Hey! I am new to Pytorch. I am trying to do transfer learning and at some point, while doing the training I want to give some additional input for each data point. So let’s say image_1 has a feature like (1.01, 0.5, .06) and image_2 has a feature like (.9, .001, .023) and so on, I would like to bundle them together.
The notebook I am following: https://github.com/WillKoehrsen/pytorch_challenge/blob/master/Transfer%20Learning%20in%20PyTorch.ipynb 7
So here it shows how we can change the fc layer at the end and insert whatever we want to. For my problem would I then need to change the very first Conv2D layer (for resnet18) and somehow pass in the features? For example, adding a fc layer there and fusing the data? Or are there other ways to do this like fusing on a tensor level?
In a programming sense, how should I go about doing this?
Thanks