so the format of a custom dataset should be like fllowing:
from torch.utils import data
'Characterizes a dataset for PyTorch'
def __init__(self, list_IDs, labels):
self.labels = labels
self.list_IDs = list_IDs
'Denotes the total number of samples'
def __getitem__(self, index):
'Generates one sample of data'
# Select sample
ID = self.list_IDs[index]
# Load data and get label
X = torch.load('data/' + ID + '.pt')
y = self.labels[ID]
return X, y
I like to have have ID information in the output in addition to x and y. So i did return X, y, ID
, but now when I do
All data returned by a dataset needs to be a tensor, if you want to use the default collate_fn of the Dataloader. You have two options: write a custom collate function and pass it to the dataloader or wrap your ID inside a tensor (which is simpler I guess) and unwrap it outside the dataloader.
Ah sorry, I implied your ID would be an integer. You cannot wrap a string to a tensor. I could think of some ways to achieve something like that, but it would not be very pytorch-like. If you are interested in these Ways you can PM me.
that’s what I thought about too. I also thought about wrapping the loader itself, but one would have to define a new iterator for this. I proposed another method, and if this method works (currently waiting for verification), I will post it here later on.