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torch_geometric.nn.norm.GraphNorm — pytorch_geometric documentation

class GraphNorm(in_channels: int, eps: float = 1e-05, device: Optional[device] = None)[source]

Bases: Module

Applies graph normalization over individual graphs as described in the “GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training” paper.

\[\mathbf{x}^{\prime}_i = \frac{\mathbf{x} - \alpha \odot \textrm{E}[\mathbf{x}]} {\sqrt{\textrm{Var}[\mathbf{x} - \alpha \odot \textrm{E}[\mathbf{x}]] + \epsilon}} \odot \gamma + \beta\]

where \(\alpha\) denotes parameters that learn how much information to keep in the mean.

Parameters:

• in_channels (int) – Size of each input sample.

• eps (float, optional) – A value added to the denominator for numerical stability. (default: 1e-5)

• device (torch.device, optional) – The device to use for the module. (default: None)

reset_parameters()[source]

Resets all learnable parameters of the module.

forward(x: Tensor, batch: Optional[Tensor] = None, batch_size: Optional[int] = None) → Tensor[source]

Forward pass.

Parameters:

• x (torch.Tensor) – The source tensor.

• batch (torch.Tensor, optional) – The batch vector \(\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N\), which assigns each element to a specific example. (default: None)

• batch_size (int, optional) – The number of examples \(B\). Automatically calculated if not given. (default: None)

Return type:

Tensor

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