models_cifm/layers/egnn_layer_void_invariant.py

import torch
from torch.nn import Linear, ReLU, SiLU, Sequential
from torch_geometric.nn import MessagePassing
from torch_scatter import scatter

from models_cifm.mlp_and_gnn import MLPBiasFree


class EGNNLayer(MessagePassing):
    """E(n) Equivariant GNN Layer

    Paper: E(n) Equivariant Graph Neural Networks, Satorras et al.
    """
    def __init__(self, emb_dim, num_mlp_layers, aggr="add"):
        """
        Args:
            emb_dim: (int) - hidden dimension `d`
            activation: (str) - non-linearity within MLPs (swish/relu)
            norm: (str) - normalisation layer (layer/batch)
            aggr: (str) - aggregation function `\oplus` (sum/mean/max)
        """
        # Set the aggregation function
        super().__init__(aggr=aggr)

        self.emb_dim = emb_dim

        self.dist_embedding = Linear(1, emb_dim, bias=False)
        self.innerprod_embedding = MLPBiasFree(in_dim=1, out_dim=1, hidden_dim=emb_dim, num_layer=num_mlp_layers)
        self.mlp_msg = MLPBiasFree(in_dim=3*emb_dim, out_dim=emb_dim, hidden_dim=emb_dim, num_layer=num_mlp_layers)
        self.mlp_pos = MLPBiasFree(in_dim=emb_dim, out_dim=1, hidden_dim=emb_dim, num_layer=num_mlp_layers)
        self.mlp_upd = MLPBiasFree(in_dim=emb_dim, out_dim=emb_dim, hidden_dim=emb_dim, num_layer=num_mlp_layers)

    def forward(self, h, pos, edge_index):
        """
        Args:
            h: (n, d) - initial node features
            pos: (n, 3) - initial node coordinates
            edge_index: (e, 2) - pairs of edges (i, j)
        Returns:
            out: [(n, d),(n,3)] - updated node features
        """
        out = self.propagate(edge_index, h=h, pos=pos)
        return out

    def message(self, h_i, h_j, pos_i, pos_j):
        # Compute messages
        pos_diff = pos_i - pos_j
        dists = torch.exp(- torch.norm(pos_diff, dim=-1).unsqueeze(1) / 30 ) # reference distances: 30um
        inner_prod = torch.mean(h_i * h_j, dim=-1).unsqueeze(1)
        msg = torch.cat([h_i, h_j, self.dist_embedding(dists)], dim=-1) * self.innerprod_embedding(inner_prod)
        msg = self.mlp_msg(msg)
        # Scale magnitude of displacement vector
        pos_diff = pos_diff * self.mlp_pos(msg)
        # NOTE: some papers divide pos_diff by (dists + 1) to stabilise model.
        return msg, pos_diff, inner_prod

    def aggregate(self, inputs, index):
        msgs, pos_diffs, inner_prod = inputs
        # Aggregate messages
        msg_aggr = scatter(msgs, index, dim=self.node_dim, reduce="add")
        # Aggregate displacement vectors
        pos_aggr = scatter(pos_diffs, index, dim=self.node_dim, reduce="add")

        counts = torch.ones_like(inner_prod)
        counts[inner_prod==0] = 0
        counts = scatter(counts, index, dim=0, reduce="add")
        counts[counts==0] = 1
        pos_aggr = pos_aggr / counts
        return msg_aggr, pos_aggr

    def update(self, aggr_out, h, pos):
        msg_aggr, pos_aggr = aggr_out
        upd_out = self.mlp_upd(msg_aggr)
        upd_pos = pos + pos_aggr
        return upd_out, upd_pos

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(emb_dim={self.emb_dim}, aggr={self.aggr})"