def test_loss_addition():
d_l = losses.DiceLoss("binary")
bf_l = losses.CrossEntropyLoss(mode="binary")
l = losses.DiceLoss("binary") * 0.5 + losses.CrossEntropyLoss(mode="binary") * 5
d_res = d_l(INP_IMG_BINARY, TARGET_IMG_BINARY)
bf_res = bf_l(INP_IMG_BINARY, TARGET_IMG_BINARY)
res = l(INP_IMG_BINARY, TARGET_IMG_BINARY)
assert res.shape == d_res.shape
assert torch.allclose(res, d_res * 0.5 + bf_res * 5)
def test_loss_addition():
inp = torch.ones(2, 1, 8, 8)
label = torch.zeros(2, 1, 8, 8)
d_l = losses.DiceLoss('binary')
bf_l = losses.BinaryFocalLoss()
l = losses.DiceLoss('binary') * 0.5 + losses.BinaryFocalLoss() * 5
d_res = d_l(inp, label)
bf_res = bf_l(inp, label)
res = l(inp, label)
assert res.shape == d_res.shape
assert res == d_res * 0.5 + bf_res * 5
def test_dice_loss_binary():
criterion = losses.DiceLoss(mode="binary", from_logits=False, eps=1e-4)
# Ideal case
y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, 1, -1)
y_true = torch.tensor(([1, 1, 1])).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(0.0, abs=EPS)
y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1)
y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(0.0, abs=EPS)
y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1)
y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(0.0, abs=EPS)
# Worst case
y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1)
y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
# It zeros loss if there is no y_true
assert float(loss) == pytest.approx(0.0, abs=EPS)
y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1)
y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(1.0, abs=EPS)
y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1)
y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(1.0, abs=EPS)
def test_dice_loss_binary():
criterion = losses.DiceLoss(mode="binary", from_logits=False)
# Ideal case
y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, 1, -1)
y_true = torch.tensor(([1, 1, 1])).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(0.0, abs=EPS)
y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1)
y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(0.0, abs=EPS)
y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1)
y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(0.0, abs=EPS)
# Worst case
y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1)
y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
# It returns 1. due to internal smoothing
assert float(loss) == pytest.approx(1.0, abs=EPS)
y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1)
y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(1.0, abs=EPS)
y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1)
y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1)
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(1.0, abs=EPS)
