I have custom classes HsImage(Image), HSImageItemList(ItemList) and HsDataBunch(ImageDataBunch). HSImageItemList has two custom keyword arguments ‘dims’ and ‘chans’ that handle whether to consider data as image data (2-dimensional) or volumetric(3-dimensional), and chans that defines which channels to use in classification.
HSImageItemList is initialized like
def __init__(self, items, dims, chans=list(range(461)), **kwargs):
super().__init__(items, **kwargs)
self.dims = dims
self.chans = chans
self.copy_new.append('dims')
self.copy_new.append('chans')
After exporting a learner that uses these, load_learner fails with TypeError: __init__() missing 1 required positional argument: 'dims'
. How can I load this with same dims
that it was saved with?