Format code and comments

conf/experiment.py

@@ -1,4 +1,3 @@
-
 """
 Configurations for the experiments and config groups, using hydra-zen.
 """

dataset/example.py

@@ -61,9 +61,9 @@ def _load(
         progress: Progress,
         job_id: TaskID,
     ) -> Tuple[Union[dict, list, Tensor], Union[dict, list, Tensor]]:
-        len = 3 if self._tiny else 20
-        progress.update(job_id, total=len)
-        for _ in range(len):
+        length = 3 if self._tiny else 20
+        progress.update(job_id, total=length)
+        for _ in range(length):
             progress.advance(job_id)
             sleep(0.001 if self._tiny else 0.1)
         return torch.rand(10000, self._img_dim, self._img_dim), torch.rand(10000, 8)

dataset/mixins/__init__.py

@@ -123,6 +123,7 @@ def __init__(
         )
         self._split = split
         self._lazy = scd_lazy  # TODO: Implement eager caching (rn the default is lazy)
+        # TODO: Refactor and reduce cyclomatic complexity
         argnames = inspect.getfullargspec(self.__class__.__init__).args
         found = False
         frame: FrameType | None = inspect.currentframe()

pyproject.toml

@@ -34,8 +34,8 @@ exclude = [
 line-length = 88
 indent-width = 4
 
-# Assume Python 3.8
-target-version = "py38"
+# Assume Python 3.11
+target-version = "py311"
 
 [tool.ruff.lint]
 # Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`)  codes by default.
@@ -69,7 +69,7 @@ line-ending = "auto"
 #
 # This is currently disabled by default, but it is planned for this
 # to be opt-out in the future.
-docstring-code-format = false
+docstring-code-format = true
 
 # Set the line length limit used when formatting code snippets in
 # docstrings.

src/base_tester.py

@@ -30,7 +30,7 @@
 
 
 console = Console()
-print = console.print
+print = console.print  # skipcq: PYL-W0603
 
 
 class BaseTester(BaseTrainer):
@@ -55,7 +55,7 @@ def __init__(
         _loss = training_loss
         self._gui = gui
         global print  # skipcq: PYL-W0603
-        print = self._gui.print
+        print = self._gui.print  # skipcq: PYL-W0603
         self._run_name = run_name
         self._model = model
         if model_ckpt_path is None:
@@ -83,7 +83,8 @@ def _test_iteration(
         batch: Union[Tuple, List, Tensor],
     ) -> Tuple[Tensor, Dict[str, Tensor]]:
         """Evaluation procedure for one batch. We want to keep the code DRY and avoid
-        making mistakes, so this code calls the BaseTrainer._train_val_iteration() method.
+        making mistakes, so this code calls the BaseTrainer._train_val_iteration()
+        method.
         Args:
             batch: The batch to process.
         Returns:
@@ -99,15 +100,16 @@ async def test(
     ) -> None:
         """Computes the average loss on the test set.
         Args:
-            visualize_every (int, optional): Visualize the model predictions every n batches.
+            visualize_every (int, optional): Visualize the model predictions every n
+            batches.
             Defaults to 0 (no visualization).
         """
         _ = kwargs
         test_loss: MeanMetric = MeanMetric()
         test_metrics: Dict[str, MeanMetric] = defaultdict(MeanMetric)
         self._model.eval()
         print(Text(f"[*] Testing {self._run_name}", style="bold green"))
-        """ ==================== Training loop for one epoch ==================== """
+        # ==================== Training loop for one epoch ====================
         pbar, update_loss_hook = self._gui.track_testing(
             self._data_loader, total=len(self._data_loader)
         )
@@ -124,7 +126,8 @@ async def test(
             if visualize_every > 0 and (i + 1) % visualize_every == 0:
                 self._visualize(batch, i)
 
