def test_hard_sampler_from_dist(distmats_and_labels) -> None: # noqa: WPS442
"""
Args:
distmats_and_labels:
list of distance matrices and valid labels
"""
sampler = HardTripletsSampler(norm_required=True)
for distmat, labels in distmats_and_labels:
ids_a, ids_p, ids_n = sampler._sample_from_distmat( # noqa: WPS437
distmat=distmat, labels=labels)
check_triplets_are_hardest(
ids_anchor=ids_a,
ids_pos=ids_p,
ids_neg=ids_n,
labels=labels,
distmat=distmat,
)
check_triplets_consistency(ids_anchor=ids_a,
ids_pos=ids_p,
ids_neg=ids_n,
labels=labels)
assert len(labels) == len(ids_a)
def test_hard_sampler_manual() -> None:
"""
Test on manual example.
"""
labels = [0, 0, 1, 1]
dist_mat = torch.tensor([
[0.0, 0.3, 0.2, 0.4],
[0.3, 0.0, 0.4, 0.8],
[0.2, 0.4, 0.0, 0.5],
[0.4, 0.8, 0.5, 0.0],
])
gt = {(0, 1, 2), (1, 0, 2), (2, 3, 0), (3, 2, 0)}
sampler = HardTripletsSampler(norm_required=True)
ids_a, ids_p, ids_n = sampler._sample_from_distmat( # noqa: WPS437
distmat=dist_mat, labels=labels)
predict = set(zip(ids_a, ids_p, ids_n))
check_triplets_consistency(ids_anchor=ids_a,
ids_pos=ids_p,
ids_neg=ids_n,
labels=labels)
assert len(labels) == len(ids_a)
assert predict == gt
def test_triplet_cluster_edge_case() -> None:
"""
Check an edge case of trivial samples for classes:
expected HardTripletsSampler and HardClusterSampler to
generate the same triplets.
"""
features_dim = 128
p, k = randint(2, 32), randint(2, 32)
# Create a list of random labels
unique_labels = torch.tensor(list(range(p)))
# Create a list of random features for all the classes
unique_features = torch.rand(size=(p, features_dim), dtype=torch.float)
labels = unique_labels.repeat((k, ))
features = unique_features.repeat((k, 1))
hard_triplet_sampler = HardTripletsSampler()
hard_cluster_sampler = HardClusterSampler()
triplets = hard_triplet_sampler.sample(features, labels)
cluster_triplets = hard_cluster_sampler.sample(features, labels)
# Concatenates tensors from triplets to use torch.unique for comparison
triplets = torch.cat(triplets, dim=1)
cluster_triplets = torch.cat(cluster_triplets, dim=1)
triplets = torch.unique(triplets, dim=0)
cluster_triplets = torch.unique(cluster_triplets, dim=0)
assert torch.allclose(triplets, cluster_triplets, atol=1e-10)
def test_hard_sampler_from_features(features_and_labels) -> None: # noqa: WPS442
"""
Args:
features_and_labels: features and valid labels
"""
sampler = HardTripletsSampler(norm_required=True)
for features, labels in features_and_labels:
ids_a, ids_p, ids_n = sampler._sample(features=features, labels=labels) # noqa: WPS437
check_triplets_consistency(ids_anchor=ids_a, ids_pos=ids_p, ids_neg=ids_n, labels=labels)
assert len(ids_a) == len(labels)