Import Spacing

Author: cvbourne

tests/test_perceptual_loss_stability.py

@@ -15,7 +15,6 @@
 
 import torch
 import torch.optim as optim
-
 from parameterized import parameterized
 
 from monai.losses.perceptual import normalize_tensor
@@ -30,44 +29,34 @@ def setUp(self):
     def tearDown(self):
         set_determinism(None)
 
-    @parameterized.expand(
-        [
-            ["e-3", 1e-3],
-            ["e-6", 1e-6],
-            ["e-9", 1e-9],
-            ["e-12", 1e-12],  # Small values 
-        ]
-    )
+    @parameterized.expand([["e-3", 1e-3], ["e-6", 1e-6], ["e-9", 1e-9], ["e-12", 1e-12]])  # Small values
     def test_normalize_tensor_stability(self, name, scale):
         """Test that small values don't produce NaNs + are handled gracefully."""
-        # Create tensor 
+        # Create tensor
         x = torch.zeros(2, 3, 10, 10, requires_grad=True)
-        
+
         optimizer = optim.Adam([x], lr=0.01)
         x_scaled = x * scale
         normalized = normalize_tensor(x_scaled)
-        
+
         # Compute to force backward pass
         loss = normalized.sum()
-        
+
         # this is where it failed before
         loss.backward()
 
         # Check for NaNs in gradients
-        self.assertFalse(
-            torch.isnan(x.grad).any(),
-            f"NaN gradients detected with scale {scale:.10e}"
-        )
-            
+        self.assertFalse(torch.isnan(x.grad).any(), f"NaN gradients detected with scale {scale:.10e}")
+
     def test_normalize_tensor_zero_input(self):
         """Test that normalize_tensor handles zero inputs gracefully."""
-        # Create tensor with zeros 
+        # Create tensor with zeros
         x = torch.zeros(2, 3, 4, 4, requires_grad=True)
-        
+
         normalized = normalize_tensor(x)
         loss = normalized.sum()
         loss.backward()
-        
+
         # Check for NaNs in gradients
         self.assertFalse(torch.isnan(x.grad).any(), "NaN gradients detected with zero input")