def v_dice_coeff(P, L, use_argmax=False, one_hot_labels=False):
"""
Calculates V-Dice for give predictions and labels.
WARNING! THIS IMPLIES SEGMENTATION CONTEXT.
Parameters
----------
P : np.ndarray
Predictions of a segmentator. Array of shape [batch_sz, W, H, num_classes].
L : np.ndarray
Labels for the segmentator. Array of shape [batch_sz, W, H]
use_argmax : bool
Converts the segmentator's predictions to one-hot format.
Example: [0.4, 0.1, 0.5] -> [0., 0., 1.]
one_hot_labels : bool
Set to True if the labels (`L`) are already one-hot encoded, i.e. have the same
shape as `P`.
"""
# P has shape [batch_sz, W, H, num_classes]
# L has shape [batch_sz, W, H]
# RESHAPE TENSORS AND ONE-HOT LABELS
# P -> [batch_sz, num_samples, num_classes]
batch_sz = len(P)
num_samples = P.shape[1] * P.shape[2]
num_classes = P.shape[-1]
if use_argmax:
P = P.argmax(axis=3)
P = P.reshape(batch_sz * num_samples)
P = one_hot(P, depth=num_classes)
P = P.reshape(batch_sz, num_samples, num_classes)
if not one_hot_labels:
# L -> [batch_sz*num_samples] -> [batch_sz*num_samples, num_classes] -> [batch_sz, num_samples, num_classes]
L = L.reshape(batch_sz * num_samples)
L = one_hot(L, depth=num_classes)
L = L.reshape(batch_sz, num_samples, num_classes)
# P has shape [batch_sz, num_samples, num_classes]
# L has shape [batch_sz, num_samples, num_classes]
R = P * L
nums = R.sum(axis=1)
P2 = P * P
P2vec = P2.sum(axis=1)
Lvec = L.sum(axis=1)
dens = P2vec + Lvec
dices_b = (2 * nums + EPSILON) / (dens + EPSILON)
dices = dices_b.mean(axis=0)
return dices.mean(), dices
def categorical_dice_coeff(P, L, use_argmax=False, ind_norm=True):
"""
Calculates V-Dice for give predictions and labels.
WARNING! THIS IMPLIES SEGMENTATION CONTEXT.
Parameters
----------
P : np.ndarray
Predictions of a segmentator. Array of shape [batch_sz, W, H, num_classes].
L : np.ndarray
Labels for the segmentator. Array of shape [batch_sz, W, H]
use_argmax : bool
Converts the segmentator's predictions to one-hot format.
Example: [0.4, 0.1, 0.5] -> [0., 0., 1.]
ind_norm : bool
Normalize each dice separately. Useful in case some classes don't appear
on some images.
"""
batch_sz = len(P)
L = np.asarray(L)
P = np.asarray(P)
num_classes = P.shape[-1]
if use_argmax:
P = P.argmax(axis=3)
P = P.reshape(-1)
P = one_hot(P, depth=num_classes)
P = P.reshape(batch_sz, -1, num_classes)
L = L.reshape(batch_sz, -1)
class_dices = np.zeros(num_classes)
class_counts = np.zeros(
num_classes) + EPSILON # Smoothing to avoid division by zero
for i in range(batch_sz):
sample_actual = L[i]
sample_pred = P[i]
for j in range(num_classes):
sub_actual = (sample_actual[:] == j).astype(np.int32)
sub_confs = sample_pred[:, j]
if np.sum(sub_actual) == 0 and np.sum(sub_confs) == 0:
continue
class_dices[j] += binary_dice(sub_confs, sub_actual)
class_counts[j] += 1
v_dice, dices = class_dices.mean() / batch_sz, class_dices / batch_sz
if ind_norm:
v_dice, dices = (class_dices /
class_counts).mean(), class_dices / class_counts
return v_dice, dices