def eval(self, M, compute_gradient=True, fM=None, **kwargs):
outputs = {}
if compute_gradient:
if self.fspace:
dM = density.zeros_like(fM)
else:
dM = density.zeros_like(M)
return 0.0, dM, outputs
else:
return 0.0, outputs
def __init__(self, expbase, cmdparams=None):
"""cryodata is a CryoData instance.
expbase is a path to the base of folder where this experiment's files
will be stored. The folder above expbase will also be searched
for .params files. These will be loaded first."""
BackgroundWorker.__init__(self)
# Create a background thread which handles IO
self.io_queue = Queue()
self.io_thread = Thread(target=self.ioworker)
self.io_thread.daemon = True
self.io_thread.start()
# General setup ----------------------------------------------------
self.expbase = expbase
self.outbase = None
# Paramter setup ---------------------------------------------------
# search above expbase for params files
_,_,filenames = os.walk(opj(expbase,'../')).next()
self.paramfiles = [opj(opj(expbase,'../'), fname) \
for fname in filenames if fname.endswith('.params')]
# search expbase for params files
_,_,filenames = os.walk(opj(expbase)).next()
self.paramfiles += [opj(expbase,fname) \
for fname in filenames if fname.endswith('.params')]
if 'local.params' in filenames:
self.paramfiles += [opj(expbase,'local.params')]
# load parameter files
self.params = Params(self.paramfiles)
self.cparams = None
if cmdparams is not None:
# Set parameter specified on the command line
for k,v in cmdparams.iteritems():
self.params[k] = v
# Dataset setup -------------------------------------------------------
self.imgpath = self.params['inpath']
psize = self.params['resolution']
if not isinstance(self.imgpath,list):
imgstk = MRCImageStack(self.imgpath,psize)
else:
imgstk = CombinedImageStack([MRCImageStack(cimgpath,psize) for cimgpath in self.imgpath])
if self.params.get('float_images',True):
imgstk.float_images()
self.ctfpath = self.params['ctfpath']
mscope_params = self.params['microscope_params']
if not isinstance(self.ctfpath,list):
ctfstk = CTFStack(self.ctfpath,mscope_params)
else:
ctfstk = CombinedCTFStack([CTFStack(cctfpath,mscope_params) for cctfpath in self.ctfpath])
self.cryodata = CryoDataset(imgstk,ctfstk)
self.cryodata.compute_noise_statistics()
if self.params.get('window_images',True):
imgstk.window_images()
minibatch_size = self.params['minisize']
testset_size = self.params['test_imgs']
partition = self.params.get('partition',0)
num_partitions = self.params.get('num_partitions',1)
seed = self.params['random_seed']
if isinstance(partition,str):
partition = eval(partition)
if isinstance(num_partitions,str):
num_partitions = eval(num_partitions)
if isinstance(seed,str):
seed = eval(seed)
self.cryodata.divide_dataset(minibatch_size,testset_size,partition,num_partitions,seed)
self.cryodata.set_datasign(self.params.get('datasign','auto'))
if self.params.get('normalize_data',True):
self.cryodata.normalize_dataset()
self.voxel_size = self.cryodata.pixel_size
# Iterations setup -------------------------------------------------
self.iteration = 0
self.tic_epoch = None
self.num_data_evals = 0
self.eval_params()
outdir = self.cparams.get('outdir',None)
if outdir is None:
if self.cparams.get('num_partitions',1) > 1:
outdir = 'partition{0}'.format(self.cparams['partition'])
else:
outdir = ''
self.outbase = opj(self.expbase,outdir)
if not os.path.isdir(self.outbase):
os.makedirs(self.outbase)
# Output setup -----------------------------------------------------
self.ostream = OutputStream(opj(self.outbase,'stdout'))
self.ostream(80*"=")
self.ostream("Experiment: " + expbase + \
" Kernel: " + self.params['kernel'])
self.ostream("Started on " + socket.gethostname() + \
" At: " + time.strftime('%B %d %Y: %I:%M:%S %p'))
self.ostream("Git SHA1: " + gitutil.git_get_SHA1())
self.ostream(80*"=")
gitutil.git_info_dump(opj(self.outbase, 'gitinfo'))
self.startdatetime = datetime.now()
# for diagnostics and parameters
self.diagout = Output(opj(self.outbase, 'diag'),runningout=False)
# for stats (per image etc)
self.statout = Output(opj(self.outbase, 'stat'),runningout=True)
# for likelihoods of individual images
self.likeout = Output(opj(self.outbase, 'like'),runningout=False)
self.img_likes = n.empty(self.cryodata.N_D)
