def BuildHGM(cf):
"""Worker for running Hierarchical Geodesic Model (HGM)
n for group geodesic estimation on a subset of individuals.
Runs HGM on this subset sequentially. The variations retuned
are summed up to get update for all individuals"""
size = Compute.GetMPIInfo()['size']
rank = Compute.GetMPIInfo()['rank']
name = Compute.GetMPIInfo()['name']
localRank = Compute.GetMPIInfo()['local_rank']
nodename = socket.gethostname()
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# just one reporter process on each node
isReporter = rank == 0
cf.study.numSubjects = len(cf.study.subjectIntercepts)
if isReporter:
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(HGMConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
#common.DebugHere()
# if MPI check if processes are greater than number of subjects. it is okay if there are more subjects than processes
if cf.compute.useMPI and (cf.study.numSubjects < cf.compute.numProcesses):
raise Exception("Please don't use more processes " +
"than total number of individuals")
# subdivide data, create subsets for this thread to work on
nodeSubjectIds = cf.study.subjectIds[rank::cf.compute.numProcesses]
nodeIntercepts = cf.study.subjectIntercepts[rank::cf.compute.numProcesses]
nodeSlopes = cf.study.subjectSlopes[rank::cf.compute.numProcesses]
nodeBaselineTimes = cf.study.subjectBaselineTimes[rank::cf.compute.
numProcesses]
sys.stdout.write(
"This is process %d of %d with name: %s on machinename: %s and local rank: %d.\nnodeIntercepts: %s\n nodeSlopes: %s\n nodeBaselineTimes: %s\n"
% (rank, size, name, nodename, localRank, nodeIntercepts, nodeSlopes,
nodeBaselineTimes))
# mem type is determined by whether or not we're using CUDA
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
# load data in memory
# load intercepts
J = [
common.LoadITKImage(f, mType) if isinstance(f, str) else f
for f in nodeIntercepts
]
# load slopes
n = [
common.LoadITKField(f, mType) if isinstance(f, str) else f
for f in nodeSlopes
]
# get imGrid from data
imGrid = J[0].grid()
# create time array with checkpointing info for group geodesic
(t, Jind, gCpinds) = HGMSetUpTimeArray(cf.optim.nTimeStepsGroup,
nodeBaselineTimes, 0.0000001)
tdiscGroup = CAvmHGMCommon.HGMSetupTimeDiscretizationGroup(
t, J, n, Jind, gCpinds, mType, nodeSubjectIds)
# create time array with checkpointing info for residual geodesic
(s, scratchInd, rCpinds) = HGMSetUpTimeArray(cf.optim.nTimeStepsResidual,
[1.0], 0.0000001)
tdiscResidual = CAvmHGMCommon.HGMSetupTimeDiscretizationResidual(
s, rCpinds, imGrid, mType)
# create group state and residual state
groupState = CAvmHGMCommon.HGMGroupState(
imGrid,
mType,
cf.vectormomentum.diffOpParamsGroup[0],
cf.vectormomentum.diffOpParamsGroup[1],
cf.vectormomentum.diffOpParamsGroup[2],
t,
cf.optim.NIterForInverse,
cf.vectormomentum.varIntercept,
cf.vectormomentum.varSlope,
cf.vectormomentum.varInterceptReg,
cf.optim.stepSizeGroup,
integMethod=cf.optim.integMethodGroup)
#ca.Copy(groupState.I0, common.LoadITKImage('/usr/sci/projects/ADNI/nikhil/software/vectormomentumtest/TestData/FlowerData/Longitudinal/GroupGeodesic/I0.mhd', mType))
# note that residual state is treated a scratch variable in this algorithm and reused for computing residual geodesics of multiple individual
residualState = CAvmHGMCommon.HGMResidualState(
None,
None,
imGrid,
mType,
