def derivative(self, forget=True, project=False):
''' Evaluates the derivative of the reduced functional for the most
recently evaluated control value. '''
# Check if we have the gradient already in the cash.
# If so, return the cached value
if self.cache is not None:
hash = value_hash([x.data() for x in self.controls])
fnspaces = [p.data().function_space() if isinstance(p.data(),
Function) else None for p in self.controls]
if hash in self._cache["derivative_cache"]:
info_green("Got a derivative cache hit.")
return cache_load(self._cache["derivative_cache"][hash], fnspaces)
# Call callback
values = [p.data() for p in self.controls]
self.derivative_cb_pre(delist(values, list_type=self.controls))
# Compute the gradient by solving the adjoint equations
dfunc_value = drivers.compute_gradient(self.functional, self.controls, forget=forget, project=project)
dfunc_value = enlist(dfunc_value)
# Reset the checkpointing state in dolfin-adjoint
adjointer.reset_revolve()
# Apply the scaling factor
scaled_dfunc_value = [utils.scale(df, self.scale) for df in list(dfunc_value)]
# Call callback
# We might have forgotten the control values already,
# in which case we can only return Nones
values = []
for c in self.controls:
try:
values.append(p.data())
except libadjoint.exceptions.LibadjointErrorNeedValue:
values.append(None)
if self.current_func_value is not None:
self.derivative_cb_post(self.scale * self.current_func_value,
delist(scaled_dfunc_value, list_type=self.controls),
delist(values, list_type=self.controls))
# Cache the result
if self.cache is not None:
info_red("Got a derivative cache miss")
self._cache["derivative_cache"][hash] = cache_store(scaled_dfunc_value, self.cache)
return scaled_dfunc_value
def derivative(self, forget=True, project=False):
''' Evaluates the derivative of the reduced functional for the most
recently evaluated control value. '''
# Check if we have the gradient already in the cash.
# If so, return the cached value
if self.cache is not None:
hash = value_hash([x.data() for x in self.controls])
fnspaces = [p.data().function_space() if isinstance(p.data(),
Function) else None for p in self.controls]
if hash in self._cache["derivative_cache"]:
info_green("Got a derivative cache hit.")
return cache_load(self._cache["derivative_cache"][hash], fnspaces)
# Compute the gradient by solving the adjoint equations
dfunc_value = drivers.compute_gradient(self.functional, self.controls, forget=forget, project=project)
dfunc_value = enlist(dfunc_value)
# Reset the checkpointing state in dolfin-adjoint
adjointer.reset_revolve()
# Apply the scaling factor
scaled_dfunc_value = [utils.scale(df, self.scale) for df in list(dfunc_value)]
# Call the user-specific callback routine
if self.derivative_cb:
if self.current_func_value is not None:
values = [p.data() for p in self.controls]
self.derivative_cb(self.scale * self.current_func_value,
delist(scaled_dfunc_value, list_type=self.controls),
delist(values, list_type=self.controls))
else:
info_red("Gradient evaluated without functional evaluation, not calling derivative callback function")
# Cache the result
if self.cache is not None:
info_red("Got a derivative cache miss")
self._cache["derivative_cache"][hash] = cache_store(scaled_dfunc_value, self.cache)
return scaled_dfunc_value
def derivative(self, forget=True, project=False):
""" Evaluates the derivative of the reduced functional at the most
recently evaluated control value.
Args:
forget (Optional[bool]): Delete the forward state while solving the
adjoint equations. If you want to reevaluate derivative at the same
point (or the Hessian) you will need to set this to False or None. Defaults to True.
project (Optional[bool]): If True, the returned value will be the L2
Riesz representer, if False it will be the l2 Riesz representative.
The L2 projection requires one additional linear solve.
Defaults to False.
Returns:
The functional derivative. The returned type is the same as the control
type.
"""
# Check if we have the gradient already in the cash.
# If so, return the cached value
if self.cache is not None:
hash = value_hash([x.data() for x in self.controls])
fnspaces = [
p.data().function_space()
if isinstance(p.data(), Function) else None
for p in self.controls
]
if hash in self._cache["derivative_cache"]:
info_green("Got a derivative cache hit.")
return cache_load(self._cache["derivative_cache"][hash],
fnspaces)
# Call callback
values = [p.data() for p in self.controls]
self.derivative_cb_pre(delist(values, list_type=self.controls))
# Compute the gradient by solving the adjoint equations
dfunc_value = drivers.compute_gradient(self.functional,
self.controls,
forget=forget,
project=project)
dfunc_value = enlist(dfunc_value)
# Reset the checkpointing state in dolfin-adjoint
adjointer.reset_revolve()
# Apply the scaling factor
scaled_dfunc_value = [
utils.scale(df, self.scale) for df in list(dfunc_value)
]
# Call callback
# We might have forgotten the control values already,
# in which case we can only return Nones
values = []
for p in self.controls:
try:
values.append(p.data())
except libadjoint.exceptions.LibadjointErrorNeedValue:
values.append(None)
if self.current_func_value is not None:
self.derivative_cb_post(
self.scale * self.current_func_value,
delist(scaled_dfunc_value, list_type=self.controls),
delist(values, list_type=self.controls))
# Cache the result
if self.cache is not None:
info_red("Got a derivative cache miss")
self._cache["derivative_cache"][hash] = cache_store(
scaled_dfunc_value, self.cache)
return scaled_dfunc_value
def derivative(self, forget=True, project=False):
""" Evaluates the derivative of the reduced functional at the most
recently evaluated control value.
Args:
forget (Optional[bool]): Delete the forward state while solving the
adjoint equations. If you want to reevaluate derivative at the same
point (or the Hessian) you will need to set this to False or None. Defaults to True.
project (Optional[bool]): If True, the returned value will be the L2
Riesz representer, if False it will be the l2 Riesz representative.
The L2 projection requires one additional linear solve.
Defaults to False.
Returns:
The functional derivative. The returned type is the same as the control
type.
"""
# Check if we have the gradient already in the cash.
# If so, return the cached value
if self.cache is not None:
hash = value_hash([x.data() for x in self.controls])
fnspaces = [p.data().function_space() if isinstance(p.data(),
Function) else None for p in self.controls]
if hash in self._cache["derivative_cache"]:
info_green("Got a derivative cache hit.")
return cache_load(self._cache["derivative_cache"][hash], fnspaces)
# Call callback
values = [p.data() for p in self.controls]
self.derivative_cb_pre(delist(values, list_type=self.controls))
# Compute the gradient by solving the adjoint equations
dfunc_value = drivers.compute_gradient(self.functional, self.controls, forget=forget, project=project)
dfunc_value = enlist(dfunc_value)
# Reset the checkpointing state in dolfin-adjoint
adjointer.reset_revolve()
# Apply the scaling factor
scaled_dfunc_value = [utils.scale(df, self.scale) for df in list(dfunc_value)]
# Call callback
# We might have forgotten the control values already,
# in which case we can only return Nones
values = []
for c in self.controls:
try:
values.append(p.data())
except libadjoint.exceptions.LibadjointErrorNeedValue:
values.append(None)
if self.current_func_value is not None:
self.derivative_cb_post(self.scale * self.current_func_value,
delist(scaled_dfunc_value, list_type=self.controls),
delist(values, list_type=self.controls))
# Cache the result
if self.cache is not None:
info_red("Got a derivative cache miss")
self._cache["derivative_cache"][hash] = cache_store(scaled_dfunc_value, self.cache)
return scaled_dfunc_value