[0]->cpu_data();
  Dtype* top_data = top[0]->mutable_cpu_data();
  const Dtype* weight = this->blobs_[0]->cpu_data();
  // bottom_data为M*K矩阵，权重为K*N矩阵，top_data为M*N矩阵
  // top_data = bottom_data * weight
  caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans,
      M_, N_, K_, (Dtype)1.,
      bottom_data, weight, (Dtype)0., top_data);
  // 如果包含有偏置项
  if (bias_term_) {
    // top_data += bias_multiplier * bias
    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
        bias_multiplier_.cpu_data(),
        this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
  }
  // 因此两步合并的结果就是 top_data = bottom_data * weight + bias_multiplier * bias
}

// CPU反向传导
template <typename Dtype>
void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  if (this->param_propagate_down_[0]) {
    const Dtype* top_diff = top[0]->cpu_diff();
    const Dtype* bottom_data = bottom[0]->cpu_data();
    // 求权重的偏导，weight_diff += top_diff * bottom_data
    if (transpose_) {
      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
          K_, N_, M_,
          (Dtype)1., bottom_data, top_diff,
          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
    } else {
      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
          N_, K_, M_,
          (Dtype)1., top_diff, bottom_data,
          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
    }
  }
  // 求偏置项的偏导，bias_diff += top_diff * bias_multiplier
  if (bias_term_ && this->param_propagate_down_[1]) {
    const Dtype* top_diff = top[0]->cpu_diff();
    // Gradient with respect to bias
    caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
        bias_multiplier_.cpu_data(), (Dtype)1.,
        this->blobs_[1]->mutable_cpu_diff());
  }
  if (propagate_down[0]) {
    const Dtype* top_diff = top[0]->cpu_diff();
    // 求bottom数据的偏导，bottom_data_diff = top_diff * weight
    if (transpose_) {
      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
          M_, K_, N_,
          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
          (Dtype)0., bottom[0]->mutable_cpu_diff());
    } else {
      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
          M_, K_, N_,
          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
          (Dtype)0., bottom[0]->mutable_cpu_diff());
    }
  }
}

// 如果CPU_ONLY模式则禁止Forward_gpu和Backward_gpu函数
#ifdef CPU_ONLY
STUB_GPU(InnerProductLayer);
#endif

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