Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
PackedSequence(data=tensor([[ 1.0349, 0.9661],
[-1.1247, -0.9683],
[-1.0911, 0.9254],
[ 0.8055, -0.9169],
[ 0.8182, -0.9944],
[-1.0771, -1.0414],
[-0.8251, -0.9499],
[ 1.0081, 0.7680],
[-0.8670, 0.9342]]), batch_sizes=tensor([3, 3, 2, 1]),
sorted_indices=tensor([0, 2, 1]), unsorted_indices=tensor([0, 2,
1]))
The output is a bit cryptic, to say the least. Let’s decipher it, piece by piece, starting
with the unsorted_indices_tensor. Even though we didn’t sort the list ourselves,
PyTorch did it internally, and it found that the longest sequence is the first (four
data points, index 0), followed by the third (three data points, index 2), and then by
the shortest one (two data points, index 1).
Once the sequences are listed in order of decreasing length, like in Figure 8.27, the
number of sequences that are at least t steps long (corresponding to the number
of columns in the figure below) is given by the batch_sizes attribute:
Figure 8.27 - Packing sequences
For example, the batch_size for the third column is two because two sequences
have at least three data points. Then, it goes through the data points in the same
658 | Chapter 8: Sequences