self.img_likes[:] = n.inf
# optimization state vars ------------------------------------------
init_model = self.cparams.get('init_model',None)
if init_model is not None:
filename = init_model
if filename.upper().endswith('.MRC'):
M = readMRC(filename)
else:
with open(filename) as fp:
M = cPickle.load(fp)
if type(M)==list:
M = M[-1]['M']
if M.shape != 3*(self.cryodata.N,):
M = cryoem.resize_ndarray(M,3*(self.cryodata.N,),axes=(0,1,2))
else:
init_seed = self.cparams.get('init_random_seed',0) + self.cparams.get('partition',0)
print "Randomly generating initial density (init_random_seed = {0})...".format(init_seed), ; sys.stdout.flush()
tic = time.time()
M = cryoem.generate_phantom_density(self.cryodata.N, 0.95*self.cryodata.N/2.0, \
5*self.cryodata.N/128.0, 30, seed=init_seed)
print "done in {0}s".format(time.time() - tic)
tic = time.time()
print "Windowing and aligning initial density...", ; sys.stdout.flush()
# window the initial density
wfunc = self.cparams.get('init_window','circle')
cryoem.window(M,wfunc)
# Center and orient the initial density
cryoem.align_density(M)
print "done in {0:.2f}s".format(time.time() - tic)
# apply the symmetry operator
init_sym = get_symmetryop(self.cparams.get('init_symmetry',self.cparams.get('symmetry',None)))
if init_sym is not None:
tic = time.time()
print "Applying symmetry operator...", ; sys.stdout.flush()
M = init_sym.apply(M)
print "done in {0:.2f}s".format(time.time() - tic)
tic = time.time()
print "Scaling initial model...", ; sys.stdout.flush()
modelscale = self.cparams.get('modelscale','auto')
mleDC, _, mleDC_est_std = self.cryodata.get_dc_estimate()
if modelscale == 'auto':
# Err on the side of a weaker prior by using a larger value for modelscale
modelscale = (n.abs(mleDC) + 2*mleDC_est_std)/self.cryodata.N
print "estimated modelscale = {0:.3g}...".format(modelscale), ; sys.stdout.flush()
self.params['modelscale'] = modelscale
self.cparams['modelscale'] = modelscale
M *= modelscale/M.sum()
print "done in {0:.2f}s".format(time.time() - tic)
if mleDC_est_std/n.abs(mleDC) > 0.05:
print " WARNING: the DC component estimate has a high relative variance, it may be inaccurate!"
if ((modelscale*self.cryodata.N - n.abs(mleDC)) / mleDC_est_std) > 3:
print " WARNING: the selected modelscale value is more than 3 std devs different than the estimated one. Be sure this is correct."
self.M = n.require(M,dtype=density.real_t)
self.fM = density.real_to_fspace(M)
self.dM = density.zeros_like(self.M)
self.step = eval(self.cparams['optim_algo'])
self.step.setup(self.cparams, self.diagout, self.statout, self.ostream)
# Objective function setup --------------------------------------------
param_type = self.cparams.get('parameterization','real')
cplx_param = param_type in ['complex','complex_coeff','complex_herm_coeff']
self.like_func = eval_objective(self.cparams['likelihood'])
self.prior_func = eval_objective(self.cparams['prior'])
if self.cparams.get('penalty',None) is not None:
self.penalty_func = eval_objective(self.cparams['penalty'])
prior_func = SumObjectives(self.prior_func.fspace, \
[self.penalty_func,self.prior_func], None)
else:
prior_func = self.prior_func
self.obj = SumObjectives(cplx_param,
[self.like_func,prior_func], [None,None])
self.obj.setup(self.cparams, self.diagout, self.statout, self.ostream)
self.obj.set_dataset(self.cryodata)
self.obj_wrapper = ObjectiveWrapper(param_type)
self.last_save = time.time()
self.logpost_history = FiniteRunningSum()
self.like_history = FiniteRunningSum()
# Importance Samplers -------------------------------------------------
self.is_sym = get_symmetryop(self.cparams.get('is_symmetry',self.cparams.get('symmetry',None)))
self.sampler_R = FixedFisherImportanceSampler('_R',self.is_sym)
self.sampler_I = FixedFisherImportanceSampler('_I')
self.sampler_S = FixedGaussianImportanceSampler('_S')
self.like_func.set_samplers(sampler_R=self.sampler_R,sampler_I=self.sampler_I,sampler_S=self.sampler_S)
def eval(self,
M=None,
fM=None,
compute_gradient=True,
all_grads=False,
**kwargs):
anyfspace = any([obj.fspace for obj in self.objs])
anyrspace = any([not obj.fspace for obj in self.objs])
N = None
if fM is None and anyfspace:
assert M is not None, 'M or fM must be set!'
