Run tests

Signed-off-by: Walter Hugo Lopez Pinaya <ianonimato@hotmail.com>

Author: Warvito

generative/losses/perceptual.py

@@ -319,7 +319,8 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         We expect that the input is normalised between [0, 1]. Given the preprocessing performed during the training at
         https://pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html#torchvision.models.ResNet50_Weights,
         we make sure that the input and target have 3 channels, and then do Z-Score normalization.
-        The outputs are normalised across the channels, and we obtain the mean from the spatial dimensions (similar approach to the lpips package).
+        The outputs are normalised across the channels, and we obtain the mean from the spatial dimensions (similar
+        approach to the lpips package).
         """
         # If input has just 1 channel, repeat channel to have 3 channels
         if input.shape[1] == 1 and target.shape[1] == 1:

tests/test_perceptual_loss.py

@@ -14,9 +14,10 @@
 import unittest
 
 import torch
-from generative.losses import PerceptualLoss
 from parameterized import parameterized
 
+from generative.losses import PerceptualLoss
+
 TEST_CASES = [
     [{"spatial_dims": 2, "network_type": "squeeze"}, (2, 1, 64, 64), (2, 1, 64, 64)],
     [