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_model/test_fno.py

@@ -2,9 +2,9 @@ import torch
 from pina.model import FNO
 
 output_channels = 5
-batch_size = 15
-resolution = [30, 40, 50]
-lifting_dim = 128
+batch_size = 4
+resolution = [4, 6, 8]
+lifting_dim = 24
 
 
 def test_constructor():

tests/test_model/test_network.py

@@ -1,49 +0,0 @@
-import torch
-import pytest
-
-from pina.model.network import Network
-from pina.model import FeedForward
-from pina import LabelTensor
-
-data = torch.rand((20, 3))
-data_lt = LabelTensor(data, ['x', 'y', 'z'])
-input_dim = 3
-output_dim = 4
-torchmodel = FeedForward(input_dim, output_dim)
-extra_feat = []
-
-
-def test_constructor():
-    Network(model=torchmodel,
-            input_variables=['x', 'y', 'z'],
-            output_variables=['a', 'b', 'c', 'd'],
-            extra_features=None)
-
-def test_forward():
-    net = Network(model=torchmodel,
-                input_variables=['x', 'y', 'z'],
-                output_variables=['a', 'b', 'c', 'd'],
-                extra_features=None)
-    out = net.torchmodel(data)
-    out_lt = net(data_lt)
-    assert isinstance(out, torch.Tensor)
-    assert isinstance(out_lt, LabelTensor)
-    assert out.shape == (20, 4)
-    assert out_lt.shape == (20, 4)
-    assert torch.allclose(out_lt, out)
-    assert out_lt.labels == ['a', 'b', 'c', 'd']
-
-    with pytest.raises(AssertionError):
-        net(data)
-
-def test_backward():
-    net = Network(model=torchmodel,
-                input_variables=['x', 'y', 'z'],
-                output_variables=['a', 'b', 'c', 'd'],
-                extra_features=None)
-    data = torch.rand((20, 3))
-    data.requires_grad = True
-    out = net.torchmodel(data)
-    l = torch.mean(out)
-    l.backward()
-    assert data._grad.shape == torch.Size([20, 3])