-        # TODO: Report metrics in a special panel? Then hang the GUI until the user is done.
+        # TODO: Report metrics in a special panel? Then hang the GUI until the user is
+        # done.
         print("=" * 81)
         print("==" + " " * 31 + " Test results " + " " * 31 + "==")
         print("=" * 81)

src/base_trainer.py

@@ -34,7 +34,7 @@
 from utils.training import visualize_model_predictions
 
 console = Console()
-print = console.print
+print = console.print  # skipcq: PYL-W0603
 
 
 class BaseTrainer:
@@ -77,7 +77,7 @@ def __init__(
         self._n_ctrl_c = 0
         self._gui = gui
         global print  # skipcq: PYL-W0603
-        print = self._gui.print
+        print = self._gui.print  # skipcq: PYL-W0603
         if model_ckpt_path is not None:
             self._load_checkpoint(model_ckpt_path)
         signal.signal(signal.SIGINT, self._terminator)
@@ -104,8 +104,9 @@ def _train_val_iteration(
         epoch: int,
         validation: bool = False,
     ) -> Tuple[Tensor, Dict[str, Tensor]]:
-        """Training or validation procedure for one batch. We want to keep the code DRY and avoid
-        making mistakes, so write this code only once at the cost of many function calls!
+        """Training or validation procedure for one batch. We want to keep the code DRY
+        and avoid making mistakes, so write this code only once at the cost of many
+        function calls!
         Args:
             batch: The batch to process.
         Returns:
@@ -136,7 +137,7 @@ def _train_epoch(
         epoch_loss: MeanMetric = MeanMetric()
         epoch_loss_components: Dict[str, MeanMetric] = defaultdict(MeanMetric)
         has_visualized = 0
-        """ ==================== Training loop for one epoch ==================== """
+        # ==================== Training loop for one epoch ====================
         pbar, update_loss_hook = self._gui.track_training(
             self._train_loader,
             total=len(self._train_loader),
@@ -188,7 +189,7 @@ def _val_epoch(self, description: str, visualize: bool, epoch: int) -> float:
             float: Average validation loss for the epoch.
         """
         has_visualized = 0
-        """ ==================== Validation loop for one epoch ==================== """
+        # ==================== Validation loop for one epoch ====================
         with torch.no_grad():
             val_loss: MeanMetric = MeanMetric()
             val_loss_components: Dict[str, MeanMetric] = defaultdict(MeanMetric)
@@ -212,7 +213,7 @@ def _val_epoch(self, description: str, visualize: bool, epoch: int) -> float:
                 for k, v in loss_components.items():
                     val_loss_components[k].update(v.item())
                 update_loss_hook(val_loss.compute())
-                """ ==================== Visualization ==================== """
+                # ==================== Visualization ====================
                 if (
                     visualize
                     and has_visualized < self._viz_n_samples
@@ -235,8 +236,8 @@ def _val_epoch(self, description: str, visualize: bool, epoch: int) -> float:
                     },
                     step=epoch,
                 )
-            # Set minimize_metric to a key in val_loss_components if you wish to minimize
-            # a specific metric instead of the validation loss:
+            # Set minimize_metric to a key in val_loss_components if you wish to
+            # minimize a specific metric instead of the validation loss:
             self._model_saver(
                 epoch,
                 mean_val_loss,
@@ -268,7 +269,7 @@ async def train(
         )
         self._viz_n_samples = visualize_n_samples
         self._gui.set_start_epoch(self._epoch)
-        """ ==================== Training loop ==================== """
+        # ==================== Training loop ====================
         last_val_loss = float("inf")
         for epoch in range(self._epoch, epochs):
             print(f"Epoch: {epoch}")
@@ -294,7 +295,7 @@ async def train(
                 last_val_loss = val_loss
             if self._scheduler is not None:
                 await asyncio.to_thread(self._scheduler.step)
-            """ ==================== Plotting ==================== """
+            # ==================== Plotting ====================
             self._gui.plot(epoch, train_loss, last_val_loss)  # , self._model_saver)
         await asyncio.to_thread(
             self._save_checkpoint,
@@ -333,18 +334,19 @@ def _save_checkpoint(self, val_loss: float, ckpt_path: str, **kwargs) -> None:
         )
 
