def normalise_callback(data,
normalise_func=normalise,
use_imagenet_stats=False):
if use_imagenet_stats:
m, s = imagenet_mean, imagenet_std
else:
x, _ = next(iter(data.train_dl))
m, s = extract_mean_std(
x[0]) if normalise_func == cap_normalise else extract_mean_std(x)
norm = partial(normalise_func, mean=m.cuda(), std=s.cuda())
return partial(BatchTransformCallback, norm)
def update_clusters_cpu(self, timeout=2 * 60):
cpu_family = self.get_ovirt_cpu_family()
api = self.engine_vm().get_api_v4(check=True)
clusters_service = api.system_service().clusters_service()
clusters = clusters_service.list()
if clusters is None:
LOGGER.debug('no clusters found: skipping')
return
for cluster in clusters:
if cluster.cpu.type == cpu_family:
continue
LOGGER.debug(
('found CPU cluster mismatch, current: {0}, required: '
'{1}').format(cluster.cpu.type, cpu_family))
cluster_service = clusters_service.cluster_service(cluster.id)
cluster_service.update(
otypes.Cluster(cpu=otypes.Cpu(type=cpu_family)))
def _assert_cluster_cpu(cluster):
cluster = clusters_service.cluster_service(cluster.id).get()
return cluster.cpu.type == cpu_family
testlib.assert_true_within(partial(_assert_cluster_cpu, cluster),
timeout=timeout)
LOGGER.debug(('successfuly changed cluster id {0} to cpu family: '
'{1}').format(cluster.id, cpu_family))
def get_cap_dls(labelled_list, train_bs, valid_bs,collate_fn = None, shuffle = True, num_workers = 0,flip = False):
if valid_bs is None:
valid_bs = train_bs
if collate_fn is not None:
collate_fn = partial(collate_fn, pad_idx = labelled_list.train.py.tokenizer.pad_token_id, flip = flip)
return (DataLoader(labelled_list.train, batch_size= train_bs, shuffle=shuffle, collate_fn=collate_fn, num_workers=num_workers),
DataLoader(labelled_list.valid, batch_size= valid_bs, shuffle=shuffle, collate_fn=collate_fn, num_workers=num_workers))
def assert_vdsm_alive(self, timeout=2 * 60):
"""
Assert service 'vdsmd' reports running on all vdsm hosts
Args:
timeout(int): timeout
Returns:
None
Raises:
AssertionError: if vdsmd is not reported running after the
given timeout, or ssh is unreachable.
"""
def _vdsm_up(host):
status = host.service('vdsmd').alive()
LOGGER.debug('vdsm status: %s', status)
return status
for host in self.host_vms():
testlib.assert_true_within(
partial(_vdsm_up, host),
timeout=timeout,
allowed_exceptions=self._get_check_running_allowed_exceptions(
),
)
def start_all_hosts(self, timeout=5 * 60):
api = self.get_api_v4(check=True)
hosts_service = api.system_service().hosts_service()
hosts = hosts_service.list(search='status=maintenance')
if hosts:
def _host_is_up(host):
h_service = hosts_service.host_service(host.id)
host_obj = h_service.get()
if host_obj.status == otypes.HostStatus.UP:
return True
if host_obj.status == otypes.HostStatus.NON_OPERATIONAL:
raise RuntimeError('Host %s is in non operational state' %
host.name)
elif host_obj.status == otypes.HostStatus.INSTALL_FAILED:
raise RuntimeError('Host %s installation failed' %
host.name)
for host in hosts:
host_service = hosts_service.host_service(host.id)
host_service.activate()
for host in hosts:
testlib.assert_true_within(partial(_host_is_up, host),
timeout=timeout)
def parse(self):
""" Parses the code
"""
def is_fn(x):
return x.startswith("fn_")
fns=filter(is_fn, self.__dict__)
self.fns=map(lambda x:x[3:], fns)
def assign_line_nbr(line, linenbr):
return (linenbr, line)
def tokenize(x):
try:
code, value=versa_int(x) #maybe scalar
assert(code==True)
return (code, value)
except AssertionError:
if x in self.fns:
return ("function", x)
return ("unknown", x)
def res((_, tokens)):
return (_, map(tokenize, tokens))
## we want non-empty lines
_=self.code.split("\n")
_=filter(not_empty, _)
_lines=map(assign_line_nbr, _, range(0, len(_))) ## (linenbr, line_data)
## since the separator is the space,
## we want to make sure that coma separated constructs
## are treated as correctly: compress ', ' to ','
f=compose([partial(freplace, ", ", ","), partial(fsplit, " ")])
_sts=map(f, _lines)
self.sts=filter(f_not_empty, _sts)
self.xsts=map(res, self.sts)
return self
def check_sds_status(self, status=None, timeout=5 * 60):
