Self-loops management in KNNGraph and RadiusGraph (#522)

* Add self-loop option to RadiusGraph and KNNGraph

pina/graph.py

@@ -3,6 +3,7 @@
 import torch
 from torch_geometric.data import Data, Batch
 from torch_geometric.utils import to_undirected
+from torch_geometric.utils.loop import remove_self_loops
 from .label_tensor import LabelTensor
 from .utils import check_consistency, is_function
 
@@ -209,6 +210,7 @@ class GraphBuilder:
         x=None,
         edge_attr=False,
         custom_edge_func=None,
+        loop=True,
         **kwargs,
     ):
         """
@@ -224,18 +226,19 @@ class GraphBuilder:
         :param x: Optional tensor of node features of shape ``(N, F)``, where
             ``F`` is the number of features per node.
         :type x: torch.Tensor | LabelTensor, optional
-        :param edge_attr: Optional tensor of edge attributes of shape ``(E, F)``
-            , where ``F`` is the number of features per edge.
-        :type edge_attr: torch.Tensor, optional
+        :param bool edge_attr: Whether to compute the edge attributes.
         :param custom_edge_func: A custom function to compute edge attributes.
             If provided, overrides ``edge_attr``.
         :type custom_edge_func: Callable, optional
+        :param bool loop: Whether to include self-loops.
         :param kwargs: Additional keyword arguments passed to the
             :class:`~pina.graph.Graph` class constructor.
         :return: A :class:`~pina.graph.Graph` instance constructed using the
             provided information.
         :rtype: Graph
         """
+        if not loop:
+            edge_index = remove_self_loops(edge_index)[0]
         edge_attr = cls._create_edge_attr(
             pos, edge_index, edge_attr, custom_edge_func or cls._build_edge_attr
         )
@@ -374,11 +377,8 @@ class KNNGraph(GraphBuilder):
             representing the edge indices of the graph.
         :rtype: torch.Tensor
         """
-
         dist = torch.cdist(points, points, p=2)
-        knn_indices = torch.topk(dist, k=neighbours + 1, largest=False).indices[
-            :, 1:
-        ]
+        knn_indices = torch.topk(dist, k=neighbours, largest=False).indices
         row = torch.arange(points.size(0)).repeat_interleave(neighbours)
         col = knn_indices.flatten()
         return torch.stack([row, col], dim=0).as_subclass(torch.Tensor)