Refactor GNO model and enhance Graph class documentation and error ha…

…ndling. Remove TemporalGraph class

pina/graph.py

@@ -25,25 +25,44 @@ def __init__(
             additional_params=None
     ):
         """
-        Constructor for the Graph class.
-        :param x: The node features.
+        Constructor for the Graph class. This object creates a list of PyTorch Geometric Data objects.
+        Based on the input of x and pos there could be the following cases:
+            1. 1 pos, 1 x: a single graph will be created
+            2. N pos, 1 x: N graphs will be created with the same node features
+            3. 1 pos, N x: N graphs will be created with the same nodes but different nodes features
+            4. N pos, N x: N graph will be created
+
+        :param x: Node features. Can be a single 2D tensor of shape [num_nodes, num_node_features],
+                  or a 3D tensor of shape [n_graphs, num_nodes, num_node_features]
+                  or a list of such 2D tensors of shape [num_nodes ,num_node_features].
         :type x: torch.Tensor or list[torch.Tensor]
-        :param pos: The node positions.
+        :param pos: Node coordinates. Can be a single 2D tensor of shape [num_nodes, num_coordinates],
+                    or a 3D tensor of shape [n_graphs, num_nodes, num_coordinates]
+                    or a list of such 2D tensors of shape [num_nodes ,num_coordinates].
         :type pos: torch.Tensor or list[torch.Tensor]
-        :param edge_index: The edge index.
+        :param edge_index: The edge index defining connections between nodes.
+                           It should be a 2D tensor of shape [2, num_edges]
+                           or a 3D tensor of shape [n_graphs, 2, n_edges]
+                           or a list of such tensors 2D tensors o shape [2, num_edges].
         :type edge_index: torch.Tensor or list[torch.Tensor]
-        :param edge_attr: The edge attributes.
-        :type edge_attr: torch.Tensor or list[torch.Tensor]
-        :param build_edge_attr: Whether to build the edge attributes.
-        :type build_edge_attr: bool
-        :param undirected: Whether to build an undirected graph.
-        :type undirected: bool
-        :param custom_build_edge_attr: Custom function to build the edge
-        attributes.
-        :type custom_build_edge_attr: function
-        :param additional_params: Additional parameters.
-        :type additional_params: dict
+        :param edge_attr: Edge features. If provided, should have the shape [num_edges, num_edge_features]
+                          or be a list of such tensors for multiple graphs.
+        :type edge_attr: torch.Tensor or list[torch.Tensor], optional
+        :param build_edge_attr: Whether to compute edge attributes during initialization.
+        :type build_edge_attr: bool, default=False
+        :param undirected: If True, converts the graph(s) into an undirected graph by adding reciprocal edges.
+        :type undirected: bool, default=False
+        :param custom_build_edge_attr: A user-defined function to generate edge attributes dynamically.
+                                       The function should take (x, pos, edge_index) as input and return a tensor
+                                       of shape [num_edges, num_edge_features].
+        :type custom_build_edge_attr: function or callable, optional
+        :param additional_params: Dictionary containing extra attributes to be added to each Data object.
+                                  Keys represent attribute names, and values should be tensors or lists of tensors.
+        :type additional_params: dict, optional
+
+        Note: if
         """
+
         self.data = []
         x, pos, edge_index = self._check_input_consistency(x, pos, edge_index)
 
@@ -85,7 +104,8 @@ def __init__(
 
         # Build the edge attributes
         edge_attr = self._check_and_build_edge_attr(edge_attr, build_edge_attr,
-                                                    data_len, edge_index, pos, x)
+                                                    data_len, edge_index, pos,
+                                                    x)
 
         # Perform the graph construction
         self._build_graph_list(x, pos, edge_index, edge_attr, additional_params)
@@ -128,14 +148,32 @@ def _check_input_consistency(x, pos, edge_index=None):
         # If x is a 3D tensor, we split it into a list of 2D tensors
         if isinstance(x, torch.Tensor) and x.ndim == 3:
             x = [x[i] for i in range(x.shape[0])]
+        elif (not (isinstance(x, list) and all(t.ndim == 2 for t in x)) and
+              not (isinstance(x, torch.Tensor) and x.ndim == 2)):
+            raise TypeError("x must be either a list of 2D tensors or a 2D "
+                            "tensor or a 3D tensor")
 
