12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Change `optimize_using` to simpler function call

release/4.3a0
Gerry Chen 2022-05-13 10:24:49 -04:00
parent 1e03c8b195
commit 78c7a6b72c
No known key found for this signature in database
GPG Key ID: E9845092D3A57286
2 changed files with 30 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
python/gtsam/tests/test_logging_optimizer.py
View File

@ -66,9 +66,9 @@ class TestOptimizeComet(GtsamTestCase):
# Wrapper function sets the hook and calls optimizer.optimize() for us.
params = gtsam.GaussNewtonParams()
actual = optimize_using(gtsam.GaussNewtonOptimizer, hook)(self.graph, self.initial)
actual = optimize_using(gtsam.GaussNewtonOptimizer, hook, self.graph, self.initial)
self.check(actual)
actual = optimize_using(gtsam.GaussNewtonOptimizer, hook)(self.graph, self.initial, params)
actual = optimize_using(gtsam.GaussNewtonOptimizer, hook, self.graph, self.initial, params)
self.check(actual)
actual = gtsam_optimize(gtsam.GaussNewtonOptimizer(self.graph, self.initial, params),
params, hook)
@ -80,10 +80,10 @@ class TestOptimizeComet(GtsamTestCase):
print(error)
params = gtsam.LevenbergMarquardtParams()
actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook)(self.graph, self.initial)
actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook, self.graph, self.initial)
self.check(actual)
actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook)(self.graph, self.initial,
params)
actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook, self.graph, self.initial,
params)
self.check(actual)
actual = gtsam_optimize(gtsam.LevenbergMarquardtOptimizer(self.graph, self.initial, params),
params, hook)

47
python/gtsam/utils/logging_optimizer.py
View File

@ -17,39 +17,42 @@ OPTIMIZER_PARAMS_MAP = {
}
def optimize_using(OptimizerClass, hook) -> Callable[[Any], gtsam.Values]:
def optimize_using(OptimizerClass, hook, *args) -> gtsam.Values:
""" Wraps the constructor and "optimize()" call for an Optimizer together and adds an iteration
hook.
Example usage:
solution = optimize_using(gtsam.GaussNewtonOptimizer, hook)(graph, init, params)
```python
def hook(optimizer, error):
print("iteration {:}, error = {:}".format(optimizer.iterations(), error))
solution = optimize_using(gtsam.GaussNewtonOptimizer, hook, graph, init, params)
```
Iteration hook's args are (optimizer, error) and return type should be None
Args:
OptimizerClass (T): A NonlinearOptimizer class (e.g. GaussNewtonOptimizer,
LevenbergMarquadrtOptimizer)
LevenbergMarquardtOptimizer)
hook ([T, double] -> None): Function to callback after each iteration. Args are (optimizer,
error) and return should be None.
*args: Arguments that would be passed into the OptimizerClass constructor, usually:
graph, init, [params]
Returns:
(Callable[*, gtsam.Values]): Call the returned function with the usual NonlinearOptimizer
arguments (will be forwarded to constructor) and it will return a Values object
representing the solution. See example usage above.
(gtsam.Values): A Values object representing the optimization solution.
"""
def wrapped_optimize(*args):
for arg in args:
if isinstance(arg, gtsam.NonlinearOptimizerParams):
arg.iterationHook = lambda iteration, error_before, error_after: hook(
optimizer, error_after)
break
else:
params = OPTIMIZER_PARAMS_MAP[OptimizerClass]()
params.iterationHook = lambda iteration, error_before, error_after: hook(
# Add the iteration hook to the NonlinearOptimizerParams
for arg in args:
if isinstance(arg, gtsam.NonlinearOptimizerParams):
arg.iterationHook = lambda iteration, error_before, error_after: hook(
optimizer, error_after)
args = (*args, params)
optimizer = OptimizerClass(*args)
hook(optimizer, optimizer.error())
return optimizer.optimize()
return wrapped_optimize
break
else:
params = OPTIMIZER_PARAMS_MAP[OptimizerClass]()
params.iterationHook = lambda iteration, error_before, error_after: hook(
optimizer, error_after)
args = (*args, params)
# Construct Optimizer and optimize
optimizer = OptimizerClass(*args)
hook(optimizer, optimizer.error()) # Call hook once with init values to match behavior below
return optimizer.optimize()
def optimize(optimizer, check_convergence, hook):