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Refactor IRLS iterations, full solves from l2->lp
Adapt Example
This commit is contained in:
D Fournier
+125
-81
@@ -144,7 +144,7 @@ class BetaSchedule(InversionDirective):
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if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
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self.invProb.beta /= self.coolingFactor
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class TargetMisfit(InversionDirective):
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@property
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@@ -238,12 +238,6 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
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# Save the file as a npz
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np.savez('{:03d}-{:s}'.format(self.opt.iter,self.fileName), iter=self.opt.iter, beta=self.invProb.beta, phi_d=self.invProb.phi_d, phi_m=self.invProb.phi_m, phi_ms=phi_ms, phi_mx=phi_mx, phi_my=phi_my, phi_mz=phi_mz,f=self.opt.f, m=self.invProb.curModel,dpred=self.invProb.dpred)
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# class UpdateReferenceModel(Parameter):
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# mref0 = None
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# def nextIter(self):
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# mref = getattr(self, 'm_prev', None)
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# if mref is None:
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# if self.debug: print 'UpdateReferenceModel is using mref0'
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@@ -254,109 +248,165 @@ class SaveOutputDictEveryIteration(_SaveEveryIteration):
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class Update_IRLS(InversionDirective):
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eps_min = None
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eps_p = None
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eps_q = None
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norms = [2.,2.,2.,2.]
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factor = None
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gamma = None
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phi_m_last = None
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phi_d_last = None
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f_old = None
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f_min_change = 1e-1
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coolingRate = 3
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maxIRLSiter = 10
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f_min_change = 1e-2
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beta_tol = 5e-2
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# Solving parameter for IRLS (mode:2)
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IRLSiter = 0
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minGNiter = 6
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maxIRLSiter = 10
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iterStart = 0
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# Beta schedule
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coolingFactor = 2.
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coolingRate = 1
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mode = 1
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@property
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def target(self):
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if getattr(self, '_target', None) is None:
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self._target = self.survey.nD
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return self._target
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@target.setter
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def target(self, val):
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self._target = val
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def initialize(self):
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self.IRLSiter = 0
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# Scale the regularization for changes in norm
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if getattr(self, 'phi_m_last', None) is not None:
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self.reg.curModel = self.invProb.curModel
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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self.reg.gamma = 1.
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phim_new = self.reg.eval(self.invProb.curModel)
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self.gamma = self.phi_m_last / phim_new
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if self.mode == 1:
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self.reg.norms = [2., 2., 2., 2.]
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# # Scale the regularization for changes in norm
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# if getattr(self, 'phi_m_last', None) is not None:
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#
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# self.reg.curModel = self.invProb.curModel
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# self.reg._Wsmall = None
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# self.reg._Wx = None
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# self.reg._Wy = None
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# self.reg._Wz = None
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# self.reg.gamma = 1.
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# phim_new = self.reg.eval(self.invProb.curModel)
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# self.gamma = self.phi_m_last / phim_new
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#
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# self.reg.curModel = self.invProb.curModel
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# self.reg.gamma = self.gamma
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# # Reset the regularization matrices so that it is
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# # recalculated with new gamma
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# self.reg._Wsmall = None
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# self.reg._Wx = None
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# self.reg._Wy = None
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# self.reg._Wz = None
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self.reg.curModel = self.invProb.curModel
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self.reg.gamma = self.gamma
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# Reset the regularization matrices so that it is
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# recalculated with new gamma
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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if getattr(self, 'phi_d_last', None) is None:
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self.phi_d_last = self.invProb.phi_d
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if getattr(self, 'f_last', None) is None:
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self.f_old = self.invProb.evalFunction(self.reg.curModel, return_g=False, return_H=False)
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print self.f_old
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def endIter(self):
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# Only update after GN iterations
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if self.opt.iter % self.coolingRate == 0:
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# if getattr(self, 'phi_d_last', None) is None:
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# self.phi_d_last = self.invProb.phi_d
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#
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# if getattr(self, 'f_last', None) is None:
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# self.f_old = self.invProb.evalFunction(self.reg.curModel, return_g=False, return_H=False)
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# print self.f_old
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def endIter(self):
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# After reaching target misfit with l2-norm, switch to IRLS (mode:2)
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if self.invProb.phi_d < self.target and self.mode == 1:
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print "Convergence with smooth l2-norm regularization: Start IRLS steps..."
