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generalconv_v2.py
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import torch
import torch.nn as nn
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.inits import glorot, zeros
from torch_geometric.utils import add_remaining_self_loops, is_undirected
from torch_scatter import scatter_add
from graphgym.config import cfg
class GeneralConvLayerV2(MessagePassing):
r"""General GNN layer
"""
def __init__(self,
in_channels,
out_channels,
improved=False,
cached=False,
bias=True,
**kwargs):
super(GeneralConvLayerV2, self).__init__(aggr=cfg.gnn.agg,
flow=cfg.gnn.flow,
**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.weight = Parameter(torch.Tensor(in_channels, out_channels))
if cfg.gnn.self_msg == 'concat':
self.weight_self = 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)
if cfg.gnn.self_msg == 'concat':
glorot(self.weight_self)
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index,
num_nodes,
edge_weight=None,
improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=dtype,
device=edge_index.device)
fill_value = 1.0 if not improved else 2.0
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
if is_undirected(edge_index, num_nodes=num_nodes):
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
norm = deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
else:
if cfg.gnn.flow == 'source_to_target':
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
else:
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-1.0)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
norm = (deg_inv_sqrt[row] if cfg.gnn.flow == 'source_to_target'
else deg_inv_sqrt[col]) * edge_weight
return edge_index, norm
def forward(self, x, edge_index, edge_weight=None, edge_feature=None):
""""""
if cfg.gnn.self_msg == 'concat':
x_self = torch.matmul(x, self.weight_self)
x = torch.matmul(x, self.weight)
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
if cfg.gnn.self_msg == 'none':
# add self-loop to ensure the final output
# has considered h_{v}^{l-1}
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, 2 if self.improved else 1,
x.size(self.node_dim))
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
x_msg = self.propagate(edge_index,
x=x,
norm=norm,
edge_feature=edge_feature)
if cfg.gnn.self_msg == 'none':
return x_msg
elif cfg.gnn.self_msg == 'add':
return x_msg + x
elif cfg.gnn.self_msg == 'concat':
return x_msg + x_self
else:
raise ValueError('self_msg {} not defined'.format(
cfg.gnn.self_msg))
def message(self, x_j, norm, edge_feature):
if edge_feature is None:
return norm.view(-1, 1) * x_j if norm is not None else x_j
else:
return norm.view(-1, 1) * (
x_j + edge_feature) if norm is not None else (x_j +
edge_feature)
def update(self, aggr_out):
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)
class GeneralEdgeConvLayerV2(MessagePassing):
r"""General GNN layer, with edge features
"""
def __init__(self,
in_channels,
out_channels,
improved=False,
cached=False,
bias=True,
**kwargs):
super(GeneralEdgeConvLayerV2, self).__init__(aggr=cfg.gnn.agg,
flow=cfg.gnn.flow,
**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.msg_direction = cfg.gnn.msg_direction
if self.msg_direction == 'single':
self.linear_msg = nn.Linear(in_channels + cfg.dataset.edge_dim,
out_channels,
bias=False)
else:
self.linear_msg = nn.Linear(in_channels * 2 + cfg.dataset.edge_dim,
out_channels,
bias=False)
if cfg.gnn.self_msg == 'concat':
self.linear_self = nn.Linear(in_channels, out_channels, bias=False)
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index,
num_nodes,
edge_weight=None,
improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1), ),
dtype=dtype,
device=edge_index.device)
fill_value = 1.0 if not improved else 2.0
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
if is_undirected(edge_index, num_nodes=num_nodes):
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
norm = deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
else:
if cfg.gnn.flow == 'source_to_target':
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
else:
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-1.0)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
norm = (deg_inv_sqrt[row] if cfg.gnn.flow == 'source_to_target'
else deg_inv_sqrt[col]) * edge_weight
return edge_index, norm
def forward(self, x, edge_index, edge_weight=None, edge_feature=None):
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
x_msg = self.propagate(edge_index,
x=x,
norm=norm,
edge_feature=edge_feature)
if cfg.gnn.self_msg == 'concat':
x_self = self.linear_self(x)
return x_self + x_msg
elif cfg.gnn.self_msg == 'add':
return x + x_msg
else:
return x_msg
def message(self, x_i, x_j, norm, edge_feature):
if self.msg_direction == 'both':
x_j = torch.cat((x_i, x_j, edge_feature), dim=-1)
else:
x_j = torch.cat((x_j, edge_feature), dim=-1)
x_j = self.linear_msg(x_j)
return norm.view(-1, 1) * x_j if norm is not None else x_j
def update(self, aggr_out):
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)