Mismatched model shape upon MegaDepth finetuning

[mattiasmar]

Mismatched model shape upon MegaDepth finetuning

[mattiasmar]

I've repeated this experiment with SIFT and I got the same error:

Exception has occurred: RuntimeError
Error(s) in loading state_dict for TwoViewPipeline:
	size mismatch for matcher.posenc.Wr.weight: copying a param with shape torch.Size([32, 2]) from checkpoint, the shape in current model is torch.Size([32, 4]).

However, with superpoint and aliked the exception does not occur. One obvious difference is that SIFT doesn't have a torch model, whereas SP and ALIKED do. Could that break the TwoViewPipeline?

[mattiasmar]

The parameter "model.matcher.add_scale_ori (in the config yaml) must be set to the same value as used during (pre)training against the homography task (obviously).

The default value of this parameters is 'false'. When set to true the number of in-features of the Learnable Fourier Positional Encoding becomes 4 otherwise it becomes 2.