N = M.shape[0]
# fM = density.real_to_fspace(M)
fM = M
elif fM is not None:
N = fM.shape[0]
if M is None and anyrspace:
assert fM is not None, 'M or fM must be set!'
N = fM.shape[0]
# M = density.fspace_to_real(fM)
M = fM
elif M is not None:
assert N is None or N == M.shape[0]
N = M.shape[0]
assert N is not None
logP = 0
logPs = []
if compute_gradient:
if all_grads:
dlogP = density.zeros_like(
fM) if self.fspace else density.zeros_like(M)
dlogPs = []
else:
if (not self.fspace) or anyrspace:
dlogPdM = density.zeros_like(M)
if self.fspace or anyfspace:
dlogPdfM = density.zeros_like(fM)
outputs = {}
for w, obj in zip(self.ws, self.objs):
if compute_gradient:
clogP, cdlogP, coutputs = obj.eval(
M=M, fM=fM, compute_gradient=compute_gradient, **kwargs)
if w is not None and w != 1:
clogP *= w
cdlogP *= w
if all_grads:
if obj.fspace == self.fspace:
dlogPs.append(cdlogP)
elif self.fspace:
# dlogPs.append(density.real_to_fspace(cdlogP))
dlogPs.append(cdlogP)
else:
# dlogPs.append(density.fspace_to_real(cdlogP))
dlogPs.append(cdlogP)
dlogP += dlogPs[-1]
else:
if obj.fspace:
dlogPdfM += cdlogP
else:
dlogPdM += cdlogP
else:
clogP, coutputs = obj.eval(M=M,
fM=fM,
compute_gradient=compute_gradient,
**kwargs)
if w is not None and w != 1:
clogP *= w
logP += clogP
logPs.append(clogP)
outputs.update(coutputs)
if compute_gradient and not all_grads:
if self.fspace:
dlogP = dlogPdfM
if anyrspace:
# dlogP += density.real_to_fspace(dlogPdM)
dlogP += dlogPdM
else:
dlogP = dlogPdM
if anyfspace:
# dlogP += density.fspace_to_real(dlogPdfM)
dlogP += dlogPdfM
outputs['all_logPs'] = logPs
if compute_gradient and all_grads:
outputs['all_dlogPs'] = dlogPs
if compute_gradient:
return logP, dlogP, outputs
else:
return logP, outputs
def eval(self, M=None, fM=None, compute_gradient=True, all_grads=False,**kwargs):
anyfspace = any([obj.fspace for obj in self.objs])
anyrspace = any([not obj.fspace for obj in self.objs])
N = None
if fM is None and anyfspace:
assert M is not None, 'M or fM must be set!'
N = M.shape[0]
fM = density.real_to_fspace(M)
elif fM is not None:
N = fM.shape[0]
if M is None and anyrspace:
assert fM is not None, 'M or fM must be set!'
N = fM.shape[0]
M = density.fspace_to_real(fM)
elif M is not None:
assert N is None or N == M.shape[0]
N = M.shape[0]
assert N is not None
logP = 0
logPs = []
if compute_gradient:
if all_grads:
dlogP = density.zeros_like(fM) if self.fspace else density.zeros_like(M)
dlogPs = []
else:
if (not self.fspace) or anyrspace:
dlogPdM = density.zeros_like(M)
if self.fspace or anyfspace:
dlogPdfM = density.zeros_like(fM)
outputs = {}
for w,obj in zip(self.ws,self.objs):
if compute_gradient:
clogP, cdlogP, coutputs = obj.eval(M = M, fM = fM,
compute_gradient = compute_gradient,
**kwargs)
if w is not None and w != 1:
clogP *= w
cdlogP *= w
if all_grads:
if obj.fspace == self.fspace:
dlogPs.append(cdlogP)
elif self.fspace:
dlogPs.append(density.real_to_fspace(cdlogP))
else:
dlogPs.append(density.fspace_to_real(cdlogP))
dlogP += dlogPs[-1]
else:
if obj.fspace:
dlogPdfM += cdlogP
else:
dlogPdM += cdlogP
else:
clogP, coutputs = obj.eval(M = M, fM = fM,
compute_gradient = compute_gradient,
**kwargs)
if w is not None and w != 1:
clogP *= w
logP += clogP
logPs.append(clogP)
outputs.update(coutputs)
if compute_gradient and not all_grads:
if self.fspace:
dlogP = dlogPdfM
if anyrspace:
dlogP += density.real_to_fspace(dlogPdM)
else:
dlogP = dlogPdM
if anyfspace:
dlogP += density.fspace_to_real(dlogPdfM)
outputs['all_logPs'] = logPs
if compute_gradient and all_grads:
outputs['all_dlogPs'] = dlogPs
if compute_gradient:
return logP, dlogP, outputs
else:
return logP, outputs