fnn update, pinn torch models, tests update. (#88)

* fnn update, remove labeltensors
* allow custom torch models
* updating tests

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Dario Coscia <dariocoscia@dhcp-031.eduroam.sissa.it>

tests/test_model/test_network.py

@@ -0,0 +1,55 @@
+import torch
+import torch.nn as nn
+import pytest
+from pina.model import Network, FeedForward
+from pina import LabelTensor
+
+
+class myFeature(torch.nn.Module):
+    """
+    Feature: sin(x)
+    """
+
+    def __init__(self):
+        super(myFeature, self).__init__()
+
+    def forward(self, x):
+        t = (torch.sin(x.extract(['x'])*torch.pi) *
+             torch.sin(x.extract(['y'])*torch.pi))
+        return LabelTensor(t, ['sin(x)sin(y)'])
+
+
+input_variables = ['x', 'y']
+output_variables = ['u']
+data = torch.rand((20, 2))
+input_ = LabelTensor(data, input_variables)
+
+
+def test_constructor():
+    net = FeedForward(2, 1)
+    pina_net = Network(model=net, input_variables=input_variables,
+                       output_variables=output_variables)
+
+
+def test_forward():
+    net = FeedForward(2, 1)
+    pina_net = Network(model=net, input_variables=input_variables,
+                       output_variables=output_variables)
+    output_ = pina_net(input_)
+    assert output_.labels == output_variables
+
+
+def test_constructor_extrafeat():
+    net = FeedForward(3, 1)
+    feat = [myFeature()]
+    pina_net = Network(model=net, input_variables=input_variables,
+                       output_variables=output_variables, extra_features=feat)
+
+
+def test_forward_extrafeat():
+    net = FeedForward(3, 1)
+    feat = [myFeature()]
+    pina_net = Network(model=net, input_variables=input_variables,
+                       output_variables=output_variables, extra_features=feat)
+    output_ = pina_net(input_)
+    assert output_.labels == output_variables