RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0. Pytorch problem

Question

I'm trying to write an autoencoder which accepts images or latent vector and returns both reconstructed image and the latent space.

dim_code = 128
class Autoencoder(nn.Module):
    def __init__(self):

       super().__init__()
       self.encoder = nn.Sequential(
          #Custom(lambda x: x.permute(0,3,1,2).float()),
          nn.ConvTranspose2d(in_channels = 3, out_channels = 6, kernel_size=3, stride=1, padding = 1),            
          nn.ReLU(),
          nn.MaxPool2d(2),                          # 8192
          nn.ConvTranspose2d(in_channels = 6, out_channels = 12, kernel_size=3, stride=1, padding = 1),
          nn.ReLU(),
          nn.MaxPool2d(2),                          # 4096
          nn.Flatten(),
          nn.Linear(3072, 512),
          nn.ReLU(),
          nn.Linear(512,dim_code)
       )
        
       self.decoder = nn.Sequential(
            nn.ReLU(),
            nn.Linear(dim_code, 512),
            nn.ReLU(),
            nn.Linear(512, 4096),
            nn.ReLU(),
            nn.Linear(4096, 64*64*3),
           # Custom(lambda x: x.view(-1,64,64,3).float())
       )
    def forward(self, x, len_batch=1, latent=False):

        if latent==False:
          latent_code = self.encoder(x)
          reconstruct = self.decoder(latent_code) 
          reconstruction = torch.reshape(reconstruct, (len_batch, 3, 64, 64))      
        else:
          reconstruct = self.decoder(X) 
          reconstruction = torch.reshape(reconstruct, (len_batch, 3, 64, 64))         
        
        return reconstruction, latent_code

device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
criterion = F.mse_loss

model = Autoencoder().to(device)

optimizer = torch.optim.Adam(model.parameters())

Then I tried to generate the latent vector and give it to the autoencoder to see the results:

z = np.random.randn(25, dim_code)
z_t = torch.tensor(z, dtype=float)

output = model(z_t.to(device), len_batch=25, latent=True)[0]
for i in range(5):
  plt.subplot(2, 3, i+1)
  plt.axis("off")
  plt.imshow(output[i].permute(1,2,0).cpu().detach().numpy())

But unfortunately it raises an error: thats it

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat2 in method wrapper_CUDA_mm)

I tried to assign tensor to cpu or use numpy.array format, but it raises the same error.

Answer 1

Try transferring len_batch variable to the available device as well.