def test_in_top_k_pre_dim_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
target = flow.tensor([3, 1], dtype=flow.int32)
prediction = flow.tensor(
[[[0.0, 1.0, 2.0, 3.0]], [[3.0, 2.0, 1.0, 0.0]]],
dtype=flow.float32)
out = flow.in_top_k(target, prediction, k=1)
test_case.assertTrue(
"The dimension of predictions must be 2" in str(context.exception))
def test_in_top_k_num_equal_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
target = flow.tensor([[3, 1]], dtype=flow.int32)
prediction = flow.tensor(
[[0.0, 1.0, 2.0, 3.0], [3.0, 2.0, 1.0, 0.0]],
dtype=flow.float32)
out = flow.in_top_k(target, prediction, k=1)
test_case.assertTrue(
"The num of targets must equal the num of predictions" in str(
context.exception))
def _test_in_top_k_impl(test_case, shape, k, device):
np_targets = np.random.randint(0, shape[1], size=shape[0])
np_predictions = np.random.rand(*shape)
of_targets = flow.tensor(np_targets,
dtype=flow.int32,
device=flow.device(device),
requires_grad=False)
of_predictions = flow.tensor(
np_predictions,
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.in_top_k(of_targets, of_predictions, k)
np_out = _in_top_k_np(np_targets, np_predictions, k)
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001, equal_nan=True))