def draw_gm(gm, layout='sfdp', size=1.5): import opengm as ogm import networkx from networkx.drawing.nx_agraph import graphviz_layout networkx.graphviz_layout = graphviz_layout ogm.visualizeGm(gm, layout=layout, relNodeSize=size)
def viz_factor_graph(gm):
"""
ut.qtensure()
gm = build_factor_graph(G, nodes, edges , n_annots, n_names, lookup_annot_idx,
use_unaries=True, edge_probs=None, operator='multiplier')
"""
ut.qtensure()
import networkx
from networkx.drawing.nx_agraph import graphviz_layout
networkx.graphviz_layout = graphviz_layout
opengm.visualizeGm(gm, show=False, layout="neato", plotUnaries=True,
iterations=1000, plotFunctions=True,
plotNonShared=False, relNodeSize=1.0)
_ = pt.show_nx(gm.G) # NOQA
f1=numpy.ones([2])
f2=numpy.ones([2,2])
"""
Grid:
- 4x4=16 variables
- second order factors in 4-neigbourhood
all connected to the same function
- higher order functions are shared
"""
size=3
gm=opengm.gm([2]*size*size)
fid=gm.addFunction(f2)
for y in range(size):
for x in range(size):
gm.addFactor(gm.addFunction(f1),x*size+y)
if(x+1<size):
gm.addFactor(fid,[x*size+y,(x+1)*size+y])
if(y+1<size):
gm.addFactor(fid,[x*size+y,x*size+(y+1)])
opengm.visualizeGm( gm,layout='spring',iterations=3000,
show=False,plotFunctions=True,
plotNonShared=True,relNodeSize=0.4)
plt.savefig("grid.png",bbox_inches='tight',dpi=300)
plt.close()
f1=numpy.ones([2])
f2=numpy.ones([2,2])
"""
Grid:
- 4x4=16 variables
- second order factors in 4-neigbourhood
all connected to the same function
- higher order functions are shared
"""
size=3
gm=opengm.gm([2]*size*size)
fid=gm.addFunction(f2)
for y in range(size):
for x in range(size):
gm.addFactor(gm.addFunction(f1),x*size+y)
if(x+1<size):
gm.addFactor(fid,[x*size+y,(x+1)*size+y])
if(y+1<size):
gm.addFactor(fid,[x*size+y,x*size+(y+1)])
opengm.visualizeGm( gm,layout='spring',iterations=3000,
show=True,plotFunctions=True,
plotNonShared=True,relNodeSize=0.4)
plt.show
plt.savefig("grid.png",bbox_inches='tight',dpi=300)
plt.close()
import numpy
import opengm
import matplotlib.pyplot as plt
f1 = numpy.ones([2])
f2 = numpy.ones([2, 2])
f3 = numpy.ones([2, 2, 2])
"""
Triangle (non-shared) :
- 3 variables
- 3 unaries
- 2 second order functions
- 1 third order factor
- functions are *non* - shared
"""
gm = opengm.gm([2, 2, 2])
gm.addFactor(gm.addFunction(f1), [0])
gm.addFactor(gm.addFunction(f1), [1])
gm.addFactor(gm.addFunction(f1), [2])
gm.addFactor(gm.addFunction(f2), [0, 1])
gm.addFactor(gm.addFunction(f2), [1, 2])
gm.addFactor(gm.addFunction(f2), [0, 2])
gm.addFactor(gm.addFunction(f3), [0, 1, 2])
opengm.visualizeGm(gm, show=False, plotFunctions=True, plotNonShared=True)
plt.savefig("triangle.png", bbox_inches='tight', dpi=300)
plt.close()
n_pixel_labels = 2
n_segment_labels = 2
pixels = np.random.random((shape + (n_pixel_labels, )))
segment_map = np.arange(n_pixels).reshape(shape)
segment_values = np.random.random(
(np.max(segment_map) + 1, n_segment_labels))
t0 = time.time()
gm = pixel_lattice_graph(
pixels, opengm.pottsFunction([n_pixel_labels, n_pixel_labels], 0.0,
0.5))
t1 = time.time()
opengm.visualizeGm(gm)
print "graph build in", t1 - t0, "seconds"
# test inference is possible
inference = opengm.inference.GraphCut(gm=gm)
t0 = time.time()
inference.infer()
t1 = time.time()
print "inference completed in", t1 - t0, "seconds"
t0 = time.time()
gm = segment_adjacency_graph(segment_values,
segment_map,
segment_regularizer=opengm.pottsFunction(
[n_segment_labels, n_segment_labels], 0.0,
def dummy_cut_example():
