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perf: Vectorize index clamping in Dataset.expand_index_in_trial #300

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perf: Vectorize index clamping in Dataset.expand_index_in_trial #300

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21 changes: 9 additions & 12 deletions cebra/data/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,25 +154,22 @@ def expand_index_in_trial(self, index, trial_ids, trial_borders):
trial_ids is in size of a length of self.index and indicate the trial id of the index belong to.
trial_borders is in size of a length of self.idnex and indicate the border of each trial.

Todo:
- rewrite
"""

# TODO(stes) potential room for speed improvements by pre-allocating these tensors/
# using non_blocking copy operation.
offset = torch.arange(-self.offset.left,
self.offset.right,
device=index.device)
index = torch.tensor(
[
torch.clamp(
i,
trial_borders[trial_ids[i]] + self.offset.left,
trial_borders[trial_ids[i] + 1] - self.offset.right,
) for i in index
],
device=self.device,
)

# Vectorized lookup and boundary calculation
batch_trial_ids = trial_ids[index]
min_borders = trial_borders[batch_trial_ids] + self.offset.left
max_borders = trial_borders[batch_trial_ids + 1] - self.offset.right

# Fast C-level clamp
index = torch.clamp(index, min=min_borders, max=max_borders)

return index[:, None] + offset[None, :]

@abc.abstractmethod
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