using for custom dataset

[KyonP]

Hi, I'm trying to apply your code to a sequential image dataset.

However, whenever I tried to input concatenated images (Batch x Timeseries x Channels x Width x Height), it gives following error.

<ipython-input-40-c130ba752793> in forward(self, input, h, c)
     29         input = input.cuda()
     30 
---> 31         combined = torch.cat((input, h), dim=1)
     32 
     33         A = self.conv(combined)

TypeError: cat received an invalid combination of arguments - got (tuple, dim=int), but expected one of:
 * (sequence[torch.cuda.FloatTensor] seq)
 * (sequence[torch.cuda.FloatTensor] seq, int dim)
      didn't match because some of the arguments have invalid types: (tuple, dim=int)

It seems like something is wrong with .cuda() declaration.

So I looked into those two Variables input and h

input has torch.FloatTensor type,

h has torch.autograd.variable.Variable type.

From this article, those two Variables have to be changed to same data type.

My question is,

i) Have you undergone the same issue like this?

ii) I've tried to change the data type of h by h.cuda() didn't work. I'm not used to Pytorch. So is there any advice I can get?

BTW thank you for your update. Gave me a lot of help

[automan000]

I think this problem is caused by the incompatilbe types of 'input' and 'h'. They should both be Variable rather than FloatTensor. Please refer to the document of 'torch.cat' for more details.