Preamble/disclaimer: I am not a seasoned Python coder. The following serves to show that.
When I try to create a data object for my ConvLearner model like this: data1608 = fastai.dataset.ImageClassifierData.from_paths(PATH)
without any tfms transformations, I get errors like these:
Putting the tfms=tfms argument back in resolves this.
Here’s my confusion;
The signature in .from_paths() says that the default value for tfms is (None, None) which suggests to me that it can be omitted during initialization of the object.
Thanks yinterian. That confirms I am trying to do something that I am not supposed to. The whole reason for me to try using a transformation-less data object is to learn the fastai library.
So allow me a follow-up question: why?
Why do I need transforms if not for data augmentation? Is it because the training images all have dimensions that do not conform to the sz * sz format? Is this the way to inform pytorch how to pre-condition my data?