Loading deepchem/models/torch_models/lcnn.py +3 −50 Original line number Diff line number Diff line Loading @@ -109,52 +109,6 @@ class LCNNBlock(nn.Module): return node_feats class AtomWiseLinear(nn.Module): """ Performs Matrix Multiplication It is used to transform each node wise feature into a scalar. """ def __init__(self, input_feature: int, output_feature: int, dropout: float = 0.0, UseBN: bool = True): """ Parameters ---------- input_feature: int Size of input feature size output_feature: int Size of output feature size dropout: float p value for dropout between 0.0 to 1.0 UseBN: bool Setting it to True will perform Batch Normalisation """ super(AtomWiseLinear, self).__init__() self.conv_weights = nn.Linear(input_feature, output_feature) def forward(self, node_feats): """ Update node representations. Parameters ---------- node_feats: torch.Tensor The node features. The shape is `(N, Node_feature_size)`. Returns ------- node_feats: torch.Tensor The updated node features. The shape is `(N, Node_feature_size)`. """ node_feats = self.conv_weights(node_feats) return node_feats class Atom_Wise_Convolution(nn.Module): """ Performs self convolution to each node Loading Loading @@ -401,8 +355,9 @@ class LCNN(nn.Module): self.LCNN_blocks = nn.Sequential(*modules) self.Atom_wise_Conv = Atom_Wise_Convolution(n_features, sitewise_n_feature) self.Atom_wise_Lin = AtomWiseLinear(sitewise_n_feature, sitewise_n_feature) self.Atom_wise_Lin = nn.Linear(sitewise_n_feature, sitewise_n_feature) self.fc = nn.Linear(sitewise_n_feature, n_task) self.activation = Shifted_softplus() def forward(self, G): """ Loading @@ -423,13 +378,11 @@ class LCNN(nn.Module): raise ImportError("This class requires DGL to be installed.") G = G.local_var() node_feats = G.ndata.pop('x') for conv in self.LCNN_blocks: node_feats = conv(G, node_feats) node_feats = self.Atom_wise_Conv(node_feats) node_feats = self.Atom_wise_Lin(node_feats) G.ndata['new'] = node_feats G.ndata['new'] = self.activation(node_feats) y = dgl.mean_nodes(G, 'new') y = self.fc(y) return y Loading Loading
deepchem/models/torch_models/lcnn.py +3 −50 Original line number Diff line number Diff line Loading @@ -109,52 +109,6 @@ class LCNNBlock(nn.Module): return node_feats class AtomWiseLinear(nn.Module): """ Performs Matrix Multiplication It is used to transform each node wise feature into a scalar. """ def __init__(self, input_feature: int, output_feature: int, dropout: float = 0.0, UseBN: bool = True): """ Parameters ---------- input_feature: int Size of input feature size output_feature: int Size of output feature size dropout: float p value for dropout between 0.0 to 1.0 UseBN: bool Setting it to True will perform Batch Normalisation """ super(AtomWiseLinear, self).__init__() self.conv_weights = nn.Linear(input_feature, output_feature) def forward(self, node_feats): """ Update node representations. Parameters ---------- node_feats: torch.Tensor The node features. The shape is `(N, Node_feature_size)`. Returns ------- node_feats: torch.Tensor The updated node features. The shape is `(N, Node_feature_size)`. """ node_feats = self.conv_weights(node_feats) return node_feats class Atom_Wise_Convolution(nn.Module): """ Performs self convolution to each node Loading Loading @@ -401,8 +355,9 @@ class LCNN(nn.Module): self.LCNN_blocks = nn.Sequential(*modules) self.Atom_wise_Conv = Atom_Wise_Convolution(n_features, sitewise_n_feature) self.Atom_wise_Lin = AtomWiseLinear(sitewise_n_feature, sitewise_n_feature) self.Atom_wise_Lin = nn.Linear(sitewise_n_feature, sitewise_n_feature) self.fc = nn.Linear(sitewise_n_feature, n_task) self.activation = Shifted_softplus() def forward(self, G): """ Loading @@ -423,13 +378,11 @@ class LCNN(nn.Module): raise ImportError("This class requires DGL to be installed.") G = G.local_var() node_feats = G.ndata.pop('x') for conv in self.LCNN_blocks: node_feats = conv(G, node_feats) node_feats = self.Atom_wise_Conv(node_feats) node_feats = self.Atom_wise_Lin(node_feats) G.ndata['new'] = node_feats G.ndata['new'] = self.activation(node_feats) y = dgl.mean_nodes(G, 'new') y = self.fc(y) return y Loading
