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import torch
import torch.nn as nn
from torch_geometric.nn.aggr import (
AttentionalAggregation,
GraphMultisetTransformer,
MaxAggregation,
MeanAggregation,
SetTransformerAggregation,
)
class CatAggregation(nn.Module):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten(1, 2)
def forward(self, x, index=None):
return self.flatten(x)
class HeterogeneousAggregator(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
output_dim,
pooling_method,
pooling_layer_idx,
input_channels,
num_classes,
):
super().__init__()
self.pooling_method = pooling_method
self.pooling_layer_idx = pooling_layer_idx
self.input_channels = input_channels
self.num_classes = num_classes
if pooling_layer_idx == "all":
self._pool_layer_idx_fn = self.get_all_layer_indices
elif pooling_layer_idx == "last":
self._pool_layer_idx_fn = self.get_last_layer_indices
elif isinstance(pooling_layer_idx, int):
self._pool_layer_idx_fn = self.get_nth_layer_indices
else:
raise ValueError(f"Unknown pooling layer index {pooling_layer_idx}")
if pooling_method == "mean":
self.pool = MeanAggregation()
elif pooling_method == "max":
self.pool = MaxAggregation()
elif pooling_method == "cat":
self.pool = CatAggregation()
elif pooling_method == "attentional_aggregation":
self.pool = AttentionalAggregation(
gate_nn=nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.SiLU(),
nn.Linear(hidden_dim, 1),
),
nn=nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.SiLU(),
nn.Linear(hidden_dim, output_dim),
),
)
elif pooling_method == "set_transformer":
self.pool = SetTransformerAggregation(
input_dim, heads=8, num_encoder_blocks=4, num_decoder_blocks=4
)
elif pooling_method == "graph_multiset_transformer":
self.pool = GraphMultisetTransformer(input_dim, k=8, heads=8)
else:
raise ValueError(f"Unknown pooling method {pooling_method}")
def get_last_layer_indices(
self, x, layer_layouts, node_mask=None, return_dense=False
):
batch_size = x.shape[0]
device = x.device
# NOTE: node_mask needs to exist in the heterogeneous case only
if node_mask is None:
node_mask = torch.ones_like(x[..., 0], dtype=torch.bool, device=device)
valid_layer_indices = (
torch.arange(node_mask.shape[1], device=device)[None, :] * node_mask
)
last_layer_indices = valid_layer_indices.topk(
k=self.num_classes, dim=1
).values.fliplr()
if return_dense:
return torch.arange(batch_size, device=device)[:, None], last_layer_indices
batch_indices = torch.arange(batch_size, device=device).repeat_interleave(
self.num_classes
)
return batch_indices, last_layer_indices.flatten()
def get_nth_layer_indices(
self, x, layer_layouts, node_mask=None, return_dense=False
):
batch_size = x.shape[0]
device = x.device
cum_layer_layout = [
torch.cumsum(torch.tensor([0] + layer_layout), dim=0)
for layer_layout in layer_layouts
]
layer_sizes = torch.tensor(
[layer_layout[self.pooling_layer_idx] for layer_layout in layer_layouts],
dtype=torch.long,
device=device,
)
batch_indices = torch.arange(batch_size, device=device).repeat_interleave(
layer_sizes
)
layer_indices = torch.cat(
[
torch.arange(
layout[self.pooling_layer_idx],
layout[self.pooling_layer_idx + 1],
device=device,
)
for layout in cum_layer_layout
]
)
return batch_indices, layer_indices
def get_all_layer_indices(
self, x, layer_layouts, node_mask=None, return_dense=False
):
"""Imitate flattening with indexing"""
batch_size, num_nodes = x.shape[:2]
device = x.device
batch_indices = torch.arange(batch_size, device=device).repeat_interleave(
num_nodes
)
layer_indices = torch.arange(num_nodes, device=device).repeat(batch_size)
return batch_indices, layer_indices
def forward(self, x, layer_layouts, node_mask=None):
# NOTE: `cat` only works with `pooling_layer_idx == "last"`
return_dense = self.pooling_method == "cat" and self.pooling_layer_idx == "last"
batch_indices, layer_indices = self._pool_layer_idx_fn(
x, layer_layouts, node_mask=node_mask, return_dense=return_dense
)
flat_x = x[batch_indices, layer_indices]
return self.pool(flat_x, index=batch_indices)
class HomogeneousAggregator(nn.Module):
def __init__(
self,
pooling_method,
pooling_layer_idx,
layer_layout,
):
super().__init__()
self.pooling_method = pooling_method
self.pooling_layer_idx = pooling_layer_idx
self.layer_layout = layer_layout
def forward(self, node_features, edge_features):
if self.pooling_method == "mean" and self.pooling_layer_idx == "all":
graph_features = node_features.mean(dim=1)
elif self.pooling_method == "max" and self.pooling_layer_idx == "all":
graph_features = node_features.max(dim=1).values
elif self.pooling_method == "mean" and self.pooling_layer_idx == "last":
graph_features = node_features[:, -self.layer_layout[-1] :].mean(dim=1)
elif self.pooling_method == "cat" and self.pooling_layer_idx == "last":
graph_features = node_features[:, -self.layer_layout[-1] :].flatten(1, 2)
elif self.pooling_method == "mean" and isinstance(self.pooling_layer_idx, int):
graph_features = node_features[
:,
self.layer_idx[self.pooling_layer_idx] : self.layer_idx[
self.pooling_layer_idx + 1
],
].mean(dim=1)
elif self.pooling_method == "cat_mean" and self.pooling_layer_idx == "all":
graph_features = torch.cat(
[
node_features[:, self.layer_idx[i] : self.layer_idx[i + 1]].mean(
dim=1
)
for i in range(len(self.layer_layout))
],
dim=1,
)
elif self.pooling_method == "mean_edge" and self.pooling_layer_idx == "all":
graph_features = edge_features.mean(dim=(1, 2))
elif self.pooling_method == "max_edge" and self.pooling_layer_idx == "all":
graph_features = edge_features.flatten(1, 2).max(dim=1).values
elif self.pooling_method == "mean_edge" and self.pooling_layer_idx == "last":
graph_features = edge_features[:, :, -self.layer_layout[-1] :].mean(
dim=(1, 2)
)
return graph_features
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