Merge remote-tracking branch 'origin/main' into publication

Author: Azure Pipelines

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
       - id: codespell
         additional_dependencies: [tomli]
@@ -45,7 +45,7 @@ repos:
         args: ["--print-width=120"]
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.21
+    rev: 0.7.22
     hooks:
       - id: mdformat
         additional_dependencies:
@@ -55,7 +55,7 @@ repos:
         args: ["--number"]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.6
+    rev: v0.11.4
     hooks:
       # try to fix what is possible
       - id: ruff

_docs/source/conf.py

@@ -201,7 +201,7 @@
         project + " Documentation",
         author,
         project,
-        "" "Miscellaneous",  # about.__docs__,
+        "Miscellaneous",  # about.__docs__,
     ),
 ]
 

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

@@ -883,7 +883,7 @@ def train_model(model, optimizer, data_loader, loss_module, num_epochs=100):
 #
 # $$acc = \frac{\#\text{correct predictions}}{\#\text{all predictions}} = \frac{TP+TN}{TP+TN+FP+FN}$$
 #
-# where TP are the true positives, TN true negatives, FP false positives, and FN the fale negatives.
+# where TP are the true positives, TN true negatives, FP false positives, and FN the false negatives.
 #
 # When evaluating the model, we don't need to keep track of the computation graph as we don't intend to calculate the gradients.
 # This reduces the required memory and speed up the model.
@@ -910,7 +910,7 @@ def eval_model(model, data_loader):
             num_preds += data_labels.shape[0]
 
     acc = true_preds / num_preds
-    print(f"Accuracy of the model: {100.0*acc:4.2f}%")
+    print(f"Accuracy of the model: {100.0 * acc:4.2f}%")
 
 
 # %%

course_UvA-DL/02-activation-functions/notebook.py

@@ -343,12 +343,12 @@ def load_model(model_path, model_name, net=None):
 
     """
     config_file, model_file = _get_config_file(model_path, model_name), _get_model_file(model_path, model_name)
-    assert os.path.isfile(
-        config_file
-    ), f'Could not find the config file "{config_file}". Are you sure this is the correct path and you have your model config stored here?'
-    assert os.path.isfile(
-        model_file
-    ), f'Could not find the model file "{model_file}". Are you sure this is the correct path and you have your model stored here?'
+    assert os.path.isfile(config_file), (
+        f'Could not find the config file "{config_file}". Are you sure this is the correct path and you have your model config stored here?'
+    )
+    assert os.path.isfile(model_file), (
+        f'Could not find the model file "{model_file}". Are you sure this is the correct path and you have your model stored here?'
+    )
     with open(config_file) as f:
         config_dict = json.load(f)
     if net is None:
@@ -548,7 +548,7 @@ def train_model(net, model_name, max_epochs=50, patience=7, batch_size=256, over
             ############
             net.train()
             true_preds, count = 0.0, 0
-            for imgs, labels in tqdm(train_loader_local, desc=f"Epoch {epoch+1}", leave=False):
+            for imgs, labels in tqdm(train_loader_local, desc=f"Epoch {epoch + 1}", leave=False):
                 imgs, labels = imgs.to(device), labels.to(device)  # To GPU
                 optimizer.zero_grad()  # Zero-grad can be placed anywhere before "loss.backward()"
                 preds = net(imgs)
@@ -566,7 +566,7 @@ def train_model(net, model_name, max_epochs=50, patience=7, batch_size=256, over
             val_acc = test_model(net, val_loader)
             val_scores.append(val_acc)
             print(
-                f"[Epoch {epoch+1:2i}] Training accuracy: {train_acc*100.0:05.2f}%, Validation accuracy: {val_acc*100.0:05.2f}%"
+                f"[Epoch {epoch + 1:2i}] Training accuracy: {train_acc * 100.0:05.2f}%, Validation accuracy: {val_acc * 100.0:05.2f}%"
             )
 
             if len(val_scores) == 1 or val_acc > val_scores[best_val_epoch]:
@@ -587,7 +587,7 @@ def train_model(net, model_name, max_epochs=50, patience=7, batch_size=256, over
 
     load_model(CHECKPOINT_PATH, model_name, net=net)
     test_acc = test_model(net, test_loader)
-    print((f" Test accuracy: {test_acc*100.0:4.2f}% ").center(50, "=") + "\n")
+    print((f" Test accuracy: {test_acc * 100.0:4.2f}% ").center(50, "=") + "\n")
     return test_acc
 
 