# the default status cannot be used in the function header, because
# the v4 sdk might not be available.
if status is None:
status = otypes.StorageDomainStatus.ACTIVE
api = self.get_api_v4(check=True)
dcs_service = api.system_service().data_centers_service()
for dc in dcs_service.list():
def _sds_state(dc_id):
dc_service = dcs_service.data_center_service(dc_id)
sds = dc_service.storage_domains_service()
return all(sd.status == status for sd in sds.list())
testlib.assert_true_within(partial(_sds_state, dc_id=dc.id),
timeout=timeout)
def stop_all_vms(self, timeout=5 * 60):
api = self.get_api_v4(check=True)
vms_service = api.system_service().vms_service()
ids = self._search_vms(api, query='status=up')
[vms_service.vm_service(id).stop() for id in ids]
def _vm_is_down(id):
vm_srv = vms_service.vm_service(id)
vm = vm_srv.get()
if vm.status == otypes.VmStatus.DOWN:
LOGGER.debug('Engine VM ID %s, is down', id)
return True
for id in ids:
testlib.assert_true_within(partial(_vm_is_down, id=id),
timeout=timeout)
def g(self):
"""Returns the left-hand side of the first equation of the system of
non-linear PDE for the primal/dual variables (w,u):
g(w,u) = - a div w + K* (Ku - z) ,
where 'u' is the real image (volume), 'z' the observed image (
projections), 'w' the variable dual to 'u', 'a' a coefficient
balancing the trade-off between data fidelity '|| K u - z||^2_L2'
and regularization 'TV(u) = || |grad u| ||_L1'.
Returns
-------
numpy.array
Array of the same dimension as the reconstruction volume 'u'.
"""
ushape = self.u.shape
assert self.K.num_voxel == ushape, "Volume data dimension mismatch"
# K u
self.K.set_volume_data(self.u)
self.K.forward()
assert self.z.shape == self.K.projection_shape, \
"Projection data mismatch"
# K* (K u - z)
self.K.set_projection_data(self.K.projection_data - self.z)
self.K.backward()
# Create slice object in order to fix dimension mismatch after
# taking derivative s = [slice(0, nn - 1) for nn in ushape]
# In order to compute the divergence of w, a generator comprehension
# is used to create an iterable object containing the 1D
# derivatives of 'w'. return - self.a * reduce(add, (np.diff(wn, 1,
# n)[s] for (n, wn) in enumerate(self.w))) + self.K.volume_data[s]
return -self.a * reduce(
add, (partial(wn, n)
for (n, wn) in enumerate(self.w))) + self.K.volume_data
def assert_engine_alive(self, timeout=2 * 60):
"""
Assert service 'ovirt-engine' reports running on the engine VM
Args:
timeout(int): timeout
Returns:
None
Raises:
AssertionError: if ovirt-engine is not reported running after the
given timeout, or ssh is unreachable.
"""
def _ovirt_engine_up(host):
status = host.service('ovirt-engine').alive()
LOGGER.debug('ovirt-engine status: %s', status)
return status
testlib.assert_true_within(partial(_ovirt_engine_up, self.engine_vm()),
timeout=timeout)
def g(self):
"""Returns the left-hand side of the first equation of the system of
non-linear PDE for the primal/dual variables (w,u):
g(w,u) = - a div w + K* (Ku - z) ,
where 'u' is the real image (volume), 'z' the observed image (
projections), 'w' the variable dual to 'u', 'a' a coefficient
balancing the trade-off between data fidelity '|| K u - z||^2_L2'
and regularization 'TV(u) = || |grad u| ||_L1'.
Returns
-------
numpy.array
Array of the same dimension as the reconstruction volume 'u'.
"""
ushape = self.u.shape
assert self.K.num_voxel == ushape, "Volume data dimension mismatch"
# K u
self.K.set_volume_data(self.u)
self.K.forward()
assert self.z.shape == self.K.projection_shape, \
"Projection data mismatch"
# K* (K u - z)
self.K.set_projection_data(self.K.projection_data - self.z)
self.K.backward()
# Create slice object in order to fix dimension mismatch after
# taking derivative s = [slice(0, nn - 1) for nn in ushape]
# In order to compute the divergence of w, a generator comprehension
# is used to create an iterable object containing the 1D
# derivatives of 'w'. return - self.a * reduce(add, (np.diff(wn, 1,
# n)[s] for (n, wn) in enumerate(self.w))) + self.K.volume_data[s]
return - self.a * reduce(add, (partial(wn, n) for (n, wn) in
enumerate(self.w))) + self.K.volume_data
def fastmatch(t1, t2):
"""
Calculates a match between t1 and t2.
