apply precommit

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+default_language_version:
+    python: python3.13
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: check-added-large-files
+        args: ["--maxkb=512"]
+      - id: check-yaml
+      - id: check-toml
+      - id: check-docstring-first
+
+  - repo: local
+    hooks:
+      - id: ruff-format
+        name: ruff format
+        entry: uv run ruff format .
+        language: system
+        types: [python]
+
+      - id: ruff-check
+        name: ruff check
+        entry: uv run ruff check --fix .
+        language: system
+        types: [python]
+
+      - id: pytest
+        name: pytest
+        entry: uv run pytest tests/ -v
+        language: system
+        pass_filenames: false
+        stages: [pre-push]

quickdraw_bot/prepare.py

@@ -21,11 +21,7 @@ class Prepare:
         np.random.seed(self.config['random_seed'])
 
     def _load_dataset(self) -> dict[str, np.ndarray]:
-        data: dict[str, list] = {
+        data: dict[str, list] = {'images': [], 'cate_names': [], 'cate_ids': []}
-            'images': [],
-            'cate_names': [],
-            'cate_ids': []
-        }
         cls_id_map: dict[str, int] = {}
         raw_data_dir = Path(self.config['data_dir']) / 'raw'
         for npy_file in sorted(raw_data_dir.glob('*.npy')):

quickdraw_bot/train.py

@@ -52,23 +52,15 @@ class Train:
             shuffle=True,
             num_workers=4,
             pin_memory=False,  # not support for mps
-            persistent_workers=True
+            persistent_workers=True,
         )
         valid_dataloader = DataLoader(
-            valid_dataset,
+            valid_dataset, batch_size=self.config['batch_size'], shuffle=False, num_workers=1, pin_memory=False, persistent_workers=True
-            batch_size=self.config['batch_size'],
-            shuffle=False,
-            num_workers=1,
-            pin_memory=False,
-            persistent_workers=True
         )
         return train_dataloader, valid_dataloader
 
     def _get_model(self) -> torch.nn.Module:
-        model = BabyCNN(
+        model = BabyCNN(num_classes=self.config['num_of_class'], dropout_p=0.3).to(self._device)
-            num_classes=self.config['num_of_class'],
-            dropout_p=0.3
-        ).to(self._device)
         model.train()
         return model
 
@@ -89,18 +81,18 @@ class Train:
         return scheduler
 
     def _get_loss(self) -> torch.nn.modules.loss._Loss:
-        loss = torch.nn.CrossEntropyLoss(
+        loss = torch.nn.CrossEntropyLoss(label_smoothing=0.1).to(self._device)
-            label_smoothing=0.1
-        ).to(self._device)
         return loss
 
     def _get_metrics(self) -> tuple[MetricCollection, ConfusionMatrix]:
-        metric_collection = MetricCollection([
+        metric_collection = MetricCollection(
-            Accuracy(task='multiclass', num_classes=self.config['num_of_class'], top_k=1),
+            [
-            Precision(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+                Accuracy(task='multiclass', num_classes=self.config['num_of_class'], top_k=1),
-            Recall(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+                Precision(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
-            F1Score(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+                Recall(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
-        ]).to(self._device)
+                F1Score(task='multiclass', num_classes=self.config['num_of_class'], average='macro'),
+            ]
+        ).to(self._device)
         confusion_matrix = ConfusionMatrix(
             task='multiclass',
             threshold=0.5,
@@ -116,16 +108,11 @@ class Train:
         loss = self._get_loss()
         metrics, _ = self._get_metrics()
 
-        with Live(
+        with Live(dir='./doc/exp/train', report='html', dvcyaml='./assets/dvc.yaml', exp_message=self.config['exp_msg']) as live:
-            dir='./doc/exp/train',
-            report='html',
-            dvcyaml='./assets/dvc.yaml',
-            exp_message=self.config['exp_msg']) as live:
-
             for epoch in tqdm(range(self.config['num_of_epochs']), desc='Training Epoch'):
                 metrics.reset()
                 model.train()
-                total_train_loss = 0.
+                total_train_loss = 0.0
                 for inputs, targets in train_dataloader:
                     inputs = inputs.to(self._device)
                     targets = targets.to(self._device)
@@ -141,7 +128,7 @@ class Train:
 
                 metrics.reset()
                 model.eval()
-                total_valid_loss = 0.
+                total_valid_loss = 0.0
                 with torch.no_grad():
                     for inputs, targets in valid_dataloader:
                         inputs = inputs.to(self._device)

quickdraw_bot/utils/dataset.py

@@ -11,14 +11,15 @@ from torchvision.transforms import v2
 
 
 class QuickDrawDataset(torch.utils.data.Dataset):
-    def __init__(self,
+    def __init__(
-                 data_npz_path: str,
+        self,
-                 image_shape: tuple[int, int, int] = (1, 28, 28),
+        data_npz_path: str,
-                 enable_data_aug: bool = False,
+        image_shape: tuple[int, int, int] = (1, 28, 28),
-                 file_lazy_load: bool = False,
+        enable_data_aug: bool = False,
-                 return_cate_name: bool = False,
+        file_lazy_load: bool = False,
-                 vis_dir: str = None,
+        return_cate_name: bool = False,
-                ) -> None:
+        vis_dir: str = None,
+    ) -> None:
         super().__init__()
         self._images: torch.Tensor | np.ndarray = []
         self._cate_names: list[str] = []
@@ -43,13 +44,15 @@ class QuickDrawDataset(torch.utils.data.Dataset):
                 v2.RandomApply([v2.RandomAffine(degrees=(-30, 30), translate=(0.2, 0.2), scale=(0.8, 1.2), shear=(-10, 10))], p=0.5),
                 v2.RandomPerspective(distortion_scale=0.15, p=0.2),
                 v2.RandomApply([v2.ElasticTransform(alpha=15.0, sigma=3.0)], p=0.2),
-                v2.RandomErasing(p=0.2, scale=(0.02, 0.2))
+                v2.RandomErasing(p=0.2, scale=(0.02, 0.2)),
             ]
-        self._transform = v2.Compose([
+        self._transform = v2.Compose(
-            *aug_pipeline,
+            [
-            v2.Resize(self._image_shape[1:]),
+                *aug_pipeline,
-            v2.ToDtype(torch.float32, scale=True),
+                v2.Resize(self._image_shape[1:]),
-        ])
+                v2.ToDtype(torch.float32, scale=True),
+            ]
+        )
 
     def _collect_data(self) -> None:
         if self._file_lazy_load:

quickdraw_bot/utils/model.py

@@ -9,9 +9,7 @@ import torch.nn.functional as F
 
 
 class BabyCNN(nn.Module):
-    def __init__(self,
+    def __init__(self, num_classes: int = 10, dropout_p: float = 0.5) -> None:
-                 num_classes: int = 10,
-                 dropout_p: float = 0.5) -> None:
         super().__init__()
 
         # Conv Block 1: 28x28 -> 14x14