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layers.py
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layers.py
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from typing import Optional, Tuple
from torch_geometric.typing import Adj, OptTensor, PairTensor
import torch
from torch import Tensor
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_sparse import SparseTensor, matmul, fill_diag, sum, mul
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops
from torch_geometric.utils.num_nodes import maybe_num_nodes
from torch_geometric.nn.inits import glorot, zeros
@torch.jit._overload
def gcn_norm(edge_index, edge_weight=None, num_nodes=None, improved=False,
add_self_loops=True, dtype=None):
# type: (Tensor, OptTensor, Optional[int], bool, bool, Optional[int]) -> PairTensor # noqa
pass
@torch.jit._overload
def gcn_norm(edge_index, edge_weight=None, num_nodes=None, improved=False,
add_self_loops=True, dtype=None):
# type: (SparseTensor, OptTensor, Optional[int], bool, bool, Optional[int]) -> SparseTensor # noqa
pass
def gcn_norm(edge_index, edge_weight=None, num_nodes=None, improved=False,
add_self_loops=True, dtype=None):
fill_value = 2. if improved else 1.
if isinstance(edge_index, SparseTensor):
adj_t = edge_index
if not adj_t.has_value():
adj_t = adj_t.fill_value(1., dtype=dtype)
if add_self_loops:
adj_t = fill_diag(adj_t, fill_value)
deg = sum(adj_t, dim=1)
deg_inv_sqrt = deg.pow_(-0.5)
deg_inv_sqrt.masked_fill_(deg_inv_sqrt == float('inf'), 0.)
adj_t = mul(adj_t, deg_inv_sqrt.view(-1, 1))
adj_t = mul(adj_t, deg_inv_sqrt.view(1, -1))
return adj_t
else:
num_nodes = maybe_num_nodes(edge_index, num_nodes)
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ), dtype=dtype,
device=edge_index.device)
if add_self_loops:
edge_index, tmp_edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
assert tmp_edge_weight is not None
edge_weight = tmp_edge_weight
row, col = edge_index[0], edge_index[1]
#print(edge_weight.shape, edge_index.shape)
#print(col.max(), num_nodes)
if len(col) > 0:
#print(col, num_nodes)
assert edge_weight.shape[0] == col.shape[0]
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
deg[deg==0] = 1
deg_inv_sqrt = deg.pow_(-0.5)
deg_inv_sqrt.masked_fill_(deg_inv_sqrt == float('inf'), 0)
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
else:
return edge_index, edge_weight
class GCS(MessagePassing):
r"""The graph convolutional operator from the `"Semi-supervised
Classification with Graph Convolutional Networks"
<https://arxiv.org/abs/1609.02907>`_ paper
.. math::
\mathbf{X}^{\prime} = \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
\mathbf{\hat{D}}^{-1/2} \mathbf{X} \mathbf{\Theta},
where :math:`\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}` denotes the
adjacency matrix with inserted self-loops and
:math:`\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}` its diagonal degree matrix.
Args:
in_channels (int): Size of each input sample.
out_channels (int): Size of each output sample.
improved (bool, optional): If set to :obj:`True`, the layer computes
:math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`.
(default: :obj:`False`)
cached (bool, optional): If set to :obj:`True`, the layer will cache
the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
\mathbf{\hat{D}}^{-1/2}` on first execution, and will use the
cached version for further executions.
This parameter should only be set to :obj:`True` in transductive
learning scenarios. (default: :obj:`False`)
normalize (bool, optional): Whether to add self-loops and apply
symmetric normalization. (default: :obj:`True`)
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`.
"""
_cached_edge_index: Optional[Tuple[Tensor, Tensor]]
_cached_adj_t: Optional[SparseTensor]
def __init__(self, in_channels: int, out_channels: int,
improved: bool = False, cached: bool = False,
add_self_loops: bool = False, normalize: bool = True,
bias: bool = True, **kwargs):
super(GCS, self).__init__(aggr='add', **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.cached = cached
self.normalize = normalize
self.add_self_loops = add_self_loops
self._cached_edge_index = None
self._cached_adj_t = None
self.weight = Parameter(torch.Tensor(in_channels, out_channels))
self.weight_skip = Parameter(torch.Tensor(in_channels, out_channels))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
glorot(self.weight)
glorot(self.weight_skip)
zeros(self.bias)
self._cached_edge_index = None
self._cached_adj_t = None
def forward(self, x: Tensor, edge_index: Adj,
edge_weight: OptTensor = None) -> Tensor:
""""""
if self.normalize:
if isinstance(edge_index, Tensor):
cache = self._cached_edge_index
if cache is None:
edge_index, edge_weight = gcn_norm( # yapf: disable
edge_index, edge_weight, x.size(self.node_dim),
self.improved, self.add_self_loops, dtype=x.dtype)
if self.cached:
self._cached_edge_index = (edge_index, edge_weight)
else:
edge_index, edge_weight = cache[0], cache[1]
elif isinstance(edge_index, SparseTensor):
cache = self._cached_adj_t
if cache is None:
edge_index = gcn_norm( # yapf: disable
edge_index, edge_weight, x.size(self.node_dim),
self.improved, self.add_self_loops, dtype=x.dtype)
if self.cached:
self._cached_adj_t = edge_index
else:
edge_index = cache
x_skip = torch.matmul(x, self.weight_skip)
x = torch.matmul(x, self.weight)
# propagate_type: (x: Tensor, edge_weight: OptTensor)
if edge_index.shape[1] > 0:
adj_t = SparseTensor(row=edge_index[1], col=edge_index[0],
value=edge_weight, sparse_sizes=(x.shape[0], x.shape[0]),
is_sorted=False)
out = self.propagate(adj_t, x=x)
else:
out = 0
#out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
# size=None)
out = out + x_skip
if self.bias is not None:
out += self.bias
return out
def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor:
return edge_weight.view(-1, 1) * x_j
def message_and_aggregate(self, adj_t: SparseTensor, x: Tensor) -> Tensor:
return matmul(adj_t, x, reduce=self.aggr)
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)