def __init__(self, in_channels, hidden_channels, out_channels, drop_out=0.1):
super(SAGENet, self).__init__()
self.conv1 = SAGEConv(in_channels, hidden_channels)
self.conv2 = SAGEConv(hidden_channels, hidden_channels)
self.conv3 = SAGEConv(hidden_channels, hidden_channels)
self.lin = torch.nn.Linear(3 * hidden_channels, out_channels)
self.drop_out = drop_out
def __init__(self, in_channels, out_channels):
super().__init__()
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, 16))
self.convs.append(SAGEConv(16, 16))
self.convs.append(SAGEConv(16, out_channels))
def __init__(self, input_dim):
super(SimpleNetWSage, self).__init__()
self.conv_succ1 = SAGEConv(input_dim, 64, flow="target_to_source")
# self.conv_succ2 = GCNConv(128, 128, flow="target_to_source")
self.conv_succ3 = SAGEConv(64, 64, flow="target_to_source")
self.conv_succ2 = SAGEConv(64, 64, flow="target_to_source")
self.conv_succ4 = SAGEConv(64, 64, flow="target_to_source")
self.conv_probs = SAGEConv(64, 1, flow="target_to_source")
self.do_nothing = Linear(64, 1)
self.value = Linear(64, 1)
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout):
super(SAGE, self).__init__()
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, hidden_channels))
for _ in range(num_layers - 2):
self.convs.append(SAGEConv(hidden_channels, hidden_channels))
self.convs.append(SAGEConv(hidden_channels, out_channels))
self.dropout = dropout
def __init__(self, in_channels: int, hidden_channels: int, num_layers: int,
out_channels: Optional[int] = None, dropout: float = 0.0,
act: Optional[Callable] = ReLU(inplace=True),
norm: Optional[torch.nn.Module] = None, jk: str = 'last',
**kwargs):
super().__init__(in_channels, hidden_channels, num_layers,
out_channels, dropout, act, norm, jk)
self.convs.append(SAGEConv(in_channels, hidden_channels, **kwargs))
for _ in range(1, num_layers):
self.convs.append(
SAGEConv(hidden_channels, hidden_channels, **kwargs))
def __init__(
self,
input_channels: int,
output_channels: int,
aggr: str,
activation_name: _typing.Optional[str] = ...,
dropout_probability: _typing.Optional[float] = ...,
):
super().__init__()
self._convolution: SAGEConv = SAGEConv(input_channels,
output_channels,
aggr=aggr)
if (activation_name is not Ellipsis and activation_name is not None
and type(activation_name) == str):
self._activation_name: _typing.Optional[str] = activation_name
else:
self._activation_name: _typing.Optional[str] = None
if (dropout_probability is not Ellipsis
and dropout_probability is not None
and type(dropout_probability) == float):
if dropout_probability < 0:
dropout_probability = 0
if dropout_probability > 1:
dropout_probability = 1
self._dropout: _typing.Optional[
torch.nn.Dropout] = torch.nn.Dropout(dropout_probability)
else:
self._dropout: _typing.Optional[torch.nn.Dropout] = None
def __init__(self, input_dimension: int, dimensions: _typing.Sequence[int],
_act: _typing.Optional[str],
_dropout: _typing.Optional[float],
aggr: _typing.Optional[str]):
super(_SAGE, self).__init__()
self._act: _typing.Optional[str] = _act
self._dropout: _typing.Optional[float] = _dropout
self.__convolution_layers: torch.nn.ModuleList = torch.nn.ModuleList()
for layer, output_dimension in enumerate(dimensions):
self.__convolution_layers.append(
SAGEConv(input_dimension if layer == 0 else dimensions[layer -
1],
output_dimension,
aggr=aggr))
def __init__(self, in_channels, out_channels):
super(Net, self).__init__()
self.conv1 = SAGEConv(in_channels, 16)
self.conv2 = SAGEConv(16, 16)
self.conv3 = SAGEConv(16, out_channels)
def init_conv(self, in_channels: Union[int, Tuple[int, int]],
out_channels: int, **kwargs) -> MessagePassing:
return SAGEConv(in_channels, out_channels, **kwargs)
def __init__(self, in_channels, out_channels):
super(SAGENet, self).__init__()
self.conv1 = SAGEConv(in_channels, 16, normalize=False)
self.conv2 = SAGEConv(16, out_channels, normalize=False)
def init_conv(self, in_channels: int, out_channels: int,
**kwargs) -> MessagePassing:
return SAGEConv(in_channels, out_channels, **kwargs)
def __init__(self):
super(Net, self).__init__()
self.conv1 = SAGEConv(dataset.num_features, 16)
self.conv2 = SAGEConv(16, 16)
self.conv3 = SAGEConv(16, dataset.num_classes)
