PyTorch - TypeError: forward() 接受 1 个位置参数，但给出了 2 个

Posted 2023-02-22

【中文标题】PyTorch - TypeError: forward() 接受 1 个位置参数，但给出了 2 个

【英文标题】：PyTorch - TypeError: forward() takes 1 positional argument but 2 were given

【发布时间】：2020-12-01 18:47:49

【问题描述】：

我不明白这个错误来自哪里，模型的参数数量似乎是正确的，下面是我的模型：

class MancalaModel(nn.Module):

    def __init__(self, n_inputs=16, n_outputs=16):
        super().__init__()

        n_neurons = 256

        def create_block(n_in, n_out):
            block = nn.ModuleList()
            block.append(nn.Linear(n_in, n_out))
            block.append(nn.ReLU())
            return block

        self.blocks = nn.ModuleList()
        self.blocks.append(create_block(n_inputs, n_neurons))
        for _ in range(6):
            self.blocks.append(create_block(n_neurons, n_neurons))

        self.actor_block = nn.ModuleList()
        self.critic_block = nn.ModuleList()
        for _ in range(2):
            self.actor_block.append(create_block(n_neurons, n_neurons))
            self.critic_block.append(create_block(n_neurons, n_neurons))

        self.actor_block.append(create_block(n_neurons, n_outputs))
        self.critic_block.append(create_block(n_neurons, 1))

        self.apply(init_weights)

    def forward(self, x):
        x = self.blocks(x)
        actor = F.softmax(self.actor_block(x))
        critics = self.critic_block(x)
        return actor, critics

然后我创建一个实例并使用随机数进行前向传递

model = MancalaModel()
x = model(torch.rand(1, 16))

然后我得到 TypeError 说参数的数量不正确：

      2 model = MancalaModel()
----> 3 x = model(torch.rand(1, 16))
      4 # summary(model, (16,), device='cpu')
      5 

d:\environments\python\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

D:\UOM\Year3\AI & Games\KalahPlayer\agents\model_agent.py in forward(self, x)
     54 
     55     def forward(self, x):
---> 56         x = self.blocks(x)
     57         actor = F.softmax(self.actor_block(x))
     58         critics = self.critic_block(x)

d:\environments\python\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

TypeError: forward() takes 1 positional argument but 2 were given

感谢任何帮助，谢谢！

【参考方案1】：

TL;DR 您正在尝试 forward 到 nn.ModuleList - 这没有定义。 您需要将self.blocks 转换为nn.Sequential：

        def create_block(n_in, n_out):
            # do not work with ModuleList here either.
            block = nn.Sequential(
              nn.Linear(n_in, n_out),
              nn.ReLU()
            )
            return block

        blocks = []  # simple list - not a member of self, for temporal use only.
        blocks.append(create_block(n_inputs, n_neurons))
        for _ in range(6):
            blocks.append(create_block(n_neurons, n_neurons))
        self.blocks = nn.Sequential(*blocks)  # convert the simple list to nn.Sequential

---

我希望你得到NotImplementedError，而不是这个TypeError，因为你的self.blocks 是nn.ModuleList 类型，它的forward 方法抛出NotImplementedError。我刚刚创建了pull request 来解决这个令人困惑的问题。更新（2021 年 4 月 22 日）： PR was merged。在未来的版本中，您应该在调用nn.ModuleList 或nn.ModuleDict 时看到NotImplementedError。

【讨论】：

我可以确认这个问题存在于torch 1.7.1

@RakshitKothari 这个拉取请求的状态是“卡住” - 它与火炬脚本无关的问题相冲突。随时在此处发表评论并引起 pytorch 开发人员的注意。

@RakshitKothari 此 PR 于 4 月 22 日合并。对于更新的 pytorch 版本，您应该获得 NotImplementedError。我用 1.9.0 版确认了这一点