Formatting changes for black 22.3.0

Author: ProGamerGov

captum/optim/_core/loss.py

@@ -267,7 +267,7 @@ class DeepDream(BaseLoss):
     def __call__(self, targets_to_values: ModuleOutputMapping) -> torch.Tensor:
         activations = targets_to_values[self.target]
         activations = activations[self.batch_index[0] : self.batch_index[1]]
-        return activations ** 2
+        return activations**2
 
 
 @loss_wrapper
@@ -587,7 +587,7 @@ def __call__(self, targets_to_values: ModuleOutputMapping) -> torch.Tensor:
             return activations * vec
 
         dot = torch.mean(activations * vec)
-        cossims = dot / (self.eps + torch.sqrt(torch.sum(activations ** 2)))
+        cossims = dot / (self.eps + torch.sqrt(torch.sum(activations**2)))
         return dot * torch.clamp(cossims, min=0.1) ** self.cossim_pow
 
 

captum/optim/_param/image/images.py

@@ -385,7 +385,7 @@ def _setup_input(
                 x = F.interpolate(init.clone(), size=(h, w), mode="bilinear")
                 x = x / 6  # Prevents output from being all white
             upsample = torch.nn.Upsample(scale_factor=scale, mode="nearest")
-            x = x * (scale ** power) / (32 ** power)
+            x = x * (scale**power) / (32**power)
             x = torch.nn.Parameter(x)
             tensor_params.append(x)
             scaler.append(upsample)

captum/optim/_param/image/transforms.py

@@ -1217,9 +1217,9 @@ def __init__(
         padding_transform: Optional[nn.Module] = nn.ConstantPad2d(2, value=0.5),
         translate: Optional[Union[int, List[int]]] = [4] * 10,
         scale: Optional[NumSeqOrTensorOrProbDistType] = [
-            0.995 ** n for n in range(-5, 80)
+            0.995**n for n in range(-5, 80)
         ]
-        + [0.998 ** n for n in 2 * list(range(20, 40))],
+        + [0.998**n for n in 2 * list(range(20, 40))],
         degrees: Optional[NumSeqOrTensorOrProbDistType] = list(range(-20, 20))
         + list(range(-10, 10))
         + list(range(-5, 5))

tests/optim/core/test_loss.py

@@ -30,7 +30,7 @@ def test_channel_deepdream(self) -> None:
         model = BasicModel_ConvNet_Optim()
         loss = opt_loss.DeepDream(model.layer)
         expected = torch.as_tensor(
-            [[[CHANNEL_ACTIVATION_0_LOSS ** 2]], [[CHANNEL_ACTIVATION_1_LOSS ** 2]]]
+            [[[CHANNEL_ACTIVATION_0_LOSS**2]], [[CHANNEL_ACTIVATION_1_LOSS**2]]]
         )[None, :]
         assertTensorAlmostEqual(self, get_loss_value(model, loss), expected, mode="max")
 
@@ -84,7 +84,7 @@ def test_l2(self) -> None:
         loss = opt_loss.L2(model.layer)
         self.assertAlmostEqual(
             get_loss_value(model, loss),
-            (CHANNEL_ACTIVATION_0_LOSS ** 2 + CHANNEL_ACTIVATION_1_LOSS ** 2) ** 0.5,
+            (CHANNEL_ACTIVATION_0_LOSS**2 + CHANNEL_ACTIVATION_1_LOSS**2) ** 0.5,
             places=5,
         )
 
@@ -267,7 +267,7 @@ def test_pow(self) -> None:
         loss = opt_loss.ChannelActivation(model.layer, 0) ** 2
         self.assertAlmostEqual(
             get_loss_value(model, loss),
-            CHANNEL_ACTIVATION_0_LOSS ** 2,
+            CHANNEL_ACTIVATION_0_LOSS**2,
             places=6,
         )
 

tests/optim/param/test_transforms.py

@@ -1912,8 +1912,8 @@ def test_transform_robustness_init_transform_values(self) -> None:
         for module in transform_robustness.jitter_transforms:
             self.assertEqual(module.pad_range, 2 * 4)
 
-        expected_scale = [0.995 ** n for n in range(-5, 80)] + [
-            0.998 ** n for n in 2 * list(range(20, 40))
+        expected_scale = [0.995**n for n in range(-5, 80)] + [
+            0.998**n for n in 2 * list(range(20, 40))
         ]
         self.assertEqual(transform_robustness.random_scale.scale, expected_scale)
         expected_degrees = (