def test_construct_metric_value_holders_one_metric_for_all_labels(self):
hvd_mock = mock.MagicMock()
hvd_mock.allreduce = lambda tensor, name: 2 * tensor
hvd_mock.local_size = lambda: 2
metric_class = remote._metric_cls()
def torch_dummy_metric(outputs, labels):
count = torch.tensor(0.)
for output, label in zip(outputs, labels):
count += 1
return count
metric_fn_groups = [[torch_dummy_metric], [torch_dummy_metric]]
label_columns = ['l1', 'l2']
construct_metric_value_holders = remote._construct_metric_value_holders_fn(
)
metric_values = construct_metric_value_holders(metric_class,
metric_fn_groups,
label_columns, hvd_mock)
assert metric_values[0][0].name == 'group_0_l1'
assert metric_values[0][1].name == 'group_0_l2'
assert metric_values[1][0].name == 'group_1_l1'
assert metric_values[1][1].name == 'group_1_l2'
def test_metric_class(self):
hvd_mock = mock.MagicMock()
hvd_mock.allreduce = lambda tensor, name: 2 * tensor
hvd_mock.local_size = lambda: 2
metric_class = remote._metric_cls()
metric = metric_class('dummy_metric', hvd_mock)
metric.update(torch.tensor(1.0))
metric.update(torch.tensor(2.0))
assert metric.sum.item() == 6.0
assert metric.n.item() == 2.0
assert metric.avg.item() == 6.0 / 2.0
