def test_single_cut_sampler_time_constraints(
max_duration, max_frames, max_samples, exception_expectation
):
# The dummy cuts have a duration of 1 second each
cut_set = DummyManifest(CutSet, begin_id=0, end_id=100)
if max_frames is None:
cut_set = cut_set.drop_features()
with exception_expectation:
sampler = SimpleCutSampler(
cut_set,
shuffle=True,
# Set an effective batch size of 10 cuts, as all have 1s duration == 100 frames
# This way we're testing that it works okay when returning multiple batches in
# a full epoch.
max_frames=max_frames,
max_samples=max_samples,
max_duration=max_duration,
)
sampler_cut_ids = []
for batch in sampler:
sampler_cut_ids.extend(batch)
# Invariant 1: we receive the same amount of items in a dataloader epoch as there we in the CutSet
assert len(sampler_cut_ids) == len(cut_set)
# Invariant 2: the items are not duplicated
assert len(set(c.id for c in sampler_cut_ids)) == len(sampler_cut_ids)
# Invariant 3: the items are shuffled, i.e. the order is different than that in the CutSet
assert [c.id for c in sampler_cut_ids] != [c.id for c in cut_set]
def test_cut_pairs_sampler_time_constraints(max_duration, max_frames,
max_samples,
exception_expectation):
# The dummy cuts have a duration of 1 second each
cut_set = DummyManifest(CutSet, begin_id=0, end_id=100)
if max_frames is None:
cut_set = cut_set.drop_features()
with exception_expectation:
sampler = CutPairsSampler(
source_cuts=cut_set,
target_cuts=cut_set,
shuffle=True,
# Set an effective batch size of 10 cuts, as all have 1s duration == 100 frames
# This way we're testing that it works okay when returning multiple batches in
# a full epoch.
max_source_frames=max_frames,
max_target_frames=max_frames /
2 if max_frames is not None else None,
max_source_samples=max_samples,
max_target_samples=max_samples /
2 if max_samples is not None else None,
max_source_duration=max_duration,
max_target_duration=max_duration /
2 if max_duration is not None else None,
)
source_cuts, target_cuts = [], []
for src_batch, tgt_batch in sampler:
source_cuts.extend(src_batch)
target_cuts.extend(tgt_batch)
# Invariant 1: we receive the same amount of items in a dataloader epoch as there we in the CutSet
assert len(source_cuts) == len(cut_set)
assert len(target_cuts) == len(cut_set)
# Invariant 2: the items are not duplicated
assert len(set(c.id for c in source_cuts)) == len(source_cuts)
assert len(set(c.id for c in target_cuts)) == len(target_cuts)
# Invariant 3: the items are shuffled, i.e. the order is different than that in the CutSet
assert [c.id for c in source_cuts] != [c.id for c in cut_set]
# Invariant 4: the source and target cuts are in the same order
assert [c.id for c in source_cuts] == [c.id for c in target_cuts]