def update(self, output: Sequence[Union[torch.Tensor, dict]]):
assert len(output) == 2, 'MeanDice metric can only support y_pred and y.'
y_pred, y = output
average = compute_meandice(y_pred, y, self.include_background, self.to_onehot_y, self.mutually_exclusive,
self.add_sigmoid, self.logit_thresh)
batch_size = len(y)
self._sum += average.item() * batch_size
self._num_examples += batch_size
def update(self, output: Sequence[Union[torch.Tensor, dict]]):
assert len(
output) == 2, 'MeanDice metric can only support y_pred and y.'
y_pred, y = output
scores = compute_meandice(y_pred, y, self.include_background,
self.to_onehot_y, self.mutually_exclusive,
self.add_sigmoid, self.logit_thresh)
# add all items in current batch
for batch in scores:
not_nan = ~torch.isnan(batch)
if not_nan.sum() == 0:
continue
class_avg = batch[not_nan].mean().item()
self._sum += class_avg
self._num_examples += 1
def test_value(self, input_data, expected_value):
result = compute_meandice(**input_data)
self.assertAlmostEqual(result.item(), expected_value, places=4)
def test_nans(self, input_data, expected_value):
result = compute_meandice(**input_data)
self.assertTrue(
np.allclose(np.isnan(result.cpu().numpy()), expected_value))
def test_value(self, input_data, expected_value):
result = compute_meandice(**input_data)
self.assertTrue(
np.allclose(result.cpu().numpy(), expected_value, atol=1e-4))