def test_reset_compute():
a = DummyMetricSum()
assert a.x == 0
a.update(tensor(5))
assert a.compute() == 5
a.reset()
assert a.compute() == 0
def test_reset_compute():
a = DummyMetricSum()
assert a.x == 0
a.update(tensor(5))
assert a.compute() == 5
a.reset()
if not _LIGHTNING_AVAILABLE or _LIGHTNING_GREATER_EQUAL_1_3:
assert a.compute() == 0
else:
assert a.compute() == 5
def test_load_state_dict(tmpdir):
""" test that metric states can be loaded with state dict """
metric = DummyMetricSum()
metric.persistent(True)
metric.update(5)
loaded_metric = DummyMetricSum()
loaded_metric.load_state_dict(metric.state_dict())
assert metric.compute() == 5
def _test_ddp_compositional_tensor(rank, worldsize):
setup_ddp(rank, worldsize)
dummy = DummyMetricSum()
dummy._reductions = {"x": torch.sum}
dummy = dummy.clone() + dummy.clone()
dummy.update(tensor(1))
val = dummy.compute()
assert val == 2 * worldsize
def test_warning_on_compute_before_update():
metric = DummyMetricSum()
# make sure everything is fine with forward
with pytest.warns(None) as record:
val = metric(1)
assert not record
metric.reset()
with pytest.warns(UserWarning, match=r'The ``compute`` method of metric .*'):
val = metric.compute()
assert val == 0.0
# after update things should be fine
metric.update(2.0)
with pytest.warns(None) as record:
val = metric.compute()
assert not record
assert val == 2.0
def test_warning_on_compute_before_update():
"""test that an warning is raised if user tries to call compute before update."""
metric = DummyMetricSum()
# make sure everything is fine with forward
with pytest.warns(None) as record:
val = metric(1)
assert not record
metric.reset()
with pytest.warns(UserWarning, match=r"The ``compute`` method of metric .*"):
val = metric.compute()
assert val == 0.0
# after update things should be fine
metric.update(2.0)
with pytest.warns(None) as record:
val = metric.compute()
assert not record
assert val == 2.0
