def test_accuracy_top1():
"""
Tests for catalyst.metrics.accuracy metric.
"""
for i in range(NUM_CLASSES):
outputs = torch.zeros((BATCH_SIZE, NUM_CLASSES))
outputs[:, i] = 1
targets = torch.ones((BATCH_SIZE, 1)) * i
top1, top3, top5 = accuracy(outputs, targets, topk=(1, 3, 5))
assert np.isclose(top1, 1)
assert np.isclose(top3, 1)
assert np.isclose(top5, 1)
def test_accuracy_top3():
"""
Tests for catalyst.metrics.accuracy metric.
"""
outputs = (torch.linspace(0, NUM_CLASSES - 1, steps=NUM_CLASSES).repeat(
1, BATCH_SIZE).view(-1, NUM_CLASSES))
for i in range(NUM_CLASSES):
targets = torch.ones((BATCH_SIZE, 1)) * i
top1, top3, top5 = accuracy(outputs, targets, topk=(1, 3, 5))
assert np.isclose(top1, 1 if i >= NUM_CLASSES - 1 else 0)
assert np.isclose(top3, 1 if i >= NUM_CLASSES - 3 else 0)
assert np.isclose(top5, 1 if i >= NUM_CLASSES - 5 else 0)
def update(self, logits: torch.Tensor,
targets: torch.Tensor) -> List[float]:
"""
Updates metric value with accuracy for new data and return intermediate metrics values.
Args:
logits: tensor of logits
targets: tensor of targets
Returns:
list of accuracy@k values
"""
values = accuracy(logits, targets, topk=self.topk_args)
values = [v.item() for v in values]
for value, metric in zip(values, self.additive_metrics):
metric.update(value, len(targets))
return values