def makeGt(shape):
gt=numpy.ones(shape,dtype='uint8')
gt[0:shape[0]/2,:] = 0
return gt
import opengm
import opengm.learning as learning
from opengm import numpy
# weight vector
nWeights = 100
weightVals = numpy.ones(nWeights) * 0.5
weights = opengm.learning.Weights(weightVals)
dataset = learning.createDataset(loss='h')
print "type of dataset", dataset
# for grid search learner
lowerBounds = numpy.zeros(nWeights)
upperBounds = numpy.ones(nWeights)
nTestPoints = numpy.ones(nWeights).astype('uint64') * 10
learner = learning.gridSearchLearner(dataset=dataset,
lowerBounds=lowerBounds,
upperBounds=upperBounds,
nTestPoints=nTestPoints)
learner.learn(infCls=opengm.inference.BeliefPropagation,
parameter=opengm.InfParam(damping=0.5))
# for struct max margin learner
smm_learnerParam = learning.StructMaxMargin_Bundle_HammingLossParameter(
1.0, 0.01, 0)
smm_learner = learning.StructMaxMargin_Bundle_HammingLoss(
dataset, smm_learnerParam)
feat.append(vigra.filters.gaussianSmoothing(randGt.astype('float32'),sigma) )
featB = []
for sigma in [1.0, 1.5]:
featB.append(vigra.filters.gaussianGradientMagnitude(randGt.astype('float32'),sigma) )
a = numpy.rollaxis(numpy.array(feat), axis=0, start=3)
b = numpy.rollaxis(numpy.array(featB), axis=0, start=3)
return a,b
for mi in range(nModels):
gm = opengm.gm(numpy.ones(numVar)*nLables)
gt = makeGt(shape)
gtFlat = gt.reshape([-1])
unaries,binaries = makeFeatures(gt)
# print unaries, binaries
for x in range(shape[0]):
for y in range(shape[1]):
uFeat = unaries[x,y,:].astype("float64")
uFeat = numpy.repeat(uFeat[:,numpy.newaxis],2,axis=1).T
uFeat[1,:]=1
lu = opengm.LUnaryFunction(weights=weights,numberOfLabels=nLables, features=uFeat, weightIds=uWeightIds)
def makeGt(shape):
gt = numpy.ones(shape, dtype='uint8')
gt[0:shape[0] / 2, :] = 0
return gt
sSmooth = [1.0, 1.2, 1.5, 2.0, 3.0, 4.0]
sGrad = [1.0, 1.5, 2.0, 4.0]
nUWeights = len(sSmooth) + 1
nBWeights = len(sGrad) + 1
nWeights = nUWeights + nBWeights
def makeGt(shape):
gt = numpy.ones(shape, dtype='uint8')
gt[0:shape[0] / 2, :] = 0
return gt
weightVals = numpy.ones(nWeights)
weights = opengm.learning.Weights(weightVals)
uWeightIds = numpy.arange(nUWeights, dtype='uint64')
bWeightIds = numpy.arange(start=nUWeights, stop=nWeights, dtype='uint64')
dataset = learning.createDataset(numWeights=nWeights)
weights = dataset.getWeights()
def makeFeatures(gt):
random = (numpy.random.rand(*gt.shape) - 0.5) * 5.0
randGt = random + gt
# vigra.imshow(randGt)
# plt.colorbar()
# create a simple model with exactly one variable with two labels
numWeights = 4
nLabels = 2
nVars = 1
# set weight ids and features for all labels
weightIds = numpy.array([[0, 1], [2, 3]])
features = numpy.array([[0.5, -0.25], [-0.5, -1.25]])
# create dataset with 2 weights and get the 2 weights
dataset = learning.createDataset(numWeights)
weights = dataset.getWeights()
# set up graphical model
gm = opengm.gm(numpy.ones(nVars) * nLabels)
fid = gm.addFunction(learning.lUnaryFunction(weights, 2, features, weightIds))
gm.addFactor(fid, [0])
# add graphical model to dataset with ground truth
ground_truth = numpy.array([0]).astype(opengm.label_type)
dataset.pushBackInstance(gm, ground_truth)
# set up learner and run
#learner = learning.structMaxMarginLearner(dataset, 0.1, 0.001, 0)
#learner = learning.subgradientSSVM(dataset, learningRate=1.0, C=100, learningMode='batch')
#learner.learn(infCls=opengm.inference.TrwsExternal, parameter=opengm.InfParam())
learner = learning.maxLikelihoodLearner(
dataset,
maximumNumberOfIterations=1500,
import opengm
import opengm.learning as learning
from opengm import numpy
# weight vector
nWeights = 100
weightVals = numpy.ones(nWeights)*0.5
weights = opengm.learning.Weights(weightVals)
dataset =learning.createDataset(loss='h')
print "type of dataset", dataset
# for grid search learner
lowerBounds = numpy.zeros(nWeights)
upperBounds = numpy.ones(nWeights)
nTestPoints =numpy.ones(nWeights).astype('uint64')*10
learner = learning.gridSearchLearner(dataset=dataset,lowerBounds=lowerBounds, upperBounds=upperBounds,nTestPoints=nTestPoints)
learner.learn(infCls=opengm.inference.BeliefPropagation,
parameter=opengm.InfParam(damping=0.5))
# for struct max margin learner
smm_learnerParam = learning.StructMaxMargin_Bundle_HammingLossParameter(1.0, 0.01, 0)