add run_if_labs tests.decorators

facebookresearch · Jan 12, 2023 · f0f12de · f0f12de
1 parent 724950f
commit f0f12de
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diff --git a/tests/labs/lie_functional/test_se3.py b/tests/labs/lie_functional/test_se3.py
@@ -10,7 +10,10 @@
 
 import torch
 
+from tests.decorators import run_if_labs
 
+
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_exp(batch_size: int, dtype: torch.dtype):
@@ -22,6 +25,7 @@ def test_exp(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "exp", 1e-6, (tangent_vector,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_log(batch_size: int, dtype: torch.dtype):
@@ -35,6 +39,7 @@ def test_log(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "log", EPS, (group,), (left_project,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_adjoint(batch_size: int, dtype: torch.dtype):
@@ -46,6 +51,7 @@ def test_adjoint(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "adjoint", TEST_EPS, (group,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_inverse(batch_size: int, dtype: torch.dtype):
@@ -57,6 +63,7 @@ def test_inverse(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "inverse", TEST_EPS, (group,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_hat(batch_size: int, dtype: torch.dtype):
@@ -68,6 +75,7 @@ def test_hat(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "hat", TEST_EPS, (tangent_vector,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_vee(batch_size: int, dtype: torch.dtype):
@@ -84,6 +92,7 @@ def test_vee(batch_size: int, dtype: torch.dtype):
     assert torch.allclose(actual_tangent_vector, tangent_vector, atol=TEST_EPS)
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_compose(batch_size: int, dtype: torch.dtype):
@@ -96,6 +105,7 @@ def test_compose(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "compose", TEST_EPS, (group0, group1))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_lift(batch_size: int, dtype: torch.dtype):
@@ -112,6 +122,7 @@ def test_lift(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "lift", TEST_EPS, (matrix,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_project(batch_size: int, dtype: torch.dtype):
@@ -129,6 +140,7 @@ def test_project(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "project", TEST_EPS, (matrix,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_left_act(batch_size: int, dtype: torch.dtype):
@@ -159,6 +171,7 @@ def test_left_act(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(se3, "left_act", TEST_EPS, (group, matrix))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_left_project(batch_size: int, dtype: torch.dtype):

diff --git a/tests/labs/lie_functional/test_so3.py b/tests/labs/lie_functional/test_so3.py
@@ -27,6 +27,7 @@ def test_exp(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(so3, "exp", 1e-6, (tangent_vector,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_log(batch_size: int, dtype: torch.dtype):
@@ -101,6 +102,7 @@ def test_vee(batch_size: int, dtype: torch.dtype):
     assert torch.allclose(actual_tangent_vector, tangent_vector, atol=TEST_EPS)
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_compose(batch_size: int, dtype: torch.dtype):
@@ -113,6 +115,7 @@ def test_compose(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(so3, "compose", TEST_EPS, (group0, group1))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_quaternion_to_rotation(batch_size: int, dtype: torch.dtype):
@@ -125,6 +128,7 @@ def test_quaternion_to_rotation(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(so3, "quaternion_to_rotation", TEST_EPS, (quaternion,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_lift(batch_size: int, dtype: torch.dtype):
@@ -141,6 +145,7 @@ def test_lift(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(so3, "lift", TEST_EPS, (matrix,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_project(batch_size: int, dtype: torch.dtype):
@@ -158,6 +163,7 @@ def test_project(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(so3, "project", TEST_EPS, (matrix,))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_left_act(batch_size: int, dtype: torch.dtype):
@@ -188,6 +194,7 @@ def test_left_act(batch_size: int, dtype: torch.dtype):
     check_lie_group_function(so3, "left_act", TEST_EPS, (group, matrix))
 
 
+@run_if_labs()
 @pytest.mark.parametrize("batch_size", [1, 20, 100])
 @pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
 def test_left_project(batch_size: int, dtype: torch.dtype):