diff --git a/examples/evolvegcnh_example.py b/examples/evolvegcnh_example.py index 84692855..3608d7cc 100644 --- a/examples/evolvegcnh_example.py +++ b/examples/evolvegcnh_example.py @@ -63,7 +63,7 @@ def forward(self, x, edge_index, edge_weight): model.train() -for epoch in range(epochs): +for _ in range(epochs): optimizer.zero_grad() x, edge_index = create_mock_data(node_count, edge_per_node, node_features) edge_weight = create_mock_edge_weight(edge_index) diff --git a/examples/evolvegcno_example.py b/examples/evolvegcno_example.py index c9a60062..fb586afc 100644 --- a/examples/evolvegcno_example.py +++ b/examples/evolvegcno_example.py @@ -61,7 +61,7 @@ def forward(self, x, edge_index, edge_weight): model.train() -for epoch in range(epochs): +for _ in range(epochs): optimizer.zero_grad() x, edge_index = create_mock_data(node_count, edge_per_node, node_features) edge_weight = create_mock_edge_weight(edge_index) diff --git a/examples/gclstm_example.py b/examples/gclstm_example.py index d1138b75..70f4e33e 100644 --- a/examples/gclstm_example.py +++ b/examples/gclstm_example.py @@ -59,7 +59,7 @@ def forward(self, x, edge_index, edge_weight): model.train() -for epoch in range(epochs): +for _ in range(epochs): optimizer.zero_grad() x, edge_index = create_mock_data(node_count, edge_per_node, node_features) edge_weight = create_mock_edge_weight(edge_index) diff --git a/examples/gconvgru_example.py b/examples/gconvgru_example.py index 16b8b615..000fd1ff 100644 --- a/examples/gconvgru_example.py +++ b/examples/gconvgru_example.py @@ -60,7 +60,7 @@ def forward(self, x, edge_index, edge_weight): model.train() -for epoch in range(epochs): +for _ in range(epochs): optimizer.zero_grad() x, edge_index = create_mock_data(node_count, edge_per_node, node_features) edge_weight = create_mock_edge_weight(edge_index) diff --git a/examples/gconvlstm_example.py b/examples/gconvlstm_example.py index 3b85805b..3cbfd4cc 100644 --- a/examples/gconvlstm_example.py +++ b/examples/gconvlstm_example.py @@ -59,7 +59,7 @@ def forward(self, x, edge_index, edge_weight): model.train() -for epoch in range(epochs): +for _ in range(epochs): optimizer.zero_grad() x, edge_index = create_mock_data(node_count, edge_per_node, node_features) edge_weight = create_mock_edge_weight(edge_index) diff --git a/torch_geometric_temporal/nn/recurrent/evolvegcno.py b/torch_geometric_temporal/nn/recurrent/evolvegcno.py index a81ebb7c..741a9fb8 100644 --- a/torch_geometric_temporal/nn/recurrent/evolvegcno.py +++ b/torch_geometric_temporal/nn/recurrent/evolvegcno.py @@ -67,5 +67,4 @@ def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, W = self.conv_layer.weight[None, :, :] W, _ = self.recurrent_layer(W) self.conv_layer.weight = torch.nn.Parameter(W.squeeze()) - X = self.conv_layer(X, edge_index, edge_weight) - return X + return self.conv_layer(X, edge_index, edge_weight) diff --git a/torch_geometric_temporal/nn/recurrent/gc_lstm.py b/torch_geometric_temporal/nn/recurrent/gc_lstm.py index df6bfce2..57bc790c 100644 --- a/torch_geometric_temporal/nn/recurrent/gc_lstm.py +++ b/torch_geometric_temporal/nn/recurrent/gc_lstm.py @@ -148,8 +148,7 @@ def _calculate_cell_state(self, X, edge_index, edge_weight, H, C, I, F): T = T + self.conv_c(H, edge_index, edge_weight) T = T + self.b_c T = torch.tanh(T) - C = F*C + I*T - return C + return F*C + I*T def _calculate_output_gate(self, X, edge_index, edge_weight, H, C): O = torch.matmul(X, self.W_o) @@ -160,8 +159,7 @@ def _calculate_output_gate(self, X, edge_index, edge_weight, H, C): def _calculate_hidden_state(self, O, C): - H = O * torch.tanh(C) - return H + return O * torch.tanh(C) def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor=None, diff --git a/torch_geometric_temporal/nn/recurrent/gconv_gru.py b/torch_geometric_temporal/nn/recurrent/gconv_gru.py index f7940f03..b5e7d490 100644 --- a/torch_geometric_temporal/nn/recurrent/gconv_gru.py +++ b/torch_geometric_temporal/nn/recurrent/gconv_gru.py @@ -125,8 +125,7 @@ def _calculate_candidate_state(self, X, edge_index, edge_weight, H, R): def _calculate_hidden_state(self, Z, H, H_tilde): - H = Z*H + (1-Z)*H_tilde - return H + return Z*H + (1-Z)*H_tilde def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, diff --git a/torch_geometric_temporal/nn/recurrent/gconv_lstm.py b/torch_geometric_temporal/nn/recurrent/gconv_lstm.py index 1a5e2ab6..9781d2ee 100644 --- a/torch_geometric_temporal/nn/recurrent/gconv_lstm.py +++ b/torch_geometric_temporal/nn/recurrent/gconv_lstm.py @@ -172,8 +172,7 @@ def _calculate_cell_state(self, X, edge_index, edge_weight, H, C, I, F): T = T + self.conv_h_c(H, edge_index, edge_weight) T = T + self.b_c T = torch.tanh(T) - C = F*C + I*T - return C + return F*C + I*T def _calculate_output_gate(self, X, edge_index, edge_weight, H, C): O = self.conv_x_o(X, edge_index, edge_weight) @@ -185,8 +184,7 @@ def _calculate_output_gate(self, X, edge_index, edge_weight, H, C): def _calculate_hidden_state(self, O, C): - H = O * torch.tanh(C) - return H + return O * torch.tanh(C) def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch.FloatTensor=None, diff --git a/torch_geometric_temporal/nn/recurrent/lrgcn.py b/torch_geometric_temporal/nn/recurrent/lrgcn.py index eb0fcc8d..06c80634 100644 --- a/torch_geometric_temporal/nn/recurrent/lrgcn.py +++ b/torch_geometric_temporal/nn/recurrent/lrgcn.py @@ -115,8 +115,7 @@ def _calculate_cell_state(self, X, edge_index, edge_type, H, C, I, F): T = self.conv_x_c(X, edge_index, edge_type) T = T + self.conv_h_c(H, edge_index, edge_type) T = torch.tanh(T) - C = F*C + I*T - return C + return F*C + I*T def _calculate_output_gate(self, X, edge_index, edge_type, H, C): @@ -127,8 +126,7 @@ def _calculate_output_gate(self, X, edge_index, edge_type, H, C): def _calculate_hidden_state(self, O, C): - H = O * torch.tanh(C) - return H + return O * torch.tanh(C) def forward(self, X: torch.FloatTensor, edge_index: torch.LongTensor, edge_type: torch.LongTensor,