Update solvers (#434)

* Enable DDP training with batch_size=None and add validity check for split sizes
* Refactoring SolverInterfaces (#435)
* Solver update + weighting
* Updating PINN for 0.2
* Modify GAROM + tests
* Adding more versatile loggers
* Disable compilation when running on Windows
* Fix tests

---------

Co-authored-by: giovanni <giovanni.canali98@yahoo.it>
Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>

tests/test_weighting/test_standard_weighting.py

@@ -0,0 +1,42 @@
+import pytest
+import torch
+
+from pina import Trainer
+from pina.solvers import PINN
+from pina.model import FeedForward
+from pina.problem.zoo import Poisson2DSquareProblem
+from pina.loss import ScalarWeighting
+
+problem = Poisson2DSquareProblem()
+model = FeedForward(len(problem.input_variables), len(problem.output_variables))
+condition_names = problem.conditions.keys()
+print(problem.conditions.keys())
+
+@pytest.mark.parametrize("weights",
+                         [1, 1., dict(zip(condition_names, [1]*len(condition_names)))])
+def test_constructor(weights):
+    ScalarWeighting(weights=weights)
+
+@pytest.mark.parametrize("weights", ['a', [1,2,3]])
+def test_wrong_constructor(weights):
+    with pytest.raises(ValueError):
+        ScalarWeighting(weights=weights)
+
+@pytest.mark.parametrize("weights",
+                         [1, 1., dict(zip(condition_names, [1]*len(condition_names)))])
+def test_aggregate(weights):
+    weighting = ScalarWeighting(weights=weights)
+    losses = dict(zip(condition_names, [torch.randn(1) for _ in range(len(condition_names))]))
+    weighting.aggregate(losses=losses)
+
+@pytest.mark.parametrize("weights",
+                         [1, 1., dict(zip(condition_names, [1]*len(condition_names)))])
+def test_train_aggregation(weights):
+    weighting = ScalarWeighting(weights=weights)
+    problem.discretise_domain(50)
+    solver = PINN(
+                problem=problem,
+                model=model,
+                weighting=weighting)
+    trainer = Trainer(solver=solver, max_epochs=5, accelerator='cpu')
+    trainer.train()