Models

class core.net.RX50GCN3Head4Channel(out_channels=7, pretrained=True, nodes=(32, 32), dropout=0, enhance_diag=True, aux_pred=True)
__init__(out_channels=7, pretrained=True, nodes=(32, 32), dropout=0, enhance_diag=True, aux_pred=True)
Parameters

  • out_channels –

  • pretrained –

  • nodes –

  • dropout –

  • enhance_diag –

  • aux_pred –

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class core.net.RX101GCN3Head4Channel(out_channels=7, pretrained=True, nodes=(32, 32), dropout=0, enhance_diag=True, aux_pred=True)
__init__(out_channels=7, pretrained=True, nodes=(32, 32), dropout=0, enhance_diag=True, aux_pred=True)
Parameters

  • out_channels –

  • pretrained –

  • nodes –

  • dropout –

  • enhance_diag –

  • aux_pred –

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also nn-init-doc).

Parameters

fn (Module -> None) – function to be applied to each submodule

Returns

self

Return type

Module

Example:

>>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
forward(x)
Parameters

x –

Returns

class core.net.SCGBlock(in_ch, hidden_ch=6, node_size=(32, 32), add_diag=True, dropout=0.2)
__init__(in_ch, hidden_ch=6, node_size=(32, 32), add_diag=True, dropout=0.2)
forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

classmethod laplacian_matrix(A, self_loop=False)

Computes normalized Laplacian matrix: A (B, N, N)

class core.net.GCNLayer(in_features, out_features, bnorm=True, activation=ReLU(), dropout=None)
__init__(in_features, out_features, bnorm=True, activation=ReLU(), dropout=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(data)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class core.net.BatchNormGCN(num_features)

Batch normalization over GCN features

__init__(num_features)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.