问不一致的错误: TypeError: super( type，obj)：obj必须是类型的实例或子类型
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Stack Overflow用户

提问于 2019-05-30 23:36:15

回答 1查看 1.9K关注 0票数 2

我有下面两个类的python代码。

import torch
import torch.nn as nn
import torch.nn.functional as F

class QNet_baseline(nn.Module):

    """
        A MLP with 2 hidden layer

        observation_dim (int): number of observation features
        action_dim (int): Dimension of each action
        seed (int): Random seed
    """

    def __init__(self, observation_dim, action_dim, seed):
        super(QNet_baseline, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(observation_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, action_dim)

    def forward(self, observations):
        """
           Forward propagation of neural network

        """

        x = F.relu(self.fc1(observations))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


class QNet_3hidden(nn.Module):

    """
        A MLP with 3 hidden layer

        observation_dim (int): number of observation features
        action_dim (int): Dimension of each action
        seed (int): Random seed
    """

    def __init__(self, observation_dim, action_dim, seed):
        super(QNet_3hidden, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(observation_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 64)
        self.fc4 = nn.Linear(64, action_dim)

    def forward(self, observations):
        """
           Forward propagation of neural network

        """

        x = F.relu(self.fc1(observations))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        return x

我使用相同的代码来实例化这两个类。QNet_baseline工作正常，但我得到了QNet_3hidden的以下错误。为什么QNet_baseline会工作，但QNet_3hidden有一个错误？我错过了什么？谢谢!

/home/workspace/QNetworks.py in __init__(self, observation_dim, action_dim, seed)
     44 
     45     def __init__(self, observation_dim, action_dim, seed):
---> 46         super(QNet_3hidden, self).__init__()
     47         self.seed = torch.manual_seed(seed)
     48         self.fc1 = nn.Linear(observation_dim, 128)

TypeError: super(type, obj): obj must be an instance or subtype of type

另外，下面是两个类如何实例化：

class DDQN_Agent():
    """Interacts with and learns from the environment.

    Attributes:
        state_size (int): dimension of each state
        action_size (int): dimension of each action
        seed (int): random seed
    """

    def __init__(self, state_size, action_size, seed, qnet="baseline", filename=None):
        """Initialize an Agent object.

        Args:
            filename: path of .pth file with trained weights
        """
        self.state_size = state_size
        self.action_size = action_size
        self.seed = random.seed(seed)

        # Q-Network
        if qnet=="3hidden":
            self.qnetwork_local = QNet_3hidden(state_size, action_size, seed).to(device)
            self.qnetwork_target = QNet_3hidden(state_size, action_size, seed).to(device)
            self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
        else:
            self.qnetwork_local = QNet_baseline(state_size, action_size, seed).to(device)
            self.qnetwork_target = QNet_baseline(state_size, action_size, seed).to(device)
            self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)

        if filename:
            weights = torch.load(filename)
            self.qnetwork_local.load_state_dict(weights)
            self.qnetwork_target.load_state_dict(weights)

        # Replay memory
        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
        # Initialize time step (for updating every UPDATE_EVERY steps)
        self.t_step = 0