Python subgradientSSVM Examples

Example #1

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))

Example #2

File: whorse.py Project: chaubold/opengm

    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)

Example #3

    #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]

Example #4

                                            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,

Example #5

File: brown_horse.py Project: chaubold/opengm

    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()

Example #6

File: toy_dataset.py Project: chaubold/opengm

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)

Example #7

File: noisy_squares_3_labels.py Project: chaubold/opengm

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)