radial field

Author: AleDinve

pina/model/block/message_passing/radial_field_network_block.py

@@ -0,0 +1,142 @@
+"""Module for the Radial Field Network block."""
+
+import torch
+from torch_geometric.nn import MessagePassing
+from ....utils import check_consistency
+
+
+class RadialFieldNetworkBlock(MessagePassing):
+    """
+    Implementation of the Radial Field Network block.
+
+    This block is used to perform message-passing between nodes and edges in a
+    graph neural network, following the scheme proposed by Köhler et al. (2020).
+    It serves as an inner block in a larger graph neural network architecture.
+
+    The message between two nodes connected by an edge is computed by applying a
+    linear transformation to the sender node features and the edge features,
+    followed by a non-linear activation function. Messages are then aggregated
+    using an aggregation scheme (e.g., sum, mean, min, max, or product).
+
+    The update step is performed by a simple addition of the incoming messages
+    to the node features.
+
+    .. seealso::
+
+        **Original reference** Köhler, J., Klein, L., & Noé, F. (2020, November). 
+        Equivariant flows: exact likelihood generative learning for symmetric densities. 
+        In International conference on machine learning (pp. 5361-5370). PMLR.
+    """
+
+        
+
+    def __init__(
+        self,
+        node_feature_dim,
+        hidden_dim,
+        edge_feature_dim,
+        activation=torch.nn.ReLU,
+        aggr="add",
+        node_dim=-2,
+        flow="source_to_target",
+    ):
+        """
+        Initialization of the :class:`RadialFieldNetworkBlock` class.
+
+        :param int node_feature_dim: The dimension of the node features.
+        :param int edge_feature_dim: The dimension of the edge features.
+        :param torch.nn.Module activation: The activation function.
+            Default is :class:`torch.nn.Tanh`.
+        :param str aggr: The aggregation scheme to use for message passing.
+            Available options are "add", "mean", "min", "max", "mul".
+            See :class:`torch_geometric.nn.MessagePassing` for more details.
+            Default is "add".
+        :param int node_dim: The axis along which to propagate. Default is -2.
+        :param str flow: The direction of message passing. Available options
+            are "source_to_target" and "target_to_source".
+            The "source_to_target" flow means that messages are sent from
+            the source node to the target node, while the "target_to_source"
+            flow means that messages are sent from the target node to the
+            source node. See :class:`torch_geometric.nn.MessagePassing` for more
+            details. Default is "source_to_target".
+        :raises ValueError: If `node_feature_dim` is not a positive integer.
+        :raises ValueError: If `edge_feature_dim` is not a positive integer.
+        """
+        super().__init__(aggr=aggr, node_dim=node_dim, flow=flow)
+
+        # Check consistency
+        check_consistency(node_feature_dim, int)
+        check_consistency(edge_feature_dim, int)
+
+        # Check values
+        if node_feature_dim <= 0:
+            raise ValueError(
+                "`node_feature_dim` must be a positive integer,"
+                f" got {node_feature_dim}."
+            )
+
+        if edge_feature_dim <= 0:
+            raise ValueError(
+                "`edge_feature_dim` must be a positive integer,"
+                f" got {edge_feature_dim}."
+            )
+
+
+        # Initialize parameters
+        self.node_feature_dim = node_feature_dim
+        self.edge_feature_dim = edge_feature_dim
+        self.hidden_dim = hidden_dim
+        self.activation = activation
+        self.layer = lambda i,o: torch.nn.Linear(
+            in_features=i,
+            out_features=o,
+            bias=True,
+        )
+        # Layer for processing node features
+        self.radial_field = torch.nn.Sequential([self.layer(1,self.hidden_dim),
+                                                 torch.nn.ReLU,
+                                                 self.layer(self.hidden_dim,1)]
+        )
+
+
+    def forward(self, x, edge_index):
+        """
+        Forward pass of the block, triggering the message-passing routine.
+
+        :param x: The node features.
+        :type x: torch.Tensor | LabelTensor
+        :param torch.Tensor edge_index: The edge indices.
+        :return: The updated node features.
+        :rtype: torch.Tensor
+        """
+        return self.propagate(edge_index=edge_index, x=x)
+
+    def message(self, x_j, x_i):
+        """
+        Compute the message to be passed between nodes and edges.
+
+        :param x_j: Concatenation of the node position and the
+        node features of the sender nodes.
+        :type x_j: torch.Tensor | LabelTensor
+        :param edge_attr: The edge attributes.
+        :type edge_attr: torch.Tensor | LabelTensor
+        :return: The message to be passed.
+        :rtype: torch.Tensor
+        """
+        r = torch.norm(x_i-x_j)*(x_i-x_j)
+        
+
+        return self.activation(self.radial_field(r))
+
+
+    def update(self, message, x):
+        """
+        Update the node features with the received messages.
+
+        :param torch.Tensor message: The message to be passed.
+        :param x: The node features.
+        :type x: torch.Tensor | LabelTensor
+        :return: The updated node features.
+        :rtype: torch.Tensor
+        """
+        return x + message