"""
GCN implementation of the Mixup algorithm from `"Mixup for Node and Graph Classification"
<https://dl.acm.org/doi/abs/10.1145/3442381.3449796>`_ paper
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch_geometric.nn as gnn
from torch import Tensor
from torch.nn import Parameter
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import zeros
from torch_geometric.typing import Adj, OptTensor
from torch_sparse import SparseTensor, matmul
from GOOD import register
from GOOD.utils.config_reader import Union, CommonArgs, Munch
from .BaseGNN import GNNBasic, BasicEncoder
from .Classifiers import Classifier
[docs]@register.model_register
class Mixup_GCN(GNNBasic):
r"""
The Graph Neural Network modified from the `"Mixup for Node and Graph Classification"
<https://dl.acm.org/doi/abs/10.1145/3442381.3449796>`_ paper and `"Semi-supervised Classification with Graph Convolutional Networks"
<https://arxiv.org/abs/1609.02907>`_ paper.
Args:
config (Union[CommonArgs, Munch]): munchified dictionary of args (:obj:`config.model.dim_hidden`, :obj:`config.model.model_layer`, :obj:`config.dataset.dim_node`, :obj:`config.dataset.num_classes`)
"""
def __init__(self, config: Union[CommonArgs, Munch]):
super().__init__(config)
self.feat_encoder = MixupGCNFeatExtractor(config)
self.classifier = Classifier(config)
self.graph_repr = None
[docs] def forward(self, *args, **kwargs) -> torch.Tensor:
r"""
The Mixup-GCN model implementation.
Args:
*args (list): argument list for the use of arguments_read. Refer to :func:`arguments_read <GOOD.networks.models.BaseGNN.GNNBasic.arguments_read>`
**kwargs (dict): key word arguments for the use of arguments_read. Refer to :func:`arguments_read <GOOD.networks.models.BaseGNN.GNNBasic.arguments_read>`
Returns (Tensor):
label predictions
"""
out_readout = self.feat_encoder(*args, **kwargs)
out = self.classifier(out_readout)
return out
[docs]class MixUpGCNConv(gnn.MessagePassing):
r"""The graph convolutional operator from the `"Mixup for Node and Graph Classification"
<https://dl.acm.org/doi/abs/10.1145/3442381.3449796>`_ paper and `"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.
The adjacency matrix can include other values than :obj:`1` representing
edge weights via the optional :obj:`edge_weight` tensor.
Its node-wise formulation is given by:
.. math::
\mathbf{x}^{\prime}_i = \mathbf{\Theta}^{\top} \sum_{j \in
\mathcal{N}(v) \cup \{ i \}} \frac{e_{j,i}}{\sqrt{\hat{d}_j
\hat{d}_i}} \mathbf{x}_j
with :math:`\hat{d}_i = 1 + \sum_{j \in \mathcal{N}(i)} e_{j,i}`, where
:math:`e_{j,i}` denotes the edge weight from source node :obj:`j` to target
node :obj:`i` (default: :obj:`1.0`)
Args:
in_channels (int): Size of each input sample, or :obj:`-1` to derive
the size from the first input(s) to the forward method.
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`)
add_self_loops (bool, optional): If set to :obj:`False`, will not add
self-loops to the input graph. (default: :obj:`True`)
normalize (bool, optional): Whether to add self-loops and compute
symmetric normalization coefficients on the fly.
(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`.
Shapes:
- **input:**
node features :math:`(|\mathcal{V}|, F_{in})`,
edge indices :math:`(2, |\mathcal{E}|)`,
edge weights :math:`(|\mathcal{E}|)` *(optional)*
- **output:** node features :math:`(|\mathcal{V}|, F_{out})`
"""
_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 = True, normalize: bool = True,
bias: bool = True, **kwargs):
kwargs.setdefault('aggr', 'add')
super().__init__(**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.cached = cached
self.add_self_loops = add_self_loops
self.normalize = normalize
self._cached_edge_index = None
self._cached_adj_t = None
self.lin = Linear(in_channels, out_channels, bias=False,
weight_initializer='glorot')
self.lin_cen = Linear(in_channels, out_channels, bias=False,
weight_initializer='glorot')
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
self.lin.reset_parameters()
zeros(self.bias)
self._cached_edge_index = None
self._cached_adj_t = None
def forward(self, x: Tensor, x_cen: 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)
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)
if self.cached:
self._cached_adj_t = edge_index
else:
edge_index = cache
x = self.lin(x)
# propagate_type: (x: Tensor, edge_weight: OptTensor)
out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
size=None) + self.lin_cen(x_cen)
if self.bias is not None:
out += self.bias
return out
[docs] def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor:
return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j
[docs] def message_and_aggregate(self, adj_t: SparseTensor, x: Tensor) -> Tensor:
return matmul(adj_t, x, reduce=self.aggr)