pytorch输入张量大小错误的尺寸Conv1D

问题描述

 def train(epoch):
      model.train()
      train_loss = 0

  for batch_idx,(data,_) in enumerate(train_loader):
    data = data[None,:,:]
    print(data.size())    # something seems to change between here

    data = data.to(device)
    optimizer.zero_grad()
    recon_batch,mu,logvar = model(data) # and here???

    loss = loss_function(recon_batch,data,logvar)
    loss.backward()
    train_loss += loss.item()

    optimizer.step()

    if batch_idx % 1000 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch,batch_idx * len(data),len(train_loader.dataset),100. * batch_idx / len(train_loader),loss.item() / len(data)))


  print('====> Epoch: {} Average loss: {:.4f}'.format(epoch,train_loss / len(train_loader.dataset)))

for epoch in range(1,4):
        train(epoch)

从训练循环来看，这很奇怪，它确实知道大小为[1,1,1998]，但是在将其发送到设备后发生了变化吗？

    torch.Size([1,1998])
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-138-70cca679f91a> in <module>()
     27 
     28 for epoch in range(1,4):
---> 29         train(epoch)

5 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in forward(self,input)
    255                             _single(0),self.dilation,self.groups)
    256         return F.conv1d(input,self.weight,self.bias,self.stride,--> 257                         self.padding,self.groups)
    258 
    259 

RuntimeError: Expected 3-dimensional input for 3-dimensional weight [12,1],but got 2-dimensional input of size [1,1998] instead

这也是我的模型（我认识到这里可能还有其他一些问题，但我在询问张量大小未注册）

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

    self.conv1 = nn.Conv1d( 1,12,kernel_size=1,stride=5,padding=0)
    self.conv1_drop = nn.Dropout2d()
    self.pool1 = nn.MaxPool1d(kernel_size=3,stride=2)

    self.fc21 = nn.Linear(198,1)
    self.fc22 = nn.Linear(198,1)

    self.fc3 = nn.Linear(1,198)
    self.fc4 = nn.Linear(198,1998)

  def encode(self,x):
    h1 = self.conv1(x)
    h1 = self.conv1_drop(h1)
    h1 = self.pool1(h1)
    h1 = F.relu(h1)
    h1 = h1.view(1,-1) # 1 is the batch size
    return self.fc21(h1),self.fc22(h1)
  
  def reparameterize(self,logvar):
    std = torch.exp(0.5*logvar)
    eps = torch.rand_like(std)
    return mu + eps*std 
  
  def decode(self,z):
    h3 = F.relu(self.fc3(z))
    return torch.sigmoid(self.fc4(h3))
  
  def forward(self,x):
    mu,logvar = self.encode(x.view(-1,1998))
    z = self.reparameterize(mu,logvar)
    return self.decode(z),logvar

那么为什么Pytorch在重塑后不保留尺寸，如果是的话，那将是正确的张量大小吗？

解决方法

当我打电话给forward()时，我刚刚发现自己的错误，self.encode(x.view(-1,1998))正在重整张量。

python pytorch tensor