use needs_cuda decorator

Author: AntoineSimoulin

test/test_transforms_v2.py

@@ -60,7 +60,6 @@
 )
 
 
-CUDA_AVAILABLE = torch.cuda.is_available()
 CVCUDA_AVAILABLE = _is_cvcuda_available()
 if CVCUDA_AVAILABLE:
     cvcuda = _import_cvcuda()
@@ -6745,7 +6744,7 @@ def test_functional_error(self):
 
 
 @pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="test requires CUDA")
+@needs_cuda
 class TestToCVCUDATensor:
     @pytest.mark.parametrize("dtype", [torch.uint8, torch.uint16, torch.float32, torch.float64])
     @pytest.mark.parametrize("device", cpu_and_cuda())
@@ -6812,7 +6811,7 @@ def test_round_trip(self, dtype, device, color_space, batch_size):
 
 
 @pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="test requires CUDA")
+@needs_cuda
 class TestCVDUDAToTensor:
     @pytest.mark.parametrize("dtype", [torch.uint8, torch.uint16, torch.float32, torch.float64])
     @pytest.mark.parametrize("device", cpu_and_cuda())