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experiments.py
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from __future__ import division
import numpy as np
na = np.newaxis
import inspect, shutil, os, abc, cPickle, datetime, warnings
from renderer.renderer import MouseScene
from renderer.load_data import load_behavior_data
import pose_models
import particle_filter
import predictive_models as pm
import predictive_distributions as pd
from util.text import progprint_xrange
from util.general import joindicts
class Experiment(object):
__metaclass__ = abc.ABCMeta
### must override this
@abc.abstractmethod
def run(self,frame_range):
pass
### should probably override this
def resume(self):
raise NotImplementedError
### probably shouldn't be overridden
def save_progress(self,particlefilter,pose_model,datapath,frame_range,**kwargs):
dct = joindicts((
{
'particlefilter':particlefilter,
'pose_model':pose_model,
'datapath':datapath,
'frame_range':frame_range,
},
kwargs
))
outfilename = os.path.join(self.cachepath(frame_range),str(particlefilter.numsteps))
with open(outfilename,'w') as outfile:
cPickle.dump(dct,outfile,protocol=2)
descripfilename = os.path.join(self.cachepath(frame_range),'description.txt')
if not os.path.exists(descripfilename):
with open(descripfilename,'w') as outfile:
outfile.write(
'''
experiment: %s
data path: %s
frame range: %s
pose model: %s
start time: %s
code.py contains the experiment code used to run the experiment; see also experiments.py
''' % (self.__class__.__name__, datapath, frame_range, pose_model, datetime.datetime.now())
)
shutil.copy(outfilename,os.path.join('results','current_run'))
def load_most_recent_progress(self,frame_range):
warnings.warn('unteseted, unused')
raise NotImplementedError, 'outdated, needs new pickle file format'
most_recent_filename = os.path.join(self.cachepath(frame_range),
max([int(x) for x in os.listdir(self.cachepath(frame_range)) if x.isdigit()]))
with open(os.path.join(self.cachepath(frame_range),most_recent_filename),'r') as infile:
particlefilter, pose_model, datapath, frame_range = cPickle.load(infile)
return particlefilter, pose_model, datapath, frame_range
### don't override this stuff
def __init__(self,frame_range):
print 'results directory: %s' % self.cachepath(frame_range)
if os.path.exists(self.cachepath(frame_range)):
response = raw_input('cache file exists: [o]verwrite, [r]esume, or do [N]othing? ').lower()
if response == 'r':
self.resume(frame_range)
elif response == 'o':
shutil.rmtree(self.cachepath(frame_range))
else:
print 'did nothing'
return
os.makedirs(self.cachepath(frame_range))
with open(os.path.join(self.cachepath(frame_range),'code.py'),'w') as outfile:
outfile.write(inspect.getsource(self.__class__))
self.run(frame_range)
def cachepath(self,frame_range):
return os.path.join('results',str(abs(hash(inspect.getsource(self.__class__)))) + ('.%d.%d' % frame_range))
#################
# Experiments #
#################
### Decent ones
class SideInfoFixedNoise(Experiment):
def run(self,frame_range):
datapath = os.path.join(os.path.dirname(__file__),"Test Data","Blurred Edge")
num_particles_firststep = 1024*50
num_particles = 1024*30
cutoff = 1024*15
xytheta_noisechol = np.diag((2.,2.))
randomwalk_noisechol = np.diag((7.,3.,0.01,2.,2.,10.,) + (20.,)*(2+2*3))
subsequent_randomwalk_noisechol = np.diag((3.,2.,0.01,0.2,0.2,1.0,) + (6.,)*(2+2*3))
pose_model = pose_models.PoseModel3()
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2])
pose_model.default_particle_pose = pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2])
def log_likelihood(stepnum,im,poses):
return ms.get_likelihood(im,particle_data=pose_model.expand_poses(poses),
x=xytheta[stepnum,0],y=xytheta[stepnum,1],theta=xytheta[stepnum,2])/500.