r"""
CommandLine:
python -m ibeis.workflow --exec-dummy_cut_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.workflow import * # NOQA
>>> result = dummy_cut_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import opengm
import numpy as np
import plottool as pt
pt.ensure_pylab_qt4()
# Matching Graph
cost_matrix = np.array([
[0.5, 0.6, 0.2, 0.4],
[0.0, 0.5, 0.2, 0.9],
[0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.0, 0.5],
])
cost_matrix += cost_matrix.T
number_of_labels = 4
num_annots = 4
#cost_matrix = (cost_matrix * 2) - 1
#gm = opengm.gm(number_of_labels)
gm = opengm.gm(np.ones(num_annots) * number_of_labels)
aids = np.arange(num_annots)
aid_pairs = np.array([(a1, a2) for a1, a2 in ut.iprod(
aids, aids) if a1 != a2], dtype=np.uint32)
aid_pairs.sort(axis=1)
# add a potts function
# penalizes neighbors for having different labels
# beta = 0 # 0.1 # strength of potts regularizer
#beta = 0.1 # 0.1 # strength of potts regularizer
# Places to look for the definition of this stupid class
# ~/code/opengm/src/interfaces/python/opengm/opengmcore/pyFunctionTypes.cxx
# /src/interfaces/python/opengm/opengmcore/function_injector.py
#shape = [number_of_labels] * 2
#regularizer = opengm.PottsGFunction(shape, 0.0, beta)
# __init__( (object)arg1, (object)shape [, (object)values=()]) -> object :
# values = np.arange(1, ut.num_partitions(num_annots) + 1)
#regularizer = opengm.PottsGFunction(shape)
#reg_fid = gm.addFunction(regularizer)
# A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
# http://arxiv.org/pdf/1404.0533.pdf
# regularizer1 = opengm.pottsFunction([number_of_labels] * 2, valueEqual=0.0, valueNotEqual=beta)
# gm.addFactors(reg_fid, aid_pairs)
# 2nd order function
pair_fid = gm.addFunction(cost_matrix)
gm.addFactors(pair_fid, aid_pairs)
if False:
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
else:
Inf = opengm.inference.Multicut
parameter = opengm.InfParam()
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self,):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
print(callback.labels)
print(cost_matrix)
pt.imshow(cost_matrix, cmap='magma')
opengm.visualizeGm(gm=gm)
pass
def dummy_multicut():
""" """
# Places to look for the definition of PottsGFunction class
# ~/code/opengm/src/interfaces/python/opengm/opengmcore/pyFunctionTypes.cxx
# /src/interfaces/python/opengm/opengmcore/function_injector.py
# A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
# http://arxiv.org/pdf/1404.0533.pdf
# __init__( (object)arg1, (object)shape [, (object)values=()]) -> object :
# values = np.arange(1, ut.num_partitions(num_annots) + 1)
# http://hci.iwr.uni-heidelberg.de/opengm2/doxygen/opengm-2.1.1/classopengm_1_1PottsGFunction.html
import opengm
import numpy as np
from itertools import product
cost_matrix = np.array([
[ 1. , 0.2, -0.6, -0.2],
[ 0.2, 1. , -0.6, 0.8],
[-0.6, -0.6, 1. , -0.8],
[-0.2, 0.8, -0.8, 1. ]])
num_vars = len(cost_matrix)
# Enumerate undirected edges (node index pairs)
var_indices = np.arange(num_vars)
varindex_pairs = np.array(
[(a1, a2) for a1, a2 in product(var_indices, var_indices)
if a1 != a2 and a1 > a2], dtype=np.uint32)
varindex_pairs.sort(axis=1)
# Create nodes in the graphical model. In this case there are <num_vars>
# nodes and each node can be assigned to one of <num_vars> possible labels
num_nodes = num_vars
space = np.full((num_nodes,), fill_value=num_vars, dtype=np.int)
gm = opengm.gm(space)
# Use one potts function for each edge
for varx1, varx2 in varindex_pairs:
