def __call__(self, m_array):
''' An implementation of the reduced functional evaluation
that accepts the control values as an array of scalars '''
# In case the annotation is not reused, we need to reset any prior annotation of the adjointer before reruning the forward model.
if not self.replays_annotation:
solving.adj_reset()
# We move in control space, so we also need to reset the factorisation cache
adj_reset_cache()
# Now its time to update the control values using the given array
m = self.rf.controls.__class__([p.data() for p in self.controls])
self.set_local(m, m_array)
return self.__base_call__(m)
def __call__(self, m_array):
''' An implementation of the reduced functional evaluation
that accepts the control values as an array of scalars '''
# In case the annotation is not reused, we need to reset any prior annotation of the adjointer before reruning the forward model.
if not self.replays_annotation:
solving.adj_reset()
# We move in control space, so we also need to reset the factorisation cache
adj_reset_cache()
# Now its time to update the control values using the given array
m = self.rf.controls.__class__([p.data() for p in self.controls])
self.set_local(m, m_array)
return self.__base_call__(m)
def __call__(self, value):
""" Evaluates the reduced functional for the given control value.
Args:
value: The point in control space where to perform the Taylor test. Must be of the same type as the Control (e.g. Function, Constant or lists of latter).
Returns:
float: The functional value.
"""
# Make sure we do not annotate
# Reset any cached data in dolfin-adjoint
adj_reset_cache()
#: The control values at which the reduced functional is to be evaluated.
value = enlist(value)
# Call callback
self.eval_cb_pre(delist(value, list_type=self.controls))
# Update the control values on the tape
ListControl(self.controls).update(value)
# Check if the result is already cached
if self.cache:
hash = value_hash(value)
if hash in self._cache["functional_cache"]:
# Found a cache
info_green("Got a functional cache hit")
return self._cache["functional_cache"][hash]
# Replay the annotation and evaluate the functional
func_value = 0.
for i in range(adjointer.equation_count):
(fwd_var, output) = adjointer.get_forward_solution(i)
if isinstance(output.data, Function):
output.data.rename(str(fwd_var),
"a Function from dolfin-adjoint")
# Call callback
self.replay_cb(fwd_var, output.data,
delist(value, list_type=self.controls))
# Check if we checkpointing is active and if yes
# record the exact same checkpoint variables as
# in the initial forward run
if adjointer.get_checkpoint_strategy() != None:
if str(fwd_var) in mem_checkpoints:
storage = libadjoint.MemoryStorage(output, cs=True)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
if str(fwd_var) in disk_checkpoints:
storage = libadjoint.MemoryStorage(output)
adjointer.record_variable(fwd_var, storage)
storage = libadjoint.DiskStorage(output, cs=True)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
if not str(fwd_var) in mem_checkpoints and not str(
fwd_var) in disk_checkpoints:
storage = libadjoint.MemoryStorage(output)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
# No checkpointing, so we record everything
else:
storage = libadjoint.MemoryStorage(output)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
if i == adjointer.timestep_end_equation(fwd_var.timestep):
func_value += adjointer.evaluate_functional(
self.functional, fwd_var.timestep)
if adjointer.get_checkpoint_strategy() != None:
adjointer.forget_forward_equation(i)
self.current_func_value = func_value
# Call callback
self.eval_cb_post(self.scale * func_value,
delist(value, list_type=self.controls))
if self.cache:
# Add result to cache
info_red("Got a functional cache miss")
self._cache["functional_cache"][hash] = self.scale * func_value
return self.scale * func_value
def __call__(self, value):
""" Evaluates the reduced functional for the given control value.
Args:
value: The point in control space where to perform the Taylor test. Must be of the same type as the Control (e.g. Function, Constant or lists of latter).
Returns:
float: The functional value.
"""
# Make sure we do not annotate
# Reset any cached data in dolfin-adjoint
adj_reset_cache()
#: The control values at which the reduced functional is to be evaluated.
value = enlist(value)
# Call callback
self.eval_cb_pre(delist(value, list_type=self.controls))
# Update the control values on the tape
ListControl(self.controls).update(value)
# Check if the result is already cached
if self.cache:
hash = value_hash(value)
if hash in self._cache["functional_cache"]:
# Found a cache
info_green("Got a functional cache hit")
return self._cache["functional_cache"][hash]
# Replay the annotation and evaluate the functional
func_value = 0.
for i in range(adjointer.equation_count):
(fwd_var, output) = adjointer.get_forward_solution(i)
if isinstance(output.data, Function):
output.data.rename(str(fwd_var), "a Function from dolfin-adjoint")
# Call callback
self.replay_cb(fwd_var, output.data, delist(value, list_type=self.controls))
# Check if we checkpointing is active and if yes
# record the exact same checkpoint variables as
# in the initial forward run
if adjointer.get_checkpoint_strategy() != None:
if str(fwd_var) in mem_checkpoints:
storage = libadjoint.MemoryStorage(output, cs = True)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
if str(fwd_var) in disk_checkpoints:
storage = libadjoint.MemoryStorage(output)
adjointer.record_variable(fwd_var, storage)
storage = libadjoint.DiskStorage(output, cs = True)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
if not str(fwd_var) in mem_checkpoints and not str(fwd_var) in disk_checkpoints:
storage = libadjoint.MemoryStorage(output)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
# No checkpointing, so we record everything
else:
storage = libadjoint.MemoryStorage(output)
storage.set_overwrite(True)
adjointer.record_variable(fwd_var, storage)
if i == adjointer.timestep_end_equation(fwd_var.timestep):
func_value += adjointer.evaluate_functional(self.functional, fwd_var.timestep)
if adjointer.get_checkpoint_strategy() != None:
adjointer.forget_forward_equation(i)
self.current_func_value = func_value
# Call callback
self.eval_cb_post(self.scale * func_value, delist(value,
list_type=self.controls))
if self.cache:
# Add result to cache
info_red("Got a functional cache miss")
self._cache["functional_cache"][hash] = self.scale*func_value
return self.scale*func_value