I was trying to generate some images using code and vary it by using randomness. I found that when I use DataLoader and then use enumerate or iter on it, then memory overshoots unexpectedly. On debugging I found that __getitem__ function is getting called right when I create an enumerate or iter object.
Q: How can I ensure that it only gets called when I call next for iterator or extract next element from enumerate?
For reference here is my toy example (to expose that issue is with calling of __getitem__ (tested on Python3.7 and 3.8)
#Testing enumeration here
from torch.utils.data import Dataset, DataLoader
import torch
class test_enum(Dataset):
    def __init__(self, root="/home/aknirala/data/clocks/", transform=None):
        self.transform = transform
        self.root = root
    def __len__(self):
        return 1000000 
    def __getitem__(self, idx):
        print(str(idx) + ", ")
        return idx


dataset = test_enum()
dataloader = torch.utils.data.DataLoader(dataset, batch_size=4,
                                            shuffle=True, num_workers=2)
print("Len of iterations per epochs: ",len(dataloader))

Now moment I do:
data_iter = iter(dataloader)
I get something like: (There are batch_sizenum_workers2 numbers printed below)
143666, 315805, 

357128, 851433, 

298989, 706179, 

432269, 11821, 

995092, 
911574, 
812109, 
598460, 
276834, 
563535, 
551306, 
343008,

Also instead if I do:
e = enumerate(dataloader)
I get something like:
640834, 336805, 

792848, 183726, 

727800, 322372, 
703700, 
870395, 
856172, 
370866, 
792898, 
415834, 
700454, 
516118, 
916330, 

733028,

Later when I call next(data_iter) or run a for loop on e, each time I see 4 numbers which is as expected (as batch_size is s4). However, why does getitem gets called while I am creating enumerate or iter object? And how can I stop it?