cf.vectormomentum.diffOpParamsResidual[0],
cf.vectormomentum.diffOpParamsResidual[1],
cf.vectormomentum.diffOpParamsResidual[2],
s,
cf.optim.NIterForInverse,
cf.vectormomentum.varIntercept,
cf.vectormomentum.varSlope,
cf.vectormomentum.varInterceptReg,
cf.optim.stepSizeResidual,
integMethod=cf.optim.integMethodResidual)
# start up the memory manager for scratch variables
ca.ThreadMemoryManager.init(imGrid, mType, 0)
# need some host memory in np array format for MPI reductions
if cf.compute.useMPI:
mpiImageBuff = None if mType == ca.MEM_HOST else ca.Image3D(
imGrid, ca.MEM_HOST)
mpiFieldBuff = None if mType == ca.MEM_HOST else ca.Field3D(
imGrid, ca.MEM_HOST)
for i in range(len(groupState.t) - 1, -1, -1):
if tdiscGroup[i].J is not None:
indx_last_individual = i
break
'''
# initial template image
ca.SetMem(groupState.I0, 0.0)
tmp = ca.ManagedImage3D(imGrid, mType)
for tdisc in tdiscGroup:
if tdisc.J is not None:
ca.Copy(tmp, tdisc.J)
groupState.I0 += tmp
del tmp
if cf.compute.useMPI:
Compute.Reduce(groupState.I0, mpiImageBuff)
# divide by total num subjects
groupState.I0 /= cf.study.numSubjects
'''
# run the loop
for it in range(cf.optim.Niter):
# compute HGM variation for group
HGMGroupVariation(groupState, tdiscGroup, residualState, tdiscResidual,
cf.io.outputPrefix, rank, it)
common.CheckCUDAError("Error after HGM iteration")
# compute gradient for momenta (m is used as scratch)
# if there are multiple nodes we'll need to sum across processes now
if cf.compute.useMPI:
# do an MPI sum
Compute.Reduce(groupState.sumSplatI, mpiImageBuff)
Compute.Reduce(groupState.sumJac, mpiImageBuff)
Compute.Reduce(groupState.madj, mpiFieldBuff)
# also sum up energies of other nodes
# intercept
Eintercept = np.array([groupState.EnergyHistory[-1][1]])
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
Eintercept,
op=mpi4py.MPI.SUM)
groupState.EnergyHistory[-1][1] = Eintercept[0]
Eslope = np.array([groupState.EnergyHistory[-1][2]])
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
Eslope,
op=mpi4py.MPI.SUM)
groupState.EnergyHistory[-1][2] = Eslope[0]
ca.Copy(groupState.m, groupState.m0)
groupState.diffOp.applyInverseOperator(groupState.m)
ca.Sub_I(groupState.m, groupState.madj)
#groupState.diffOp.applyOperator(groupState.m)
# now take gradient step in momenta for group
if cf.optim.method == 'FIXEDGD':
# take fixed stepsize gradient step
ca.Add_MulC_I(groupState.m0, groupState.m, -cf.optim.stepSizeGroup)
else:
raise Exception("Unknown optimization scheme: " + cf.optim.method)
# end if
# now divide to get the new base image for group
ca.Div(groupState.I0, groupState.sumSplatI, groupState.sumJac)
# keep track of energy in this iteration
if isReporter and cf.io.plotEvery > 0 and ((
(it + 1) % cf.io.plotEvery == 0) or (it == cf.optim.Niter - 1)):
HGMPlots(cf,
groupState,
tdiscGroup,
residualState,
tdiscResidual,
indx_last_individual,
writeOutput=True)
if isReporter:
(VEnergy, IEnergy, SEnergy) = groupState.EnergyHistory[-1]
print datetime.datetime.now().time(
), " Iter", it, "of", cf.optim.Niter, ":", VEnergy + IEnergy + SEnergy, '(Total) = ', VEnergy, '(Vector) + ', IEnergy, '(Intercept) + ', SEnergy, '(Slope)'
# write output images and fields
HGMWriteOutput(cf, groupState, tdiscGroup, isReporter)
def BuildAtlas(cf):
"""Worker for running Atlas construction on a subset of individuals.
