def test_focal_class_is_scribtable():
"""check that script gives the same results"""
input_bad = torch.Tensor([-1, 2, 0]).float()
target = torch.Tensor([1, 0, 1])
loss = losses.FocalLoss()(input_bad, target)
jit_class = torch.jit.script(losses.FocalLoss())
loss_jit = jit_class(input_bad, target)
assert torch.allclose(loss, loss_jit)
def test_binary_focal_3d():
"""This test validates that it works for 3d by comparing results to 1d reshaped data"""
INP_3d = torch.rand(BS, 1, IM_SIZE, IM_SIZE, IM_SIZE)
TARGET_3d = torch.randint(0, 2, (BS, 1, IM_SIZE, IM_SIZE, IM_SIZE)).float()
my_focal_3d = losses.FocalLoss(mode="binary")(INP_3d, TARGET_3d)
my_focal_1d = losses.FocalLoss(mode="binary")(INP_3d.view(BS, 1, -1), TARGET_3d.view(BS, 1, -1))
assert torch.allclose(my_focal_3d, my_focal_1d)
def test_focal_loss_modes():
# check that multilabel == one-hot-encoded multiclass
fl_multiclass = losses.FocalLoss(mode="multiclass",
reduction="sum")(INP_IMG,
TARGET_IMG_MULTICLASS)
fl_multilabel = losses.FocalLoss(mode="multilabel",
reduction="sum")(INP_IMG,
TARGET_IMG_MULTILABEL)
assert fl_multiclass == fl_multilabel
# check that ignore index works for multiclass
fl = losses.FocalLoss(mode="multiclass",
reduction="none")(INP_IMG, TARGET_IMG_MULTICLASS)
loss_diff = fl[:, :, :2, :2].sum()
y_true = TARGET_IMG_MULTICLASS.clone()
y_true[:, :2, :2] = -100
fl_i = losses.FocalLoss(mode="multiclass",
reduction="sum",
ignore_label=-100)(INP_IMG, y_true)
assert torch.allclose(fl.sum() - loss_diff, fl_i)
# check that ignore index works for binary
fl = losses.FocalLoss(mode="binary", reduction="none")(INP_IMG_BINARY,
TARGET_IMG_BINARY)
loss_diff = fl[:, :, :2, :2].sum()
y_true = TARGET_IMG_BINARY.clone()
y_true[:, :, :2, :2] = -100
fl_i = losses.FocalLoss(mode="binary", reduction="sum",
ignore_label=-100)(INP_IMG_BINARY, y_true)
assert torch.allclose(fl.sum() - loss_diff, fl_i)
def test_focal_incorrect_reduction():
with pytest.raises(ValueError):
losses.FocalLoss(reduction="some_reduction")
def test_focal_incorrect_mode():
with pytest.raises(ValueError):
losses.FocalLoss(mode="some_mode")
