def test_compute(self, input_params, expected_avg):
dice_metric = MeanDice(**input_params)
y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])
y = torch.Tensor([[[0], [1]], [[0], [1]]])
dice_metric.update([y_pred, y])
y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])
y = torch.Tensor([[[0], [1]], [[1], [0]]])
dice_metric.update([y_pred, y])
avg_dice = dice_metric.compute()
self.assertAlmostEqual(avg_dice, expected_avg, places=4)
def test_compute(self, input_params, expected_avg, details_shape):
dice_metric = MeanDice(**input_params)
# set up engine
def _val_func(engine, batch):
pass
engine = Engine(_val_func)
dice_metric.attach(engine=engine, name="mean_dice")
y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])
y = torch.Tensor([[[0], [1]], [[0], [1]]])
dice_metric.update([y_pred, y])
y_pred = [torch.Tensor([[0], [1]]), torch.Tensor([[1], [0]])]
y = [torch.Tensor([[0], [1]]), torch.Tensor([[1], [0]])]
dice_metric.update([y_pred, y])
avg_dice = dice_metric.compute()
self.assertAlmostEqual(avg_dice, expected_avg, places=4)
self.assertTupleEqual(
tuple(engine.state.metric_details["mean_dice"].shape),
details_shape)