cost = cost_matrix[varx1, varx2]
potts_func = opengm.PottsFunction((num_vars, num_vars), valueEqual=0, valueNotEqual=cost)
potts_func_id = gm.addFunction(potts_func)
var_indicies = np.array([varx1, varx2])
gm.addFactor(potts_func_id, var_indicies)
#opengm.visualizeGm(gm=gm)
InfAlgo = opengm.inference.Multicut
parameter = opengm.InfParam()
inf = InfAlgo(gm, parameter=parameter)
inf.infer()
labels = inf.arg()
print(labels)
import plottool as pt
#varindex_pairs = np.vstack(np.triu_indices_from(cost_matrix)).T
# Dummy unaries
#for varx in var_indices:
# unary_func = np.ones(num_vars)
# unary_func_id = gm.addFunction(unary_func)
# gm.addFactor(unary_func_id, varx1)
#pt.ensure_pylab_qt4()
# add a potts function
#shape = [num_vars] * 2
# num_parts = 5 # possible number paritions with 4 variables
# num_parts = ut.get_nth_bell_number(num_vars - 1)
# Causes a segfault if values is passed in
# values = np.arange(1, num_parts + 1).astype(np.float64)
# gpotts_func = opengm.PottsGFunction(shape, values)
#gpotts_func = opengm.PottsGFunction(shape)
#gpotts_fid = gm.addFunction(gpotts_func)
# Commenting out the next line results in a segfault
#gm.addFactors(gpotts_fid, varindex_pairs)
# 2nd order function
# Seems to cause OpenGM error: Invalid Model for Multicut-Solver! Solver requires a generalized potts model!
# pair_fid = gm.addFunction(cost_matrix)
# gm.addFactors(pair_fid, varindex_pairs)
InfAlgo = opengm.inference.Multicut
# Not sure what parameters are allowed to be passed here.
parameter = opengm.InfParam()
inf = InfAlgo(gm, parameter=parameter)
inf.infer()
class PyCallback(object):
def __init__(self,):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
print(callback.labels)
print(cost_matrix)
pt.imshow(cost_matrix, cmap='magma')
opengm.visualizeGm(gm=gm)
import numpy
import opengm
import matplotlib.pyplot as plt
f1=numpy.ones([2])
f2=numpy.ones([2,2])
f3=numpy.ones([2,2,2])
"""
Triangle (non-shared) :
- 3 variables
- 3 unaries
- 2 second order functions
- 1 third order factor
- functions are *non* - shared
"""
gm=opengm.gm([2,2,2])
gm.addFactor(gm.addFunction(f1),[0])
gm.addFactor(gm.addFunction(f1),[1])
gm.addFactor(gm.addFunction(f1),[2])
gm.addFactor(gm.addFunction(f2),[0,1])
gm.addFactor(gm.addFunction(f2),[1,2])
gm.addFactor(gm.addFunction(f2),[0,2])
gm.addFactor(gm.addFunction(f3),[0,1,2])
opengm.visualizeGm( gm,show=False,plotFunctions=True,
plotNonShared=True)
plt.savefig("triangle.png",bbox_inches='tight',
dpi=300)
plt.close()
import opengm
import matplotlib.pyplot as plt
f1=numpy.ones([2])
f2=numpy.ones([2,2])
#Chain (non-shared functions):
numVar=5
gm=opengm.gm([2]*numVar)
for vi in xrange(numVar):
gm.addFactor(gm.addFunction(f1),vi)
if(vi+1<numVar):
gm.addFactor(gm.addFunction(f2),[vi,vi+1])
# visualize gm
opengm.visualizeGm( gm,show=False,layout='spring',plotFunctions=True,
plotNonShared=True,relNodeSize=0.4)
plt.savefig("chain_non_shared.png",bbox_inches='tight',dpi=300)
plt.close()
#Chain (shared high order functions):
numVar=5
gm=opengm.gm([2]*numVar)
fid2=gm.addFunction(f2)
for vi in xrange(numVar):
gm.addFactor(gm.addFunction(f1),vi)
if(vi+1<numVar):
gm.addFactor(fid2,[vi,vi+1])
# visualize gm
opengm.visualizeGm( gm,show=False,layout='spring',plotFunctions=True,
plotNonShared=True,relNodeSize=0.4)
f1=numpy.ones([2])
f2=numpy.ones([2,2])
"""
Full Connected (non-shared):
- all possible pairwise connections
- functions are *non* - shared
"""
numVar=4
gm=opengm.gm([2]*numVar)
for vi0 in xrange(numVar):