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: \
pm.Concatenation(
components=(
pm.SideInfo(noiseclass=lambda: pd.FixedNoise(xytheta_noisechol)),
pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
),
arggetters=(
lambda d: {'sideinfo':d['sideinfo'][:2]},
lambda d: {'lagged_outputs': map(lambda x: x[2:],d['lagged_outputs'])}
)
)
) for itr in range(num_particles_firststep)])
pf.step(images[0],particle_kwargs={'sideinfo':xytheta[0]})
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
for i in progprint_xrange(1,images.shape[0],perline=10):
if i % 10 == 0:
self.save_progress(pf,pose_model,datapath,frame_range)
pf.step(images[i],particle_kwargs={'sideinfo':xytheta[i]})
print len(np.unique([p.track[1][0] for p in pf.particles]))
print ''
self.save_progress(pf,frame_range)
class RandomWalkFixedNoise(Experiment):
def run(self,frame_range):
datapath = os.path.join(os.path.dirname(__file__),"Test Data","Blurred Edge")
num_particles_firststep = 1024*50
num_particles = 1024*30
cutoff = 1024*15
randomwalk_noisechol = np.diag((3.,3.,7.,3.,0.01,2.,2.,10.,) + (20.,)*(2+2*3))
subsequent_randomwalk_noisechol = np.diag((2.,2.,3.,2.,0.01,0.2,0.2,1.0,) + (6.,)*(2+2*3))
pose_model = pose_models.PoseModel3()
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
def log_likelihood(stepnum,im,poses):
return ms.get_likelihood(im,particle_data=pose_model.expand_poses(poses),
x=xytheta[stepnum,0],y=xytheta[stepnum,1],theta=xytheta[stepnum,2])/500.
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
for i in progprint_xrange(1,images.shape[0],perline=10):
if i % 10 == 0:
self.save_progress(pf,pose_model,datapath,frame_range)
pf.step(images[i])
print len(np.unique([p.track[1][0] for p in pf.particles]))
print ''
class RandomWalkFixedNoiseFrozenTrack(Experiment):
# should look a lot like RandomWalkFixedNoise
def run(self,frame_range):
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*80
num_particles = 1024*60
cutoff = 1024*30
lag = 15
randomwalk_noisechol = np.diag((3.,3.,7.,3.,0.01,2.,2.,10.,) + (20.,)*(2+2*3))
subsequent_randomwalk_noisechol = np.diag((1.5,1.5,3.,0.25,0.01,1e-6,1e-6,1e-6,) + (5.,)*(2+2*3))
# TODO check z size
# TODO try cutting scale, fit on first 10 or so
# pose_model = pose_models.PoseModel3()
pose_model = pose_models.PoseModel10()
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
def log_likelihood(stepnum,im,poses):
return ms.get_likelihood(im,particle_data=pose_model.expand_poses(poses),
x=xytheta[stepnum,0],y=xytheta[stepnum,1],theta=xytheta[stepnum,2])/3000.
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
for i in progprint_xrange(1,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 5) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class RandomWalkFixedNoiseFrozenTrackParallel(Experiment):
# should look a lot like RandomWalkFixedNoise
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*80
num_particles = 1024*60
cutoff = 1024*30
lag = 15
randomwalk_noisechol = np.diag((3.,3.,7.,3.,0.01,2.,2.,10.,) + (20.,)*(2+2*3))
subsequent_randomwalk_noisechol = np.diag((1.5,1.5,3.,0.25,0.01,1e-6,1e-6,1e-6,) + (5.,)*(2+2*3))
# pose_model = pose_models.PoseModel3()
pose_model = pose_models.PoseModel10()
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
# engines should be started in git root, where this file is
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/2000.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
for i in progprint_xrange(1,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 5) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class RandomWalkFixedNoiseFrozenTrack_AW_5Joints_simplified(Experiment):
# should look a lot like RandomWalkFixedNoise
def run(self,frame_range):
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*10
num_particles = 1024*4
cutoff = 1024*4
lag = 15
pose_model = pose_models.PoseModel_5Joint_origweights_AW()
variances = {
'x': {'init':3.0, 'subsq':1.5},
'y': {'init':3.0, 'subsq':1.5},
'theta_yaw': {'init':7.0, 'subsq':3.0},
'z': {'init':3.0, 'subsq':0.25},
'theta_roll': {'init':0.01, 'subsq':0.01},
's_w': {'init':2.0, 'subsq':1e-6},
's_l': {'init':2.0, 'subsq':1e-6},
's_h': {'init':1.0, 'subsq':1e-6}
}
particle_fields = pose_model.ParticlePose._fields
joint_names = [j for j in particle_fields if 'psi_' in j]
[variances.update({j:{'init':20.0, 'subsq':5.0}}) for j in joint_names]
randomwalk_noisechol = np.diag([variances[p]['init'] for p in particle_fields])
subsequent_randomwalk_noisechol = np.diag([variances[p]['subsq'] for p in particle_fields])
# TODO check z size
# TODO try cutting scale, fit on first 10 or so
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
def log_likelihood(stepnum,im,poses):
return ms.get_likelihood(im,particle_data=pose_model.expand_poses(poses),
x=xytheta[stepnum,0],y=xytheta[stepnum,1],theta=xytheta[stepnum,2])/2000.
