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indices_tuple: Add assertion that each pair should be either positive or negative #743

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Merged
merged 3 commits into from
Apr 20, 2025
Merged

indices_tuple: Add assertion that each pair should be either positive or negative #743

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b7d9eb6
add check that each pair should be either positive or negative
lucamarini22 Apr 2, 2025
e9d6a90
more efficient implementation
lucamarini22 Apr 4, 2025
61d47c9
remove outer miner test from test_cross_batch_memory.py
KevinMusgrave Apr 20, 2025
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5 changes: 5 additions & 0 deletions src/pytorch_metric_learning/losses/generic_pair_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ def mat_based_loss(self, mat, indices_tuple):
pos_mask, neg_mask = torch.zeros_like(mat), torch.zeros_like(mat)
pos_mask[a1, p] = 1
neg_mask[a2, n] = 1
self._assert_either_pos_or_neg(pos_mask, neg_mask)
return self._compute_loss(mat, pos_mask, neg_mask)

def pair_based_loss(self, mat, indices_tuple):
Expand All @@ -38,3 +39,7 @@ def pair_based_loss(self, mat, indices_tuple):
if len(a2) > 0:
neg_pair = mat[a2, n]
return self._compute_loss(pos_pair, neg_pair, indices_tuple)

@staticmethod
def _assert_either_pos_or_neg(pos_mask, neg_mask):
assert not torch.any((pos_mask != 0) & (neg_mask != 0)), "Each pair should be either be positive or negative"
32 changes: 0 additions & 32 deletions tests/losses/test_cross_batch_memory.py
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Expand Up @@ -238,7 +238,6 @@ def test_loss(self):
batch_size = 32
for inner_loss in [ContrastiveLoss(), MultiSimilarityLoss()]:
inner_miner = MultiSimilarityMiner(0.3)
outer_miner = MultiSimilarityMiner(0.2)
self.loss = CrossBatchMemory(
loss=inner_loss,
embedding_size=self.embedding_size,
Expand Down Expand Up @@ -267,10 +266,6 @@ def test_loss(self):
labels = torch.randint(0, num_labels, (batch_size,)).to(TEST_DEVICE)
loss = self.loss(embeddings, labels)
loss_with_miner = self.loss_with_miner(embeddings, labels)
oa1, op, oa2, on = outer_miner(embeddings, labels)
loss_with_miner_and_input_indices = self.loss_with_miner2(
embeddings, labels, (oa1, op, oa2, on)
)
all_embeddings = torch.cat([all_embeddings, embeddings])
all_labels = torch.cat([all_labels, labels])

Expand Down Expand Up @@ -308,33 +303,6 @@ def test_loss(self):
torch.isclose(loss_with_miner, correct_loss_with_miner)
)

# loss with inner and outer miner
indices_tuple = inner_miner(
embeddings, labels, all_embeddings, all_labels
)
a1, p, a2, n = lmu.remove_self_comparisons(
indices_tuple,
self.loss_with_miner2.curr_batch_idx,
self.loss_with_miner2.memory_size,
)
a1 = torch.cat([oa1, a1])
p = torch.cat([op, p])
a2 = torch.cat([oa2, a2])
n = torch.cat([on, n])
correct_loss_with_miner_and_input_indice = inner_loss(
embeddings,
labels,
(a1, p, a2, n),
all_embeddings,
all_labels,
)
self.assertTrue(
torch.isclose(
loss_with_miner_and_input_indices,
correct_loss_with_miner_and_input_indice,
)
)

def test_queue(self):
for test_enqueue_mask in [False, True]:
for dtype in TEST_DTYPES:
Expand Down