def test_input_indices_tuple(self):
batch_size = 32
pair_miner = PairMarginMiner(pos_margin=0,
neg_margin=1,
use_similarity=False)
triplet_miner = TripletMarginMiner(margin=1)
self.loss = CrossBatchMemory(loss=ContrastiveLoss(),
embedding_size=self.embedding_size,
memory_size=self.memory_size)
for i in range(30):
embeddings = torch.randn(batch_size, self.embedding_size)
labels = torch.arange(batch_size)
self.loss(embeddings, labels)
for curr_miner in [pair_miner, triplet_miner]:
input_indices_tuple = curr_miner(embeddings, labels)
all_labels = torch.cat([labels, self.loss.label_memory], dim=0)
a1ii, pii, a2ii, nii = lmu.convert_to_pairs(
input_indices_tuple, labels)
a1i, pi, a2i, ni = lmu.get_all_pairs_indices(
labels, self.loss.label_memory)
a1, p, a2, n = self.loss.create_indices_tuple(
batch_size, embeddings, labels, self.loss.embedding_memory,
self.loss.label_memory, input_indices_tuple)
self.assertTrue(not torch.any((all_labels[a1] -
all_labels[p]).bool()))
self.assertTrue(
torch.all((all_labels[a2] - all_labels[n]).bool()))
self.assertTrue(len(a1) == len(a1i) + len(a1ii))
self.assertTrue(len(p) == len(pi) + len(pii))
self.assertTrue(len(a2) == len(a2i) + len(a2ii))
self.assertTrue(len(n) == len(ni) + len(nii))
def compute_loss(self, embeddings, labels, indices_tuple, pos_num):
mat = lmu.get_pairwise_mat(embeddings[:pos_num, :], embeddings,
self.use_similarity, self.squared_distances)
indices_tuple = lmu.convert_to_pairs(indices_tuple, labels)
return self.loss_method(mat, labels, indices_tuple)