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self.mode = 2
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print self.eps_p, self.eps_q, self.norms
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self.reg.eps_p = self.eps_p
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self.reg.eps_q = self.eps_q
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self.reg.norms = self.norms
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self.coolingFactor = 1.
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self.coolingRate = 1
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self.iterStart = self.opt.iter
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self.phi_d_last = self.invProb.phi_d
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self.phi_m_last = self.invProb.phi_m_last
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self.reg.l2model = self.invProb.curModel
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self.reg.curModel = self.invProb.curModel
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if getattr(self, 'f_old', None) is None:
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self.f_old = self.reg.eval(self.invProb.curModel)#self.invProb.evalFunction(self.invProb.curModel, return_g=False, return_H=False)
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if self.opt.iter > 0 and self.opt.iter % self.coolingRate == 0:
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if self.debug: print 'BetaSchedule is cooling Beta. Iteration: %d' % self.opt.iter
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self.invProb.beta /= self.coolingFactor
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# Only update after GN iterations
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if (self.opt.iter-self.iterStart) % self.minGNiter == 0 and self.mode==2:
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self.IRLSiter += 1
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self.f_change = np.abs(self.f_old - self.opt.f_last) / self.f_old
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print "Function decrease" + str(self.f_change)
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phim_new = self.reg.eval(self.invProb.curModel)
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self.f_change = np.abs(self.f_old - phim_new) / self.f_old
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print "Regularization decrease: %6.3e" % (self.f_change)
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# Check for maximum number of IRLS cycles
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if self.IRLSiter == self.maxIRLSiter:
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print "Reach maximum number of IRLS cycles: %i" % self.maxIRLSiter
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self.opt.stopNextIteration = True
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return
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# Check if the function has changed enough
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if self.f_change < self.f_min_change:
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if self.f_change < self.f_min_change and self.IRLSiter > 1:
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print "Minimum decrease in regularization. End of IRLS"
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self.opt.stopNextIteration = True
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return
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else:
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self.f_old = self.opt.f_last
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return
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else:
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self.f_old = phim_new
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# Cool the threshold parameter if required
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if getattr(self, 'factor', None) is not None:
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eps = self.reg.eps / self.factor
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if getattr(self, 'eps_min', None) is not None:
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self.reg.eps = np.max([self.eps_min,eps])
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else:
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self.reg.eps = eps
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# Get phi_m at the end of current iteration
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self.phi_m_last = self.invProb.phi_m_last
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# Reset the regularization matrices so that it is
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# recalculated for current model
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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# Update the model used for the IRLS weights
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self.reg.curModel = self.invProb.curModel
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# Temporarely set gamma to 1. to get raw phi_m
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self.reg.gamma = 1.
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# Compute new model objective function value
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phim_new = self.reg.eval(self.invProb.curModel)
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# Update gamma to scale the regularization between IRLS iterations
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self.reg.gamma = self.phi_m_last / phim_new
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# Reset the regularization matrices again for new gamma
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self.reg._Wsmall = None
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self.reg._Wx = None
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self.reg._Wy = None
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self.reg._Wz = None
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# Compute the change in
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# Check if misfit is within the tolerance, otherwise adjust beta
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val = self.invProb.phi_d / (self.survey.nD*0.5)
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if np.abs(1.-val) > self.beta_tol:
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self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
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class Update_lin_PreCond(InversionDirective):
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"""
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Create a Jacobi preconditioner for the linear problem
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@@ -409,21 +459,15 @@ class Update_Wj(InversionDirective):
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self.reg.wght = JtJdiag
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class Scale_Beta(InversionDirective):
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"""
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Instead of a linear cooling schedule, beta is allowed to change based
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on the ratio between the target misfit and the current data misfit. The
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update is done only if the misfit is outside some threshold bounds.
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"""
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tol = 0.05
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coolingRate=5
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def endIter(self):
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# Only update after GN iterations
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if self.opt.iter % self.coolingRate == 0:
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# Check if misfit is within the tolerance, otherwise adjust beta
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val = self.invProb.phi_d / (self.survey.nD*0.5)
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if np.abs(1.-val) > self.tol:
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self.invProb.beta = self.invProb.beta * self.survey.nD*0.5 / self.invProb.phi_d
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#class Scale_Beta(InversionDirective):
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# """
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# Instead of a linear cooling schedule, beta is allowed to change based
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# on the ratio between the target misfit and the current data misfit. The
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# update is done only if the misfit is outside some threshold bounds.
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# """
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# tol = 0.05
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# coolingRate=5
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#
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# def endIter(self):
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#
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#
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