def test_similarity_fn_unknown_loss():
"""
Test dissimilarity function raises an error
if an unknonw loss type is passed.
"""
config = {"name": "random"}
with pytest.raises(ValueError):
label.get_dissimilarity_fn(config)
def test_unknown_cases(self):
"""
Test dissimilarity function raises an error
if an unknonw loss type is passed.
"""
config = {"name": "random"}
with pytest.raises(ValueError) as err_info:
label.get_dissimilarity_fn(config)
assert "Unknown loss type" in str(err_info.value)
def test_similarity_fn_single_scale():
"""
Asserting loss function returned by get dissimilarity
function when appropriate strings passed.
"""
config = {"name": "single_scale", "single_scale": "jaccard"}
assert isinstance(label.get_dissimilarity_fn(config), FunctionType)
def add_label_loss(
model: tf.keras.Model,
grid_fixed: tf.Tensor,
fixed_label: (tf.Tensor, None),
pred_fixed_label: (tf.Tensor, None),
loss_config: dict,
) -> tf.keras.Model:
"""
Add label dissimilarity loss of ddf into model.
:param model: tf.keras.Model
:param grid_fixed: tensor of shape (f_dim1, f_dim2, f_dim3, 3)
:param fixed_label: tensor of shape (batch, f_dim1, f_dim2, f_dim3)
:param pred_fixed_label: tensor of shape (batch, f_dim1, f_dim2, f_dim3)
:param loss_config: config for loss
"""
if fixed_label is not None:
loss_label = tf.reduce_mean(
label_loss.get_dissimilarity_fn(
config=loss_config["dissimilarity"]["label"])(
y_true=fixed_label, y_pred=pred_fixed_label))
weighted_loss_label = (loss_label *
loss_config["dissimilarity"]["label"]["weight"])
model.add_loss(weighted_loss_label)
model.add_metric(loss_label,
name="loss/label_dissimilarity",
aggregation="mean")
model.add_metric(
weighted_loss_label,
name="loss/weighted_label_dissimilarity",
aggregation="mean",
)
# metrics
dice_binary = label_loss.dice_score(y_true=fixed_label,
y_pred=pred_fixed_label,
binary=True)
dice_float = label_loss.dice_score(y_true=fixed_label,
y_pred=pred_fixed_label,
binary=False)
tre = label_loss.compute_centroid_distance(y_true=fixed_label,
y_pred=pred_fixed_label,
grid=grid_fixed)
foreground_label = label_loss.foreground_proportion(y=fixed_label)
foreground_pred = label_loss.foreground_proportion(y=pred_fixed_label)
model.add_metric(dice_binary,
name="metric/dice_binary",
aggregation="mean")
model.add_metric(dice_float,
name="metric/dice_float",
aggregation="mean")
model.add_metric(tre, name="metric/tre", aggregation="mean")
model.add_metric(foreground_label,
name="metric/foreground_label",
aggregation="mean")
model.add_metric(foreground_pred,
name="metric/foreground_pred",
aggregation="mean")
return model
def test_known_cases(self, config):
"""
Asserting loss function returned by get dissimilarity
function when appropriate strings passed.
"""
loss_fn = label.get_dissimilarity_fn(config)
loss = loss_fn(y_true=self.y_true, y_pred=self.y_pred)
assert loss.shape == (self.batch_size, )