Runs Atlas on this subset sequentially. The variations retuned are
summed up to get update for all individuals
"""
localRank = Compute.GetMPIInfo()['local_rank']
rank = Compute.GetMPIInfo()['rank']
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# just one reporter process on each node
isReporter = rank == 0
cf.study.numSubjects = len(cf.study.subjectImages)
if isReporter:
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(AtlasConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
#common.DebugHere()
# if MPI check if processes are greater than number of subjects. it is okay if there are more subjects than processes
if cf.compute.useMPI and (cf.study.numSubjects < cf.compute.numProcesses):
raise Exception("Please don't use more processes " +
"than total number of individuals")
# subdivide data, create subsets for this thread to work on
nodeSubjectIds = cf.study.subjectIds[rank::cf.compute.numProcesses]
nodeImages = cf.study.subjectImages[rank::cf.compute.numProcesses]
nodeWeights = cf.study.subjectWeights[rank::cf.compute.numProcesses]
numLocalSubjects = len(nodeImages)
print 'rank:', rank, ', localRank:', localRank, ', nodeImages:', nodeImages, ', nodeWeights:', nodeWeights
# mem type is determined by whether or not we're using CUDA
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
# load data in memory
# load intercepts
J_array = [
common.LoadITKImage(f, mType) if isinstance(f, str) else f
for f in nodeImages
]
# get imGrid from data
imGrid = J_array[0].grid()
# atlas image
atlas = ca.Image3D(imGrid, mType)
# allocate memory to store only the initial momenta for each individual in this thread
m_array = [ca.Field3D(imGrid, mType) for i in range(numLocalSubjects)]
# allocate only one copy of scratch memory to be reused for each local individual in this thread in loop
p = WarpVariables(imGrid,
mType,
cf.vectormomentum.diffOpParams[0],
cf.vectormomentum.diffOpParams[1],
cf.vectormomentum.diffOpParams[2],
cf.optim.NIterForInverse,
cf.vectormomentum.sigma,
cf.optim.stepSize,
integMethod=cf.optim.integMethod)
# memory to accumulate numerators and denominators for atlas from
# local individuals which will be summed across MPI threads
sumSplatI = ca.Image3D(imGrid, mType)
sumJac = ca.Image3D(imGrid, mType)
# start up the memory manager for scratch variables
ca.ThreadMemoryManager.init(imGrid, mType, 0)
# need some host memory in np array format for MPI reductions
if cf.compute.useMPI:
mpiImageBuff = None if mType == ca.MEM_HOST else ca.Image3D(
imGrid, ca.MEM_HOST)
t = [
x * 1. / (cf.optim.nTimeSteps) for x in range(cf.optim.nTimeSteps + 1)
]
cpinds = range(1, len(t))
msmtinds = [
len(t) - 2
] # since t=0 is not in cpinds, thats just identity deformation so not checkpointed
cpstates = [(ca.Field3D(imGrid, mType), ca.Field3D(imGrid, mType))
for idx in cpinds]
gradAtMsmts = [ca.Image3D(imGrid, mType) for idx in msmtinds]
EnergyHistory = []
# TODO: better initializations
# initialize atlas image with zeros.
ca.SetMem(atlas, 0.0)
# initialize momenta with zeros
for m0_individual in m_array:
ca.SetMem(m0_individual, 0.0)
'''
# initial template image
ca.SetMem(groupState.I0, 0.0)
tmp = ca.ManagedImage3D(imGrid, mType)
for tdisc in tdiscGroup:
if tdisc.J is not None:
ca.Copy(tmp, tdisc.J)
groupState.I0 += tmp
del tmp
if cf.compute.useMPI:
Compute.Reduce(groupState.I0, mpiImageBuff)
# divide by total num subjects
groupState.I0 /= cf.study.numSubjects
'''
