DeepGraphInfomax GNNModular pretraining task #3358
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rbharath
reviewed
Apr 19, 2023
deepchem/models/torch_models/gnn.py
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| """ | ||
| The forward method takes two inputs, `x` (local node representations) and `summary` (global graph representations), both of shape `(batch_size, hidden_dim)`. | ||
| It computes the product of `summary` and `self.weight`, and then calculates the element-wise product of `x` and the resulting matrix `h`. | ||
| Finally, it returns the sum of the element-wise product along dimension 1 (i.e., summing over the `hidden_dim`), resulting in a tensor of shape `(batch_size,)`, which represents the similarity scores between the local and global representations. |
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Can you add a numpydoc style Parameters field here?
deepchem/models/torch_models/gnn.py
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| @@ -178,6 +180,36 @@ def forward(self, data): | |||
| return out | |||
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| class Discriminator(nn.Module): | |||
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Can you add a unit test for this layer?
rbharath
approved these changes
Apr 24, 2023
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Description
Adds in the unsupervised deepgraphinfomax pretraining task for GNNModular. This maximizes the mutual information between positive (same graph) and negative (different graphs) graph representations.
Type of change
Please check the option that is related to your PR.
Checklist
yapf -i <modified file>and check no errors (yapf version must be 0.32.0)mypy -p deepchemand check no errorsflake8 <modified file> --countand check no errorspython -m doctest <modified file>and check no errors