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
for i in progprint_xrange(1,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 5) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class RandomWalkFixedNoiseParallelSimplified(Experiment):
# combo of previous two
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*40
num_particles = 1024*20
cutoff = 1024*10
lag = 10
pose_model = pose_models.PoseModel_5Joint_origweights_AW()
variances = {
'x': {'init':3.0, 'subsq':1.5},
'y': {'init':3.0, 'subsq':1.5},
'theta_yaw': {'init':7.0, 'subsq':3.0},
'z': {'init':3.0, 'subsq':0.25},
'theta_roll': {'init':0.01, 'subsq':0.01},
's_w': {'init':2.0, 'subsq':1e-6},
's_l': {'init':2.0, 'subsq':1e-6},
's_h': {'init':1.0, 'subsq':1e-6}
}
particle_fields = pose_model.ParticlePose._fields
joint_names = [j for j in pose_model.ParticlePose._fields if 'psi_' in j]
[variances.update({j:{'init':20.0, 'subsq':5.0}}) for j in joint_names]
randomwalk_noisechol = np.diag([variances[p]['init'] for p in particle_fields])
subsequent_randomwalk_noisechol = np.diag([variances[p]['subsq'] for p in particle_fields])
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/2500.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
for i in progprint_xrange(1,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 10) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class MomentumLearnedNoiseFrozenTrackParallel(Experiment):
# should look a lot like RandomWalkFixedNoise
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*50
num_particles = 1024*20
cutoff = 1024*10
lag = 15
randomwalk_noisechol = np.diag((3.,3.,7.,3.,0.01,2.,2.,10.,) + (20.,)*(2+2*3))
subsequent_randomwalk_noisechol = np.diag((1.5,1.5,3.,0.25,0.01,1e-6,1e-6,1e-6,) + (5.,)*(2+2*3))
# pose_model = pose_models.PoseModel3()
pose_model = pose_models.PoseModel10()
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
# engines should be started in git root, where this file is
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/2000.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
pf.change_numparticles(num_particles)
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
pf.step(images[1])
# now switch to momentum!
starters = pf.particles
propmatrix = np.hstack((1.5*np.eye(pose_model.particle_pose_tuple_len),-0.5*np.eye(pose_model.particle_pose_tuple_len)))
invwishparams = (20,20*subsequent_randomwalk_noisechol)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=2,
previous_outputs=(p.track[1],p.track[0]),
baseclass=lambda: pm.Momentum(propmatrix=propmatrix,noiseclass=lambda: pd.InverseWishartNoise(*invwishparams)),
) for p in starters]
)
for i in progprint_xrange(2,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 5) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class MomentumLearnedNoiseParallelSimplified(Experiment):
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*40
num_particles = 1024*10
cutoff = 1024*5
lag = 10
pose_model = pose_models.PoseModel_5Joint_origweights_AW()
variances = {
'x': {'init':3.0, 'subsq':1.5},
'y': {'init':3.0, 'subsq':1.5},
'theta_yaw': {'init':7.0, 'subsq':3.0},
'z': {'init':3.0, 'subsq':0.25},
'theta_roll': {'init':0.01, 'subsq':0.01},
's_w': {'init':2.0, 'subsq':1e-6},
's_l': {'init':2.0, 'subsq':1e-6},
's_h': {'init':1.0, 'subsq':1e-6}
}
particle_fields = pose_model.ParticlePose._fields
joint_names = [j for j in pose_model.ParticlePose._fields if 'psi_' in j]
[variances.update({j:{'init':20.0, 'subsq':5.0}}) for j in joint_names]
randomwalk_noisechol = np.diag([variances[p]['init'] for p in particle_fields])
subsequent_randomwalk_noisechol = np.diag([variances[p]['subsq'] for p in particle_fields])
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/2500.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
pf.step(images[1])
pf.change_numparticles(num_particles)
