classdef SegmentationAccuracy < dagnn.Loss

properties (Transient)

pixelAccuracy = 0

meanAccuracy = 0

meanIntersectionUnion = 0

confusion = 0

nClass = 59;

end

methods

function outputs = forward(obj, inputs, params)

predictions = gather(inputs{1}) ;

labels = gather(inputs{2}) ;

[~,predictions] = max(predictions, [], 3) ;

% compute statistics only on accumulated pixels

ok = labels > 0 ;

numPixels = sum(ok(:)) ;

obj.confusion = obj.confusion + ...

accumarray([labels(ok),predictions(ok)],1,[obj.nClass obj.nClass]) ;

% compute various statistics of the confusion matrix

pos = sum(obj.confusion,2) ;

res = sum(obj.confusion,1)' ;

tp = diag(obj.confusion) ;

obj.pixelAccuracy = sum(tp) / max(1,sum(obj.confusion(:))) ;

obj.meanAccuracy = mean(tp ./ max(1, pos)) ;

obj.meanIntersectionUnion = mean(tp ./ max(1, pos + res - tp)) ;

obj.average = [obj.pixelAccuracy ; obj.meanAccuracy ; obj.meanIntersectionUnion] ;

obj.numAveraged = obj.numAveraged + numPixels ;

outputs{1} = obj.average ;

end

function [derInputs, derParams] = backward(obj, inputs, params, derOutputs)

derInputs{1} = [] ;

derInputs{2} = [] ;

derParams = {} ;

end

function reset(obj)

obj.confusion = 0 ;

obj.pixelAccuracy = 0 ;

obj.meanAccuracy = 0 ;

obj.meanIntersectionUnion = 0 ;

obj.average = [0;0;0] ;

obj.numAveraged = 0 ;

end

function str = toString(obj)

str = sprintf('acc:%.2f, mAcc:%.2f, mIU:%.2f', ...

obj.pixelAccuracy, obj.meanAccuracy, obj.meanIntersectionUnion) ;

end

function obj = SegmentationAccuracy(varargin)

obj.load(varargin) ;

end

end