Pytorch 可复现性设置随机数种子

为了实现pytorch训练神经网络可复现性需要进行以下操作：

def setup_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    # os.environ['CUDA_LAUNCH_BLOCKING'] = str(1)
    os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.enabled = False
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True
    torch.use_deterministic_algorithms(True)

其中torch.use_deterministic_algorithms(True)会强制pytorch的操作是可复现性的，如果存在非复现性操作就会报错，并指示出相关操作。

重点在于设置shuffle=False并且num_workers=0

    for (i, x_val) in enumerate(x_train):
        x_val = torch.tensor(x_train[i], dtype=torch.float)
        if link_load:
            y_val = torch.tensor(y_train[i].flatten(), dtype=torch.float)
        else:
            y_val = torch.tensor(y_train[i].flatten(), dtype=torch.int64)

        data = Data(x=x_val, y=y_val, edge_index=edge_index)
        train_dataset.append(data)

    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=False, sampler=sampler, num_workers=0)

在使用PyG写图神经网络的时候遇到了data.edge_index在进入卷积层的时候会遇到scatter_add操作是不可复现的，所以这里使用torch_sparse.SparseTensor来代替可以解决。

    def forward(self, data):
        x, edge_index = data.x, data.edge_index

        edge_index = torch_sparse.SparseTensor(row=edge_index[0], col=edge_index[1])