layers.gcc_module
¶
Module Contents¶
Classes¶
Squeeze-and-excitation networks |
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Update the node feature hv with MLP, BN and ReLU. |
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MLP with linear output |
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MPNN from |
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GIN model |
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MPNN from |
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class
layers.gcc_module.
SELayer
(in_channels, se_channels)[source]¶ Bases:
torch.nn.Module
Squeeze-and-excitation networks
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class
layers.gcc_module.
ApplyNodeFunc
(mlp, use_selayer)[source]¶ Bases:
torch.nn.Module
Update the node feature hv with MLP, BN and ReLU.
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class
layers.gcc_module.
MLP
(num_layers, input_dim, hidden_dim, output_dim, use_selayer)[source]¶ Bases:
torch.nn.Module
MLP with linear output
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class
layers.gcc_module.
UnsupervisedGAT
(node_input_dim, node_hidden_dim, edge_input_dim, num_layers, num_heads)[source]¶ Bases:
torch.nn.Module
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class
layers.gcc_module.
UnsupervisedMPNN
(output_dim=32, node_input_dim=32, node_hidden_dim=32, edge_input_dim=32, edge_hidden_dim=32, num_step_message_passing=6, lstm_as_gate=False)[source]¶ Bases:
torch.nn.Module
MPNN from Neural Message Passing for Quantum Chemistry
- node_input_dimint
Dimension of input node feature, default to be 15.
- edge_input_dimint
Dimension of input edge feature, default to be 15.
- output_dimint
Dimension of prediction, default to be 12.
- node_hidden_dimint
Dimension of node feature in hidden layers, default to be 64.
- edge_hidden_dimint
Dimension of edge feature in hidden layers, default to be 128.
- num_step_message_passingint
Number of message passing steps, default to be 6.
- num_step_set2setint
Number of set2set steps
- num_layer_set2setint
Number of set2set layers
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forward
(self, g, n_feat, e_feat)[source]¶ Predict molecule labels
- gDGLGraph
Input DGLGraph for molecule(s)
- n_feattensor of dtype float32 and shape (B1, D1)
Node features. B1 for number of nodes and D1 for the node feature size.
- e_feattensor of dtype float32 and shape (B2, D2)
Edge features. B2 for number of edges and D2 for the edge feature size.
res : Predicted labels
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class
layers.gcc_module.
UnsupervisedGIN
(num_layers, num_mlp_layers, input_dim, hidden_dim, output_dim, final_dropout, learn_eps, graph_pooling_type, neighbor_pooling_type, use_selayer)[source]¶ Bases:
torch.nn.Module
GIN model
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class
layers.gcc_module.
GraphEncoder
(positional_embedding_size=32, max_node_freq=8, max_edge_freq=8, max_degree=128, freq_embedding_size=32, degree_embedding_size=32, output_dim=32, node_hidden_dim=32, edge_hidden_dim=32, num_layers=6, num_heads=4, num_step_set2set=6, num_layer_set2set=3, norm=False, gnn_model='mpnn', degree_input=False, lstm_as_gate=False)[source]¶ Bases:
torch.nn.Module
MPNN from Neural Message Passing for Quantum Chemistry
- node_input_dimint
Dimension of input node feature, default to be 15.
- edge_input_dimint
Dimension of input edge feature, default to be 15.
- output_dimint
Dimension of prediction, default to be 12.
- node_hidden_dimint
Dimension of node feature in hidden layers, default to be 64.
- edge_hidden_dimint
Dimension of edge feature in hidden layers, default to be 128.
- num_step_message_passingint
Number of message passing steps, default to be 6.
- num_step_set2setint
Number of set2set steps
- num_layer_set2setint
Number of set2set layers
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forward
(self, g, return_all_outputs=False)[source]¶ Predict molecule labels
- gDGLGraph
Input DGLGraph for molecule(s)
- n_feattensor of dtype float32 and shape (B1, D1)
Node features. B1 for number of nodes and D1 for the node feature size.
- e_feattensor of dtype float32 and shape (B2, D2)
Edge features. B2 for number of edges and D2 for the edge feature size.
res : Predicted labels