optimizer and scheduler classes

pina/__init__.py

@@ -6,13 +6,17 @@ __all__ = [
     "Condition",
     "SamplePointDataset",
     "SamplePointLoader",
+    "TorchOptimizer",
+    "TorchScheduler",
 ]
 
 from .meta import *
-#from .label_tensor import LabelTensor
+from .label_tensor import LabelTensor
 from .solvers.solver import SolverInterface
 from .trainer import Trainer
 from .plotter import Plotter
 from .condition import Condition
 from .dataset import SamplePointDataset
 from .dataset import SamplePointLoader
+from .optimizer import TorchOptimizer
+from .scheduler import TorchScheduler

pina/optimizer.py

@@ -0,0 +1,21 @@
+""" Module for PINA Optimizer """
+
+import torch
+from .utils import check_consistency
+
+class Optimizer: # TODO improve interface
+    pass
+
+
+class TorchOptimizer(Optimizer):
+
+    def __init__(self, optimizer_class, **kwargs):
+        check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True)
+
+        self.optimizer_class = optimizer_class
+        self.kwargs = kwargs
+
+    def hook(self, parameters):
+        self.optimizer_instance = self.optimizer_class(
+            parameters, **self.kwargs
+        )

pina/scheduler.py

@@ -0,0 +1,29 @@
+""" Module for PINA Scheduler """
+
+try:
+    from torch.optim.lr_scheduler import LRScheduler  # torch >= 2.0
+except ImportError:
+    from torch.optim.lr_scheduler import (
+        _LRScheduler as LRScheduler,
+    )  # torch < 2.0
+from .optimizer import Optimizer
+from .utils import check_consistency
+
+
+class Scheduler: # TODO improve interface
+    pass
+
+
+class TorchScheduler(Scheduler):
+
+    def __init__(self, scheduler_class, **kwargs):
+        check_consistency(scheduler_class, LRScheduler, subclass=True)
+
+        self.scheduler_class = scheduler_class
+        self.kwargs = kwargs
+
+    def hook(self, optimizer):
+        check_consistency(optimizer, Optimizer)
+        self.scheduler_instance = self.scheduler_class(
+            optimizer.optimizer_instance, **self.kwargs
+        )

tests/test_optimizer.py

@@ -0,0 +1,20 @@
+
+import torch
+import pytest
+from pina import TorchOptimizer
+
+opt_list = [
+    torch.optim.Adam,
+    torch.optim.AdamW,
+    torch.optim.SGD,
+    torch.optim.RMSprop
+]
+
+@pytest.mark.parametrize("optimizer_class", opt_list)
+def test_constructor(optimizer_class):
+    TorchOptimizer(optimizer_class, lr=1e-3)
+
+@pytest.mark.parametrize("optimizer_class", opt_list)
+def test_hook(optimizer_class):
+    opt = TorchOptimizer(optimizer_class, lr=1e-3)
+    opt.hook(torch.nn.Linear(10, 10).parameters())

tests/test_scheduler.py

@@ -0,0 +1,27 @@
+
+import torch
+import pytest
+from pina import TorchOptimizer, TorchScheduler
+
+opt_list = [
+    torch.optim.Adam,
+    torch.optim.AdamW,
+    torch.optim.SGD,
+    torch.optim.RMSprop
+]
+
+sch_list = [
+    torch.optim.lr_scheduler.ConstantLR
+]
+
+@pytest.mark.parametrize("scheduler_class", sch_list)
+def test_constructor(scheduler_class):
+    TorchScheduler(scheduler_class)
+
+@pytest.mark.parametrize("optimizer_class", opt_list)
+@pytest.mark.parametrize("scheduler_class", sch_list)
+def test_hook(optimizer_class, scheduler_class):
+    opt = TorchOptimizer(optimizer_class, lr=1e-3)
+    opt.hook(torch.nn.Linear(10, 10).parameters())
+    sch = TorchScheduler(scheduler_class)
+    sch.hook(opt)