### now switch to momentum!
starters = pf.particles
propmatrix = np.hstack((1.25*np.eye(pose_model.particle_pose_tuple_len),-0.25*np.eye(pose_model.particle_pose_tuple_len)))
invwishparams = (100,100*subsequent_randomwalk_noisechol)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=2,
previous_outputs=(p.track[1],p.track[0]),
baseclass=lambda: pm.Momentum(propmatrix=propmatrix,noiseclass=lambda: pd.InverseWishartNoise(*invwishparams)),
) for p in starters]
)
for i in progprint_xrange(2,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 10) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class MomentumLearnedNoiseParallelSuperSimplified(Experiment):
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*40
num_particles = 1024*10
cutoff = 1024*5
lag = 10
pose_model = pose_models.PoseModel_4Joint_origweights_AW()
variances = {
'x': {'init':3.0, 'subsq':1.5},
'y': {'init':3.0, 'subsq':1.5},
'theta_yaw': {'init':7.0, 'subsq':3.0},
'z': {'init':3.0, 'subsq':0.25},
'theta_roll': {'init':0.01, 'subsq':0.01},
's_w': {'init':2.0, 'subsq':1e-6},
's_l': {'init':2.0, 'subsq':1e-6},
's_h': {'init':1.0, 'subsq':1e-6}
}
particle_fields = pose_model.ParticlePose._fields
joint_names = [j for j in pose_model.ParticlePose._fields if 'psi_' in j]
[variances.update({j:{'init':20.0, 'subsq':5.0}}) for j in joint_names]
randomwalk_noisechol = np.diag([variances[p]['init'] for p in particle_fields])
subsequent_randomwalk_noisechol = np.diag([variances[p]['subsq'] for p in particle_fields])
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/2500.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
pf.step(images[1])
pf.change_numparticles(num_particles)
### now switch to momentum!
starters = pf.particles
propmatrix = np.hstack((1.25*np.eye(pose_model.particle_pose_tuple_len),-0.25*np.eye(pose_model.particle_pose_tuple_len)))
invwishparams = (50,50*subsequent_randomwalk_noisechol)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=2,
previous_outputs=(p.track[1],p.track[0]),
baseclass=lambda: pm.Momentum(propmatrix=propmatrix,noiseclass=lambda: pd.InverseWishartNoise(*invwishparams)),
) for p in starters]
)
for i in progprint_xrange(2,lag):
pf.step(images[i])
self.save_progress(pf,pose_model,datapath,frame_range,means=[])
# now step with freezing means
means = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
means.append(np.sum(pf.weights_norm[:,na] * np.array([p.track[i-lag] for p in pf.particles]),axis=0))
print '\nsaved a mean for index %d with %d unique particles!\n' % \
(i-lag,len(np.unique([p.track[i-15][0] for p in pf.particles])))
pf.step(images[i])
if (i % 10) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
self.save_progress(pf,pose_model,datapath,frame_range,means=means)
class OneJoint(Experiment):
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*40
num_particles = 1024*30
cutoff = 1024*15
lag = 10
pose_model = pose_models.OneJoint()
variances = {
'x': {'init':3.0, 'subsq':1.5},
'y': {'init':3.0, 'subsq':1.5},
'theta_yaw': {'init':7.0, 'subsq':3.0},
'z': {'init':3.0, 'subsq':0.25},
'theta_roll': {'init':0.01, 'subsq':0.01},
's_w': {'init':2.0, 'subsq':1e-1},
's_l': {'init':2.0, 'subsq':1e-1},
's_h': {'init':1.0, 'subsq':1e-1}
}
particle_fields = pose_model.ParticlePose._fields
joint_names = [j for j in pose_model.ParticlePose._fields if 'psi_' in j]
[variances.update({j:{'init':20.0, 'subsq':5.0}}) for j in joint_names]
randomwalk_noisechol = np.diag([variances[p]['init'] for p in particle_fields])
subsequent_randomwalk_noisechol = np.diag([variances[p]['subsq'] for p in particle_fields])
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/3000.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=1,
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
pf.step(images[0])
randomwalk_noisechol[:] = subsequent_randomwalk_noisechol[:]
pf.step(images[1])
pf.change_numparticles(num_particles)
### now switch to momentum!
starters = pf.particles
# TODO tune these
propmatrix = np.hstack((1.25*np.eye(pose_model.particle_pose_tuple_len),-0.25*np.eye(pose_model.particle_pose_tuple_len)))
invwishparams = (30,30*subsequent_randomwalk_noisechol)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.AR(
numlags=2,
previous_outputs=(p.track[1],p.track[0]),
baseclass=lambda: pm.Momentum(propmatrix=propmatrix,noiseclass=lambda: pd.InverseWishartNoise(*invwishparams)),
) for p in starters]
)
for i in progprint_xrange(2,lag):
pf.step(images[i])
# now step with freezing traces
traces = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
traces.append((i,[p.track[-lag:] for p in pf.particles]))
pf.step(images[i])
if (i % 10) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,traces=traces)
self.save_progress(pf,pose_model,datapath,frame_range,traces=traces)
class ThreeJoints(Experiment):
def run(self,frame_range):
raw_input('be sure engines are started in git root!')