See figure 10 in reference.
"""
M = Bimap()
label(t1)
label(t2)
depth = max(get_depth(t1), get_depth(t2))
while 0 <= depth:
nodes1 = get_depth_nodes(t1, depth)
nodes2 = get_depth_nodes(t2, depth)
equal = utils.partial(depth_equal, 0.6, 0.5, M)
_match(nodes1, nodes2, M, equal)
depth -= 1
return M
def fastmatch(t1, t2):
"""
Calculates a match between t1 and t2.
See figure 10 in reference.
"""
M = Bimap()
label(t1)
label(t2)
depth = max(get_depth(t1), get_depth(t2))
while 0 <= depth:
nodes1 = get_depth_nodes(t1, depth)
nodes2 = get_depth_nodes(t2, depth)
equal = utils.partial(depth_equal, 0.6, 0.5, M)
_match(nodes1, nodes2, M, equal)
depth -= 1
return M
def __init__(self,
visual_model,
num_visual_features,
textual_features,
vocab_size,
pad_token_id,
max_len=49,
encoding_drop=0.1,
N=6,
heads=8,
attn_drop=0.1,
ff_drop=0.1,
d_ff=2048,
activation='GELU'):
super().__init__()
self.visual_backbone = visual_model
self.th = Xcoder(True,
N,
textual_features,
h=heads,
d_ff=d_ff,
ff_drop=ff_drop,
attn_drop=attn_drop,
activation=activation)
self.visual_features = []
self.lin_projection = nn.Linear(num_visual_features, textual_features)
self.embed = WordEmbedding(vocab_size,
textual_features,
padding_index=pad_token_id)
self.pos_enc = PositionalEncoding(textual_features, max_len,
encoding_drop)
self._register_hook(self.visual_backbone,
partial(self.hook_function, self.visual_features))
self.lin_out = nn.Linear(textual_features, vocab_size)
self.lin_out.weight = self.embed.emb.weight
self.pad_tok_id = pad_token_id
def matrix_norm(self, iterations, vol_init=1.0,
tv_norm=False, return_volume=False,
intermediate_results=False):
"""The matrix norm || K ||_2 of 'K' defined here as largest
singular value of 'K'. Employs the generic power method to obtain a
scalar 's' which tends to || K ||_2 as the iterations N increase.
To be implemented: optionally return volume 'x', such that it can be
re-used as initializer to continue the iteration.
Parameters
----------
:type iterations: int
:param iterations: Number of iterations of the generic power method.
:type vol_init: float | ndarray (default 1.0)
:param vol_init: in I, initial image to start with.
:type intermediate_results: bool
:param intermediate_results: Returns list of intermediate results
instead of scalar.
:type return_volume: bool
:param return_volume: Return volume in order to resume iteration via
passing it over as initial volume.
Returns
-------
:rtype: float | numpy.ndarray, numpay.array (optional)
:returns: s, vol
s: Scalar of final iteration or numpy.ndarray containing all
results during iteration.
vol: Volume vector
"""
geom = self.geom
vol = self.recon_space.element(vol_init)
proj = Rn(geom.proj_size).zero()
# projector = Projector(geom, vol.space, proj.space)
projector = Projector(geom)
# print 'projector scaling factor', projector.scal_fac
tmp = None
if intermediate_results:
s = np.zeros(iterations)
else:
s = 0
# Power method loop
for n in range(iterations):
# step 4: x_{n+1} <- K^T K x_n
if tv_norm:
# K = (A, grad) instead of K = A
# Compute: - div grad x_n
# use sum over generator expression
tmp = -reduce(add,
(partial(
partial(vol.data.reshape(geom.vol_shape),
dim, geom.voxel_width[dim]),
dim, geom.voxel_width[dim]) for dim in
range(geom.vol_ndim)))
# x_n <- A^T (A x_n)
vol = projector.backward(projector.forward(vol))
vol *= self.adj_scal_fac
if tv_norm:
# x_n <- x_n - div grad x_n
# print 'n: {2}. vol: min = {0}, max = {1}'.format(
# vol.data.min(), vol.data.max(), n)
# print 'n: {2}. tv: min = {0}, max = {1}'.format(tmp.min(),
# tmp.max(), n)
vol.data[:] += tmp.ravel()
# step 5:
# x_n <- x_n/||x_n||_2
vol /= vol.norm()
# step 6:
# s_n <-|| K x ||_2
if intermediate_results:
# proj <- A^T x_n
proj = projector.forward(vol)
s[n] = proj.norm()
if tv_norm:
s[n] = np.sqrt(s[n] ** 2 +
reduce(add,
(np.linalg.norm(
partial(vol.data.reshape(
geom.vol_shape), dim,
geom.voxel_width[dim])) ** 2
for dim in range(geom.vol_ndim))))
# step 6: || K x ||_2
if not intermediate_results:
proj = projector.forward(vol)
s = proj.norm()
if tv_norm:
s = np.sqrt(s ** 2 + reduce(add,
(np.linalg.norm(partial(
vol.data.reshape(
geom.vol_shape), dim,
geom.voxel_width[dim])) ** 2
for dim in range(geom.vol_ndim))))
# Clear ASTRA memory
projector.clear_astra_memory()
# Returns
if not return_volume:
return s
else:
return s, vol.data
def least_squares(self, iterations=1, L=None, tau=None, sigma=None,
theta=None, non_negativiy_constraint=False,
tv_norm=False,
verbose=True):
"""Least-squares problem with optional TV-regularisation and/or
non-negativity constraint.
