infer_type fails with Tuple index out of range error. about nir

[sheiksadique]

Torch models still expect input with the batch dimension included. It should be ignored only at the conversion stage to NIR.

[sheiksadique]

I see that the input type for the input node is (1, 2, 32, 32). So the batch dim is appearing there. I assume this is not supposed to be there?

[stevenabreu7]

Yeah, the batch dimension shouldn't be there

[Jegp]

Could the batch dimension be related to the batch dimension in the sample data? On line 40 it's torch.rand((1, 2, 34, 34)).

[Jegp]

Is that true? This code works well for me:

import torch
torch.nn.Linear(1, 2)(torch.zeros(1))

[SirineArfa]

I get the same error when running infer_type() on an NIRGraph generated by snntorch.export_to_nir() for an input of the following size: [1, 150, 2, 32, 32] including the batch size dimension. Is there a current fix for this?

[SirineArfa]

I get the same error when running infer_type() on an NIRGraph generated by snntorch.export_to_nir() for an input of the following size: [1, 150, 2, 32, 32] including the batch size dimension. Is there a current fix for this?

I found a current fix for this --> Adjust (if necessary and only in case of mismatch) dimensions of padding, dilation, and stride in ir._calculate_conv_output() to match the dimension of input_shape

[stevenabreu7]

To re-open this, do we agree that all NIR graphs should have the batch dimension removed? Or is this something that should go into the newly added metadata field?

We could also add a helper method to add/remove the batch dimension from a NIR graph (should not be too difficult to simply do something like unsqueeze(0) to all nodes in the graph).

Thoughts? @Jegp @SirineArfa @matjobst (and anyone else)

[Jegp]

Happy to put that in the spec. Ideally, the graphs should be independent of batches, IMO. Shouldn't batches be independent of the computation? We're not doing any batch norming in NIR, for instance.