Lazy initialization of MelScale.fb throws when loading module

Lazy initialization refers to the declaration here and the initialization here. When loading saved model (saved with torch.save()), this causes problem:

size mismatch for reduction.mel_scale.fb: copying a param with shape torch.Size([41, 16]) from checkpoint, the shape in current model is torch.Size([0]).

The problem can be sidestepped by running the model at least once before loading weights. But I’d rather not have to rely on such tricks.

Why is this being lazily initialized anyway?

[vincentqb]

The lazy evaluation means that we do not have to specify any dimensions when defining MelScale, and they are then inferred at evaluation time against the matrix used, is that right? This would seem like a nice feature to have for MelScale.

However, for MelSpectrogram, we already know the size of the matrix at initialization, and so we could infer the size at that time. Is that correct? If this is so, then we could go for that as a default behavior since waiting does not give us more information.

[jamarshon]

Going to close this as from #246, we can now load from state_dict for MelSpectrogram in this code snippet https://github.com/pytorch/audio/issues/245#issuecomment-522602035

Also, we can load from state_dict for MelScale (see test_melscale_load_save and test_melspectrogram_load_save)

Concluded that inferring matrix size at runtime for MelScale (i.e. lazy initialization) is a nice property to have as long as it does not impede other use cases. Feel free to reopen if we did not address anything