Parameters
----------
:type iterations: int (default 1)
:param iterations: Number of iterations the optimization should
run for.
:type L: float (defaul: None)
:param L: Matrix norm of forward projector. If 'None' matrix_norm is
called with 20 iterations.
:type tau: float (default 1/L)
:param tau:
:type sigma: float (default 1/L)
:param sigma:
:type theta: float (default 1)
:param theta:
:type non_negativiy_constraint: bool (default False)
:param non_negativiy_constraint: Add non-negativity constraint to
optimization problem (via indicator function).
:type tv_norm: bool | float (default False)
:param tv_norm: Unless False, coincides with the numerical value of
the parameter lambda for TV-Regularisation.
:type verbose: bool (default False)
:param verbose: Show intermediate reconstructions and
convergence measures during iteration.
Returns
-------
:rtype: odl.Vector, odl.Vector, numpy.ndarray, numpy.ndarray
:returns: u, p, cpd, l2_du
u: vector of reconstructed volume
p: vector of dual projection variable
cpd: condition primal-dual gap (convergence measure)
l2_du: l2-norm of constraint-induced convergence measure
"""
# step 1:
if L is None:
L = self.matrix_norm(20)
if tau is None:
tau = 1 / L
if sigma is None:
sigma = 1 / L
if theta is None:
theta = 1
# print 'tau:', tau
# print 'sigma:', sigma
# print 'theta:', theta
geom = self.geom
g = self.proj # domain: D
# l2-norm of (volume update / tau)
l2_du = np.zeros(iterations)
# conditional primal-dual gap
cpd = np.zeros(iterations)
# step 2: initialize u and p with zeros
u = self.recon_space.zero() # domain: I
p = g.space.zero() # domain: D
# q: spatial vector = list of ndarrays in I (not Rn vectors)
if tv_norm:
ndim = geom.vol_ndim
# domain of q: V = [I, I, ...]
q = [np.zeros(geom.vol_shape, dtype=u.data.dtype) for _ in range(
ndim)]
# step 3: ub <- u
ub = u.copy() # domain: I
# initialize projector
# A = Projector(geom, u.space, p.space)
A = Projector(geom)
# visual output instance
disp = DisplayIntermediates(verbose=verbose, vol=u.data.reshape(
geom.vol_shape), cpd=cpd, l2_du=l2_du)
# step 4: repeat
for n in range(iterations):
# step 5: p_{n+1} <- (p_n + sigma(A^T ub_n - g)) / (1 + sigma)
if n >= 0:
# with(Timer('proj:')):
# # p_tmp <- A ub
# p_tmp = A.forward(ub)
# # p_tmp <- p_tmp - g
# p_tmp -= g
# # p <- p + sigma * p_tmp
# p += sigma * p_tmp
# p_n <- p_n + sigma(A ub -g )
tmp = A.forward(ub)
# print 'p:', p.data.shape, 'Au:', tmp.data.shape, 'g:', \
# g.data.shape
p += sigma * (A.forward(ub) - g)
else:
p -= sigma * g
# p <- p / (1 + sigma)
p /= 1 + sigma
# TV step 6: q_{n+1} <- lambda(q_n + sigma grad ub_n) /
# max(lambda 1_I, |q_n + sigma grad ub_n|)
if tv_norm:
for dim in range(ndim):
# q_n <- q_n + sigma * grad ub_n
q[dim] += sigma * partial(ub.data.reshape(
self.geom.vol_shape), dim, geom.voxel_width[dim])
# |q_n|: isotropic TV
# use div_q to save memory, q = [qi] where qi are ndarrays
div_q = np.sqrt(reduce(add, (qi ** 2 for qi in q)))
# max(lambda 1_I, |q_n + sigma diff ub_n|)
# print 'q_mag:', div_q.min(), div_q.max()
div_q[div_q < tv_norm] = tv_norm
# q_n <- lambda * q_n / |q_n|
for dim in range(ndim):
q[dim] /= div_q
q[dim] *= tv_norm
# div q_{n+1}
div_q = reduce(add, (partial(qi, dim, geom.voxel_width[dim])
for (dim, qi) in enumerate(q)))
div_q *= tau
