Source code for graphwar.nn.models.sgc

import torch.nn as nn

from graphwar.utils import wrapper
from graphwar.nn.layers import SGConv, Sequential, activations


[docs]class SGC(nn.Module): r"""The simplified graph convolutional operator from the `"Simplifying Graph Convolutional Networks" <https://arxiv.org/abs/1902.07153>`_ paper (ICML'19) Parameters ---------- in_channels : int, the input dimensions of model out_channels : int, the output dimensions of model hids : list, optional the number of hidden units for each hidden layer, by default [] acts : list, optional the activation function for each hidden layer, by default [] K : int, optional the number of propagation steps, by default 2 dropout : float, optional the dropout ratio of model, by default 0. bias : bool, optional whether to use bias in the layers, by default True cached : bool, optional whether the layer will cache the computation of :math:`(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2})^K` on first execution, and will use the cached version for further executions, by default True bn: bool, optional whether to use :class:`BatchNorm1d` after the convolution layer, by default False Note ---- To accept a different graph as inputs, please call :meth:`cache_clear` first to clear cached results. It is convenient to extend the number of layers with different or the same hidden units (activation functions) using :meth:`graphwar.utils.wrapper`. See Examples below: Examples -------- >>> # SGC without hidden layer >>> model = SGC(100, 10) >>> # SGC with two hidden layers >>> model = SGC(100, 10, hids=[32, 16], acts=['relu', 'elu']) >>> # SGC with two hidden layers, without activation at the first layer >>> model = SGC(100, 10, hids=[32, 16], acts=[None, 'relu']) >>> # SGC with very deep architectures, each layer has elu as activation function >>> model = SGC(100, 10, hids=[16]*8, acts=['elu']) See also -------- :class:`graphwar.nn.layers.SGConv` """ @wrapper def __init__(self, in_channels, out_channels, hids: list = [], acts: list = [], K: int = 2, dropout: float = 0., bias: bool = True, cached: bool = True, bn: bool = False): super().__init__() conv = [] for i, (hid, act) in enumerate(zip(hids, acts)): if i == 0: conv.append(SGConv(in_channels, hid, bias=bias, K=K, cached=cached)) else: conv.append(nn.Linear(in_channels, hid, bias=bias)) if bn: conv.append(nn.BatchNorm1d(hid)) conv.append(activations.get(act)) conv.append(nn.Dropout(dropout)) in_channels = hid if not hids: conv.append(SGConv(in_channels, out_channels, bias=bias, K=K, cached=cached)) else: conv.append(nn.Linear(in_channels, out_channels, bias=bias)) self.conv = Sequential(*conv)
[docs] def reset_parameters(self): self.conv.reset_parameters()
[docs] def cache_clear(self): """Clear cached inputs or intermediate results.""" for layer in self.conv: if hasattr(layer, 'cache_clear'): layer.cache_clear() return self
[docs] def forward(self, x, edge_index, edge_weight=None): return self.conv(x, edge_index, edge_weight)