# preprocessinput
# assign atlas reference to p.I0. This reference will not change.
p.I0 = atlas
# run the loop
for it in range(cf.optim.Niter):
# run one iteration of warp for each individual and update
# their own initial momenta and also accumulate SplatI and Jac
ca.SetMem(sumSplatI, 0.0)
ca.SetMem(sumJac, 0.0)
TotalVEnergy = np.array([0.0])
TotalIEnergy = np.array([0.0])
for itsub in range(numLocalSubjects):
# initializations for this subject, this only assigns
# reference to image variables
p.m0 = m_array[itsub]
Imsmts = [J_array[itsub]]
# run warp iteration
VEnergy, IEnergy = RunWarpIteration(nodeSubjectIds[itsub], cf, p,
t, Imsmts, cpinds, cpstates,
msmtinds, gradAtMsmts, it)
# gather relevant results
ca.Add_I(sumSplatI, p.sumSplatI)
ca.Add_I(sumJac, p.sumJac)
TotalVEnergy[0] += VEnergy
TotalIEnergy[0] += IEnergy
# if there are multiple nodes we'll need to sum across processes now
if cf.compute.useMPI:
# do an MPI sum
Compute.Reduce(sumSplatI, mpiImageBuff)
Compute.Reduce(sumJac, mpiImageBuff)
# also sum up energies of other nodes
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
TotalVEnergy,
op=mpi4py.MPI.SUM)
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
TotalIEnergy,
op=mpi4py.MPI.SUM)
EnergyHistory.append([TotalVEnergy[0], TotalIEnergy[0]])
# now divide to get the new atlas image
ca.Div(atlas, sumSplatI, sumJac)
# keep track of energy in this iteration
if isReporter and cf.io.plotEvery > 0 and ((
(it + 1) % cf.io.plotEvery == 0) or (it == cf.optim.Niter - 1)):
# plots
AtlasPlots(cf, p, atlas, m_array, EnergyHistory)
if isReporter:
# print out energy
(VEnergy, IEnergy) = EnergyHistory[-1]
print "Iter", it, "of", cf.optim.Niter, ":", VEnergy + IEnergy, '(Total) = ', VEnergy, '(Vector) + ', IEnergy, '(Image)'
# write output images and fields
AtlasWriteOutput(cf, atlas, m_array, nodeSubjectIds, isReporter)
def GeoRegIteration(subid, cf, p, t, Imsmts, cpinds, cpstates, msmtinds,
gradAtMsmts, EnergyHistory, it):
# compute gradient for regression
(grad_m, sumJac, sumSplatI, VEnergy,
IEnergy) = GeoRegGradient(p, t, Imsmts, cpinds, cpstates, msmtinds,
gradAtMsmts)
# do energy related stuff for printing and bookkeeping
#if it>0:
EnergyHistory.append([VEnergy + IEnergy, VEnergy, IEnergy])
print VEnergy + IEnergy, '(Total) = ', VEnergy, '(Vector)+', IEnergy, '(Image)'
# plot some stuff
if cf.io.plotEvery > 0 and (((it + 1) % cf.io.plotEvery) == 0
or it == cf.optim.Niter - 1):
GeoRegPlots(subid, cf, p, t, Imsmts, cpinds, cpstates, msmtinds,
gradAtMsmts, EnergyHistory)
# end if
if cf.optim.method == 'FIXEDGD':
# automatic stepsize selection in the first three steps
if it == 1:
# TODO: BEWARE There are hardcoded numbers here for 2D and 3D
#first find max absolute value across voxels in gradient
temp = ca.Field3D(grad_m.grid(), ca.MEM_HOST)
ca.Copy(temp, grad_m)
temp_x, temp_y, temp_z = temp.asnp()
temp1 = np.square(temp_x.flatten()) + np.square(
temp_y.flatten()) + np.square(temp_z.flatten())
medianval = np.median(temp1[temp1 > 0.0000000001])
del temp, temp1, temp_x, temp_y, temp_z
#2D images for 2000 iters
#p.stepSize = float(0.000000002*medianval)
#3D images for 2000 iters
p.stepSize = float(0.000002 * medianval)
print 'rank:', Compute.GetMPIInfo(
)['rank'], ', localRank:', Compute.GetMPIInfo(
)['local_rank'], 'subid: ', subid, ' Selecting initial step size in the beginning to be ', str(
p.stepSize)
if it > 3:
totalEnergyDiff = EnergyHistory[-1][0] - EnergyHistory[-2][0]
if totalEnergyDiff > 0.0:
if cf.optim.maxPert is not None:
print 'rank:', Compute.GetMPIInfo(
)['rank'], ', localRank:', Compute.GetMPIInfo(
)['local_rank'], 'subid: ', subid, ' Reducing stepsize for gradient descent by ', str(
cf.optim.maxPert *
100), '%. The new step size is ', str(
p.stepSize * (1 - cf.optim.maxPert))
p.stepSize = p.stepSize * (1 - cf.optim.maxPert)
# take gradient descent step
ca.Add_MulC_I(p.m0, grad_m, -p.stepSize)
else:
raise Exception("Unknown optimization scheme: " + cf.optim.optMethod)
# end if
# now divide to get new base image
ca.Div(p.I0, sumSplatI, sumJac)
return (EnergyHistory)