# step 6: u_{n+1} <- u_{n} - tau * A^T p_{n+1}
# TV step 7: u_{n+1} <- u_{n} - tau * A^T p_{n+1} + div q_{n+1}
# ub_tmp <- A^T p
ub_tmp = A.backward(p)
ub_tmp *= tau
ub_tmp *= self.adj_scal_fac
# l2-norm per voxel of ub_tmp = A^T p
l2_du[n:] = ub_tmp.norm() # / u.data.size
if tv_norm:
l2_du[n:] += np.linalg.norm(div_q.ravel()) # / u.data.size
# store current u_n temporarily in ub_n
ub = -u.copy()
# u <- u - tau ub_tmp
u -= ub_tmp
# TV: u <- u + tau div q
if tv_norm:
print('{0}: u - A^T p: min = {1}, max = {2}'.format(
n, u.data.min(), u.data.max()))
print('{0}: div q: min = {1}, max = {2}'.format(
n, div_q.min(), div_q.max()))
u.data[:] += div_q.ravel()
# Positivity constraint
if non_negativiy_constraint:
u.data[u.data < 0] = 0
# print '\nu:', u.data.min(), u.data.max()
# conditional primal-dual gap for current u and p
# 1/2||A u - g||_2^2 + 1/2||p||_2^2 + <p,g>_D
# p_tmp <- A u
# p_tmp = A.forward(u)
# p_tmp -= g
# cpd[n:] = (0.5 * p_tmp.norm() ** 2 +
cpd[n:] = (0.5 * p.space.norm(A.forward(u) - g) ** 2 +
0.5 * p.norm() ** 2 +
p.inner(g)) # / p.data.size
if tv_norm:
cpd[n:] += tv_norm * np.linalg.norm(
reduce(add, (partial(u.data.reshape(geom.vol_shape),
dim, geom.voxel_width[dim]) for dim
in range(geom.vol_ndim))
).ravel(), ord=1) # / u.data.size
# step 7 / TV step 8: ub_{n+1} <- u_{n+1} + theta(u_{n+1} - u_n)
# ub <- ub + u_{n+1}, remember ub = -u_n
ub += u
# ub <- theta * ub
ub *= theta
# ub <- ub + u_{n+1}
ub += u
# visual output
disp.update()
A.clear_astra_memory()
# Should avoid window freezing
disp.show()
return u, p, cpd, l2_du
def fit_voxel_grid(voxel_grid,
max_num_fitted_models=5,
use_sphere=True,
use_cuboid=True,
use_capsule=True,
visualize_intermediate=False,
loss_type=LossType.BEST_EFFORT,
use_cuda=False,
cuda_device=None,
component_threshold=0.05):
fitted_models = []
num_voxels_total = voxel_grid.sum().item()
voxels_remaining = voxel_grid.clone()
connected_components = voxel.connected_components(voxels_remaining)
i = 0
while len(connected_components) > 0 and i < max_num_fitted_models:
component = argmax(connected_components, lambda comp: comp.shape[0])
if (float(component.shape[0]) /
num_voxels_total) <= component_threshold:
break
component_points = component.float()
if use_cuda:
component_points = component_points.cuda(device=cuda_device)
lambda_ = 1.0
potential_models = []
if use_sphere:
potential_models.append(SphereModel(component_points, lambda_))
if use_cuboid:
potential_models.append(CuboidModel(component_points, lambda_))
if use_capsule:
potential_models.append(CapsuleModel(component_points, lambda_))
if use_cuda:
for model in potential_models:
model.cuda(device=cuda_device)
best_model = argmin(
potential_models,
partial(optimize, component_points, loss_type=loss_type))
if visualize_intermediate:
fig = plt.figure()
ax = fig.gca(projection='3d')
draw.draw_voxels(ax, voxels_remaining)
best_model.draw(ax)
plt.show()
points_inside_mask = best_model.exact_containment(component_points)
indices_covered = component[points_inside_mask, :]
voxel.batch_set(voxels_remaining, indices_covered, False)
if points_inside_mask.sum().item() > 0:
fitted_models.append(best_model)
else:
# We failed to fit to any of the voxels in this component, so just ignore it
# Todo: try splitting the component up and fit to those pieces
voxel.batch_set(voxels_remaining, component, False)
i += 1
connected_components = voxel.connected_components(voxels_remaining)
