Function main

examples/BGRL_L2L.py:123–145

()

def main():
    device = torch.device('cuda')
    path = osp.join(osp.expanduser('~'), 'datasets', 'WikiCS')
    dataset = WikiCS(path, transform=T.NormalizeFeatures())
    data = dataset[0].to(device)

    aug1 = A.Compose([A.EdgeRemoving(pe=0.5), A.FeatureMasking(pf=0.1)])
    aug2 = A.Compose([A.EdgeRemoving(pe=0.5), A.FeatureMasking(pf=0.1)])

    gconv = GConv(input_dim=dataset.num_features, hidden_dim=256, num_layers=2).to(device)
    encoder_model = Encoder(encoder=gconv, augmentor=(aug1, aug2), hidden_dim=256).to(device)
    contrast_model = BootstrapContrast(loss=L.BootstrapLatent(), mode='L2L').to(device)

    optimizer = Adam(encoder_model.parameters(), lr=0.01)

    with tqdm(total=100, desc='(T)') as pbar:
        for epoch in range(1, 101):
            loss = train(encoder_model, contrast_model, data, optimizer)
            pbar.set_postfix({'loss': loss})
            pbar.update()

    test_result = test(encoder_model, data)
    print(f'(E): Best test F1Mi={test_result["micro_f1"]:.4f}, F1Ma={test_result["macro_f1"]:.4f}')


if __name__ == '__main__':