LPAC neural network architecture.
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| | __init__ (self, in_config) |
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| torch.Tensor | forward (self, torch_geometric.data.Data data) |
| | Forward pass of the LPAC model.
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| None | load_compiled_state_dict (self, str model_state_dict_path) |
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| None | load_model (self, str model_state_dict_path) |
| | Load the model from the state dict.
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| None | load_model_state_dict (self, dict model_state_dict) |
| | Load the model from the state dict.
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| None | load_cnn_backbone (self, str model_path) |
| | Load the CNN backbone from the model path.
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| None | load_gnn_backbone (self, str model_path) |
| | Load the GNN backbone from the model path.
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| None | parse (self, dict config) |
| | Parse the configuration for the GNN model.
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LPAC neural network architecture.
Definition at line 40 of file lpac.py.
◆ __init__()
| __init__ |
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| self, |
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| in_config ) |
◆ forward()
| torch.Tensor forward |
( |
| self, |
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torch_geometric.data.Data | data ) |
Forward pass of the LPAC model.
Definition at line 59 of file lpac.py.
◆ load_cnn_backbone()
| None load_cnn_backbone |
( |
| self, |
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str | model_path ) |
Load the CNN backbone from the model path.
Definition at line 106 of file lpac.py.
◆ load_compiled_state_dict()
| None load_compiled_state_dict |
( |
| self, |
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str | model_state_dict_path ) |
◆ load_gnn_backbone()
| None load_gnn_backbone |
( |
| self, |
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str | model_path ) |
Load the GNN backbone from the model path.
Definition at line 112 of file lpac.py.
◆ load_model()
| None load_model |
( |
| self, |
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str | model_state_dict_path ) |
Load the model from the state dict.
Definition at line 94 of file lpac.py.
◆ load_model_state_dict()
| None load_model_state_dict |
( |
| self, |
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dict | model_state_dict ) |
Load the model from the state dict.
Definition at line 100 of file lpac.py.
◆ cnn_backbone
◆ cnn_config
| cnn_config = in_config["CNNBackBone"] |
◆ gnn_backbone
| gnn_backbone = GNNBackBone(self.config, self.cnn_backbone.latent_size + 2) |
◆ gnn_mlp
| gnn_mlp = MLP([self.latent_size, 32, 32]) |
◆ output_dim
◆ output_linear
| output_linear = torch.nn.Linear(32, self.output_dim) |