def train_step(warper, weights, optimizer, mov, fix):
"""
Train step function for backprop using gradient tape
:param warper: warping function returned from layer.Warping
:param weights: trainable ddf [1, f_dim1, f_dim2, f_dim3, 3]
:param optimizer: tf.optimizers
:param mov: moving image [1, m_dim1, m_dim2, m_dim3]
:param fix: fixed image [1, f_dim1, f_dim2, f_dim3]
:return:
loss: overall loss to optimise
loss_image: image dissimilarity
loss_deform: deformation regularisation
"""
with tf.GradientTape() as tape:
pred = warper(inputs=[weights, mov])
loss_image = image_loss.dissimilarity_fn(y_true=fix,
y_pred=pred,
name=image_loss_name)
loss_deform = deform_loss.local_displacement_energy(
weights, deform_loss_name)
loss = loss_image + weight_deform_loss * loss_deform
gradients = tape.gradient(loss, [weights])
optimizer.apply_gradients(zip(gradients, [weights]))
return loss, loss_image, loss_deform
def test_local_displacement_energy():
"""test the computation of local displacement energy for ddf"""
# bending energy
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.local_displacement_energy(tensor, "bending")
expect = tf.zeros([4])
assert is_equal_tf(get, expect)
# l1 norm on gradient
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.local_displacement_energy(tensor, "gradient-l1")
expect = tf.zeros([4])
assert is_equal_tf(get, expect)
# l2 norm on gradient
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.local_displacement_energy(tensor, "gradient-l2")
expect = tf.zeros([4])
assert is_equal_tf(get, expect)
# not supported energy type
tensor = tf.ones([4, 50, 50, 50, 3])
with pytest.raises(ValueError):
deform.local_displacement_energy(tensor, "a wrong string")
def add_ddf_loss(model: tf.keras.Model, ddf: tf.Tensor,
loss_config: dict) -> tf.keras.Model:
"""
add regularization loss of ddf into model
:param model: tf.keras.Model
:param ddf: tensor of shape (batch, m_dim1, m_dim2, m_dim3, 3)
:param loss_config: config for loss
"""
loss_reg = tf.reduce_mean(
deform_loss.local_displacement_energy(ddf,
**loss_config["regularization"]))
weighted_loss_reg = loss_reg * loss_config["regularization"]["weight"]
model.add_loss(weighted_loss_reg)
model.add_metric(loss_reg, name="loss/regularization", aggregation="mean")
model.add_metric(weighted_loss_reg,
name="loss/weighted_regularization",
aggregation="mean")
return model