[FIXED] Pythonic squeeze and unsqueeze data dimensions

Issue

For giving the data in good dimensions to a PyTorch Model, I use squeeze en unsqueeze function like this:

   inps = torch.FloatTensor(data[0])
   tgts = torch.FloatTensor(data[1])
   
   tgts = torch.unsqueeze(tgts, -1)
   tgts = torch.unsqueeze(tgts, -1)
   tgts = torch.unsqueeze(tgts, -1)
   inps = torch.unsqueeze(inps, -1)
   inps = torch.unsqueeze(inps, -1)
   inps = torch.unsqueeze(inps, -1)

and this:

    inps = torch.FloatTensor(data[0])
    tgts = torch.FloatTensor(data[1])
      
    tgts = torch.unsqueeze(tgts, 1)
    tgts = torch.unsqueeze(tgts, 1)
    tgts = torch.unsqueeze(tgts, 1)
    inps = torch.unsqueeze(inps, 1)
    inps = torch.unsqueeze(inps, 1)
    inps = torch.unsqueeze(inps, 1)

But of course, I'm kinda embarrassed to have this repetitive part in my code. Is there another way, more pythonic and clean, to write this code, please?

Solution

You can use torch.Tensor.view like below:

how_many_unsqueeze = 3
extra_dims = (1,) * how_many_unsqueeze
# extra_dims -> (1,1,1)

inps.view(-1, *extra_dims) # -> (-1,1,1,1)
tgts.view(-1, *extra_dims) # -> (-1,1,1,1)

You can use torch.reshape like below: But after using like in your question you need back to original shape

Instead of unsqueeze

inps = torch.reshape(inps, (len(data[0]),1,1,1))
tgts = torch.reshape(tgts, (len(data[1]),1,1,1))

Instead of squeeze

inps = torch.reshape(inps, (len(data[0]),))
tgts = torch.reshape(tgts, (len(data[1]),))

Answered By - I'mahdi