将列表列表转换为CNN的张量输入

Question

我试图用CNN编码一个自动编码器，我必须使用我自己的数据。数据以列表的形式出现，我已将其转换（或试图转换）为pytorch张量

tensor_x = torch.Tensor([np.array(v) for v in testPulses])
print(tensor_x)
my_dataset = TensorDataset(tensor_x) # create your datset
data_loader = DataLoader(my_dataset) # create your dataloader

#HERE PROCESS THE DATA
tensor_x = torch.Tensor([np.array(v) for v in testPulses])
print(tensor_x)
my_dataset = TensorDataset(tensor_x) # create your datset
data_loader = DataLoader(my_dataset) # create your dataloader
tensor([[ 0.0009, -0.0010, -0.0003,  ...,  0.0033,  0.0079,  0.0030],
        [-0.0020,  0.0071,  0.0093,  ...,  0.0401,  0.0352,  0.0300],
        [ 0.0002, -0.0031,  0.0042,  ...,  0.0341,  0.0338,  0.0326],
        ...,
        [ 0.0060,  0.0084,  0.0047,  ...,  0.0380,  0.0365,  0.0334],
        [-0.0047,  0.0017, -0.0065,  ...,  0.0038,  0.0090,  0.0051],
        [-0.0061, -0.0009, -0.0030,  ...,  0.0327,  0.0336,  0.0333]]) 

我的自动编码器是

class Autoencoder(nn.Module):
  def __init__(self):
    # N, 1, 28*28
    super().__init__()
    self.encoder = nn.Sequential(
        nn.Conv1d(1, 16, 5, stride = 10, padding  = 1),  # N, 16, 1639
        nn.ReLU(),
        nn.Conv1d(16, 32, 9, stride = 10, padding = 0), # N, 32, 164
        nn.ReLU(),
        nn.Conv1d(32, 64, 5, stride = 10, padding = 1), # N, 64, 17
        nn.ReLU(),
        nn.Conv1d(64, 128, 9, stride = 10, padding = 2), # N, 128, 2
    )

    # N, 64, 1, 1
    self.decoder = nn.Sequential(
        nn.ConvTranspose1d(128, 64, 9, stride = 10, padding = 2), # N, 64, 17
        nn.ReLU(),
        nn.ConvTranspose1d(64, 32, 5, stride = 10, padding = 1), # N, 32, 164 
        nn.ReLU(),
        nn.ConvTranspose1d(32, 16, 9, stride = 10, padding = 0), # N, 16, 1639
        nn.ReLU(),
        nn.ConvTranspose1d(16, 1, 5, stride = 10, padding = 1), # N, 1, 16384 
        nn.ReLU()
    )
  
  def forward(self, x):
    encoded = self.encoder(x)
    decoded = self.decoder(encoded)
    return decoded

在这一点上，我创建了实际的模型

model = Autoencoder()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3, weight_decay = 1e-5)

然后我对其进行训练并收到以下错误

num_epochs = 10
outputs = []
dataTemp = [img for img in data_loader]
halfData = dataTemp[0:len(dataTemp)//2]
for epoch in range(num_epochs):
  for img in halfData:
    recon = model(img)
    loss = criterion(recon, img)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
  
  print(f'Epoch:{epoch+1}, Loss:{loss.item():.4f}')
  outputs.append((epoch, img, recon))

for img in dataTemp:
    recon = model(img)
    loss = criterion(recon, img)
print(f'Final Loss:{loss.item():.4f}')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-68-78e6fd204df1> in <module>()
      5 for epoch in range(num_epochs):
      6   for img in halfData:
----> 7     recon = model(img)
      8     loss = criterion(recon, img)
      9 

6 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    293                             _single(0), self.dilation, self.groups)
    294         return F.conv1d(input, weight, bias, self.stride,
--> 295                         self.padding, self.dilation, self.groups)
    296 
    297     def forward(self, input: Tensor) -> Tensor:

TypeError: conv1d() received an invalid combination of arguments - got (list, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (!list!, !Parameter!, !Parameter!, !tuple!, !tuple!, !tuple!, int)
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (!list!, !Parameter!, !Parameter!, !tuple!, !tuple!, !tuple!, int)

我完全不知道如何解决这个问题。我是pytorch的新手，所以任何帮助都会很好：）