def _compute(self):
device = f"cuda:{dist.get_rank()}" if torch.cuda.is_available() else "cpu"
metric = ConfusionMatrix(include_background=True, metric_name="tpr")
if dist.get_rank() == 0:
y_pred = torch.tensor(
[
[[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],
[[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],
],
device=device,
)
y = torch.tensor(
[
[[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]]],
[[[0.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]],
],
device=device,
)
metric.update([y_pred, y])
if dist.get_rank() == 1:
y_pred = torch.tensor(
[[[[0.0, 1.0], [1.0, 0.0]], [[1.0, 0.0], [1.0, 1.0]], [[0.0, 1.0], [0.0, 0.0]]]],
device=device,
)
y = torch.tensor(
[[[[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]]]],
device=device,
)
metric.update([y_pred, y])
avg_metric = metric.compute()
np.testing.assert_allclose(avg_metric, 0.7, rtol=1e-04, atol=1e-04)
def test_compute_seg(self, input_params, expected_avg):
metric = ConfusionMatrix(**input_params)
def _val_func(engine, batch):
pass
engine = Engine(_val_func)
metric.attach(engine, "confusion_matrix")
y_pred = data_1["y_pred"]
y = data_1["y"]
metric.update([y_pred, y])
y_pred = data_2["y_pred"]
y = data_2["y"]
metric.update([y_pred, y])
avg_metric = metric.compute()
self.assertAlmostEqual(avg_metric, expected_avg, places=4)
def test_compute(self):
matrix_metric = ConfusionMatrix(to_onehot_y=True, activation="softmax")
y_pred = torch.Tensor([[0.1, 0.9], [0.3, 1.4]])
y = torch.Tensor([[0], [1]])
matrix_metric.update([y_pred, y])
y_pred = torch.Tensor([[0.2, 0.1], [0.1, 0.5]])
y = torch.Tensor([[0], [1]])
matrix_metric.update([y_pred, y])
value = matrix_metric.compute()
np.testing.assert_allclose(0.75, value)
def test_compute(self, input_params, expected_avg):
metric = ConfusionMatrix(**input_params)
y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])
y = torch.Tensor([[[0], [1]], [[0], [1]]])
metric.update([y_pred, y])
y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])
y = torch.Tensor([[[0], [1]], [[1], [0]]])
metric.update([y_pred, y])
avg_metric = metric.compute()
self.assertAlmostEqual(avg_metric, expected_avg, places=4)
def test_compute_seg(self, input_params, expected_avg):
metric = ConfusionMatrix(**input_params)
y_pred = data_1["y_pred"]
y = data_1["y"]
metric.update([y_pred, y])
y_pred = data_2["y_pred"]
y = data_2["y"]
metric.update([y_pred, y])
avg_metric = metric.compute()
self.assertAlmostEqual(avg_metric, expected_avg, places=4)
def test_compute(self, input_params, expected_avg):
metric = ConfusionMatrix(**input_params)
# test input a list of channel-first tensor
y_pred = [torch.Tensor([[0], [1]]), torch.Tensor([[1], [0]])]
y = [torch.Tensor([[0], [1]]), torch.Tensor([[0], [1]])]
metric.update([y_pred, y])
y_pred = torch.Tensor([[[0], [1]], [[1], [0]]])
y = torch.Tensor([[[0], [1]], [[1], [0]]])
metric.update([y_pred, y])
avg_metric = metric.compute()
torch.testing.assert_allclose(avg_metric, expected_avg)
def test_shape_mismatch(self, input_params, _expected):
metric = ConfusionMatrix(**input_params)
with self.assertRaises((AssertionError, ValueError)):
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.ones((2, 3))
metric.update([y_pred, y])
with self.assertRaises((AssertionError, ValueError)):
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.ones((3, 2))
metric.update([y_pred, y])
