-
Notifications
You must be signed in to change notification settings - Fork 64
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
added message passing model and gnn layers
- Loading branch information
Showing
2 changed files
with
183 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,110 @@ | ||
import torch | ||
import torch.nn as nn | ||
from torch.nn import SiLU | ||
from torch_geometric.data import Data | ||
from torch_geometric.nn import MessagePassing, InstanceNorm | ||
from torch_cluster import radius_graph | ||
|
||
class GNN_Layer(MessagePassing): | ||
""" | ||
Message passing layer | ||
""" | ||
def __init__(self, | ||
in_features: int, | ||
out_features: int, | ||
hidden_features: int, | ||
time_window: int, | ||
n_variables: int): | ||
""" | ||
Initialize message passing layers | ||
Args: | ||
in_features (int): number of node input features | ||
out_features (int): number of node output features | ||
hidden_features (int): number of hidden features | ||
time_window (int): number of input/output timesteps (temporal bundling) | ||
n_variables (int): number of equation specific parameters used in the solver | ||
""" | ||
super(GNN_Layer, self).__init__(node_dim=-2, aggr='mean') | ||
self.in_features = in_features | ||
self.out_features = out_features | ||
self.hidden_features = hidden_features | ||
self.time_window = time_window | ||
self.n_variables = n_variables | ||
|
||
# Message network -- equation 8 | ||
# Notice: spacial dimension is hard-coded and set to 1. For now, we deal with 1d-spatial PDEs. | ||
# TODO: manage spacial dimension - to be passed as a parameter? | ||
self.message_net_1 = nn.Sequential(nn.Linear(2*self.in_features + self.time_window + 1 + self.n_variables, self.hidden_features), SiLU()) | ||
self.message_net_2 = nn.Sequential(nn.Linear(self.hidden_features, self.hidden_features), SiLU()) | ||
|
||
# Update network -- equation 9 | ||
self.update_net_1 = nn.Sequential(nn.Linear(self.in_features + self.hidden_features + self.n_variables, self.hidden_features), SiLU()) | ||
self.update_net_2 = nn.Sequential(nn.Linear(self.hidden_features, self.out_features), SiLU()) | ||
|
||
self.norm = InstanceNorm(self.hidden_features) | ||
|
||
|
||
def forward(self, graph): | ||
""" | ||
Propagate messages along edges | ||
""" | ||
f = self.propagate(edge_index=graph.edge_index, | ||
x=graph.x, | ||
u=graph.u, | ||
pos=graph.pos, | ||
variables=graph.variables) | ||
return f | ||
|
||
|
||
def message(self, x_i, x_j, u_i, u_j, pos_i, pos_j, variables_i): | ||
""" | ||
Message update following formula 8 of the paper | ||
""" | ||
message = self.message_net_1(torch.cat((x_i, x_j, u_i - u_j, pos_i - pos_j, variables_i), dim=-1)) | ||
message = self.message_net_2(message) | ||
return message | ||
|
||
|
||
def update(self, message, x, variables): | ||
""" | ||
Node update following formula 9 of the paper | ||
""" | ||
update = self.update_net_1(torch.cat((x, message, variables), dim=-1)) | ||
update = self.update_net_2(update) | ||
if self.in_features == self.out_features: | ||
return x + update | ||
else: | ||
return update | ||
|
||
|
||
class GraphHandler(): | ||
def __init__(self, coordinates, variables, num_neighs=10): | ||
super().__init__() | ||
self.n = num_neighs | ||
self.graph = self.create_ball_graph(coordinates, variables) | ||
|
||
def create_ball_graph(self, coordinates, variables): | ||
# Get the smallest distance between the coordinates | ||
if len(coordinates.shape) == 1: | ||
dx = coordinates[1]-coordinates[0] | ||
else: | ||
dx = torch.pdist(coordinates).min() | ||
|
||
# Set the radius so as to include the nearest neighbours | ||
radius = self.n * dx + 0.000001 | ||
|
||
edge_index = radius_graph(coordinates, r=radius, loop=False) | ||
|
||
# Features x are computed by the encoder preceeding the gnn_layer | ||
graph = Data(x = None, edge_index = edge_index, edge_attr = None) | ||
graph.pos = coordinates | ||
graph.u = None | ||
graph.variables = variables | ||
return graph | ||
|
||
def data_to_graph(self, data): | ||
self.graph.u = data | ||
|
||
# Probably not useful | ||
# def update_graph(self, features): | ||
# self.graph.x = features |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,73 @@ | ||
import torch | ||
import torch.nn as nn | ||
from layers.gnn_layer import GNN_Layer | ||
|
||
class Model(torch.nn.Module): | ||
""" | ||
Message passing model class. | ||
""" | ||
def __init__(self, | ||
handler, | ||
time_window: int, | ||
n_variables: int, | ||
input_dimension: int, | ||
output_dimension: int, | ||
embedding_dimension: int = 128, | ||
processing_layers: int = 6): | ||
""" | ||
Initialize Message Passing model class. | ||
Args: | ||
handler: GraphHandler object to manage the graph | ||
time_window: temporal bundling parameter | ||
n_variables: number of paramaters of the PDE | ||
input_dimension: dimension of the input | ||
output_dimension: dimension of the output | ||
embedding_dimension: dimension of node features | ||
processing_layers: number of message passing layers | ||
""" | ||
|
||
super().__init__() | ||
self.input_dimension = input_dimension | ||
self.output_dimension = output_dimension | ||
self.embedding_dimension = embedding_dimension | ||
self.processing_layers = processing_layers | ||
self.handler = handler | ||
self.time_window = time_window | ||
self.n_variables = n_variables | ||
|
||
# Encoder | ||
self.encoder = nn.Sequential(nn.Linear(self.input_dimension, self.embedding_dimension), nn.SiLU(), | ||
nn.Linear(self.embedding_dimension, self.embedding_dimension), nn.SiLU()) | ||
|
||
# GNN layers | ||
self.gnn_layers = torch.nn.ModuleList(modules=(GNN_Layer(in_features=self.embedding_dimension, | ||
hidden_features=self.embedding_dimension, | ||
out_features=self.embedding_dimension, | ||
time_window=self.time_window, | ||
n_variables=self.n_variables) for _ in range(self.processing_layers))) | ||
|
||
# Decoder | ||
# TODO: to be transformed in a 1d-CNN | ||
self.decoder = torch.nn.Linear(self.embedding_dimension, self.output_dimension) | ||
# parameters to be set in a correct way | ||
# self.decoder = nn.Sequential(nn.Conv1d(in_channels=1, out_channels=8, kernel_size=15, stride=4), | ||
# nn.Swish(), | ||
# nn.Conv1d(in_channels=8, out_channels=1, kernel_size=10, stride=1)) | ||
|
||
def forward(self, x): | ||
# Insert graph.u data for message passing | ||
self.handler.data_to_graph(x.extract(['k'])) | ||
graph = self.handler.graph | ||
|
||
# Encoder | ||
input = torch.cat((graph.u, graph.pos, graph.variables), dim = -1) | ||
graph.x = self.encoder(input) | ||
|
||
# Processor | ||
for i in range(self.processing_layers): | ||
h = self.gnn_layers[i](graph) | ||
graph.x = h | ||
|
||
# Decoder | ||
out = self.decoder(graph.x) | ||
return out |