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local SpatialGraph, parent = torch.class('nn.SpatialGraph', 'nn.Module')
local help_desc =
[[Creates an edge-weighted graph from a set of N feature
maps.
The input is a 3D tensor width x height x nInputPlane, the
output is a 3D tensor width x height x 2. The first slice
of the output contains horizontal edges, the second vertical
edges.
The input features are assumed to be >= 0.
More precisely:
+ dist == 'euclid' and norm == true: the input features should
also be <= 1, to produce properly normalized distances (btwn 0 and 1);
+ dist == 'cosine': the input features do not need to be bounded,
as the cosine dissimilarity normalizes with respect to each vector.
An epsilon is automatically added, so that components that are == 0
are properly considered as being similar.
]]
function SpatialGraph:__init(...)
parent.__init(self)
xlua.unpack_class(
self, {...},
'nn.SpatialGraph', help_desc,
{arg='dist', type='string', help='distance metric to use', default='euclid'},
{arg='normalize', type='boolean', help='normalize euclidean distances btwn 0 and 1 (assumes input range to be btwn 0 and 1)', default=true},
{arg='connex', type='number', help='connexity', default=4}
)
if self.connex ~= 4 then
xlua.error('4 is the only connexity supported, for now', 'nn.SpatialGraph',self.usage)
end
self.dist = ((self.dist == 'euclid') and 0) or ((self.dist == 'cosine') and 1)
or xerror('euclid is the only distance supported, for now','nn.SpatialGraph',self.usage)
self.normalize = (self.normalize and 1) or 0
if self.dist == 'cosine' and self.normalize == 1 then
xerror('normalized cosine is not supported for now [just because I couldnt figure out the gradient :-)]',
'nn.SpatialGraph', self.usage)
end
end
function SpatialGraph:updateOutput(input)
self.output:resize(self.connex / 2, input:size(2), input:size(3))
input.nn.SpatialGraph_updateOutput(self, input)
return self.output
end
function SpatialGraph:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input)
input.nn.SpatialGraph_updateGradInput(self, input, gradOutput)
return self.gradInput
end
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