Fine tuning makes sense. Look at the ImageNet example 137 or the Transfer learning tutorial 104.
I suspect it’ll be easier to scale and/or crop your images than to try to adapt InceptionV3 to a different image size. What size images do you have?
For smaller images, you’ll have to zero-pad or scale and crop them.
For larger images, you can scale and crop them or apply them in a “fully convolutional” manner. Scaling and cropping will be more efficient.
To apply them in a FC manner, replace the nn.Linear layers with 1x1 nn.Conv2d convolutions. You’ll then get multiple predictions per-image. This can be significantly more computationally expensive, but might give you a bit better predictions. (Image detection and segmentation networks tend to use this method.)
You could also try adding layers to the Inception definition, but I wouldn’t recommend it. I don’t think the initializing from the pre-trained weights would help much in that case.
Here’s the inception model definition 126 for reference.
Again, I think your best bet is to scale and crop your images. But if that’s not acceptable for some reason, you can try one of the methods above.