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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})"
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