Bump version of black to 22.3.0

.pre-commit-config.yaml

@@ -41,7 +41,7 @@ repos:
       - id: isort
 
   - repo: https://github.com/psf/black
-    rev: 21.12b0
+    rev: 22.3.0
     hooks:
       - id: black
         name: Format code

course_UvA-DL/01-introduction-to-pytorch/Introduction_to_PyTorch.py

@@ -314,7 +314,7 @@
 
 # %%
 a = x + 2
-b = a ** 2
+b = a**2
 c = b + 3
 y = c.mean()
 print("Y", y)

course_UvA-DL/03-initialization-and-optimization/Initialization_and_Optimization.py

@@ -1042,8 +1042,8 @@ def train_curve(optimizer_func, curve_func=pathological_curve_loss, num_updates=
 # %%
 def bivar_gaussian(w1, w2, x_mean=0.0, y_mean=0.0, x_sig=1.0, y_sig=1.0):
     norm = 1 / (2 * np.pi * x_sig * y_sig)
-    x_exp = (-1 * (w1 - x_mean) ** 2) / (2 * x_sig ** 2)
-    y_exp = (-1 * (w2 - y_mean) ** 2) / (2 * y_sig ** 2)
+    x_exp = (-1 * (w1 - x_mean) ** 2) / (2 * x_sig**2)
+    y_exp = (-1 * (w2 - y_mean) ** 2) / (2 * y_sig**2)
     return norm * torch.exp(x_exp + y_exp)
 
 

course_UvA-DL/07-deep-energy-based-generative-models/Deep_Energy_Models.py

@@ -497,7 +497,7 @@ def training_step(self, batch, batch_idx):
         real_out, fake_out = self.cnn(inp_imgs).chunk(2, dim=0)
 
         # Calculate losses
-        reg_loss = self.hparams.alpha * (real_out ** 2 + fake_out ** 2).mean()
+        reg_loss = self.hparams.alpha * (real_out**2 + fake_out**2).mean()
         cdiv_loss = fake_out.mean() - real_out.mean()
         loss = reg_loss + cdiv_loss
 

course_UvA-DL/11-vision-transformer/Vision_Transformer.py

@@ -287,7 +287,7 @@ def __init__(
         self.patch_size = patch_size
 
         # Layers/Networks
-        self.input_layer = nn.Linear(num_channels * (patch_size ** 2), embed_dim)
+        self.input_layer = nn.Linear(num_channels * (patch_size**2), embed_dim)
         self.transformer = nn.Sequential(
             *(AttentionBlock(embed_dim, hidden_dim, num_heads, dropout=dropout) for _ in range(num_layers))
         )
@@ -403,8 +403,8 @@ def train_model(**kwargs):
         model = ViT.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)
 
     # Test best model on validation and test set
-    val_result = trainer.test(model, test_dataloaders=val_loader, verbose=False)
-    test_result = trainer.test(model, test_dataloaders=test_loader, verbose=False)
+    val_result = trainer.test(model, dataloaders=val_loader, verbose=False)
+    test_result = trainer.test(model, dataloaders=test_loader, verbose=False)
     result = {"test": test_result[0]["test_acc"], "val": val_result[0]["test_acc"]}
 
     return model, result