datapath = os.path.join(os.path.dirname(__file__),"Test Data")
num_particles_firststep = 1024*40
num_particles = 1024*30
cutoff = 1024*15
lag = 10
pose_model = pose_models.ThreeJoints()
variances = {
'x': {'init':3.0, 'subsq':2.0},
'y': {'init':3.0, 'subsq':2.0},
'theta_yaw': {'init':7.0, 'subsq':3.0},
'z': {'init':3.0, 'subsq':0.25},
'theta_roll': {'init':0.01, 'subsq':0.01},
's_w': {'init':2.0, 'subsq':1e-1},
's_l': {'init':2.0, 'subsq':1e-1},
's_h': {'init':1.0, 'subsq':1e-1}
}
particle_fields = pose_model.ParticlePose._fields
joint_names = [j for j in pose_model.ParticlePose._fields if 'psi_' in j]
[variances.update({j:{'init':20.0, 'subsq':5.0}}) for j in joint_names]
randomwalk_noisechol = np.diag([variances[p]['init'] for p in particle_fields])
subsequent_randomwalk_noisechol = np.diag([variances[p]['subsq'] for p in particle_fields])
_build_mousescene(pose_model.scenefilepath)
images, xytheta = _load_data(datapath,frame_range)
pose_model.default_renderer_pose = \
pose_model.default_renderer_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
pose_model.default_particle_pose = \
pose_model.default_particle_pose._replace(theta_yaw=xytheta[0,2],x=xytheta[0,0],y=xytheta[0,1])
import parallel
dv = parallel.go_parallel(pose_model.scenefilepath,datapath,frame_range)
def log_likelihood(stepnum,_,poses):
dv.scatter('poses',pose_model.expand_poses(poses),block=True)
dv.execute('''likelihoods = ms.get_likelihood(images[%d],particle_data=poses,
x=xytheta[%d,0],y=xytheta[%d,1],theta=xytheta[%d,2])/4000.'''
% (stepnum,stepnum,stepnum,stepnum),block=True)
return dv.gather('likelihoods',block=True)
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.LimitedAR(
numlags=1,
minmaxipars=[(0,320),(0,240),(-np.inf,np.inf),(-60,200),(-30,30),(-np.inf,np.inf),(-np.inf,np.inf),(-np.inf,np.inf),(-30,30),(-20,50),(-50,20)],
previous_outputs=(pose_model.default_particle_pose,),
baseclass=lambda: pm.RandomWalk(noiseclass=lambda: pd.FixedNoise(randomwalk_noisechol))
) for itr in range(num_particles_firststep)])
# do the first few
pf.step(images[0])
pf.step(images[1])
pf.change_numparticles(num_particles)
starters = pf.particles
propmatrix = np.hstack((1.25*np.eye(pose_model.particle_pose_tuple_len),-0.25*np.eye(pose_model.particle_pose_tuple_len)))
pf = particle_filter.ParticleFilter(
pose_model.particle_pose_tuple_len,
cutoff,
log_likelihood,
[particle_filter.LimitedAR(
numlags=2,
minmaxipars=[(0,320),(0,240),(-np.inf,np.inf),(-60,200),(-30,30),(-np.inf,np.inf),(-np.inf,np.inf),(-np.inf,np.inf),(-30,30),(-20,50),(-50,20)],
previous_outputs=(p.track[1],p.track[0]),
baseclass=lambda: pm.Momentum(propmatrix=propmatrix,noiseclass=lambda: pd.FixedNoise(subsequent_randomwalk_noisechol))
) for p in starters]
)
for i in progprint_xrange(2,lag):
pf.step(images[i])
# now step with freezing traces
traces = []
for i in progprint_xrange(lag,images.shape[0],perline=10):
traces.append((i,[p.track[-lag:] for p in pf.particles]))
print 'unique: %d' % len(np.unique([p.track[-lag] for p in pf.particles]))
pf.step(images[i])
if (i % 10) == 0:
self.save_progress(pf,pose_model,datapath,frame_range,traces=traces)
self.save_progress(pf,pose_model,datapath,frame_range,traces=traces)
### currently busted
class RandomWalkWithInjection(Experiment):
def run(self,frame_range):
datapath = os.path.join(os.path.dirname(__file__),"Test Data","Blurred Edge")
num_particles_firststep = 1024*50
num_particles = 1024*30
cutoff = 1024*15
xytheta_dart_noisechol = np.diag((2.,2.,7.))