import copy
from typing import Union, Tuple, Optional, Callable
from torch_geometric.typing import PairTensor, Adj, OptTensor
from torch import Tensor
from torch.nn import ModuleList, Linear, ReLU
from torch_sparse import SparseTensor, masked_select_nnz
from torch_geometric.nn.conv import MessagePassing
from ..inits import reset
[docs]class FiLMConv(MessagePassing):
r"""The FiLM graph convolutional operator from the
`"GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation"
<https://arxiv.org/abs/1906.12192>`_ paper
.. math::
\mathbf{x}^{\prime}_i = \sum_{r \in \mathcal{R}}
\sum_{j \in \mathcal{N}(i)} \sigma \left(
\boldsymbol{\gamma}_{r,i} \odot \mathbf{W}_r \mathbf{x}_j +
\boldsymbol{\beta}_{r,i} \right)
where :math:`\boldsymbol{\beta}_{r,i}, \boldsymbol{\gamma}_{r,i} =
g(\mathbf{x}_i)` with :math:`g` being a single linear layer by default.
Self-loops are automatically added to the input graph and represented as
its own relation type.
.. note::
For an example of using FiLM, see `examples/gcn.py
<https://github.com/rusty1s/pytorch_geometric/blob/master/examples/
film.py>`_.
Args:
in_channels (int or tuple): Size of each input sample. A tuple
corresponds to the sizes of source and target dimensionalities.
out_channels (int): Size of each output sample.
num_relations (int, optional): Number of relations. (default: :obj:`1`)
nn (torch.nn.Module, optional): The neural network :math:`g` that
maps node features :obj:`x_i` of shape
:obj:`[-1, in_channels]` to shape :obj:`[-1, 2 * out_channels]`.
If set to :obj:`None`, :math:`g` will be implemented as a single
linear layer. (default: :obj:`None`)
act (callable, optional): Activation function :math:`\sigma`.
(default: :meth:`torch.nn.ReLU()`)
aggr (string, optional): The aggregation scheme to use
(:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`).
(default: :obj:`"mean"`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
"""
def __init__(
self,
in_channels: Union[int, Tuple[int, int]],
out_channels: int,
num_relations: int = 1,
nn: Optional[Callable] = None,
act: Optional[Callable] = ReLU(),
aggr: str = 'mean',
**kwargs,
):
super(FiLMConv, self).__init__(aggr=aggr, **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_relations = max(num_relations, 1)
self.act = act
if isinstance(in_channels, int):
in_channels = (in_channels, in_channels)
self.lins = ModuleList()
self.films = ModuleList()
for _ in range(num_relations):
self.lins.append(Linear(in_channels[0], out_channels, bias=False))
if nn is None:
film = Linear(in_channels[1], 2 * out_channels)
else:
film = copy.deepcopy(nn)
self.films.append(film)
self.lin_skip = Linear(in_channels[1], self.out_channels, bias=False)
if nn is None:
self.film_skip = Linear(in_channels[1], 2 * self.out_channels,
bias=False)
else:
self.film_skip = copy.deepcopy(nn)
self.reset_parameters()
[docs] def reset_parameters(self):
for lin, film in zip(self.lins, self.films):
lin.reset_parameters()
reset(film)
self.lin_skip.reset_parameters()
reset(self.film_skip)
[docs] def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj,
edge_type: OptTensor = None) -> Tensor:
""""""
if isinstance(x, Tensor):
x: PairTensor = (x, x)
beta, gamma = self.film_skip(x[1]).split(self.out_channels, dim=-1)
out = gamma * self.lin_skip(x[1]) + beta
if self.act is not None:
out = self.act(out)
# propagate_type: (x: Tensor, beta: Tensor, gamma: Tensor)
if self.num_relations <= 1:
beta, gamma = self.films[0](x[1]).split(self.out_channels, dim=-1)
out += self.propagate(edge_index, x=self.lins[0](x[0]), beta=beta,
gamma=gamma, size=None)
else:
for i, (lin, film) in enumerate(zip(self.lins, self.films)):
beta, gamma = film(x[1]).split(self.out_channels, dim=-1)
if isinstance(edge_index, SparseTensor):
edge_type = edge_index.storage.value()
assert edge_type is not None
mask = edge_type == i
out += self.propagate(
masked_select_nnz(edge_index, mask, layout='coo'),
x=lin(x[0]), beta=beta, gamma=gamma, size=None)
else:
assert edge_type is not None
mask = edge_type == i
out += self.propagate(edge_index[:, mask], x=lin(x[0]),
beta=beta, gamma=gamma, size=None)
return out
def message(self, x_j: Tensor, beta_i: Tensor, gamma_i: Tensor) -> Tensor:
out = gamma_i * x_j + beta_i
if self.act is not None:
out = self.act(out)
return out
def __repr__(self):
return '{}({}, {}, num_relations={})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels,
self.num_relations)