def __init__(self,
in_feat,
out_feat,
rel_names,
num_bases,
*,
weight=True,
bias=True,
activation=None,
self_loop=False,
dropout=0.0):
super(RelGraphConvLayer, self).__init__()
self.in_feat = in_feat
self.out_feat = out_feat
self.rel_names = rel_names
self.num_bases = num_bases
self.bias = bias
self.activation = activation
self.self_loop = self_loop
self.batchnorm = False
self.conv = dglnn.HeteroGraphConv({
rel: dglnn.GraphConv(in_feat,
out_feat,
norm='right',
weight=False,
bias=False)
for rel in rel_names
})
self.use_weight = weight
self.use_basis = num_bases < len(self.rel_names) and weight
if self.use_weight:
if self.use_basis:
self.basis = dglnn.WeightBasis((in_feat, out_feat), num_bases,
len(self.rel_names))
else:
self.weight = nn.Parameter(
th.Tensor(len(self.rel_names), in_feat, out_feat))
nn.init.xavier_uniform_(self.weight,
gain=nn.init.calculate_gain('relu'))
# bias
if bias:
self.h_bias = nn.Parameter(th.Tensor(out_feat))
nn.init.zeros_(self.h_bias)
# weight for self loop
if self.self_loop:
self.loop_weight = nn.Parameter(th.Tensor(in_feat, out_feat))
nn.init.xavier_uniform_(self.loop_weight,
gain=nn.init.calculate_gain('relu'))
# define batch norm layer
if self.batchnorm:
self.bn = nn.BatchNorm1d(out_feat)
self.dropout = nn.Dropout(dropout)
def __init__(self,
in_feat,
out_feat,
rel_names,
ntype_names,
num_bases,
*,
weight=True,
bias=True,
activation=None,
self_loop=False,
dropout=0.0,
use_gcn_checkpoint=False,
**kwargs):
super(RelGraphConvLayer, self).__init__()
self.in_feat = in_feat
self.out_feat = out_feat
self.rel_names = rel_names
self.ntype_names = ntype_names
self.num_bases = num_bases
self.bias = bias
self.activation = activation
self.self_loop = self_loop
self.use_gcn_checkpoint = use_gcn_checkpoint
self.use_weight = weight
self.use_basis = num_bases < len(self.rel_names) and weight
if self.use_weight:
if self.use_basis:
self.basis = dglnn.WeightBasis((in_feat, out_feat), num_bases,
len(self.rel_names))
else:
self.weight = nn.Parameter(
th.Tensor(len(self.rel_names), in_feat, out_feat))
# nn.init.xavier_uniform_(self.weight, gain=nn.init.calculate_gain('relu'))
nn.init.xavier_normal_(self.weight)
# TODO
# think of possibility switching to GAT
# rel : dglnn.GATConv(in_feat, out_feat, num_heads=4)
# rel : dglnn.GraphConv(in_feat, out_feat, norm='right', weight=False, bias=False, allow_zero_in_degree=True)
self.create_conv(in_feat, out_feat, rel_names)
# bias
if bias:
self.bias_dict = nn.ParameterDict()
for ntype_name in self.ntype_names:
self.bias_dict[ntype_name] = nn.Parameter(
torch.Tensor(1, out_feat))
nn.init.normal_(self.bias_dict[ntype_name])
# self.h_bias = nn.Parameter(th.Tensor(1, out_feat))
# nn.init.normal_(self.h_bias)
# weight for self loop
if self.self_loop:
# if self.use_basis:
# self.loop_weight_basis = dglnn.WeightBasis((in_feat, out_feat), num_bases, len(self.ntype_names))
# else:
# self.loop_weight = nn.Parameter(th.Tensor(len(self.ntype_names), in_feat, out_feat))
# # nn.init.xavier_uniform_(self.weight, gain=nn.init.calculate_gain('relu'))
# nn.init.xavier_normal_(self.loop_weight)
self.loop_weight = nn.Parameter(th.Tensor(in_feat, out_feat))
nn.init.xavier_uniform_(self.loop_weight,
gain=nn.init.calculate_gain('tanh'))
# # nn.init.xavier_normal_(self.loop_weight)
self.dropout = nn.Dropout(dropout)