     def _load_checkpoint(self, ckpt_path: str, model_only: bool = False) -> None:
-        """Loads the model and optimizer state from a checkpoint file. This method should remain in
-        this class because it should be extendable in classes inheriting from this class, instead
-        of being overwritten/modified. That would be a source of bugs and a bad practice.
+        """Loads the model and optimizer state from a checkpoint file. This method
+        should remain in this class because it should be extendable in classes
+        inheriting from this class, instead of being overwritten/modified. That would be
+        a source of bugs and a bad practice.
         Args:
             ckpt_path (str): The path to the checkpoint file.
-            model_only (bool): If True, only the model is loaded (useful for BaseTester).
+            model_only (bool): If True, only the model is loaded (useful for
+            BaseTester).
         Returns:
             None
         """
         print(f"[*] Restoring from checkpoint: {ckpt_path}")
         ckpt = torch.load(ckpt_path)
-        # If the model was optimized with torch.optimize() we need to remove the "_orig_mod"
         # prefix:
         if "_orig_mod" in list(ckpt["model_ckpt"].keys())[0]:
             ckpt["model_ckpt"] = {
@@ -355,7 +357,8 @@ def _load_checkpoint(self, ckpt_path: str, model_only: bool = False) -> None:
         except Exception:
             if project_conf.PARTIALLY_LOAD_MODEL_IF_NO_FULL_MATCH:
                 print(
-                    "[!] Partially loading model weights (no full match between model and checkpoint)"
+                    "[!] Partially loading model weights "
+                    + "(no full match between model and checkpoint)"
                 )
                 self._model.load_state_dict(ckpt["model_ckpt"], strict=False)
         if not model_only:
@@ -378,7 +381,8 @@ def _terminator(self, sig, frame):
             and self._n_ctrl_c == 0
         ):
             print(
-                f"[!] SIGINT received. Waiting for epoch to end for {self._run_name}. Press Ctrl+C again to abort."
+                f"[!] SIGINT received. Waiting for epoch to end for {self._run_name}."
+                + " Press Ctrl+C again to abort."
             )
             self._n_ctrl_c += 1
         elif (

utils/__init__.py

@@ -13,7 +13,7 @@
 
 # import sys
 import traceback
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional
 
 # import IPython
 import numpy as np

utils/gui.py

@@ -63,7 +63,12 @@ def __init__(
             classes: The CSS classes of the plotter widget.
             disabled: Whether the plotter widget is disabled or not.
         """
-        super().__init__(name=name, id=id, classes=classes, disabled=disabled)
+        super().__init__(
+            name=name,
+            id=id,
+            classes=classes,
+            disabled=disabled,
+        )
         self._title = title
         self._log_scale = use_log_scale
         self._train_losses: list[float] = []
@@ -171,8 +176,9 @@ def __init__(self):
 
             def update_loss_hook(self, loss: float) -> None:
                 """Update the loss value in the progress bar."""
-                # TODO: min_val_loss during validation, val_loss during training. Ideally the
-                # second parameter would be super flexible (use a dict then).
+                # TODO: min_val_loss during validation, val_loss during training.
+                # Ideally the second parameter would be super flexible (use a dict
+                # then).
                 self._loss = loss
 
         class SeqWrapper(abc.Iterator, LossHook):
@@ -235,24 +241,11 @@ def reset_hook():
             plabel.update(self.DESCRIPTIONS[Task.IDLE])
 
         wrapper = None
-        update_p, reset_p = (
-            partial(update_hook),
-            partial(reset_hook),
-        )
+        update_p, reset_p = partial(update_hook), partial(reset_hook)
         if isinstance(iterable, abc.Sequence):
-            wrapper = SeqWrapper(
-                iterable,
-                total,
-                update_p,
-                reset_p,
-            )
+            wrapper = SeqWrapper(iterable, total, update_p, reset_p)
         elif isinstance(iterable, (abc.Iterator, DataLoader)):
-            wrapper = IteratorWrapper(
-                iterable,
-                total,
-                update_p,
-                reset_p,
-            )
+            wrapper = IteratorWrapper(iterable, total, update_p, reset_p)
         else:
             raise ValueError(
                 f"iterable must be a Sequence or an Iterator, got {type(iterable)}"
@@ -264,7 +257,9 @@ def reset_hook():
 