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

@@ -580,19 +580,19 @@ def _get_result_file(model_path, model_name):
 def load_model(model_path, model_name, net=None):
     config_file = _get_config_file(model_path, model_name)
     model_file = _get_model_file(model_path, model_name)
-    assert os.path.isfile(
-        config_file
-    ), f'Could not find the config file "{config_file}". Are you sure this is the correct path and you have your model config stored here?'
-    assert os.path.isfile(
-        model_file
-    ), f'Could not find the model file "{model_file}". Are you sure this is the correct path and you have your model stored here?'
+    assert os.path.isfile(config_file), (
+        f'Could not find the config file "{config_file}". Are you sure this is the correct path and you have your model config stored here?'
+    )
+    assert os.path.isfile(model_file), (
+        f'Could not find the model file "{model_file}". Are you sure this is the correct path and you have your model stored here?'
+    )
     with open(config_file) as f:
         config_dict = json.load(f)
     if net is None:
         act_fn_name = config_dict["act_fn"].pop("name").lower()
-        assert (
-            act_fn_name in act_fn_by_name
-        ), f'Unknown activation function "{act_fn_name}". Please add it to the "act_fn_by_name" dict.'
+        assert act_fn_name in act_fn_by_name, (
+            f'Unknown activation function "{act_fn_name}". Please add it to the "act_fn_by_name" dict.'
+        )
         act_fn = act_fn_by_name[act_fn_name]()
         net = BaseNetwork(act_fn=act_fn, **config_dict)
     net.load_state_dict(torch.load(model_file))
@@ -678,7 +678,7 @@ def train_model(net, model_name, optim_func, max_epochs=50, batch_size=256, over
     plt.show()
     plt.close()
 
-    print((f" Test accuracy: {results['test_acc']*100.0:4.2f}% ").center(50, "=") + "\n")
+    print((f" Test accuracy: {results['test_acc'] * 100.0:4.2f}% ").center(50, "=") + "\n")
     return results
 
 
@@ -700,7 +700,7 @@ def epoch_iteration(net, loss_module, optimizer, train_loader_local, val_loader,
         # Record statistics during training
         true_preds += (preds.argmax(dim=-1) == labels).sum().item()
         count += labels.shape[0]
-        t.set_description(f"Epoch {epoch+1}: loss={loss.item():4.2f}")
+        t.set_description(f"Epoch {epoch + 1}: loss={loss.item():4.2f}")
         epoch_losses.append(loss.item())
     train_acc = true_preds / count
 
@@ -709,7 +709,7 @@ def epoch_iteration(net, loss_module, optimizer, train_loader_local, val_loader,
     ##############
     val_acc = test_model(net, val_loader)
     print(
-        f"[Epoch {epoch+1:2i}] Training accuracy: {train_acc*100.0:05.2f}%, Validation accuracy: {val_acc*100.0:05.2f}%"
+        f"[Epoch {epoch + 1:2i}] Training accuracy: {train_acc * 100.0:05.2f}%, Validation accuracy: {val_acc * 100.0:05.2f}%"
     )
     return train_acc, val_acc, epoch_losses
 

course_UvA-DL/04-inception-resnet-densenet/notebook.py

@@ -1183,8 +1183,8 @@ def forward(self, x):
 table = [
     [
         model_name,
-        f"{100.0*model_results['val']:4.2f}%",
-        f"{100.0*model_results['test']:4.2f}%",
+        f"{100.0 * model_results['val']:4.2f}%",
+        f"{100.0 * model_results['test']:4.2f}%",
         f"{sum(np.prod(p.shape) for p in model.parameters()):,}",
     ]
     for model_name, model_results, model in all_models

course_UvA-DL/13-contrastive-learning/SimCLR.py

@@ -642,7 +642,7 @@ def get_smaller_dataset(original_dataset, num_imgs_per_label):
 plt.show()
 
 for k, score in zip(dataset_sizes, test_scores):
-    print(f"Test accuracy for {k:3d} images per label: {100*score:4.2f}%")
+    print(f"Test accuracy for {k:3d} images per label: {100 * score:4.2f}%")
 
 # %% [markdown]
 # As one would expect, the classification performance improves the more data we have.
@@ -781,8 +781,8 @@ def train_resnet(batch_size, max_epochs=100, **kwargs):
 
 # %%
 resnet_model, resnet_result = train_resnet(batch_size=64, num_classes=10, lr=1e-3, weight_decay=2e-4, max_epochs=100)
-print(f"Accuracy on training set: {100*resnet_result['train']:4.2f}%")
-print(f"Accuracy on test set: {100*resnet_result['test']:4.2f}%")
+print(f"Accuracy on training set: {100 * resnet_result['train']:4.2f}%")
+print(f"Accuracy on test set: {100 * resnet_result['test']:4.2f}%")
 
 # %% [markdown]
 # The ResNet trained from scratch achieves 73.31% on the test set.