add dataset and dataloader for sample points (#195)

* add dataset and dataloader for sample points
* unittests

pina/label_tensor.py

@@ -96,6 +96,28 @@ class LabelTensor(torch.Tensor):
 
         self._labels = labels   # assign the label
 
+    @staticmethod
+    def vstack(label_tensors):
+        """
+        Stack tensors vertically. For more details, see
+        :meth:`torch.vstack`.
+
+        :param list(LabelTensor) label_tensors: the tensors to stack. They need
+            to have equal labels.
+        :return: the stacked tensor
+        :rtype: LabelTensor
+        """
+        if len(label_tensors) == 0:
+            return []
+
+        all_labels = [label for lt in label_tensors for label in lt.labels]
+        if set(all_labels) != set(label_tensors[0].labels):
+            raise RuntimeError('The tensors to stack have different labels')
+
+        labels = label_tensors[0].labels
+        tensors = [lt.extract(labels) for lt in label_tensors]
+        return LabelTensor(torch.vstack(tensors), labels)
+
     # TODO remove try/ except thing IMPORTANT
     # make the label None of default
     def clone(self, *args, **kwargs):
@@ -183,6 +205,18 @@ class LabelTensor(torch.Tensor):
 
         return extracted_tensor
 
+    def detach(self):
+        detached = super().detach()
+        if hasattr(self, '_labels'):
+            detached._labels = self._labels
+        return detached
+
+
+    def requires_grad_(self, mode = True) -> Tensor:
+        lt = super().requires_grad_(mode)
+        lt.labels = self.labels
+        return lt
+
     def append(self, lt, mode='std'):
         """
         Return a copy of the merged tensors.
@@ -232,7 +266,7 @@ class LabelTensor(torch.Tensor):
             len_index = len(index)
         except TypeError:
             len_index = 1
-
+        
         if isinstance(index, int) or len_index == 1:
             if selected_lt.ndim == 1:
                 selected_lt = selected_lt.reshape(1, -1)
@@ -246,8 +280,14 @@ class LabelTensor(torch.Tensor):
                     selected_lt.labels = [self.labels[i] for i in index[1]]
                 else:
                     selected_lt.labels = self.labels[index[1]]
+        else:
+            selected_lt.labels = self.labels
                     
         return selected_lt
+    
+    @property
+    def tensor(self):
+        return self.as_subclass(Tensor)
 
     def __len__(self) -> int:
         return super().__len__()