Source code for spateo.external.STAGATE_pyG.gat_conv

from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, NoneType, OptPairTensor, OptTensor, Size
from torch_geometric.utils import add_self_loops, remove_self_loops, softmax


[docs]class GATConv(MessagePassing): r"""The graph attentional operator from the `"Graph Attention Networks" <https://arxiv.org/abs/1710.10903>`_ paper .. math:: \mathbf{x}^{\prime}_i = \alpha_{i,i}\mathbf{\Theta}\mathbf{x}_{i} + \sum_{j \in \mathcal{N}(i)} \alpha_{i,j}\mathbf{\Theta}\mathbf{x}_{j}, where the attention coefficients :math:`\alpha_{i,j}` are computed as .. math:: \alpha_{i,j} = \frac{ \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top} [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_j] \right)\right)} {\sum_{k \in \mathcal{N}(i) \cup \{ i \}} \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top} [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_k] \right)\right)}. Args: in_channels (int or tuple): Size of each input sample, or :obj:`-1` to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities. out_channels (int): Size of each output sample. heads (int, optional): Number of multi-head-attentions. (default: :obj:`1`) concat (bool, optional): If set to :obj:`False`, the multi-head attentions are averaged instead of concatenated. (default: :obj:`True`) negative_slope (float, optional): LeakyReLU angle of the negative slope. (default: :obj:`0.2`) dropout (float, optional): Dropout probability of the normalized attention coefficients which exposes each node to a stochastically sampled neighborhood during training. (default: :obj:`0`) add_self_loops (bool, optional): If set to :obj:`False`, will not add self-loops to the input graph. (default: :obj:`True`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. """
[docs] _alpha: OptTensor
def __init__( self, in_channels: Union[int, Tuple[int, int]], out_channels: int, heads: int = 1, concat: bool = True, negative_slope: float = 0.2, dropout: float = 0.0, add_self_loops: bool = True, bias: bool = True, **kwargs ): kwargs.setdefault("aggr", "add") super(GATConv, self).__init__(node_dim=0, **kwargs)
[docs] self.in_channels = in_channels
[docs] self.out_channels = out_channels
[docs] self.heads = heads
[docs] self.concat = concat
[docs] self.negative_slope = negative_slope
[docs] self.dropout = dropout
[docs] self.add_self_loops = add_self_loops
# In case we are operating in bipartite graphs, we apply separate # transformations 'lin_src' and 'lin_dst' to source and target nodes: # if isinstance(in_channels, int): # self.lin_src = Linear(in_channels, heads * out_channels, # bias=False, weight_initializer='glorot') # self.lin_dst = self.lin_src # else: # self.lin_src = Linear(in_channels[0], heads * out_channels, False, # weight_initializer='glorot') # self.lin_dst = Linear(in_channels[1], heads * out_channels, False, # weight_initializer='glorot')
[docs] self.lin_src = nn.Parameter(torch.zeros(size=(in_channels, out_channels)))
nn.init.xavier_normal_(self.lin_src.data, gain=1.414)
[docs] self.lin_dst = self.lin_src
# The learnable parameters to compute attention coefficients:
[docs] self.att_src = Parameter(torch.Tensor(1, heads, out_channels))
[docs] self.att_dst = Parameter(torch.Tensor(1, heads, out_channels))
nn.init.xavier_normal_(self.att_src.data, gain=1.414) nn.init.xavier_normal_(self.att_dst.data, gain=1.414) # if bias and concat: # self.bias = Parameter(torch.Tensor(heads * out_channels)) # elif bias and not concat: # self.bias = Parameter(torch.Tensor(out_channels)) # else: # self.register_parameter('bias', None) self._alpha = None
[docs] self.attentions = None
# self.reset_parameters() # def reset_parameters(self): # self.lin_src.reset_parameters() # self.lin_dst.reset_parameters() # glorot(self.att_src) # glorot(self.att_dst) # # zeros(self.bias)
[docs] def forward( self, x: Union[Tensor, OptPairTensor], edge_index: Adj, size: Size = None, return_attention_weights=None, attention=True, tied_attention=None, ): r""" Args: return_attention_weights (bool, optional): If set to :obj:`True`, will additionally return the tuple :obj:`(edge_index, attention_weights)`, holding the computed attention weights for each edge. (default: :obj:`None`) """ H, C = self.heads, self.out_channels # We first transform the input node features. If a tuple is passed, we # transform source and target node features via separate weights: if isinstance(x, Tensor): assert x.dim() == 2, "Static graphs not supported in 'GATConv'" # x_src = x_dst = self.lin_src(x).view(-1, H, C) x_src = x_dst = torch.mm(x, self.lin_src).view(-1, H, C) else: # Tuple of source and target node features: x_src, x_dst = x assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'" x_src = self.lin_src(x_src).view(-1, H, C) if x_dst is not None: x_dst = self.lin_dst(x_dst).view(-1, H, C) x = (x_src, x_dst) if not attention: return x[0].mean(dim=1) # return x[0].view(-1, self.heads * self.out_channels) if tied_attention == None: # Next, we compute node-level attention coefficients, both for source # and target nodes (if present): alpha_src = (x_src * self.att_src).sum(dim=-1) alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1) alpha = (alpha_src, alpha_dst) self.attentions = alpha else: alpha = tied_attention from torch_sparse import SparseTensor, set_diag if self.add_self_loops: if isinstance(edge_index, Tensor): # We only want to add self-loops for nodes that appear both as # source and target nodes: num_nodes = x_src.size(0) if x_dst is not None: num_nodes = min(num_nodes, x_dst.size(0)) num_nodes = min(size) if size is not None else num_nodes edge_index, _ = remove_self_loops(edge_index) edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes) elif isinstance(edge_index, SparseTensor): edge_index = set_diag(edge_index) # propagate_type: (x: OptPairTensor, alpha: OptPairTensor) out = self.propagate(edge_index, x=x, alpha=alpha, size=size) alpha = self._alpha assert alpha is not None self._alpha = None if self.concat: out = out.view(-1, self.heads * self.out_channels) else: out = out.mean(dim=1) # if self.bias is not None: # out += self.bias from torch_sparse import SparseTensor, set_diag if isinstance(return_attention_weights, bool): if isinstance(edge_index, Tensor): return out, (edge_index, alpha) elif isinstance(edge_index, SparseTensor): return out, edge_index.set_value(alpha, layout="coo") else: return out
[docs] def message( self, x_j: Tensor, alpha_j: Tensor, alpha_i: OptTensor, index: Tensor, ptr: OptTensor, size_i: Optional[int] ) -> Tensor: # Given egel-level attention coefficients for source and target nodes, # we simply need to sum them up to "emulate" concatenation: alpha = alpha_j if alpha_i is None else alpha_j + alpha_i # alpha = F.leaky_relu(alpha, self.negative_slope) alpha = torch.sigmoid(alpha) alpha = softmax(alpha, index, ptr, size_i) self._alpha = alpha # Save for later use. alpha = F.dropout(alpha, p=self.dropout, training=self.training) return x_j * alpha.unsqueeze(-1)
[docs] def __repr__(self): return "{}({}, {}, heads={})".format(self.__class__.__name__, self.in_channels, self.out_channels, self.heads)