Model with more than one input and DataBlock API

I am having trouble understanding something, I am trying to build a model like the Siamese:

class Model(Module):
    def __init__(self, encoder, head):
        self.encoder,self.head = encoder,head
    def forward(self, x1, x2):
        ftrs =[self.encoder(x1), self.encoder(x2)], dim=1)
        return self.head(ftrs)

with 2 inputs.
If I use the datablock approach proposed on the siamese tutorial, it does not work. Because the batch that is created has lenght 2, it is on the form:

((img1, img2), y)

instead of form:

(img1, img2, y)

More generally, i want to understand where I should modify my code to * the batch into two items.
What I found, is that the learner does this in the _split function, doing self.xb = b[:i], if I do self.xb[0] i get the desired behavior.

I want to be able to create custom “Tuple” inherited classes, and clean-up my models using multiple inputs.

Why not just pass it to your forward function then unpack it there?


forward(self, x):
  a,b = x

It is what I am doing. I just want to understand why the batch is created differently with the data block API. The siamese tutorial does not work with the DataBLock pipeline.
I re did a Datablock pipeline using the Transforms pipeline, and found thath my models did not work out of the box.

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I meant instead of x1,x2 like you show (see my edit)

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you mean:

forward(self, x):
  a,b = x
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Yes, sorry :slight_smile: