def resample(self, shape):
from diffeo2c import diffeo_resample
d2 = diffeo_resample(self.d, shape)
# this might give negative values
v2 = scipy_image_resample(self.variance, shape)
np.clip(v2, 0, 1, out=v2)
return Diffeomorphism2D(d2, v2)
def resample(self, shape):
from diffeo2c import diffeo_resample
d2 = diffeo_resample(self.d, shape)
# this might give negative values
v2 = scipy_image_resample(self.variance, shape)
np.clip(v2, 0, 1, out=v2)
return Diffeomorphism2D(d2, v2)
def update(self, y0, y1):
# init structures if not already
if not self.initialized():
self.init_structures(y0.shape)
warnings.warn('remove')
self.stop_at_convergence = True
if self.reached_end and self.stop_at_convergence:
if self.total_obs % 100 == 1:
self.info('Not integrating anymore (%s)' % self.total_obs)
self.total_obs += 1
msg = 'Reached convergence and stop_at_converged=True.'
raise Diffeo2dEstimatorInterface.LearningConverged(msg)
phase = self.phases[self.current_phase]
estimator = self.estimators[self.current_phase]
target_shape = phase.shape
y0 = scipy_image_resample(y0, target_shape)
y1 = scipy_image_resample(y1, target_shape)
estimator.update(y0, y1)
self.phase_obs += 1
warnings.warn('remove this')
self.check_every = 50
if self.phase_obs % self.check_every == 0:
if self.should_switch():
if self.current_phase < len(self.phases) - 1:
self.info('Switching to next phase.')
self.next_phase()
else:
self.info('Reached end of phases.')
self.reached_end = True
self.total_obs += 1
def update(self, y0, y1):
# init structures if not already
if not self.initialized():
self.init_structures(y0.shape)
warnings.warn('remove')
self.stop_at_convergence = True
if self.reached_end and self.stop_at_convergence:
if self.total_obs % 100 == 1:
self.info('Not integrating anymore (%s)' % self.total_obs)
self.total_obs += 1
msg = 'Reached convergence and stop_at_converged=True.'
raise Diffeo2dEstimatorInterface.LearningConverged(msg)
phase = self.phases[self.current_phase]
estimator = self.estimators[self.current_phase]
target_shape = phase.shape
y0 = scipy_image_resample(y0, target_shape)
y1 = scipy_image_resample(y1, target_shape)
estimator.update(y0, y1)
self.phase_obs += 1
warnings.warn('remove this')
self.check_every = 50
if self.phase_obs % self.check_every == 0:
if self.should_switch():
if self.current_phase < len(self.phases) - 1:
self.info('Switching to next phase.')
self.next_phase()
else:
self.info('Reached end of phases.')
self.reached_end = True
self.total_obs += 1
