def test_compute_batch_images():
acc = CategoricalAccuracy()
y_pred = torch.softmax(torch.rand(2, 3, 2, 2), dim=1)
y = torch.LongTensor([[[0, 1], [0, 1]], [[0, 2], [0, 2]]])
indices = torch.max(y_pred, dim=1)[1]
acc.update((y_pred, y))
assert isinstance(acc.compute(), float)
assert accuracy_score(
y.view(-1).data.numpy(),
indices.view(-1).data.numpy()) == pytest.approx(acc.compute())
def test_compute():
acc = CategoricalAccuracy()
y_pred = torch.eye(4)
y = torch.ones(4).type(torch.LongTensor)
acc.update((y_pred, y))
assert acc.compute() == 0.25
acc.reset()
y_pred = torch.eye(2)
y = torch.ones(2).type(torch.LongTensor)
acc.update((y_pred, y))
assert acc.compute() == 0.5
def test_compute():
acc = CategoricalAccuracy()
y_pred = torch.softmax(torch.rand(4, 4), dim=1)
y = torch.ones(4).type(torch.LongTensor)
indices = torch.max(y_pred, dim=1)[1]
acc.update((y_pred, y))
assert isinstance(acc.compute(), float)
assert accuracy_score(
y.view(-1).data.numpy(),
indices.view(-1).data.numpy()) == pytest.approx(acc.compute())
acc.reset()
y_pred = torch.softmax(torch.rand(2, 2), dim=1)
y = torch.ones(2).type(torch.LongTensor)
indices = torch.max(y_pred, dim=1)[1]
acc.update((y_pred, y))
assert isinstance(acc.compute(), float)
assert accuracy_score(
y.view(-1).data.numpy(),
indices.view(-1).data.numpy()) == pytest.approx(acc.compute())
def test_compute_batch_images():
acc = CategoricalAccuracy()
y_pred = torch.zeros(2, 3, 2, 2)
y_pred[0, 1, :] = 1
y_pred[0, 2, :] = 1
y = torch.LongTensor([[[0, 1], [0, 1]], [[0, 2], [0, 2]]])
acc.update((y_pred, y))
assert isinstance(acc.compute(), float)
assert acc.compute() == 0.5
acc.reset()
y_pred = torch.zeros(2, 3, 2, 2)
y_pred[0, 1, :] = 1
y_pred[1, 2, :] = 1
y = torch.LongTensor([[[2, 1], [1, 1]], [[2, 2], [0, 2]]])
acc.update((y_pred, y))
assert isinstance(acc.compute(), float)
assert acc.compute() == 0.75
def test_zero_div():
acc = CategoricalAccuracy()
with pytest.raises(NotComputableError):
acc.compute()
