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local SpatialReplicationPadding, parent =
torch.class('nn.SpatialReplicationPadding', 'nn.Module')
function SpatialReplicationPadding:__init(pad_l, pad_r, pad_t, pad_b)
parent.__init(self)
self.pad_l = pad_l
self.pad_r = pad_r or self.pad_l
self.pad_t = pad_t or self.pad_l
self.pad_b = pad_b or self.pad_l
end
function SpatialReplicationPadding:updateOutput(input)
if input:dim() == 3 or input:dim() == 4 then
input.THNN.SpatialReplicationPadding_updateOutput(
input:cdata(), self.output:cdata(),
self.pad_l, self.pad_r, self.pad_t, self.pad_b)
else
error('input must be 3 or 4-dimensional')
end
return self.output
end
function SpatialReplicationPadding:updateGradInput(input, gradOutput)
if input:dim() == 3 and gradOutput:dim() == 3 then
assert(input:size(1) == gradOutput:size(1)
and input:size(2) + self.pad_t + self.pad_b == gradOutput:size(2)
and input:size(3) + self.pad_l + self.pad_r == gradOutput:size(3),
'input and gradOutput must be compatible in size')
elseif input:dim() == 4 and gradOutput:dim() == 4 then
assert(input:size(1) == gradOutput:size(1)
and input:size(2) == gradOutput:size(2)
and input:size(3) + self.pad_t + self.pad_b == gradOutput:size(3)
and input:size(4) + self.pad_l + self.pad_r == gradOutput:size(4),
'input and gradOutput must be compatible in size')
else
error(
[[input and gradOutput must be 3 or 4-dimensional
and have equal number of dimensions]]
)
end
input.THNN.SpatialReplicationPadding_updateGradInput(
input:cdata(), gradOutput:cdata(), self.gradInput:cdata(),
self.pad_l, self.pad_r, self.pad_t, self.pad_b)
return self.gradInput
end
function SpatialReplicationPadding:__tostring__()
return torch.type(self) ..
string.format('(l=%d, r=%d, t=%d, b=%d)', self.pad_l, self.pad_r,
self.pad_t, self.pad_b)
end
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