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

Output
-----------------------------------------------------------------
ValueError
Traceback (most recent call last)
<ipython-input-154-9b17f363443c> in <module>
----> 1 torch.as_tensor([x0, x1, x2])
ValueError: expected sequence of length 4 at dim 1 (got 2)
We can use PyTorch’s nn.utils.rnn.pad_sequence() to perform the padding for
us. It takes as arguments a list of sequences, a padding value (default is zero), and
the option to make the result batch-first. Let’s give it a try:
seq_tensors = [torch.as_tensor(seq).float() for seq in all_seqs]
padded = rnn_utils.pad_sequence(seq_tensors, batch_first=True)
padded
Output
tensor([[[ 1.0349, 0.9661],
[ 0.8055, -0.9169],
[-0.8251, -0.9499],
[-0.8670, 0.9342]],
[[-1.0911, 0.9254],
[-1.0771, -1.0414],
[ 0.0000, 0.0000],
[ 0.0000, 0.0000]],
[[-1.1247, -0.9683],
[ 0.8182, -0.9944],
[ 1.0081, 0.7680],
[ 0.0000, 0.0000]]])
Both the second and the third sequences were shorter than the first, so they got
padded accordingly to match the length of the longest sequence.
Variable-Length Sequences | 655