Hey there.
So, I noticed that when I use ‘pin_memory=True’ for my Dataloaders what is returned is actually slightly different from what should be (and is, when ‘pin_memory=False’) returned.
Mostly, what I have noticed so far is that PackPadded sequences become just the tensors that form them (data and batch_sizes) and tuples get changed to lists.
This is an example of what my current dataloader should return, and is returning if ‘pin_memory=False’ (and it is what it is return at the collate function level, regardless of the ‘pin_memory’ setting):
((PackedSequence(data=tensor([[-0.3763, -0.9540,  0.1961,  ..., -0.2956,  1.2341,  0.2977],
        [-0.3763, -0.9540,  0.1961,  ..., -0.1619,  1.2689, -0.7896],
        [ 0.1915,  0.2300, -0.2894,  ..., -0.8150,  1.8169,  2.0587],
        ...,
        [ 0.2478, -0.2490,  0.0222,  ..., -2.9116,  1.4836, -0.5898],
        [-0.9930, -0.5766,  0.2790,  ...,  0.7489,  1.2063, -0.0581],
        [-0.8871, -0.2004, -1.0601,  ...,  0.0524, -0.0852,  0.0805]]),
                 batch_sizes=tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1])),
  ([5, 9, 28, 47, 78, 80, 82, 84, 103, 118, 121, 131, 133, 135, 160, 212, 246, 263, 298, 310, 337, 347, 365, 399, 400, 401, 413, 320, 442, 455, 486, 496, 515, 530, 561, 568, 579, 582, 585, 587, 590, 599, 105, 625, 633, 648, 656, 701, 707, 714, 722, 568, 734, 738, 739, 741, 743, 745, 764, 785, 801, 843, 876, 895, 906, 910, 915, 932, 951, 956, 959, 981, 987, 1014, 1017, 1029, 1042, 1045, 1068, 1081],
   None)),
 (tensor([0, 0]),
  tensor([[ 1,  2, 16,  4,  6, 17,  9, 10, 18, 19, 20, 21, 10, 22, 12, 23, 24,  6, 25, 15],
          [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,  0,  0,  0, 0,  0]])))

and this is what it returns when ‘pin_memory=True’:
[[[tensor([[-0.3763, -0.9540,  0.1961,  ..., -0.2956,  1.2341,  0.2977],
           [-0.3763, -0.9540,  0.1961,  ..., -0.1619,  1.2689, -0.7896],
           [ 0.1915,  0.2300, -0.2894,  ..., -0.8150,  1.8169,  2.0587],
           ...,
           [ 0.2478, -0.2490,  0.0222,  ..., -2.9116,  1.4836, -0.5898],
           [-0.9930, -0.5766,  0.2790,  ...,  0.7489,  1.2063, -0.0581],
           [-0.8871, -0.2004, -1.0601,  ...,  0.0524, -0.0852,  0.0805]]),
   tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1])],
  [[5, 9, 28, 47, 78, 80, 82, 84, 103, 118, 121, 131, 133, 135, 160, 212, 246, 263, 298, 310, 337, 347, 365, 399, 400, 401, 413, 320, 442, 455, 486, 496, 515, 530, 561, 568, 579, 582, 585, 587, 590, 599, 105, 625, 633, 648, 656, 701, 707, 714, 722, 568, 734, 738, 739, 741, 743, 745, 764, 785, 801, 843, 876, 895, 906, 910, 915, 932, 951, 956, 959, 981, 987, 1014, 1017, 1029, 1042, 1045, 1068, 1081],
   None]],
 [tensor([0, 0]),
  tensor([[ 1,  2, 16,  4,  6, 17,  9, 10, 18, 19, 20, 21, 10, 22, 12, 23, 24,  6, 25, 15],
          [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,  0,  0,  0, 0,  0]])]]

Is this the expected behaviour?