Created
January 12, 2021 10:28
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Convert nested number lists in to pytorch tensors, which will be useful for batching a bunch of tensors, during text data processing.
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from collections import defaultdict | |
import torch | |
def _nested_number_list_to_tensors(nested: list, padding=0, example=None): | |
"""Turn a list of list of list of list ... of integers to a tensor with the given padding""" | |
ndim_max = defaultdict(lambda: 0) | |
def _count_nested_max(nested, depth): | |
if not isinstance(nested, list): | |
return | |
ndim_max[depth] = max(ndim_max[depth], len(nested)) | |
for x in nested: | |
_count_nested_max(x, depth + 1) | |
_count_nested_max(nested, 0) | |
ndim_max = [ndim_max[d] for d in sorted(ndim_max.keys())] | |
def _get_padding_at_depth(depth): | |
size = ndim_max[depth:] | |
lump = padding | |
for i in reversed(size): | |
lump = [lump] * i | |
return lump | |
def _pad_nested(nested, depth): | |
if not isinstance(nested, list): | |
return nested | |
if len(nested) < ndim_max[depth]: | |
nested = nested + [_get_padding_at_depth(depth + 1)] * (ndim_max[depth] - len(nested)) | |
return [_pad_nested(x, depth + 1) for x in nested] | |
full_fledged = _pad_nested(nested, 0) | |
dev = dtype = None | |
if example is not None: | |
dev = example.device | |
dtype = example.dtype | |
return torch.tensor(full_fledged, device=dev, dtype=dtype) | |
if __name__ == '__main__': | |
for case in ( | |
[[[], []], [[3, 3], [1, 2, 3]], [[7], [1, 3], [9, 9, 9, 9]]], | |
[0, 1, 2], | |
[[], [1], [2]], | |
[[], [1], [2, 3]], | |
[[1], [2, 3], [3, 3, 3]], | |
): | |
t = _nested_number_list_to_tensors(case) | |
print(t, t.size()) | |
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