message ConvolutionParameter {
 optional uint32 num_output = 1; // The number of outputs for the layer
 optional bool bias_term = 2 [default = true]; // whether to have bias terms

 // Pad, kernel size, and stride are all given as a single value for equal
 // dimensions in all spatial dimensions, or once per spatial dimension.
 repeated uint32 pad = 3; // The padding size; defaults to 0
 repeated uint32 kernel_size = 4; // The kernel size
 repeated uint32 stride = 6; // The stride; defaults to 1
 // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
 // holes. (Kernel dilation is sometimes referred to by its use in the
 // algorithme à trous from Holschneider et al. 1987.)
 repeated uint32 dilation = 18; // The dilation; defaults to 1

 // For 2D convolution only, the *_h and *_w versions may also be used to
 // specify both spatial dimensions.
 optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
 optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
 optional uint32 kernel_h = 11; // The kernel height (2D only)
 optional uint32 kernel_w = 12; // The kernel width (2D only)
 optional uint32 stride_h = 13; // The stride height (2D only)
 optional uint32 stride_w = 14; // The stride width (2D only)

 optional uint32 group = 5 [default = 1]; // The group size for group conv

 optional FillerParameter weight_filler = 7; // The filler for the weight
 optional FillerParameter bias_filler = 8; // The filler for the bias
 enum Engine {
 DEFAULT = 0;
 CAFFE = 1;
 CUDNN = 2;
 }
 optional Engine engine = 15 [default = DEFAULT];
 // The axis to interpret as "channels" when performing convolution.
 // Preceding dimensions are treated as independent inputs;
 // succeeding dimensions are treated as "spatial".
 // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
 // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
 // groups g>1) filters across the spatial axes (H, W) of the input.
 // With (N, C, D, H, W) inputs, and axis == 1, we perform
 // N independent 3D convolutions, sliding (C/g)-channels
 // filters across the spatial axes (D, H, W) of the input.
 optional int32 axis = 16 [default = 1];
 // Whether to force use of the general ND convolution, even if a specific
 // implementation for blobs of the appropriate number of spatial dimensions
 // is available. (Currently, there is only a 2D-specific convolution
 // implementation; for input blobs with num_axes != 2, this option is
 // ignored and the ND implementation will be used.)
 optional bool force_nd_im2col = 17 [default = false];
}