 
 class GUI(App):
-    """A Textual app to serve as *useful* GUI/TUI for my pytorch-based micro framework."""
+    """
+    A Textual app to serve as *useful* GUI/TUI for my pytorch-based micro framework.
+    """
 
     TITLE = "Matchbox TUI"
     CSS_PATH = "style.css"
@@ -327,11 +322,7 @@ def print(self, message: Any):
         if isinstance(message, (RenderableType, str)):
             logger.write(
                 Group(
-                    Text(
-                        datetime.now().strftime("[%H:%M] "),
-                        style="dim cyan",
-                        end="",
-                    ),
+                    Text(datetime.now().strftime("[%H:%M] "), style="dim cyan", end=""),
                     message,
                 ),
             )
@@ -379,28 +370,31 @@ def print(self, message: Any):
                     )
 
     def track_training(self, iterable, total: int) -> Tuple[Iterable, Callable]:
-        """Return an iterable that tracks the progress of the training process, and a progress bar
-        hook to update the loss value at each iteration."""
+        """Return an iterable that tracks the progress of the training process, and a
+        progress bar hook to update the loss value at each iteration."""
         return self.query_one(DatasetProgressBar).track_iterable(
             iterable, Task.TRAINING, total
         )
 
     def track_validation(self, iterable, total: int) -> Tuple[Iterable, Callable]:
-        """Return an iterable that tracks the progress of the validation process, and a progress bar
-        hook to update the loss value at each iteration."""
+        """Return an iterable that tracks the progress of the validation process, and a
+        progress bar hook to update the loss value at each iteration."""
         return self.query_one(DatasetProgressBar).track_iterable(
             iterable, Task.VALIDATION, total
         )
 
     def track_testing(self, iterable, total: int) -> Tuple[Iterable, Callable]:
-        """Return an iterable that tracks the progress of the testing process, and a progress bar
-        hook to update the loss value at each iteration."""
+        """Return an iterable that tracks the progress of the testing process, and a
+        progress bar hook to update the loss value at each iteration."""
         return self.query_one(DatasetProgressBar).track_iterable(
             iterable, Task.TESTING, total
         )
 
     def plot(
-        self, epoch: int, train_loss: float, val_loss: Optional[float] = None
+        self,
+        epoch: int,
+        train_loss: float,
+        val_loss: Optional[float] = None,
     ) -> None:
         """Plot the training and validation losses for the current epoch."""
         self.query_one(PlotterWidget).loading = False
@@ -432,8 +426,8 @@ async def run_my_app():
         await asyncio.sleep(0.1)
     await asyncio.sleep(2)
     mnist = MNIST(root="data", train=False, download=True, transform=to_tensor)
-    # Somehow, the dataloader will crash if it's not forked when using multiprocessing along with
-    # Textual.
+    # Somehow, the dataloader will crash if it's not forked when using multiprocessing
+    # along with Textual.
     mp.set_start_method("fork")
     dataloader = DataLoader(mnist, 32, shuffle=True, num_workers=2)
     pbar, update_progress_loss = gui.track_validation(dataloader, len(dataloader))
@@ -444,7 +438,8 @@ async def run_my_app():
             update_progress_loss(random())
             gui.plot(epoch=i, train_loss=random(), val_loss=random())
             gui.print(
-                f"[{i+1}/{len(dataloader)}]: We can also iterate over PyTorch dataloaders!"
+                f"[{i+1}/{len(dataloader)}]: "
+                + "We can also iterate over PyTorch dataloaders!"
             )
         if i == 0:
             gui.print(batch)