def test_binary_jaccard_loss():
eps = 1e-5
criterion = JaccardLoss(mode="binary", from_logits=False)
# Ideal case
y_pred = torch.tensor([1.0]).view(1, 1, 1, 1)
y_true = torch.tensor(([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)
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, 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, eps)
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
self.model = object_from_dict(self.hparams["model"])
if "resume_from_checkpoint" in self.hparams:
corrections: Dict[str, str] = {"model.": ""}
state_dict = state_dict_from_disk(
file_path=self.hparams["resume_from_checkpoint"],
rename_in_layers=corrections,
)
self.model.load_state_dict(state_dict)
self.losses = [
("jaccard", 0.1, JaccardLoss(mode="binary", from_logits=True)),
("focal", 0.9, BinaryFocalLoss()),
]
def __init__(self, hparams):
super().__init__()
self.hparams = hparams
self.model = object_from_dict(self.hparams["model"])
if "resume_from_checkpoint" in self.hparams:
corrections: Dict[str, str] = {"model.": ""}
checkpoint = load_checkpoint(
file_path=self.hparams["resume_from_checkpoint"],
rename_in_layers=corrections,
)
self.model.load_state_dict(checkpoint["state_dict"])
if hparams["sync_bn"]:
self.model = apex.parallel.convert_syncbn_model(self.model)
self.losses = [
("jaccard", 0.1, JaccardLoss(mode="binary", from_logits=True)),
("focal", 0.9, BinaryFocalLoss()),
]
def test_multilabel_jaccard_loss():
eps = 1e-5
criterion = JaccardLoss(mode="multilabel", from_logits=False)
# Ideal case
y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
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, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]])
y_true = 1 - y_pred
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(1.0, abs=eps)
# 1 - 1/3 case
y_pred = torch.tensor([[[0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0]]])
y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]])
loss = criterion(y_pred, y_true)
assert float(loss) == pytest.approx(1.0 - 1.0 / 3.0, abs=eps)