def test_compute_centroid_d():
"""
Testing compute centroid distance between equal
tensors returns 0s.
"""
array_ones = np.ones((2, 2))
tensor_mask = np.zeros((3, 2, 2, 2))
tensor_mask[0, :, :, :] = array_ones
tensor_mask = tf.convert_to_tensor(tensor_mask, dtype=tf.float32)
tensor_grid = np.zeros((2, 2, 2, 3))
tensor_grid[:, :, :, 0] = array_ones
tensor_grid = tf.convert_to_tensor(tensor_grid, dtype=tf.float32)
get = label.compute_centroid_distance(tensor_mask, tensor_mask,
tensor_grid)
expect = np.zeros((3))
assert is_equal_tf(get, expect)
def calculate_metrics(
fixed_image: tf.Tensor,
fixed_label: (tf.Tensor, None),
pred_fixed_image: (tf.Tensor, None),
pred_fixed_label: (tf.Tensor, None),
fixed_grid_ref: tf.Tensor,
sample_index: int,
) -> dict:
"""
Calculate image/label based metrics.
:param fixed_image: shape=(batch, f_dim1, f_dim2, f_dim3)
:param fixed_label: shape=(batch, f_dim1, f_dim2, f_dim3) or None
:param pred_fixed_image: shape=(batch, f_dim1, f_dim2, f_dim3)
:param pred_fixed_label: shape=(batch, f_dim1, f_dim2, f_dim3) or None
:param fixed_grid_ref: shape=(1, f_dim1, f_dim2, f_dim3, 3)
:param sample_index: int,
:return: dictionary of metrics
"""
if pred_fixed_image is not None:
y_true = fixed_image[sample_index:(sample_index + 1), :, :, :]
y_pred = pred_fixed_image[sample_index:(sample_index + 1), :, :, :]
y_true = tf.expand_dims(y_true, axis=4)
y_pred = tf.expand_dims(y_pred, axis=4)
ssd = image_loss.SumSquaredDifference()(y_true=y_true,
y_pred=y_pred).numpy()
else:
ssd = None
if fixed_label is not None and pred_fixed_label is not None:
y_true = fixed_label[sample_index:(sample_index + 1), :, :, :]
y_pred = pred_fixed_label[sample_index:(sample_index + 1), :, :, :]
dice = label_loss.DiceScore(binary=True)(y_true=y_true,
y_pred=y_pred).numpy()
tre = label_loss.compute_centroid_distance(
y_true=y_true, y_pred=y_pred,
grid=fixed_grid_ref[0, :, :, :, :]).numpy()[0]
else:
dice = None
tre = None
return dict(image_ssd=ssd, label_binary_dice=dice, label_tre=tre)
def build_loss(self):
"""Build losses according to configs."""
# input metrics
fixed_image = self._inputs["fixed_image"]
moving_image = self._inputs["moving_image"]
self.log_tensor_stats(tensor=moving_image, name="moving_image")
self.log_tensor_stats(tensor=fixed_image, name="fixed_image")
# image loss, conditional model does not have this
if "pred_fixed_image" in self._outputs:
pred_fixed_image = self._outputs["pred_fixed_image"]
self._build_loss(
name="image",
inputs_dict=dict(y_true=fixed_image, y_pred=pred_fixed_image),
)
if self.labeled:
# input metrics
fixed_label = self._inputs["fixed_label"]
moving_label = self._inputs["moving_label"]
self.log_tensor_stats(tensor=moving_label, name="moving_label")
self.log_tensor_stats(tensor=fixed_label, name="fixed_label")
# label loss
pred_fixed_label = self._outputs["pred_fixed_label"]
self._build_loss(
name="label",
inputs_dict=dict(y_true=fixed_label, y_pred=pred_fixed_label),
)
# additional label metrics
tre = compute_centroid_distance(y_true=fixed_label,
y_pred=pred_fixed_label,
grid=self.grid_ref)
self._model.add_metric(tre, name="metric/TRE", aggregation="mean")