for vi1 in xrange(vi0+1,numVar):
gm.addFactor(gm.addFunction(f2),[vi0,vi1])
opengm.visualizeGm( gm,show=False,layout='neato',
iterations=1000,plotFunctions=True,
plotNonShared=True,relNodeSize=0.4)
plt.savefig("full_non_shared.png",bbox_inches='tight',dpi=300)
plt.close()
"""
Full Connected (shared):
- 5 variables
- 10 second order factors
(all possible pairwise connections)
- functions are *non* - shared
"""
numVar=4
gm=opengm.gm([2]*numVar)
fid2=gm.addFunction(f2)
def dummy_cut_example():
r"""
CommandLine:
python -m ibeis.workflow --exec-dummy_cut_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.workflow import * # NOQA
>>> result = dummy_cut_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import opengm
import numpy as np
import plottool as pt
pt.ensure_pylab_qt4()
# Matching Graph
cost_matrix = np.array([
[0.5, 0.6, 0.2, 0.4],
[0.0, 0.5, 0.2, 0.9],
[0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.0, 0.5],
])
cost_matrix += cost_matrix.T
number_of_labels = 4
num_annots = 4
#cost_matrix = (cost_matrix * 2) - 1
#gm = opengm.gm(number_of_labels)
gm = opengm.gm(np.ones(num_annots) * number_of_labels)
aids = np.arange(num_annots)
aid_pairs = np.array([(a1, a2)
for a1, a2 in ut.iprod(aids, aids) if a1 != a2],
dtype=np.uint32)
aid_pairs.sort(axis=1)
# add a potts function
# penalizes neighbors for having different labels
# beta = 0 # 0.1 # strength of potts regularizer
#beta = 0.1 # 0.1 # strength of potts regularizer
# Places to look for the definition of this stupid class
# ~/code/opengm/src/interfaces/python/opengm/opengmcore/pyFunctionTypes.cxx
# /src/interfaces/python/opengm/opengmcore/function_injector.py
#shape = [number_of_labels] * 2
#regularizer = opengm.PottsGFunction(shape, 0.0, beta)
# __init__( (object)arg1, (object)shape [, (object)values=()]) -> object :
# values = np.arange(1, ut.num_partitions(num_annots) + 1)
#regularizer = opengm.PottsGFunction(shape)
#reg_fid = gm.addFunction(regularizer)
# A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
# http://arxiv.org/pdf/1404.0533.pdf
# regularizer1 = opengm.pottsFunction([number_of_labels] * 2, valueEqual=0.0, valueNotEqual=beta)
# gm.addFactors(reg_fid, aid_pairs)
# 2nd order function
pair_fid = gm.addFunction(cost_matrix)
gm.addFactors(pair_fid, aid_pairs)
if False:
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10,
damping=0.5,
convergenceBound=0.001)
else:
Inf = opengm.inference.Multicut
parameter = opengm.InfParam()
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self, ):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector), ))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
print(callback.labels)
print(cost_matrix)
pt.imshow(cost_matrix, cmap='magma')
opengm.visualizeGm(gm=gm)
pass
def dummy_multicut():
""" """
# Places to look for the definition of PottsGFunction class
# ~/code/opengm/src/interfaces/python/opengm/opengmcore/pyFunctionTypes.cxx
# /src/interfaces/python/opengm/opengmcore/function_injector.py
# A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
# http://arxiv.org/pdf/1404.0533.pdf
# __init__( (object)arg1, (object)shape [, (object)values=()]) -> object :
# values = np.arange(1, ut.num_partitions(num_annots) + 1)
# http://hci.iwr.uni-heidelberg.de/opengm2/doxygen/opengm-2.1.1/classopengm_1_1PottsGFunction.html
import opengm
import numpy as np
from itertools import product
cost_matrix = np.array([[1., 0.2, -0.6, -0.2], [0.2, 1., -0.6, 0.8],
[-0.6, -0.6, 1., -0.8], [-0.2, 0.8, -0.8, 1.]])
