def test_metrics_lambda_reset():
m0 = ListGatherMetric(0)
m1 = ListGatherMetric(1)
m2 = ListGatherMetric(2)
m0.update([1, 10, 100])
m1.update([1, 10, 100])
m2.update([1, 10, 100])
def fn(x, y, z, t):
return 1
m = MetricsLambda(fn, m0, m1, z=m2, t=0)
# initiating a new instance of MetricsLambda must reset
# its argument metrics
assert m0.list_ is None
assert m1.list_ is None
assert m2.list_ is None
m0.update([1, 10, 100])
m1.update([1, 10, 100])
m2.update([1, 10, 100])
m.reset()
assert m0.list_ is None
assert m1.list_ is None
assert m2.list_ is None
def test_metrics_lambda_update_and_attach_together():
y_pred = torch.randint(0, 2, size=(15, 10, 4)).float()
y = torch.randint(0, 2, size=(15, 10, 4)).long()
def update_fn(engine, batch):
y_pred, y = batch
return y_pred, y
engine = Engine(update_fn)
precision = Precision(average=False)
recall = Recall(average=False)
def Fbeta(r, p, beta):
return torch.mean((1 + beta**2) * p * r / (beta**2 * p + r)).item()
F1 = MetricsLambda(Fbeta, recall, precision, 1)
F1.attach(engine, "f1")
with pytest.raises(
ValueError,
match=r"MetricsLambda is already attached to an engine"):
F1.update((y_pred, y))
y_pred = torch.randint(0, 2, size=(15, 10, 4)).float()
y = torch.randint(0, 2, size=(15, 10, 4)).long()
F1 = MetricsLambda(Fbeta, recall, precision, 1)
F1.update((y_pred, y))
engine = Engine(update_fn)
with pytest.raises(ValueError,
match=r"The underlying metrics are already updated"):
F1.attach(engine, "f1")
F1.reset()
F1.attach(engine, "f1")
def test_metrics_lambda_update():
"""
Test if the underlying metrics are updated
"""
y_pred = torch.randint(0, 2, size=(15, 10, 4)).float()
y = torch.randint(0, 2, size=(15, 10, 4)).long()
precision = Precision(average=False)
recall = Recall(average=False)
def Fbeta(r, p, beta):
return torch.mean((1 + beta ** 2) * p * r / (beta ** 2 * p + r)).item()
F1 = MetricsLambda(Fbeta, recall, precision, 1)
F1.update((y_pred, y))
assert precision._updated
assert recall._updated
F1.reset()
assert not precision._updated
assert not recall._updated
"""
Test multiple updates and if the inputs of
the underlying metrics are updated multiple times
"""
y_pred1 = torch.randint(0, 2, size=(15,))
y1 = torch.randint(0, 2, size=(15,))
y_pred2 = torch.randint(0, 2, size=(15,))
y2 = torch.randint(0, 2, size=(15,))
F1.update((y_pred1, y1))
F1.update((y_pred2, y2))
# Compute true_positives and positives for precision
correct1 = y1 * y_pred1
all_positives1 = y_pred1.sum(dim=0)
if correct1.sum() == 0:
true_positives1 = torch.zeros_like(all_positives1)
else:
true_positives1 = correct1.sum(dim=0)
correct2 = y2 * y_pred2
all_positives2 = y_pred2.sum(dim=0)
if correct2.sum() == 0:
true_positives2 = torch.zeros_like(all_positives2)
else:
true_positives2 = correct2.sum(dim=0)
true_positives = true_positives1 + true_positives2
positives = all_positives1 + all_positives2
assert precision._type == "binary"
assert precision._true_positives == true_positives
assert precision._positives == positives
# Computing positivies for recall is different
positives1 = y1.sum(dim=0)
positives2 = y2.sum(dim=0)
positives = positives1 + positives2
assert recall._type == "binary"
assert recall._true_positives == true_positives
assert recall._positives == positives
"""
Test compute
"""
F1.reset()
F1.update((y_pred1, y1))
F1_metrics_lambda = F1.compute()
F1_sklearn = f1_score(y1.numpy(), y_pred1.numpy())
assert pytest.approx(F1_metrics_lambda) == F1_sklearn
