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Add flag to skip per-node projection for torch models. #55

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2 changes: 2 additions & 0 deletions fs_mol/modules/gnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,8 @@ def add_gnn_model_arguments(parser: argparse.ArgumentParser):
parser.add_argument(
"--num_gnn_layers", type=int, default=10, help="Number of GNN layers to use."
)
parser.add_argument("--skip-node-embedding", action="store_true",
help="Skip learning a per-node embedding. Input dim then equals model dim.")


def make_gnn_config_from_args(args: argparse.Namespace) -> GNNConfig:
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13 changes: 9 additions & 4 deletions fs_mol/modules/graph_feature_extractor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@ class GraphFeatureExtractorConfig:
gnn_config: GNNConfig = GNNConfig()
readout_config: GraphReadoutConfig = GraphReadoutConfig()
output_norm: Literal["off", "layer", "batch"] = "off"
skip_node_proj: bool = False


def add_graph_feature_extractor_arguments(parser: argparse.ArgumentParser):
Expand All @@ -37,6 +38,7 @@ def make_graph_feature_extractor_config_from_args(
initial_node_feature_dim=initial_node_feature_dim,
gnn_config=make_gnn_config_from_args(args),
readout_config=make_graph_readout_config_from_args(args),
skip_node_proj=args.skip_node_embedding,
)


Expand All @@ -45,10 +47,13 @@ def __init__(self, config: GraphFeatureExtractorConfig):
super().__init__()
self.config = config

# Initial (per-node) layers:
self.init_node_proj = nn.Linear(
config.initial_node_feature_dim, config.gnn_config.hidden_dim, bias=False
)
# Learn a per-node node embedding or skip this layer.
if config.skip_node_proj:
self.init_node_proj = nn.Identity(config.gnn_config.hidden_dim, config.gnn_config.hidden_dim)
else:
self.init_node_proj = nn.Linear(
config.initial_node_feature_dim, config.gnn_config.hidden_dim, bias=False
)

self.gnn = GNN(self.config.gnn_config)

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