module = nn.SpatialSubSampling(nInputPlane, kW, kH, [dW], [dH])

Applies a 2D sub-sampling over an input image composed of several input planes. The input tensor in forward(input) is expected to be a 3D tensor (width x height x nInputPlane). The number of output planes will be the same as nInputPlane.

The parameters are the following:

nInputPlane
The number of expected input planes in the image given into forward().
kW
The kernel width of the sub-sampling
kH
The kernel height of the sub-sampling
dW
The step of the sub-sampling in the width dimension. Default is 1.
dH
The step of the sub-sampling in the height dimension. Default is 1.

Note that depending of the size of your kernel, several (of the last) columns or rows of the input image might be lost. It is up to the user to add proper padding in images.

If the input image is a 3D tensor width x height x nInputPlane, the output image size will be owidth x oheight x nInputPlane where 

owidth  = (width  - kW) / dW + 1
oheight = (height - kH) / dH + 1 .

The parameters of the sub-sampling can be found in self.weight (Tensor of size nInputPlane) and self.bias (Tensor of size nInputPlane). The corresponding gradients can be found in self.gradWeight and self.gradBias.

The output value of the layer can be precisely described as: 

output[i][j][k] = bias[k]
  + weight[k] sum_{s=1}^kW sum_{t=1}^kH input[dW*(i-1)+s)][dH*(j-1)+t][k]