return fitted_models
# This can be used once we want to support integer as integer values and
# not as numeric anymore (using masked arrays ?).
acls2dtype = {'real' : np.float, 'integer' : np.float, 'numeric' : np.float}
acls2conv = {'real' : safe_float, 'integer' : safe_float, 'numeric' : safe_float}
descr = []
convertors = []
if not hasstr:
for name, value in attr:
type = parse_type(value)
if type == 'date':
raise ValueError("date type not supported yet, sorry")
elif type == 'nominal':
n = maxnomlen(value)
descr.append((name, 'S%d' % n))
pvalue = get_nom_val(value)
convertors.append(partial(safe_nominal, pvalue = pvalue))
else:
descr.append((name, acls2dtype[type]))
convertors.append(safe_float)
#dc.append(acls2conv[type])
#sdescr.append((name, acls2sdtype[type]))
else:
# How to support string efficiently ? Ideally, we should know the max
# size of the string before allocating the numpy array.
raise NotImplementedError("String attributes not supported yet, sorry")
ni = len(convertors)
# Get the delimiter from the first line of data:
def next_data_line(row_iter):
"""Assumes we are already in the data part (eg after @data)."""
def label_by_df(cls,items,csv_path,index_name,column_name,proc_x = None,proc_y = None):
df = pd.read_csv(csv_path)
df.set_index(index_name,inplace = True)
return cls(items,label_all(items,partial(label_func,df,column_name)), proc_x = proc_x, proc_y = proc_y)
from pathlib import Path
from optimizers import sgd_step, StatefulOptimizer
from callbacks import *
from torch import tensor
from torch.cuda import amp
def param_getter(m):
return m.parameters()
def ifNone(a, b):
return b if a is None else a
sgd_opt = partial(StatefulOptimizer, steppers=[sgd_step])
class Learner():
def __init__(self,
model,
data,
loss_func,
opt_func=sgd_opt,
lr=1e-2,
splitter=param_getter,
cbs=None,
cb_funcs=None,
path=None):
self.model, self.data, self.loss_func, self.opt_func, self.lr, self.splitter = model, data, loss_func, opt_func, lr, splitter
self.in_train, self.logger, self.opt = False, print, None
def least_squares(self,
iterations=1,
L=None,
tau=None,
sigma=None,
theta=None,
non_negativiy_constraint=False,
tv_norm=False,
verbose=True):
"""Least-squares problem with optional TV-regularisation and/or
non-negativity constraint.
Parameters
----------
:type iterations: int (default 1)
:param iterations: Number of iterations the optimization should
run for.
:type L: float (defaul: None)
:param L: Matrix norm of forward projector. If 'None' matrix_norm is
called with 20 iterations.
:type tau: float (default 1/L)
:param tau:
:type sigma: float (default 1/L)
:param sigma:
:type theta: float (default 1)
:param theta:
:type non_negativiy_constraint: bool (default False)
:param non_negativiy_constraint: Add non-negativity constraint to
optimization problem (via indicator function).
:type tv_norm: bool | float (default False)
:param tv_norm: Unless False, coincides with the numerical value of
the parameter lambda for TV-Regularisation.
:type verbose: bool (default False)
:param verbose: Show intermediate reconstructions and
convergence measures during iteration.
Returns
-------
:rtype: odl.Vector, odl.Vector, numpy.ndarray, numpy.ndarray
:returns: u, p, cpd, l2_du
u: vector of reconstructed volume
p: vector of dual projection variable
cpd: condition primal-dual gap (convergence measure)
l2_du: l2-norm of constraint-induced convergence measure
"""
# step 1:
if L is None:
L = self.matrix_norm(20)
if tau is None:
tau = 1 / L
if sigma is None:
sigma = 1 / L
if theta is None:
theta = 1
# print 'tau:', tau
# print 'sigma:', sigma
# print 'theta:', theta
geom = self.geom
g = self.proj # domain: D
# l2-norm of (volume update / tau)
l2_du = np.zeros(iterations)
# conditional primal-dual gap
cpd = np.zeros(iterations)
# step 2: initialize u and p with zeros
u = self.recon_space.zero() # domain: I
p = g.space.zero() # domain: D
# q: spatial vector = list of ndarrays in I (not Rn vectors)
if tv_norm:
