def test_distance_weighted_miner(self):
embedding_angles = torch.arange(0, 180)
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=torch.float) #2D embeddings
labels = torch.randint(low=0, high=2, size=(180, ))
a, _, n = lmu.get_all_triplets_indices(labels)
all_an_dist = torch.nn.functional.pairwise_distance(
embeddings[a], embeddings[n], 2)
min_an_dist = torch.min(all_an_dist)
for non_zero_cutoff_int in range(5, 15):
non_zero_cutoff = (float(non_zero_cutoff_int) / 10.) - 0.01
miner = DistanceWeightedMiner(0, non_zero_cutoff)
a, p, n = miner(embeddings, labels)
anchors, positives, negatives = embeddings[a], embeddings[
p], embeddings[n]
an_dist = torch.nn.functional.pairwise_distance(
anchors, negatives, 2)
self.assertTrue(torch.max(an_dist) <= non_zero_cutoff)
an_dist_var = torch.var(an_dist)
an_dist_mean = torch.mean(an_dist)
target_var = ((non_zero_cutoff - min_an_dist)**
2) / 12 # variance formula for uniform distribution
target_mean = (non_zero_cutoff - min_an_dist) / 2
self.assertTrue(
torch.abs(an_dist_var - target_var) / target_var < 0.1)
self.assertTrue(
torch.abs(an_dist_mean - target_mean) / target_mean < 0.1)
def test_with_distance_weighted_miner(self):
for dtype in TEST_DTYPES:
memory_size = 256
inner_loss = NTXentLoss(temperature=0.1)
inner_miner = DistanceWeightedMiner(cutoff=0.5,
nonzero_loss_cutoff=1.4)
loss_with_miner = CrossBatchMemory(
loss=inner_loss,
embedding_size=2,
memory_size=memory_size,
miner=inner_miner,
)
for i in range(20):
embedding_angles = torch.arange(0, 32)
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype,
).to(TEST_DEVICE) # 2D embeddings
labels = torch.randint(low=0, high=10,
size=(32, )).to(TEST_DEVICE)
loss_val = loss_with_miner(embeddings, labels)
loss_val.backward()
self.assertTrue(
True) # just check if we got here without an exception
def test_distance_weighted_miner(self, with_ref_labels=False):
for dtype in TEST_DTYPES:
embedding_angles = torch.arange(0, 256)
embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=dtype).to(self.device) #2D embeddings
ref_embeddings = embeddings.clone() if with_ref_labels else None
labels = torch.randint(low=0, high=2, size=(256,))
ref_labels = torch.randint(low=0, high=2, size=(256,)) if with_ref_labels else None
a,_,n = lmu.get_all_triplets_indices(labels, ref_labels)
if with_ref_labels:
all_an_dist = torch.nn.functional.pairwise_distance(embeddings[a], ref_embeddings[n], 2)
else:
all_an_dist = torch.nn.functional.pairwise_distance(embeddings[a], embeddings[n], 2)
min_an_dist = torch.min(all_an_dist)
for non_zero_cutoff_int in range(5, 15):
non_zero_cutoff = (float(non_zero_cutoff_int) / 10.) - 0.01
miner = DistanceWeightedMiner(0, non_zero_cutoff)
a, p, n = miner(embeddings, labels, ref_embeddings, ref_labels)
if with_ref_labels:
anchors, positives, negatives = embeddings[a], ref_embeddings[p], ref_embeddings[n]
else:
anchors, positives, negatives = embeddings[a], embeddings[p], embeddings[n]
an_dist = torch.nn.functional.pairwise_distance(anchors, negatives, 2)
self.assertTrue(torch.max(an_dist)<=non_zero_cutoff)
an_dist_var = torch.var(an_dist)
an_dist_mean = torch.mean(an_dist)
target_var = ((non_zero_cutoff - min_an_dist)**2) / 12 # variance formula for uniform distribution
target_mean = (non_zero_cutoff - min_an_dist) / 2
self.assertTrue(torch.abs(an_dist_var-target_var)/target_var < 0.1)
self.assertTrue(torch.abs(an_dist_mean-target_mean)/target_mean < 0.1)
def test_empty_output(self):
miner = DistanceWeightedMiner(0.1, 0.5)
batch_size = 32
embeddings = torch.randn(batch_size, 64)
labels = torch.arange(batch_size)
a, p, n = miner(embeddings, labels)
self.assertTrue(len(a) == 0)
self.assertTrue(len(p) == 0)
self.assertTrue(len(n) == 0)
def test_empty_output(self):
miner = DistanceWeightedMiner(0.1, 0.5)
batch_size = 32
for dtype in TEST_DTYPES:
embeddings = torch.randn(batch_size, 64).type(dtype).to(self.device)
labels = torch.arange(batch_size)
a, p, n = miner(embeddings, labels)
self.assertTrue(len(a)==0)
self.assertTrue(len(p)==0)
self.assertTrue(len(n)==0)