def getGradient(self, coeff=1.0):
px1 = -self.endPointGradient()
A = self.affB.getTransforms(self.Afft)
# A = [np.zeros([self.Tsize, self.dim, self.dim]), np.zeros([self.Tsize, self.dim])]
dim2 = self.dim**2
# if self.affineDim > 0:
# for t in range(self.Tsize):
# AB = np.dot(self.affineBasis, self.Afft[t])
# A[0][t] = AB[0:dim2].reshape([self.dim, self.dim])
# A[1][t] = AB[dim2:dim2+self.dim]
foo = evol.landmarkHamiltonianGradient(self.fv0.vertices, self.at, px1, self.param.KparDiff, self.regweight, affine=A)
grd = Direction()
grd.diff = foo[0]/(coeff*self.Tsize)
grd.aff = np.zeros(self.Afft.shape)
if self.affineDim > 0:
dA = foo[1]
db = foo[2]
#dAfft = 2*np.multiply(self.affineWeight.reshape([1, self.affineDim]), self.Afft)
grd.aff = 2 * self.Afft
for t in range(self.Tsize):
dAff = np.dot(self.affineBasis.T, np.vstack([dA[t].reshape([dim2,1]), db[t].reshape([self.dim, 1])]))
grd.aff[t] -= np.divide(dAff.reshape(grd.aff[t].shape), self.affineWeight.reshape(grd.aff[t].shape))
grd.aff /= (coeff*self.Tsize)
# dAfft[:,0:self.dim**2]/=100
return grd
def getGradient(self, coeff=1.0):
px1 = -self.endPointGradient()
A = [np.zeros([self.Tsize, self.dim, self.dim]), np.zeros([self.Tsize, self.dim])]
dim2 = self.dim**2
if self.affineDim > 0:
for t in range(self.Tsize):
AB = np.dot(self.affineBasis, self.Afft[t])
A[0][t] = AB[0:dim2].reshape([self.dim, self.dim])
A[1][t] = AB[dim2:dim2+self.dim]
foo = evol.landmarkHamiltonianGradient(self.x0, self.at, px1, self.param.KparDiff, self.regweight, affine=A, getCovector=True)
grd = Direction()
grd.diff = foo[0]/(coeff*self.Tsize)
grd.aff = np.zeros(self.Afft.shape)
if self.affineDim > 0:
dA = foo[1]
db = foo[2]
grd.aff = 2*np.multiply(self.affineWeight.reshape([1, self.affineDim]), self.Afft)
#grd.aff = 2 * self.Afft
for t in range(self.Tsize):
dAff = np.dot(self.affineBasis.T, np.vstack([dA[t].reshape([dim2,1]), db[t].reshape([self.dim, 1])]))
#grd.aff[t] -= np.divide(dAff.reshape(grd.aff[t].shape), self.affineWeight.reshape(grd.aff[t].shape))
grd.aff[t] -= dAff.reshape(grd.aff[t].shape)
grd.aff /= (self.coeffAff*coeff*self.Tsize)
# dAfft[:,0:self.dim**2]/=100
if self.symmetric:
grd.initx = (self.initPointGradient() - foo[4][0,...])/(self.coeffInitx * coeff)
return grd
def gradientComponent(self, q, kk):
px1 = - self.param.fun_objGrad(self.fvDef[kk], self.fv1[kk], self.param.KparDist) / self.param.sigmaError**2
#print 'in fun', kk
A = [np.zeros([self.Tsize, self.dim, self.dim]), np.zeros([self.Tsize, self.dim])]
dim2 = self.dim**2
if self.affineDim > 0:
for t in range(self.Tsize):
AB = np.dot(self.affineBasis, self.AfftAll[kk][t])
#print self.dim, dim2, AB.shape, self.affineBasis.shape
A[0][t] = AB[0:dim2].reshape([self.dim, self.dim])
A[1][t] = AB[dim2:dim2+self.dim]
foo = evol.landmarkHamiltonianGradient(self.fvTmpl.vertices, self.atAll[kk], px1, self.param.KparDiff, self.regweight, getCovector = True, affine=A)
#print foo[0].shape, foo[1].shape
grd = foo[0:3]
pxTmpl = foo[-1][0, :, :]
#print 'before put', kk
q.put([kk, grd, pxTmpl])
def getGradient(self, coeff=1.0):
#grd0 = np.zeros(self.atAll.shape)
#pxIncr = np.zeros([self.Ntarg, self.atAll.shape[1], self.atAll.shape[2]])
pxTmpl = np.zeros(self.at.shape[1:3])
q = mp.Queue()
#procGrd = []
# for kk in range(Ntarg):
# procGrd.append(mp.Process(target = gradientComponent, args=(q,kk,fvTmpl, fv1[kk], xt[kk], at[kk], KparDist, KparDiff, regWeight, sigmaError,)))
# for kk in range(Ntarg):
# procGrd[kk].start()
# for kk in range(Ntarg):
# procGrd[kk].join()
# print 'all joined'
grd = Direction()
for kk in range(self.Ntarg):
self.gradientComponent(q, kk)
grd.all.append(smatch.Direction())
dim2 = self.dim**2
for kk in range(self.Ntarg):
foo = q.get()
dat = foo[1][0]/(coeff*self.Tsize)
dAfft = np.zeros(self.AfftAll[kk].shape)
if self.affineDim > 0:
dA = foo[1][1]
db = foo[1][2]
dAfft = 2 * self.AfftAll[kk]
for t in range(self.Tsize):
dAff = np.dot(self.affineBasis.T, np.vstack([dA[t].reshape([dim2,1]), db[t].reshape([self.dim, 1])]))
dAfft[t] -= np.divide(dAff.reshape(dAfft[t].shape), self.affineWeight.reshape(dAfft[t].shape))
dAfft /= (coeff*self.Tsize)
#print dat.shape
grd.all[foo[0]].diff = dat
grd.all[foo[0]].aff = dAfft
#print foo[0], grd[foo[0]][1].shape
pxTmpl += foo[2]
#print pxTmpl.shape
foo2 = evol.landmarkHamiltonianGradient(self.fv0.vertices, self.at, pxTmpl/self.lambdaPrior, self.param.KparDiff, self.regweight)
# xtPrior = np.copy(foo2[1])
grd.prior = foo2[0] / (self.tmplCoeff*coeff*self.Tsize)
return grd
