def report_scores(self, y, t):
with chainer.no_backprop_mode():
if self.nested_label:
dice = mean_dice_coefficient(
dice_coefficient(y[:, 0:2, ...], t[:, 0, ...]))
for i in range(1, t.shape[1]):
dices = dice_coefficient(y[:, 2 * i:2 * (i + 1), ...],
t[:, i, ...])
dice = F.concat((dice, mean_dice_coefficient(dices)),
axis=0)
else:
dice = dice_coefficient(y, t)
mean_dice = mean_dice_coefficient(dice)
if self.nested_label:
b, c, h, w, d = t.shape
y = F.reshape(y, (b, 2, h * c, w, d))
t = F.reshape(t, (b, h * c, w, d))
accuracy = F.accuracy(y, t)
reporter.report({'acc': accuracy, 'mean_dc': mean_dice})
xp = cuda.get_array_module(y)
for i in range(len(dice)):
if not xp.isnan(dice.data[i]):
reporter.report({'dc_{}'.format(i): dice[i]})
def compute_dice_coef(self, y, t):
if self.nested_label:
dice = mean_dice_coefficient(dice_coefficient(y[:, 0:2, ...], t[:, 0, ...]))
for i in range(1, t.shape[1]):
dices = dice_coefficient(y[:, 2*i:2*(i+1), ...], t[:, i, ...])
dice = F.concat((dice, mean_dice_coefficient(dices)), axis=0)
else:
dice = dice_coefficient(y, t, is_brats=self.is_brats)
return dice