What is the difference between view() and unsqueeze() in Torch?

Question

Use of unsqueeze():

input = torch.Tensor(2, 4, 3) # input: 2 x 4 x 3 print(input.unsqueeze(0).size()) # prints - torch.size([1, 2, 4, 3]) 

Use of view():

input = torch.Tensor(2, 4, 3) # input: 2 x 4 x 3 print(input.view(1, -1, -1, -1).size()) # prints - torch.size([1, 2, 4, 3]) 

According to documentation, unsqueeze() inserts singleton dim at position given as parameter and view() creates a view with different dimensions of the storage associated with tensor.

What view() does is clear to me, but I am unable to distinguish it from unsqueeze(). Moreover, I don't understand when to use view() and when to use unsqueeze()?

Any help with good explanation would be appreciated!

Answer 1

view() can only take a single -1 argument.

So, if you want to add a singleton dimension, you would need to provide all the dimensions as arguments. For e.g., if A is a 2x3x4 tensor, to add a singleton dimension, you would need to do A:view(2, 1, 3, 4).

However, sometimes, the dimensionality of the input is unknown when the operation is being used. Thus, we dont know that A is 2x3x4, but we would still like to insert a singleton dimension. This happens a lot when using minibatches of tensors, where the last dimension is usually unknown. In these cases, the nn.Unsqueeze is useful and lets us insert the dimension without explicitly being aware of the other dimensions when writing the code.