Implementation of DataLoader and DataModule (#383)

Refactoring for 0.2
* Data module, data loader and dataset
* Refactor LabelTensor
* Refactor solvers

Co-authored-by: dario-coscia <dariocos99@gmail.com>

pina/model/layers/lowrank_layer.py

@@ -119,6 +119,7 @@ class LowRankBlock(torch.nn.Module):
         :rtype: torch.Tensor
         """
         # extract basis
+        coords = coords.as_subclass(torch.Tensor)
         basis = self._basis(coords)
         # reshape [B, N, D, 2*rank]
         shape = list(basis.shape[:-1]) + [-1, 2 * self.rank]

pina/model/network.py

@@ -29,7 +29,8 @@ class Network(torch.nn.Module):
         # check model consistency
         check_consistency(model, nn.Module)
         check_consistency(input_variables, str)
-        check_consistency(output_variables, str)
+        if output_variables is not None:
+            check_consistency(output_variables, str)
 
         self._model = model
         self._input_variables = input_variables
@@ -67,16 +68,15 @@ class Network(torch.nn.Module):
         # in case `input_variables = []` all points are used
         if self._input_variables:
             x = x.extract(self._input_variables)
-
         # extract features and append
         for feature in self._extra_features:
             x = x.append(feature(x))
 
         # perform forward pass + converting to LabelTensor
-        output = self._model(x).as_subclass(LabelTensor)
-
-        # set the labels for LabelTensor
-        output.labels = self._output_variables
+        x = x.as_subclass(torch.Tensor)
+        output = self._model(x)
+        if self._output_variables is not None:
+            output = LabelTensor(output, self._output_variables)
 
         return output
 
@@ -97,15 +97,9 @@ class Network(torch.nn.Module):
             This function does not extract the input variables, all the variables
             are used for both tensors. Output variables are correctly applied.
         """
-        # convert LabelTensor s to torch.Tensor s
-        x = list(map(lambda x: x.as_subclass(torch.Tensor), x))
 
         # perform forward pass (using torch.Tensor) + converting to LabelTensor
-        output = self._model(x).as_subclass(LabelTensor)
-
-        # set the labels for LabelTensor
-        output.labels = self._output_variables
-
+        output = LabelTensor(self._model(x.tensor), self._output_variables)
         return output
 
     @property