num_vars = len(cost_matrix)
# Enumerate undirected edges (node index pairs)
var_indices = np.arange(num_vars)
varindex_pairs = np.array([(a1, a2)
for a1, a2 in product(var_indices, var_indices)
if a1 != a2 and a1 > a2],
dtype=np.uint32)
varindex_pairs.sort(axis=1)
# Create nodes in the graphical model. In this case there are <num_vars>
# nodes and each node can be assigned to one of <num_vars> possible labels
num_nodes = num_vars
space = np.full((num_nodes, ), fill_value=num_vars, dtype=np.int)
gm = opengm.gm(space)
# Use one potts function for each edge
for varx1, varx2 in varindex_pairs:
cost = cost_matrix[varx1, varx2]
potts_func = opengm.PottsFunction((num_vars, num_vars),
valueEqual=0,
valueNotEqual=cost)
potts_func_id = gm.addFunction(potts_func)
var_indicies = np.array([varx1, varx2])
gm.addFactor(potts_func_id, var_indicies)
#opengm.visualizeGm(gm=gm)
InfAlgo = opengm.inference.Multicut
parameter = opengm.InfParam()
inf = InfAlgo(gm, parameter=parameter)
inf.infer()
labels = inf.arg()
print(labels)
import plottool as pt
#varindex_pairs = np.vstack(np.triu_indices_from(cost_matrix)).T
# Dummy unaries
#for varx in var_indices:
# unary_func = np.ones(num_vars)
# unary_func_id = gm.addFunction(unary_func)
# gm.addFactor(unary_func_id, varx1)
#pt.ensure_pylab_qt4()
# add a potts function
#shape = [num_vars] * 2
# num_parts = 5 # possible number paritions with 4 variables
# num_parts = ut.get_nth_bell_number(num_vars - 1)
# Causes a segfault if values is passed in
# values = np.arange(1, num_parts + 1).astype(np.float64)
# gpotts_func = opengm.PottsGFunction(shape, values)
#gpotts_func = opengm.PottsGFunction(shape)
#gpotts_fid = gm.addFunction(gpotts_func)
# Commenting out the next line results in a segfault
#gm.addFactors(gpotts_fid, varindex_pairs)
# 2nd order function
# Seems to cause OpenGM error: Invalid Model for Multicut-Solver! Solver requires a generalized potts model!
# pair_fid = gm.addFunction(cost_matrix)
# gm.addFactors(pair_fid, varindex_pairs)
InfAlgo = opengm.inference.Multicut
# Not sure what parameters are allowed to be passed here.
parameter = opengm.InfParam()
inf = InfAlgo(gm, parameter=parameter)
inf.infer()
class PyCallback(object):
def __init__(self, ):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector), ))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
print(callback.labels)
print(cost_matrix)
pt.imshow(cost_matrix, cmap='magma')
opengm.visualizeGm(gm=gm)
