def test_gconv_lstm_layer():
"""
Testing the GConvLSTM Layer.
"""
number_of_nodes = 100
edge_per_node = 10
in_channels = 64
out_channels = 16
K = 2
X, edge_index = create_mock_data(number_of_nodes, edge_per_node,
in_channels)
edge_weight = create_mock_edge_weight(edge_index)
layer = GConvLSTM(in_channels=in_channels, out_channels=out_channels, K=K)
H, C = layer(X, edge_index)
assert H.shape == (number_of_nodes, out_channels)
assert C.shape == (number_of_nodes, out_channels)
H, C = layer(X, edge_index, edge_weight)
assert H.shape == (number_of_nodes, out_channels)
assert C.shape == (number_of_nodes, out_channels)
H, C = layer(X, edge_index, edge_weight, H, C)
assert H.shape == (number_of_nodes, out_channels)
assert C.shape == (number_of_nodes, out_channels)
def test_gconv_lstm_layer():
"""
Testing the GConvLSTM Layer.
"""
number_of_nodes = 100
edge_per_node = 10
in_channels = 64
out_channels = 16
K = 2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
X, edge_index = create_mock_data(number_of_nodes, edge_per_node,
in_channels)
X = X.to(device)
edge_index = edge_index.to(device)
edge_weight = create_mock_edge_weight(edge_index).to(device)
layer = GConvLSTM(in_channels=in_channels, out_channels=out_channels,
K=K).to(device)
H, C = layer(X, edge_index)
assert H.shape == (number_of_nodes, out_channels)
assert C.shape == (number_of_nodes, out_channels)
H, C = layer(X, edge_index, edge_weight)
assert H.shape == (number_of_nodes, out_channels)
assert C.shape == (number_of_nodes, out_channels)
H, C = layer(X, edge_index, edge_weight, H, C)
assert H.shape == (number_of_nodes, out_channels)
assert C.shape == (number_of_nodes, out_channels)
def __init__(self, node_features, num_classes):
super(RecurrentGCN, self).__init__()
self.recurrent_1 = GConvLSTM(node_features, 32, 5)
self.recurrent_2 = GConvLSTM(32, 16, 5)
self.linear = torch.nn.Linear(16, num_classes)
def __init__(self, node_features):
super(RecurrentGCN, self).__init__()
self.recurrent = GConvLSTM(node_features, 32, 1)
self.linear = torch.nn.Linear(32, 1)
def __init__(self, input_feat=2, Conv_outputs=[5], LSTM_output=[5],
K=1, linear_output=3):
super(social_stgcn, self).__init__()
self.input_feat = input_feat
self.Conv_outputs = Conv_outputs
self.LSTM_output = LSTM_output
self.linear_output = linear_output
self.K = K
self.gcn1 = GCNConv(in_channels=self.input_feat,
out_channels=self.Conv_outputs[0],
improved=True)
self.gcn2 = GCNConv(in_channels=self.Conv_outputs[0],
out_channels=self.Conv_outputs[1],
improved=True)
self.gclstm1 = GConvLSTM(in_channels=self.Conv_outputs[1],
out_channels=self.Conv_outputs[1],
K=K, normalization="sym", bias=True)
self.gclstm2 = GConvLSTM(in_channels=self.Conv_outputs[1],
out_channels=self.Conv_outputs[1],
K=K, normalization="sym", bias=True)
self.gclstm3 = GConvLSTM(in_channels=self.Conv_outputs[1],
out_channels=self.LSTM_output[0],
K=K, normalization="sym", bias=True)
self.no_lstm = 3
self.linear = nn.Linear(in_features=self.LSTM_output[0],
out_features=self.linear_output)
self.gcn = Sequential('x, edge_index, h_none', [
(GCNConv(in_channels=self.input_feat,
out_channels=self.Conv_outputs[0],
improved=True), 'x, edge_index -> x'),
(nn.ReLU(), 'x -> x'),
(GCNConv(in_channels=self.Conv_outputs[0],
out_channels=self.Conv_outputs[1],
improved=True), 'x, edge_index -> x'),
(nn.ReLU(), 'x -> x'),
(GConvLSTM(in_channels=self.Conv_outputs[1],
out_channels=self.Conv_outputs[1],
K=K, normalization="sym", bias=True), 'x, edge_index -> h, c'),
(GConvLSTM(in_channels=self.Conv_outputs[1],
out_channels=self.Conv_outputs[1],
K=K, normalization="sym", bias=True), 'h, edge_index, h_none, c -> h, c'),
(GConvLSTM(in_channels=self.Conv_outputs[1],
out_channels=self.LSTM_output[0],
K=K, normalization="sym", bias=True), 'h, edge_index, h_none, c-> h, c'),
# TO BE TESTED LATER
# (GCLSTM(in_channels=self.Conv_outputs[1],
# out_channels=self.LSTM_output[0],
# K=K, normalization="sym", bias=True), 'x, edge_index -> h, c'),
#
# (GCLSTM(in_channels=self.LSTM_output[0],
# out_channels=self.LSTM_output[1],
# K=K, normalization="sym", bias=True), 'x, edge_index, h, c -> h, c'),
#
# (GCLSTM(in_channels=self.LSTM_output[1],
# out_channels=self.LSTM_output[2],
# K=K, normalization="sym", bias=True), 'h, edge_index, c -> h, _'),
(nn.ReLU(), "h -> h"),
(nn.Linear(in_features=self.LSTM_output[0],
out_features=self.linear_output), "h -> x")])