style: 🎨 run black

local.py

@@ -15,8 +15,10 @@ def show_one_image(image_path):
     image = imageio.imread(image_path)
     plt.imshow(image)
 
+
 def unnormalize(tensor):
-    return (tensor+1)/2.
+    return (tensor + 1) / 2.0
+
 
 class NucleiDataset(Dataset):
     """A PyTorch dataset to load cell images and nuclei masks"""
@@ -89,6 +91,7 @@ def show_random_dataset_image(dataset):
     print("Image size is %s" % {img[0].shape})
     plt.show()
 
+
 def pad_to_size(small_tensor, target_size):
     if small_tensor.size() > target_size:
         msg = f"Can't pad tensor of size {small_tensor.size()} to tensor of size {target_size}."
@@ -97,10 +100,11 @@ def pad_to_size(small_tensor, target_size):
         return small_tensor
     pad_twoside = []
     for small_size, large_size in zip(small_tensor.shape, target_size):
-        pad_twoside.append(math.floor((large_size - small_size)/2))
-        pad_twoside.append(math.ceil((large_size-small_size)/2))
+        pad_twoside.append(math.floor((large_size - small_size) / 2))
+        pad_twoside.append(math.ceil((large_size - small_size) / 2))
     return torch.nn.functional.pad(small_tensor, pad_twoside[::-1])
 
+
 def apply_and_show_random_image(f, ds):
     # pick random raw image from dataset
     img_tensor = ds[np.random.randint(len(ds))][0]

solution.py

@@ -41,7 +41,7 @@
     plot_receptive_field,
     show_random_dataset_image,
     pad_to_size,
-    unnormalize
+    unnormalize,
 )
 
 # %% tags=[]
@@ -1113,9 +1113,10 @@ def train(
                     global_step=step,
                 )
                 combined_image = torch.cat(
-                    [x, pad_to_size(y, x.size()), pad_to_size(prediction, x.size())], dim=3
+                    [x, pad_to_size(y, x.size()), pad_to_size(prediction, x.size())],
+                    dim=3,
                 )
-                
+
                 tb_logger.add_images(
                     tag="input_target_prediction",
                     img_tensor=combined_image,