Get appropriate model in/output type programmatically

1

Hi all,

Of course, the datatype of the data you’re trying to propagate through your network matters.
Feeding your model input with wrong input type might give you:
RuntimeError: Input type (torch.LongTensor) and weight type (torch.FloatTensor) should be the same
And feeding model output of wrong type might yield something along the lines of:
RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 'target'

To build a more flexible system I was wondering: How can one find the data type that will fit a model programmatically?

For output I’ve tried list(model.parameters())[-1] which might be a bit naive.

2

Might not be the best way, but for the inputs you could do something like:

current_children = model
while list(current_children.children()) != []: 
   current_children = list(current_children.children())[0]

for p_name, p in current_children.named_parameters():
    print('{}: {}'.format(p_name, p.dtype))