| from __future__ import annotations |
|
|
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
|
|
|
|
| class DenseGraphAttentionLayer(nn.Module): |
| def __init__(self, input_dimensions: int, output_dimensions: int, heads: int) -> None: |
| super().__init__() |
| self.output_dimensions = output_dimensions |
| self.heads = heads |
| self.weight = nn.Parameter( |
| torch.randn(heads, input_dimensions, output_dimensions) * 0.1 |
| ) |
| self.source_attention = nn.Parameter(torch.randn(heads, output_dimensions) * 0.1) |
| self.target_attention = nn.Parameter(torch.randn(heads, output_dimensions) * 0.1) |
|
|
| def forward( |
| self, |
| features: torch.Tensor, |
| adjacency: torch.Tensor, |
| ) -> tuple[torch.Tensor, torch.Tensor]: |
| projected = torch.einsum("ni,hio->hno", features, self.weight) |
| source = torch.einsum("hno,ho->hn", projected, self.source_attention) |
| target = torch.einsum("hno,ho->hn", projected, self.target_attention) |
| scores = F.leaky_relu(source[:, :, None] + target[:, None, :], 0.2) |
| mask = adjacency.bool() | torch.eye(len(adjacency), device=adjacency.device).bool() |
| scores = scores.masked_fill(~mask[None], -torch.inf) |
| attention = torch.softmax(scores, dim=2) |
| output = torch.einsum("hij,hjo->hio", attention, projected) |
| return output.permute(1, 0, 2).reshape(len(features), -1), attention |
|
|
|
|
| class MeshGraphGAT(nn.Module): |
| def __init__(self, input_dimensions: int = 8) -> None: |
| super().__init__() |
| self.first = DenseGraphAttentionLayer(input_dimensions, 16, heads=4) |
| self.second = DenseGraphAttentionLayer(64, 2, heads=1) |
|
|
| def forward( |
| self, |
| features: torch.Tensor, |
| adjacency: torch.Tensor, |
| *, |
| return_attention: bool = False, |
| ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: |
| hidden, first_attention = self.first(features, adjacency) |
| hidden = F.elu(hidden) |
| hidden = F.dropout(hidden, p=0.15, training=self.training) |
| logits, _ = self.second(hidden, adjacency) |
| if return_attention: |
| return logits, first_attention |
| return logits |
|
|
|
|
| def parameter_count(model: nn.Module) -> int: |
| return sum(parameter.numel() for parameter in model.parameters()) |
|
|
|
|
