def test_accuracy_computation(self):
accuracy = BooleanAccuracy()
predictions = torch.Tensor([[0, 1],
[2, 3],
[4, 5],
[6, 7]])
targets = torch.Tensor([[0, 1],
[2, 2],
[4, 5],
[7, 7]])
accuracy(predictions, targets)
assert accuracy.get_metric() == 2. / 4
mask = torch.ones(4, 2)
mask[1, 1] = 0
accuracy(predictions, targets, mask)
assert accuracy.get_metric() == 5. / 8
targets[1, 1] = 3
accuracy(predictions, targets)
assert accuracy.get_metric() == 8. / 12
accuracy.reset()
accuracy(predictions, targets)
assert accuracy.get_metric() == 3. / 4
def test_accuracy_computation(self):
accuracy = BooleanAccuracy()
predictions = torch.Tensor([[0, 1], [2, 3], [4, 5], [6, 7]])
targets = torch.Tensor([[0, 1], [2, 2], [4, 5], [7, 7]])
accuracy(predictions, targets)
assert accuracy.get_metric() == 2. / 4
mask = torch.ones(4, 2)
mask[1, 1] = 0
accuracy(predictions, targets, mask)
assert accuracy.get_metric() == 5. / 8
targets[1, 1] = 3
accuracy(predictions, targets)
assert accuracy.get_metric() == 8. / 12
accuracy.reset()
accuracy(predictions, targets)
assert accuracy.get_metric() == 3. / 4
def test_accuracy_computation(self, device: str):
accuracy = BooleanAccuracy()
predictions = torch.tensor([[0, 1], [2, 3], [4, 5], [6, 7]], device=device)
targets = torch.tensor([[0, 1], [2, 2], [4, 5], [7, 7]], device=device)
accuracy(predictions, targets)
assert accuracy.get_metric() == 2 / 4
mask = torch.ones(4, 2, device=device).bool()
mask[1, 1] = 0
accuracy(predictions, targets, mask)
assert accuracy.get_metric() == 5 / 8
targets[1, 1] = 3
accuracy(predictions, targets)
assert accuracy.get_metric() == 8 / 12
accuracy.reset()
accuracy(predictions, targets)
assert accuracy.get_metric() == 3 / 4