         # If pos is a 3D tensor, we split it into a list of 2D tensors
         if isinstance(pos, torch.Tensor) and pos.ndim == 3:
             pos = [pos[i] for i in range(pos.shape[0])]
+        elif not (isinstance(pos, list) and all(
+                t.ndim == 2 for t in pos)) and not (
+                isinstance(pos, torch.Tensor) and pos.ndim == 2):
+            raise TypeError("pos must be either a list of 2D tensors or a 2D "
+                            "tensor or a 3D tensor")
 
         # If edge_index is a 3D tensor, we split it into a list of 2D tensors
-        if isinstance(edge_index, torch.Tensor) and edge_index.ndim == 3:
-            edge_index = [edge_index[i] for i in range(edge_index.shape[0])]
+        if edge_index is not None:
+            if isinstance(edge_index, torch.Tensor) and edge_index.ndim == 3:
+                edge_index = [edge_index[i] for i in range(edge_index.shape[0])]
+            elif not (isinstance(edge_index, list) and all(
+                    t.ndim == 2 for t in edge_index)) and not (
+                    isinstance(edge_index,
+                               torch.Tensor) and edge_index.ndim == 2):
+                raise TypeError(
+                    "edge_index must be either a list of 2D tensors or a 2D "
+                    "tensor or a 3D tensor")
+
         return x, pos, edge_index
 
     @staticmethod
@@ -185,7 +223,7 @@ def _check_and_build_edge_attr(self, edge_attr, build_edge_attr, data_len,
             return [edge_attr] * data_len
 
         if build_edge_attr:
-            return [self._build_edge_attr(x,pos_, edge_index_) for
+            return [self._build_edge_attr(x, pos_, edge_index_) for
                     pos_, edge_index_ in zip(pos, edge_index)]
 
 
@@ -256,34 +294,3 @@ def _knn_graph(points, k):
         col = knn_indices.flatten()
         edge_index = torch.stack([row, col], dim=0)
         return edge_index
-
-
-class TemporalGraph(Graph):
-    def __init__(
-            self,
-            x,
-            pos,
-            t,
-            edge_index=None,
-            edge_attr=None,
-            build_edge_attr=False,
-            undirected=False,
-            r=None
-    ):
-
-        x, pos, edge_index = self._check_input_consistency(x, pos, edge_index)
-        print(len(pos))
-        if edge_index is None:
-            edge_index = [RadiusGraph._radius_graph(p, r) for p in pos]
-        additional_params = {'t': t}
-        self._check_time_consistency(pos, t)
-        super().__init__(x=x, pos=pos, edge_index=edge_index,
-                         edge_attr=edge_attr,
-                         build_edge_attr=build_edge_attr,
-                         undirected=undirected,
-                         additional_params=additional_params)
-
-    @staticmethod
-    def _check_time_consistency(pos, times):
-        if len(pos) != len(times):
-            raise ValueError("pos and times must have the same length.")

tests/test_graph.py

@@ -1,7 +1,6 @@
 import pytest
 import torch
-from pina import Graph
-from pina.graph import RadiusGraph, KNNGraph, TemporalGraph
+from pina.graph import RadiusGraph, KNNGraph
 
 
 @pytest.mark.parametrize(
@@ -146,19 +145,6 @@ def test_additional_parameters_2(additional_parameters):
     assert all(hasattr(d, 'y') for d in data)
     assert all(torch.isclose(d_.x, x_).all() for (d_, x_) in zip(data, x))
 
-
-def test_temporal_graph():
-    x = torch.rand(3, 10, 2)
-    pos = torch.rand(3, 10, 2)
-    t = torch.rand(3)
-    graph = TemporalGraph(x=x, pos=pos, build_edge_attr=True, r=.3, t=t)
-    assert len(graph.data) == 3
-    data = graph.data
-    assert all(torch.isclose(d_.x, x_).all() for (d_, x_) in zip(data, x))
-    assert all(hasattr(d, 't') for d in data)
-    assert all(d_.t == t_ for (d_, t_) in zip(data, t))
-
-
 def test_custom_build_edge_attr_func():
     x = torch.rand(3, 10, 2)
     pos = torch.rand(3, 10, 2)