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local Narrow, parent = torch.class('nn.Narrow', 'nn.Module')
local help_desc =
[[Selects a subset of a dimension of a nxpxqx.. Tensor.]]
local help_example =
[[mlp=nn.Sequential();
mlp:add(nn.Narrow(1,3,2))
require "lab"
x=lab.randn(10,5)
print(x)
print(mlp:forward(x))
-- gives the output:
0.9720 -0.0836 0.0831 -0.2059 -0.0871
0.8750 -2.0432 -0.1295 -2.3932 0.8168
0.0369 1.1633 0.6483 1.2862 0.6596
0.1667 -0.5704 -0.7303 0.3697 -2.2941
0.4794 2.0636 0.3502 0.3560 -0.5500
-0.1898 -1.1547 0.1145 -1.1399 0.1711
-1.5130 1.4445 0.2356 -0.5393 -0.6222
-0.6587 0.4314 1.1916 -1.4509 1.9400
0.2733 1.0911 0.7667 0.4002 0.1646
0.5804 -0.5333 1.1621 1.5683 -0.1978
[torch.Tensor of dimension 10x5]
0.0369 1.1633 0.6483 1.2862 0.6596
0.1667 -0.5704 -0.7303 0.3697 -2.2941
[torch.Tensor of dimension 2x5] ]]
function Narrow:__init(dimension,offset,length)
parent.__init(self)
self.dimension=dimension
self.index=offset
self.length=length or 1
if not dimension or not offset then
error(xlua.usage('nn.Narrow', help_desc, help_example,
{type='number', help='dimension', req=true},
{type='number', help='offset', req=true},
{type='number', help='length', default=1}))
end
end
function Narrow:forward(input)
local output=input:narrow(self.dimension,self.index,self.length);
self.output:resizeAs(output)
return self.output:copy(output)
end
function Narrow:backward(input, gradOutput)
self.gradInput:resizeAs(input)
self.gradInput:zero();
self.gradInput:narrow(self.dimension,self.index,self.length):copy(gradOutput)
return self.gradInput
end
function Narrow:write(file)
parent.write(self, file)
file:writeInt(self.dimension)
file:writeLong(self.index)
file:writeLong(self.length)
end
function Narrow:read(file, version)
parent.read(self, file)
self.dimension = file:readInt()
self.index = file:readLong()
self.length = file:readLong()
end
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