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PyTorch dataset extended with map, cache etc. (tensorflow.data like)

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First, thanks for an elegant library that has saved me a significant amount of time over the past couple years. Now, the problem: Since the name change, Ive been trying...

``` Traceback (most recent call last): File "test-dataset.py", line 1, in from dataset import func File "/root/torch-cache-test/dataset/func.py", line 3, in from . import nocache File "/root/torch-cache-test/dataset/nocache.py", line 1, in from...

Using `.cache()` (with the default memory cacher) does nothing when the Dataset is used in a multi-process DataLoader. This is a gotcha that should probably pointed out in the documentation...

Concatenation of two datasets with the logical operator works as intended: `concat_2 = images | images` While concatenation of more datasets (`concat_3 = images | images | images`) yields a...

Hi! From what I can see currently there is no simple way in pytorch to perform a stratified subsampling of the training dataset. I think it fits this library scope...

Can you share some benchmark when using this library vs when not use it? Thank you.

Thanks for this amazing library. I was wondering for large datasets with millions of images, would it make sense to cache in a single file (e.g., HDF5) instead of creating...

enhancement