Convolutional neural network throws ErrorTypeError: init() takes 1 positional argument but 2 were given

Question

我正在传递一批 256 张图像 x_s，尺寸如下 [256, 3, 560, 448]。但是，每当我尝试将我的图像提供给 CNN 时，我都会收到以下错误：

ErrorTypeError: __init__() takes 1 positional argument but 2 were given'

不确定“1 个位置参数”在这里是什么意思。我正在使用我创建的迭代器传递图像。下面是我的训练循环代码，直到它中断：

for e in range(num_epochs):
  print(f'Epoch{e+1:04d}/ {num_epochs:04d}', end='\n================\n')
  dl_source_iter = iter(dl_source)
  dl_target_iter = iter(dl_target)

  for batch in range(max_batches):
    optimizer.zero_grad()

    p = float(batch + e * max_batches) / (num_epochs *max_batches)
    grl_lambda = 2. / (1. + np.exp(-10 * p)) - 1

    x_s, y_s = next(dl_source_iter)

    y_s_domain = torch.zeros(256, dtype=torch.long)

    class_pred, domain_pred = Cnn(x_s, grl_lambda) #This is the line which throws an error

这是我的卷积神经网络：

class Cnn(nn.Module):
  def __init__(self):
    super(Cnn, self).__init__()

    self.feature_extract= nn.Sequential(
        nn.Conv2d(3, 64, 5, 1, 1),
        nn.BatchNorm2d(64),
        nn.MaxPool2d(2),
        nn.ReLU(True),
        nn.Conv2d(64, 50, 5, 1, 1),
        nn.BatchNorm2d(50),
        nn.MaxPool2d(2),
        nn.ReLU(True),
        nn.Dropout2d(),
    )

    self.num_cnn_features = 50*5*5
    self.class_classifier = nn.Sequential(
        nn.Linear(self.num_cnn_features, 200),
        nn.BatchNorm1d(200),
        nn.Dropout2d(),
        nn.ReLU(True),
        nn.Linear(200, 200),
        nn.BatchNorm1d(200),
        nn.ReLU(True),
        nn.Linear(200, 182),
        nn.LogSoftmax(dim = 1),

    )

    self.DomainClassifier = nn.Sequential(
        nn.Linear(self.num_cnn_features, 100),
        nn.BatchNorm1d(100),
        nn.ReLU(True),
        nn.Linear(100, 2),
        nn.LogSoftmax(dim=1)    
        
    )

  def forward(self, x, grl_lambda=1.0):

    features = self.feature_extract(x)
    features = features.view(-1, self.num_cnn_features)
    features_grl = GradientReversalFn(features, grl_lambda)
    class_pred = self.class_classifier(features)
    domain_pred = self.DomainClassifier(features_grl)
    return class_pred, domain_pred

有没有人猜到为什么会发生这种情况？我似乎无法弄清楚出了什么问题。任何帮助将不胜感激。

Answer 1

您需要先创建一个 Cnn 对象，然后才能将数据传递给它。您正在调用 Cnn class 构造函数 __init__，它不需要参数，而不是 Cnn class 实例的 forward 方法，这才是你真正想要做的。

# outside of loop
model = Cnn()
 
# inside loop
class_pred, domain_pred = model(x_s, grl_lambda)

Convolutional neural network throws ErrorTypeError: init() takes 1 positional argument but 2 were given

Convolutional neural network throws ErrorTypeError: init() takes 1 positional argument but 2 were given

pytorch

conv-neural-network

runtime-error

training-data

Convolutional neural network throws ErrorTypeError: __init__() takes 1 positional argument but 2 were given

Convolutional neural network throws ErrorTypeError: __init__() takes 1 positional argument but 2 were given

pytorch

conv-neural-network

runtime-error

training-data

Convolutional neural network throws ErrorTypeError: init() takes 1 positional argument but 2 were given

Convolutional neural network throws ErrorTypeError: init() takes 1 positional argument but 2 were given