def test_key_mismatch(self):
lossA = ContrastiveLoss()
lossB = TripletMarginLoss(0.1)
self.assertRaises(
AssertionError,
lambda: MultipleLosses(
losses={
"lossA": lossA,
"lossB": lossB
},
weights={
"blah": 1,
"lossB": 0.23
},
),
)
minerA = MultiSimilarityMiner()
self.assertRaises(
AssertionError,
lambda: MultipleLosses(
losses={
"lossA": lossA,
"lossB": lossB
},
weights={
"lossA": 1,
"lossB": 0.23
},
miners={"blah": minerA},
),
)
def test_input_indices_tuple(self):
lossA = ContrastiveLoss()
lossB = TripletMarginLoss(0.1)
miner = MultiSimilarityMiner()
loss_func1 = MultipleLosses(losses={
"lossA": lossA,
"lossB": lossB
},
weights={
"lossA": 1,
"lossB": 0.23
})
loss_func2 = MultipleLosses(losses=[lossA, lossB], weights=[1, 0.23])
for loss_func in [loss_func1, loss_func2]:
for dtype in TEST_DTYPES:
embedding_angles = torch.arange(0, 180)
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=(180, ))
indices_tuple = miner(embeddings, labels)
loss = loss_func(embeddings, labels, indices_tuple)
loss.backward()
correct_loss = (
lossA(embeddings, labels, indices_tuple) +
lossB(embeddings, labels, indices_tuple) * 0.23)
self.assertTrue(torch.isclose(loss, correct_loss))
def test_length_mistmatch(self):
lossA = ContrastiveLoss()
lossB = TripletMarginLoss(0.1)
self.assertRaises(
AssertionError,
lambda: MultipleLosses(losses=[lossA, lossB], weights=[1]))
minerA = MultiSimilarityMiner()
self.assertRaises(
AssertionError,
lambda: MultipleLosses(
losses=[lossA, lossB],
weights=[1, 0.2],
miners=[minerA],
),
)
def test_multiple_losses(self):
lossA = ContrastiveLoss()
lossB = TripletMarginLoss(0.1)
loss_func = MultipleLosses(losses={
"lossA": lossA,
"lossB": lossB
},
weights={
"lossA": 1,
"lossB": 0.23
})
for dtype in TEST_DTYPES:
embedding_angles = torch.arange(0, 180)
embeddings = torch.tensor(
[c_f.angle_to_coord(a) for a in embedding_angles],
requires_grad=True,
dtype=dtype).to(self.device) #2D embeddings
labels = torch.randint(low=0, high=10, size=(180, ))
loss = loss_func(embeddings, labels)
loss.backward()
correct_loss = lossA(embeddings,
labels) + lossB(embeddings, labels) * 0.23
self.assertTrue(torch.isclose(loss, correct_loss))