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local THNN = require 'nn.THNN'
local SpatialClassNLLCriterion, parent = torch.class('nn.SpatialClassNLLCriterion', 'nn.Criterion')
function SpatialClassNLLCriterion:__init(weights, sizeAverage)
parent.__init(self)
if sizeAverage ~= nil then
self.sizeAverage = sizeAverage
else
self.sizeAverage = true
end
if weights then
assert(weights:dim() == 1, "weights input should be 1-D Tensor")
self.weights = weights
end
self.output_tensor = torch.zeros(1)
self.total_weight_tensor = torch.ones(1)
self.target = torch.zeros(1):long()
end
function SpatialClassNLLCriterion:__len()
if (self.weights) then
return #self.weights
else
return 0
end
end
function SpatialClassNLLCriterion:updateOutput(input, target)
if type(target) == 'number' then
if torch.typename(input):find('torch%.Cuda.*Tensor') then
self.target = torch.CudaLongTensor and self.target:cudaLong() or self.target:cuda()
else
self.target = self.target:long()
end
self.target[1] = target
elseif torch.typename(input):find('torch%.Cuda.*Tensor') then
self.target = torch.CudaLongTensor and target:cudaLong() or target
else
self.target = target:long()
end
input.THNN.SpatialClassNLLCriterion_updateOutput(
input:cdata(),
self.target:cdata(),
self.output_tensor:cdata(),
self.sizeAverage,
THNN.optionalTensor(self.weights),
self.total_weight_tensor:cdata()
)
self.output = self.output_tensor[1]
return self.output, self.total_weight_tensor[1]
end
function SpatialClassNLLCriterion:updateGradInput(input, target)
if type(target) == 'number' then
if torch.typename(input):find('torch%.Cuda.*Tensor') then
self.target = torch.CudaLongTensor and self.target:cudaLong() or self.target:cuda()
else
self.target = self.target:long()
end
self.target[1] = target
elseif torch.typename(input):find('torch%.Cuda.*Tensor') then
self.target = torch.CudaLongTensor and target:cudaLong() or target
else
self.target = target:long()
end
self.gradInput:resizeAs(input):zero()
input.THNN.SpatialClassNLLCriterion_updateGradInput(
input:cdata(),
self.target:cdata(),
self.gradInput:cdata(),
self.sizeAverage,
THNN.optionalTensor(self.weights),
self.total_weight_tensor:cdata()
)
return self.gradInput
end
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