ndim = geom.vol_ndim
# domain of q: V = [I, I, ...]
q = [
np.zeros(geom.vol_shape, dtype=u.data.dtype)
for _ in range(ndim)
]
# step 3: ub <- u
ub = u.copy() # domain: I
# initialize projector
# A = Projector(geom, u.space, p.space)
A = Projector(geom)
# visual output instance
disp = DisplayIntermediates(verbose=verbose,
vol=u.data.reshape(geom.vol_shape),
cpd=cpd,
l2_du=l2_du)
# step 4: repeat
for n in range(iterations):
# step 5: p_{n+1} <- (p_n + sigma(A^T ub_n - g)) / (1 + sigma)
if n >= 0:
# with(Timer('proj:')):
# # p_tmp <- A ub
# p_tmp = A.forward(ub)
# # p_tmp <- p_tmp - g
# p_tmp -= g
# # p <- p + sigma * p_tmp
# p += sigma * p_tmp
# p_n <- p_n + sigma(A ub -g )
tmp = A.forward(ub)
# print 'p:', p.data.shape, 'Au:', tmp.data.shape, 'g:', \
# g.data.shape
p += sigma * (A.forward(ub) - g)
else:
p -= sigma * g
# p <- p / (1 + sigma)
p /= 1 + sigma
# TV step 6: q_{n+1} <- lambda(q_n + sigma grad ub_n) /
# max(lambda 1_I, |q_n + sigma grad ub_n|)
if tv_norm:
for dim in range(ndim):
# q_n <- q_n + sigma * grad ub_n
q[dim] += sigma * partial(
ub.data.reshape(self.geom.vol_shape), dim,
geom.voxel_width[dim])
# |q_n|: isotropic TV
# use div_q to save memory, q = [qi] where qi are ndarrays
div_q = np.sqrt(reduce(add, (qi**2 for qi in q)))
# max(lambda 1_I, |q_n + sigma diff ub_n|)
# print 'q_mag:', div_q.min(), div_q.max()
div_q[div_q < tv_norm] = tv_norm
# q_n <- lambda * q_n / |q_n|
for dim in range(ndim):
q[dim] /= div_q
q[dim] *= tv_norm
# div q_{n+1}
div_q = reduce(add, (partial(qi, dim, geom.voxel_width[dim])
for (dim, qi) in enumerate(q)))
div_q *= tau
# step 6: u_{n+1} <- u_{n} - tau * A^T p_{n+1}
# TV step 7: u_{n+1} <- u_{n} - tau * A^T p_{n+1} + div q_{n+1}
# ub_tmp <- A^T p
ub_tmp = A.backward(p)
ub_tmp *= tau
ub_tmp *= self.adj_scal_fac
# l2-norm per voxel of ub_tmp = A^T p
l2_du[n:] = ub_tmp.norm() # / u.data.size
if tv_norm:
l2_du[n:] += np.linalg.norm(div_q.ravel()) # / u.data.size
# store current u_n temporarily in ub_n
ub = -u.copy()
# u <- u - tau ub_tmp
u -= ub_tmp
# TV: u <- u + tau div q
if tv_norm:
print('{0}: u - A^T p: min = {1}, max = {2}'.format(
n, u.data.min(), u.data.max()))
print('{0}: div q: min = {1}, max = {2}'.format(
n, div_q.min(), div_q.max()))
u.data[:] += div_q.ravel()
# Positivity constraint
if non_negativiy_constraint:
u.data[u.data < 0] = 0
# print '\nu:', u.data.min(), u.data.max()
# conditional primal-dual gap for current u and p
# 1/2||A u - g||_2^2 + 1/2||p||_2^2 + <p,g>_D
# p_tmp <- A u
# p_tmp = A.forward(u)
# p_tmp -= g
# cpd[n:] = (0.5 * p_tmp.norm() ** 2 +
cpd[n:] = (0.5 * p.space.norm(A.forward(u) - g)**2 +
0.5 * p.norm()**2 + p.inner(g)) # / p.data.size
if tv_norm:
cpd[n:] += tv_norm * np.linalg.norm(reduce(
add, (partial(u.data.reshape(geom.vol_shape), dim,
geom.voxel_width[dim])
for dim in range(geom.vol_ndim))).ravel(),
ord=1) # / u.data.size
# step 7 / TV step 8: ub_{n+1} <- u_{n+1} + theta(u_{n+1} - u_n)
# ub <- ub + u_{n+1}, remember ub = -u_n
ub += u
# ub <- theta * ub
ub *= theta
# ub <- ub + u_{n+1}
ub += u
# visual output
disp.update()
A.clear_astra_memory()
# Should avoid window freezing
disp.show()
return u, p, cpd, l2_du
'real': safe_float,
'integer': safe_float,
'numeric': safe_float
}
descr = []
convertors = []
if not hasstr:
for name, value in attr:
type = parse_type(value)
if type == 'date':
raise ValueError("date type not supported yet, sorry")
elif type == 'nominal':
n = maxnomlen(value)
descr.append((name, 'S%d' % n))
pvalue = get_nom_val(value)
convertors.append(partial(safe_nominal, pvalue=pvalue))
else:
descr.append((name, acls2dtype[type]))
convertors.append(safe_float)
#dc.append(acls2conv[type])
#sdescr.append((name, acls2sdtype[type]))
else:
