Determinism use fix

Signed-off-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: ericspod

.github/workflows/pythonapp.yml

@@ -94,7 +94,7 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==2.4.1 torchvision==0.19.1+cpu --index-url https://download.pytorch.org/whl/cpu
+        python -m pip install torch==2.5.1 torchvision==0.20.1+cpu --index-url https://download.pytorch.org/whl/cpu
     - if: runner.os == 'Linux'
       name: Install itk pre-release (Linux only)
       run: |
@@ -103,7 +103,7 @@ jobs:
     - name: Install the dependencies
       run: |
         python -m pip install --user --upgrade pip wheel
-        python -m pip install torch==2.4.1 torchvision==0.19.1
+        python -m pip install torch==2.5.1 torchvision==0.20.1
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt
         python -m pip list
@@ -155,7 +155,7 @@ jobs:
         # install the latest pytorch for testing
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
-        python -m pip install torch>=2.4.1 torchvision
+        python -m pip install torch>=2.5.1 torchvision
     - name: Check packages
       run: |
         pip uninstall monai

tests/integration/test_reg_loss_integration.py

@@ -19,6 +19,7 @@
 from parameterized import parameterized
 
 from monai.losses import BendingEnergyLoss, GlobalMutualInformationLoss, LocalNormalizedCrossCorrelationLoss
+from monai.utils import set_determinism
 from tests.test_utils import SkipIfBeforePyTorchVersion
 
 TEST_CASES = [
@@ -33,14 +34,11 @@
 
 class TestRegLossIntegration(unittest.TestCase):
     def setUp(self):
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-        torch.manual_seed(0)
+        set_determinism(0)
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu:0")
 
     def tearDown(self):
-        torch.backends.cudnn.deterministic = False
-        torch.backends.cudnn.benchmark = True
+        set_determinism(None)
 
     @parameterized.expand(TEST_CASES)
     @SkipIfBeforePyTorchVersion((1, 9))