Augmented Lagrangian method #223
Conversation
pypose/optim/alm_optimizer.py
Outdated
| ############ | ||
| # Update Needed Parameters: | ||
| # 1. model params: \thetta, gradient_descent, J -> self.update_parameter(pg['params'], D) > | ||
| # D: used linear solver, update with LM | ||
| # 2. lambda multiplier: \lambda, \lambda_{t+1} = \lambda_{t} + pf * error_C | ||
| # -> self.update_parameter(pg['params'], D) | ||
| # 3. penalty factor(Optional): update_para * penalty factor |
There was a problem hiding this comment.
convert comments into docstring to generate docs.
pypose/optim/alm_optimizer.py
Outdated
| # 2. lambda multiplier: \lambda, \lambda_{t+1} = \lambda_{t} + pf * error_C | ||
| # -> self.update_parameter(pg['params'], D) | ||
| # 3. penalty factor(Optional): update_para * penalty factor | ||
| class Augmented_Lagrangian_Algorithm(_Optimizer): |
There was a problem hiding this comment.
rename to AugmentedLagrangian, capitalized names don't need underscores.
pypose/optim/alm_optimizer.py
Outdated
| convergence_tolerance_constrains=100, convergence_tolerance_model= 1e-3, min=1e-6, max=1e32,\ | ||
| update_tolerance=2, scheduler=None, penalty_factor=5, lm_rate=0.01, num_iter=10, kernel=None \ | ||
| ): | ||
| defaults = {**{'min':min, 'max':max}} |
There was a problem hiding this comment.
defaults = {'min':min, 'max':max}
alm_test.py
Outdated
There was a problem hiding this comment.
- move
alm_test.pyinto the test folder, and follow the format of other tests.
pypose/optim/alm_optimizer.py
Outdated
| for _ in range(self.num_iter): | ||
| self.loss = optim.step(input) | ||
| model_eror, error= self.model(input), self.constraints(input) | ||
| print(torch.norm(error), torch.norm(model_eror)) |
pypose/optim/alm_optimizer.py
Outdated
| class Generalized_SGD(_Optimizer): | ||
| def __init__(self, model, solver=None, strategy=None, kernel=None, corrector=None, \ | ||
| learning_rate=0.1, momentum=0.9, min=1e-6, max=1e32, vectorize=True): | ||
| assert min > 0, ValueError("min value has to be positive: {}".format(min)) | ||
| assert max > 0, ValueError("max value has to be positive: {}".format(max)) | ||
| self.strategy = TrustRegion() if strategy is None else strategy | ||
| defaults = {**{'min':min, 'max':max}, **self.strategy.defaults} | ||
| super().__init__(model.parameters(), defaults=defaults) | ||
| self.momentum = momentum | ||
| self.lr = learning_rate | ||
| self.model = RobustModel(model, kernel, auto=False) |
alm_test.py
Outdated
| while scheduler.continual(): | ||
| loss = optimizer.step(input) | ||
| scheduler.step(loss) | ||
| losses.append(torch.norm(loss).cpu().detach().numpy()) | ||
| constraint_violation.append(torch.norm(constraints(input)).cpu().detach().numpy()) | ||
| objective_loss.append(torch.norm(invnet(input)).cpu().detach().numpy()) |
There was a problem hiding this comment.
refer to point 8 in the contributor guideline
|
The latest version for review contains two files: the test file in the tests/optim/test_alm.py; the optimizer file in pypose/optim/optimizer_alm.py. The test file includes three examples: two tensor problems and one LieTensor problem. |
…pypose into imnotprepared/alm
|
|
||
| R, C = self.model(inputs) | ||
| self.lmd = self.lmd if hasattr(self, 'lmd') \ | ||
| else torch.zeros((C.shape[0], )) |
There was a problem hiding this comment.
An error occured in my computer when running tests/optim/test_cnstopt.py,
Traceback (most recent call last): File "tests/optim/test_cnstopt.py", line 48, in <module> test.test_tensor() File "tests/optim/test_cnstopt.py", line 39, in test_tensor loss, lmd, = optimizer.step(input) File "/home/xukuan/anaconda3/envs/pypose_cnst/lib/python3.8/site-packages/torch-2.0.1-py3.8-linux-x86_64.egg/torch/optim/optimizer.py", line 280, in wrapper out = func(*args, **kwargs) File "/home/xukuan/projects/pypose/tmp/pypose/pypose/optim/cnstopt.py", line 78, in step else self.alm_model(inputs) File "/home/xukuan/anaconda3/envs/pypose_cnst/lib/python3.8/site-packages/torch-2.0.1-py3.8-linux-x86_64.egg/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/home/xukuan/projects/pypose/tmp/pypose/pypose/optim/cnstopt.py", line 33, in forward L = R + (self.lmd @ C) + self.pf * penalty_term / 2 RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument tensor in method wrapper_CUDA__dot)
Should change this line to self.lmd = self.lmd if hasattr(self, 'lmd') else torch.zeros((C.shape[0], )).to(C.device) ?
There was a problem hiding this comment.
yes, plz. We tested all our samples in pure cpu environ. You can do it too at first. Then I will check this and update a new version at my earliest convenience.
|
Ok for me. |
add the implementation of augmented lagrangian method and its test(version: 0.0):
1. Unconstrained_Model(nn.Module): convert a constrained problem to unconstrained one
2. Unconstrained_Model(_Optimizer): alm optimizer
3. InvNet(nn.Module): an example of user defined objective function
4. ConstrainNet(nn.Module): an example of user defined constraints
5. test: see the "main", illustration of how to obtain optimized parameters and lagrangian multiplier can be found.