def __init__(
self,
in_channels,
out_channels,
dim,
kernel_size,
hidden_channels=None,
dilation=1,
bias=True,
**kwargs,
):
super(XConv, self).__init__()
self.in_channels = in_channels
if hidden_channels is None:
hidden_channels = in_channels // 4
assert hidden_channels > 0
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.dim = dim
self.kernel_size = kernel_size
self.dilation = dilation
self.kwargs = kwargs
C_in, C_delta, C_out = in_channels, hidden_channels, out_channels
D, K = dim, kernel_size
self.mlp1 = S(
L(dim, C_delta),
ELU(),
BN(C_delta),
L(C_delta, C_delta),
ELU(),
BN(C_delta),
Reshape(-1, K, C_delta),
)
self.mlp2 = S(
L(D * K, K**2),
ELU(),
BN(K**2),
Reshape(-1, K, K),
Conv1d(K, K**2, K, groups=K),
ELU(),
BN(K**2),
Reshape(-1, K, K),
Conv1d(K, K**2, K, groups=K),
BN(K**2),
Reshape(-1, K, K),
)
C_in = C_in + C_delta
depth_multiplier = int(ceil(C_out / C_in))
self.conv = S(
Conv1d(C_in, C_in * depth_multiplier, K, groups=C_in),
Reshape(-1, C_in * depth_multiplier),
L(C_in * depth_multiplier, C_out, bias=bias),
)
self.reset_parameters()
def __init__(self, in_channels, out_channels, dim, kernel_size,
hidden_channels=None, dilation=1, bias=True, BiLinear=BiLinear, BiConv1d=BiConv1d, ifFirst=False, **kwargs):
super(BiXConv, self).__init__()
if knn_graph is None:
raise ImportError('`XConv` requires `torch-cluster`.')
self.in_channels = in_channels
if hidden_channels is None:
hidden_channels = in_channels // 4
assert hidden_channels > 0
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.dim = dim
self.kernel_size = kernel_size
self.dilation = dilation
self.kwargs = kwargs
C_in, C_delta, C_out = in_channels, hidden_channels, out_channels
D, K = dim, kernel_size
if ifFirst:
Lin1 = Lin
else:
Lin1 = BiLinear
self.mlp1 = S(
Lin1(dim, C_delta),
Hardtanh(),
BN(C_delta),
BiLinear(C_delta, C_delta),
Hardtanh(),
BN(C_delta),
Reshape(-1, K, C_delta),
)
self.mlp2 = S(
Lin1(D * K, K**2),
Hardtanh(),
BN(K**2),
Reshape(-1, K, K),
BiConv1d(K, K**2, K, groups=K),
Hardtanh(),
BN(K**2),
Reshape(-1, K, K),
BiConv1d(K, K**2, K, groups=K),
BN(K**2),
Reshape(-1, K, K),
)
C_in = C_in + C_delta
depth_multiplier = int(ceil(C_out / C_in))
self.conv = S(
BiConv1d(C_in, C_in * depth_multiplier, K, groups=C_in),
Reshape(-1, C_in * depth_multiplier),
BiLinear(C_in * depth_multiplier, C_out, bias=bias),
)
self.reset_parameters()
def __init__(self, in_channels: int, out_channels: int, dim: int,
kernel_size: int, hidden_channels: Optional[int] = None,
dilation: int = 1, bias: bool = True, num_workers: int = 1):
super(XConv, self).__init__()
if knn_graph is None:
raise ImportError('`XConv` requires `torch-cluster`.')
self.in_channels = in_channels
if hidden_channels is None:
hidden_channels = in_channels // 4
assert hidden_channels > 0
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.dim = dim
self.kernel_size = kernel_size
self.dilation = dilation
self.num_workers = num_workers
C_in, C_delta, C_out = in_channels, hidden_channels, out_channels
D, K = dim, kernel_size
self.mlp1 = S(
L(dim, C_delta),
ELU(),
BN(C_delta),
L(C_delta, C_delta),
ELU(),
BN(C_delta),
Reshape(-1, K, C_delta),
)
self.mlp2 = S(
L(D * K, K**2),
ELU(),
BN(K**2),
Reshape(-1, K, K),
Conv1d(K, K**2, K, groups=K),
ELU(),
BN(K**2),
Reshape(-1, K, K),
Conv1d(K, K**2, K, groups=K),
BN(K**2),
Reshape(-1, K, K),
)
C_in = C_in + C_delta
depth_multiplier = int(ceil(C_out / C_in))
self.conv = S(
Conv1d(C_in, C_in * depth_multiplier, K, groups=C_in),
Reshape(-1, C_in * depth_multiplier),
L(C_in * depth_multiplier, C_out, bias=bias),
)
self.reset_parameters()