# How to support string efficiently ? Ideally, we should know the max
# size of the string before allocating the numpy array.
raise NotImplementedError("String attributes not supported yet, sorry")
ni = len(convertors)
# Get the delimiter from the first line of data:
def next_data_line(row_iter):
"""Assumes we are already in the data part (eg after @data)."""
def matrix_norm(self,
iterations,
vol_init=1.0,
tv_norm=False,
return_volume=False,
intermediate_results=False):
"""The matrix norm || K ||_2 of 'K' defined here as largest
singular value of 'K'. Employs the generic power method to obtain a
scalar 's' which tends to || K ||_2 as the iterations N increase.
To be implemented: optionally return volume 'x', such that it can be
re-used as initializer to continue the iteration.
Parameters
----------
:type iterations: int
:param iterations: Number of iterations of the generic power method.
:type vol_init: float | ndarray (default 1.0)
:param vol_init: in I, initial image to start with.
:type intermediate_results: bool
:param intermediate_results: Returns list of intermediate results
instead of scalar.
:type return_volume: bool
:param return_volume: Return volume in order to resume iteration via
passing it over as initial volume.
Returns
-------
:rtype: float | numpy.ndarray, numpay.array (optional)
:returns: s, vol
s: Scalar of final iteration or numpy.ndarray containing all
results during iteration.
vol: Volume vector
"""
geom = self.geom
vol = self.recon_space.element(vol_init)
proj = Rn(geom.proj_size).zero()
# projector = Projector(geom, vol.space, proj.space)
projector = Projector(geom)
# print 'projector scaling factor', projector.scal_fac
tmp = None
if intermediate_results:
s = np.zeros(iterations)
else:
s = 0
# Power method loop
for n in range(iterations):
# step 4: x_{n+1} <- K^T K x_n
if tv_norm:
# K = (A, grad) instead of K = A
# Compute: - div grad x_n
# use sum over generator expression
tmp = -reduce(add, (partial(
partial(vol.data.reshape(geom.vol_shape), dim,
geom.voxel_width[dim]), dim, geom.voxel_width[dim])
for dim in range(geom.vol_ndim)))
# x_n <- A^T (A x_n)
vol = projector.backward(projector.forward(vol))
vol *= self.adj_scal_fac
if tv_norm:
# x_n <- x_n - div grad x_n
# print 'n: {2}. vol: min = {0}, max = {1}'.format(
# vol.data.min(), vol.data.max(), n)
# print 'n: {2}. tv: min = {0}, max = {1}'.format(tmp.min(),
# tmp.max(), n)
vol.data[:] += tmp.ravel()
# step 5:
# x_n <- x_n/||x_n||_2
vol /= vol.norm()
# step 6:
# s_n <-|| K x ||_2
if intermediate_results:
# proj <- A^T x_n
proj = projector.forward(vol)
s[n] = proj.norm()
if tv_norm:
s[n] = np.sqrt(
s[n]**2 +
reduce(add, (np.linalg.norm(
partial(vol.data.reshape(geom.vol_shape), dim,
geom.voxel_width[dim]))**2
for dim in range(geom.vol_ndim))))
# step 6: || K x ||_2
if not intermediate_results:
proj = projector.forward(vol)
s = proj.norm()
if tv_norm:
s = np.sqrt(s**2 +
reduce(add, (np.linalg.norm(
partial(vol.data.reshape(geom.vol_shape), dim,
geom.voxel_width[dim]))**2
for dim in range(geom.vol_ndim))))
# Clear ASTRA memory
projector.clear_astra_memory()
# Returns
if not return_volume:
return s
else:
return s, vol.data
def begin_fit(self):
self.mbar = master_bar(range(self.epochs))
self.mbar.on_iter_begin()
self.run.logger = partial(self.mbar.write, table=True)