Passing a vector alongside sequences in a batch

Hi all, I hope you are well,

I have a problem where I have a CNN with two inputs, one is a 75 x 4 matrix and the other one is a single value. With a batch size of 512, the size of the training batch matrix is naturally 512 x 75 x 4, and the network trains fine without the second input.
But, as soon as I try to add the vector of single values, each 1 x 75 x 4 Matrix is passed alongside the whole vector (512 entries) which raises an error.

During the training, I would like to forward pass each 1 x 75 x 4 matrix alongside its corresponding single value but I’ve yet to find how,

Thanks in advance,


Could you post the error message and a small code snippet so that we could have a look?

Hi, thank you for your response, sure :

Here is my collate function which retrieves the length of one-hot-encoded sequences lengths, and then returns padded sequence, and lengths. (tag corresponds to the y, this is a regression problem).

def collate(batch):

    ## Get sequence lengths 
    lengths = torch.IntTensor([np.array(t['seq']).shape[0] for t in batch  ])
    ## padd
    batch_ = [torch.Tensor(t['seq']).to(device) for t in batch]
    batch_ = torch.nn.utils.rnn.pad_sequence(batch_, batch_first=True)
    #batch_ = torch.nn.utils.rnn.pack_padded_sequence(batch_, batch_first=True)
    # Get tags
    tags = [t['tag'] for t in batch]
    tags = torch.FloatTensor(tags)
    return batch_, lengths, tags

Here is a snippet of my forward function in my CNN :

    def forward(self, x, length):
        # Would like to resize x based on the length here :
        # Something like x = x[:length,:]

        x = self.conv1(x)
        x = F.relu(x)
        x = self.batch1(x)

And this is my training loop :

    for i_batch, item in enumerate(train_loader):
        seqs = item[0]
        tags = item[2]
        lengths = item[1]
        # (1) :
        outputs = custom_model(seqs, lengths)  

the line after #(1): raises an error because lengths is a vector. But I would like to apply the resize function to each of my sequences based on the corresponding value in the length vector.

If you would have more questions, don’t hesitate !


Thanks for the code! :slight_smile:
Could you please also post the error message?

Welcome !
Instead of the #resize line, I’ve used :

def forward(self, x, length):
      x = torch.narrow(0,0,length) #this line instead of the resize line in the previous forward code
      x = self.conv1(x)

And it raises the error : narrow(): argument ‘length’ (position 4) must be int, not Tensor

Further analysis showed that ‘length’ is my 512 x 1 tensor with the 512 different lengths (512 being the batch size).
I would like to apply the narrow function to each sequence, so that each sequence is “narrowed” by it’s corresponding length in the length vector.

Thanks for your time,

Unfortunately this won’t work. as x would contain tensors with different lengths, if I understand the use case correctly.
Could you zero-pad x and use lengths as a mask?
If not, could you explain your use case a bit, please?

1 Like

Hi again !
Here is an explanation of my use-case :

I have one-hot encoded sequences which are L x 4 in dimensions (L being the lenght of the sequence).
They look like this :
[1,0,0,0]] (just a silly example).

These sequences are associated with a value that I am trying to predict.

They have various sizes, and I’ve tried 0 padding them before (which works) but I am afraid this will lead to some problems because we are looking at aligned motifs. (example a [1,0,0,0] followed by a [0,0,1,0] that are always at the same position). I am afraid that padding would remove this information.
As for a mask, I’ve not thought of this, do you have any general idea how it could be implemented so that the network “cuts” the padded part of the sequences ?

Edit : For now I’ve gone around the problem by using a batch_size of one, but it greatly increases computing time, which is going to be a burden when I try to scale this up to datasets of over 1,2 or 3M sequences.

Thank you very much for your answers,

What would be the desired shape of x after the narrow operation?
Would it be in the desired use case [batch_size, 4, varying_shape]?

For now, my model works with tensor of shapes : [batch_size, 1, varying_shape, 4]