Class BaseConvolutionLayer

include/caffe/layers/base_conv_layer.hpp:18–170

 */
template <typename Dtype>
class BaseConvolutionLayer : public Layer<Dtype> {
 public:
  explicit BaseConvolutionLayer(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 int MinBottomBlobs() const { return 1; }
  virtual inline int MinTopBlobs() const { return 1; }
  virtual inline bool EqualNumBottomTopBlobs() const { return true; }

 protected:
  // Helper functions that abstract away the column buffer and gemm arguments.
  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
  // we just called weight_cpu_gemm with the same input.
  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
      Dtype* output, bool skip_im2col = false);
  void forward_cpu_bias(Dtype* output, const Dtype* bias);
  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
      Dtype* output);
  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
      weights);
  void backward_cpu_bias(Dtype* bias, const Dtype* input);

#ifndef CPU_ONLY
  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
      Dtype* output, bool skip_im2col = false);
  void forward_gpu_bias(Dtype* output, const Dtype* bias);
  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
      Dtype* col_output);
  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
      weights);
  void backward_gpu_bias(Dtype* bias, const Dtype* input);
#endif

  /// @brief The spatial dimensions of the input.
  inline int input_shape(int i) {
    return (*bottom_shape_)[channel_axis_ + i];
  }
  // reverse_dimensions should return true iff we are implementing deconv, so
  // that conv helpers know which dimensions are which.
  virtual bool reverse_dimensions() = 0;
  // Compute height_out_ and width_out_ from other parameters.
  virtual void compute_output_shape() = 0;

  /// @brief The spatial dimensions of a filter kernel.
  Blob<int> kernel_shape_;
  /// @brief The spatial dimensions of the stride.
  Blob<int> stride_;
  /// @brief The spatial dimensions of the padding.
  Blob<int> pad_;
  /// @brief The spatial dimensions of the dilation.
  Blob<int> dilation_;
  /// @brief The spatial dimensions of the convolution input.
  Blob<int> conv_input_shape_;
  /// @brief The spatial dimensions of the col_buffer.