infCls = opengm.inference.TrwsExternal param = opengm.InfParam() if False: print "construct learner" learner = learning.maxLikelihoodLearner(dataset) print "start to learn" learner.learn() print "exit" else: learner = learning.subgradientSSVM(dataset, learningRate=0.5, C=100, learningMode='batch', maxIterations=500, averaging=-1, nConf=0) learner.learn(infCls=infCls, parameter=param, connectedComponents=True, infMode='n') # predict on test test for (rgbImg, gtImg, gm) in test_set: # infer for test image inf = opengm.inference.TrwsExternal(gm) inf.infer() arg = inf.arg() arg = arg.reshape(numpy.squeeze(gtImg.shape))
vigra.colors.transform_RGB2Lab, vigra.colors.transform_RGB2Luv, partial(vigra.filters.gaussianGradientMagnitude, sigma=1.0), partial(vigra.filters.gaussianGradientMagnitude, sigma=2.0), ] dataset, test_set = secondOrderImageDataset(imgs=imgs, gts=gt, numberOfLabels=2, fUnary=fUnary, fBinary=fBinary, addConstFeature=False) learner = learning.subgradientSSVM(dataset, learningRate=0.1, C=100, learningMode='batch', maxIterations=10) learner.learn(infCls=opengm.inference.QpboExternal, parameter=opengm.InfParam()) # predict on test test for (rgbImg, gtImg, gm) in test_set: # infer for test image inf = opengm.inference.QpboExternal(gm) inf.infer() arg = inf.arg() arg = arg.reshape(numpy.squeeze(gtImg.shape)) vigra.segShow(rgbImg, arg + 2)
#partial(labStructTensorEv, sigma=1.0), #partial(labStructTensorEv, sigma=2.0), partial(gmag, sigma=1.0), partial(gmag, sigma=2.0), ] dataset,test_set = superpixelDataset(imgs=imgs,sps=sps, gts=gts, numberOfLabels=2, fUnary=fUnary, fBinary=fBinary, addConstFeature=True) learner = learning.subgradientSSVM(dataset, learningRate=0.1, C=0.1, learningMode='batch',maxIterations=2000, averaging=-1) #learner = learning.structMaxMarginLearner(dataset, 0.1, 0.001, 0) learner.learn(infCls=opengm.inference.QpboExternal, parameter=opengm.InfParam()) w = dataset.getWeights() for wi in range(len(w)): print "wi ",w[wi]
numberOfLabels=3, fUnary=fUnary, fBinary=fBinary, addConstFeature=True) learningModi = [ 'normal', 'reducedinference', 'selfFusion', 'reducedinferenceSelfFusion' ] lm = 0 infCls = opengm.inference.TrwsExternal param = opengm.InfParam() learner = learning.subgradientSSVM(dataset, learningRate=1.0, C=0.9, learningMode='batch', maxIterations=5, averaging=-1) learner.learn(infCls=infCls, parameter=param, connectedComponents=False, infMode='n') learner = learning.rws(dataset, learningRate=1.0, C=1.0, maxIterations=5000, p=100, sigma=1.3) learner.learn(infCls=infCls, parameter=param,
partial(labStructTensorEv, sigma=1.0), partial(labStructTensorEv, sigma=2.0), partial(vigra.filters.gaussianGradientMagnitude, sigma=1.0), partial(vigra.filters.gaussianGradientMagnitude, sigma=2.0), ] dataset,test_set = secondOrderImageDataset(imgs=imgs, gts=gts, numberOfLabels=2, fUnary=fUnary, fBinary=fBinary, addConstFeature=False) learner = learning.subgradientSSVM(dataset, learningRate=0.05, C=100, learningMode='batch',maxIterations=1000) #learner = learning.structMaxMarginLearner(dataset, 0.1, 0.001, 0) learner.learn(infCls=opengm.inference.LazyFlipper, parameter=opengm.InfParam(maxSubgraphSize=3)) # predict on test test for (rgbImg, gtImg, gm) in test_set : # infer for test image inf = opengm.inference.QpboExternal(gm) inf.infer()
learningModi = ['normal','reducedinference','selfFusion','reducedinferenceSelfFusion'] lm = 0 infCls = opengm.inference.TrwsExternal param = opengm.InfParam() if False: print "construct learner" learner = learning.maxLikelihoodLearner(dataset) print "start to learn" learner.learn() print "exit" else: learner = learning.subgradientSSVM(dataset, learningRate=0.5, C=100, learningMode='batch',maxIterations=500,averaging=-1,nConf=0) learner.learn(infCls=infCls,parameter=param,connectedComponents=True,infMode='n') # predict on test test for (rgbImg, gtImg, gm) in test_set : # infer for test image inf = opengm.inference.TrwsExternal(gm) inf.infer() arg = inf.arg() arg = arg.reshape( numpy.squeeze(gtImg.shape)) vigra.imshow(rgbImg) vigra.show() vigra.imshow(arg+2)
learningModi = ['normal','reducedinference','selfFusion','reducedinferenceSelfFusion'] lm = 0 infCls = opengm.inference.TrwsExternal param = opengm.InfParam() learner = learning.subgradientSSVM(dataset, learningRate=1.0, C=0.9, learningMode='batch',maxIterations=5, averaging=-1) learner.learn(infCls=infCls,parameter=param,connectedComponents=False,infMode='n') learner = learning.rws(dataset, learningRate=1.0, C=1.0,maxIterations=5000, p=100, sigma=1.3) learner.learn(infCls=infCls,parameter=param,connectedComponents=False,infMode='n') # predict on test test for (rgbImg, gtImg, gm) in test_set : # infer for test image inf = opengm.inference.TrwsExternal(gm) inf.infer() arg = inf.arg() arg = arg.reshape( numpy.squeeze(gtImg.shape)) vigra.imshow(rgbImg)