Files
lerobot/tests/datasets
Pepijn 32b0d7d1ef refactor(datasets): fold deterministic mode into EpisodeAwareSampler
Instead of a parallel DeterministicEpisodeAwareSampler class, extend the
existing EpisodeAwareSampler with a deterministic=True mode (seeded
Feistel permutation, epoch auto-advance, state_dict/load_state_dict).

The default mode is behavior-identical: same torch.randperm consumption
and the same generator contract accelerate synchronizes; the O(N) Python
index list is replaced by O(num_episodes) boundary arrays in both modes,
with `indices` kept as a back-compat property. Passing a generator
together with deterministic=True is rejected, and the state/seek methods
raise outside deterministic mode.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-06-11 11:37:44 +02:00
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