Function main

examples/InfoGraph.py:118–140

()

def main():
    device = torch.device('cuda')
    path = osp.join(osp.expanduser('~'), 'datasets')
    dataset = TUDataset(path, name='PTC_MR')
    dataloader = DataLoader(dataset, batch_size=128)
    input_dim = max(dataset.num_features, 1)

    gconv = GConv(input_dim=input_dim, hidden_dim=32, activation=torch.nn.ReLU, num_layers=2).to(device)
    fc1 = FC(hidden_dim=32 * 2)
    fc2 = FC(hidden_dim=32 * 2)
    encoder_model = Encoder(encoder=gconv, local_fc=fc1, global_fc=fc2).to(device)
    contrast_model = SingleBranchContrast(loss=L.JSD(), mode='G2L').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, dataloader, optimizer)
            pbar.set_postfix({'loss': loss})
            pbar.update()

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


if __name__ == '__main__':