build(deps): bump ruff from 0.1.14 to 0.4.10

.github/collect_env.py

@@ -168,14 +168,13 @@ def get_nvidia_smi():
 def get_platform():
     if sys.platform.startswith("linux"):
         return "linux"
-    elif sys.platform.startswith("win32"):
+    if sys.platform.startswith("win32"):
         return "win32"
-    elif sys.platform.startswith("cygwin"):
+    if sys.platform.startswith("cygwin"):
         return "cygwin"
-    elif sys.platform.startswith("darwin"):
+    if sys.platform.startswith("darwin"):
         return "darwin"
-    else:
-        return sys.platform
+    return sys.platform
 
 
 def get_mac_version(run_lambda):

.github/labeler.yml

@@ -60,4 +60,10 @@
 
 'topic: build':
 - changed-files:
-  - any-glob-to-any-file: setup.py
+  - any-glob-to-any-file:
+    - setup.py
+    - pyproject.toml
+
+'topic: style':
+- changed-files:
+  - any-glob-to-any-file: .pre-commit-config.yaml

.github/verify_labels.py

@@ -82,9 +82,7 @@ def parse_args():
     )
 
     parser.add_argument("pr", type=int, help="PR number")
-    args = parser.parse_args()
-
-    return args
+    return parser.parse_args()
 
 
 if __name__ == "__main__":

.github/workflows/style.yml

@@ -21,7 +21,8 @@ jobs:
           architecture: x64
       - name: Run ruff
         run: |
-          pip install ruff==0.1.14
+          python -m pip install --upgrade uv
+          uv pip install --system -e '.[quality]'
           ruff --version
           ruff check --diff .
 
@@ -44,10 +45,8 @@ jobs:
           key: ${{ runner.os }}-python-${{ matrix.python }}-${{ hashFiles('pyproject.toml') }}-quality
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          pip install -e '.[quality]'
-      - name: Run mypy
-        run: |
+          python -m pip install --upgrade uv
+          uv pip install --system -e '.[quality]'
           mypy --version
           mypy
 
@@ -65,7 +64,8 @@ jobs:
           architecture: x64
       - name: Run ruff
         run: |
-          pip install ruff==0.1.14
+          python -m pip install --upgrade uv
+          uv pip install --system -e '.[quality]'
           ruff --version
           ruff format --check --diff .
 
@@ -83,7 +83,8 @@ jobs:
           architecture: x64
       - name: Run ruff
         run: |
-          pip install pre-commit
+          python -m pip install --upgrade uv
+          uv pip install --system -e '.[quality]'
           git checkout -b temp
           pre-commit install
           pre-commit --version

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
         args: ['--branch', 'main']
       - id: trailing-whitespace
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.1.14'
+    rev: 'v0.4.10'
     hooks:
       - id: ruff
         args:

Makefile

@@ -7,7 +7,7 @@ quality:
 # this target runs checks on all files and potentially modifies some of them
 style:
 	ruff format .
-	ruff --fix .
+	ruff check --fix .
 
 # Run tests for the library
 test:

api/app/routes/classification.py

@@ -13,7 +13,7 @@
 
 
 @router.post("/", status_code=status.HTTP_200_OK, summary="Perform image classification")
-async def classify(file: UploadFile = File(...)) -> ClsCandidate:
+def classify(file: UploadFile = File(...)) -> ClsCandidate:
     """Runs holocron vision model to analyze the input image"""
     probs = classify_image(decode_image(file.file.read()))
 

docs/source/conf.py

@@ -220,7 +220,7 @@ def inject_checkpoint_metadata(app, what, name, obj, options, lines):
             table.extend((
                 ("url", f"`link <{meta.url}>`__"),
                 ("sha256", meta.sha256[:16]),
-                ("size", f"{meta.size / 1024 ** 2:.1f}MB"),
+                ("size", f"{meta.size / 1024**2:.1f}MB"),
                 ("num_params", f"{meta.num_params / 1000000.0:.1f}M"),
             ))
             # Wrap the text

holocron/models/classification/sknet.py

@@ -110,9 +110,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         b, m, c = paths.shape[:3]
         z = self.sa(paths.sum(dim=1)).view(b, m, c, 1, 1)
         attention_factors = torch.softmax(z, dim=1)
-        out = (attention_factors * paths).sum(dim=1)
-
-        return out
+        return (attention_factors * paths).sum(dim=1)
 
 
 class SKBottleneck(_ResBlock):

holocron/models/classification/tridentnet.py

@@ -41,7 +41,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         dilations = [1] * self.num_branches if self.dilation[0] == 1 else [1 + idx for idx in range(self.num_branches)]
 
         # Use shared weight to apply the convolution
-        out = torch.cat(
+        return torch.cat(
             [
                 F.conv2d(
                     _x,
@@ -57,8 +57,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             1,
         )
 
-        return out
-
 
 class Tridentneck(_ResBlock):
     expansion: int = 4

holocron/models/detection/yolo.py

@@ -339,9 +339,7 @@ def _format_outputs(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
     def _forward(self, x: Tensor) -> Tensor:
         out = self.backbone(x)
         out = self.block4(out)
-        out = self.classifier(out)
-
-        return out
+        return self.classifier(out)
 
     def forward(
         self, x: Tensor, target: Optional[List[Dict[str, Tensor]]] = None

holocron/models/detection/yolov2.py

@@ -208,9 +208,7 @@ def _forward(self, x: Tensor) -> Tensor:
         out = torch.cat((passthrough, out), 1)
         out = self.block6(out)
 
-        out = self.head(out)
-
-        return out
+        return self.head(out)
 
     def forward(
         self, x: Union[Tensor, List[Tensor], Tuple[Tensor, ...]], target: Optional[List[Dict[str, Tensor]]] = None

holocron/models/detection/yolov4.py

@@ -628,15 +628,14 @@ def forward(
         y3 = self.yolo3(o3, target)
 
         if not self.training:
-            detections = [
+            return [
                 {
                     "boxes": torch.cat((det1["boxes"], det2["boxes"], det3["boxes"]), dim=0),
                     "scores": torch.cat((det1["scores"], det2["scores"], det3["scores"]), dim=0),
                     "labels": torch.cat((det1["labels"], det2["labels"], det3["labels"]), dim=0),
                 }
                 for det1, det2, det3 in zip(y1, y2, y3)
             ]
-            return detections
 
         return {k: y1[k] + y2[k] + y3[k] for k in y1}
 

holocron/models/segmentation/unet.py

@@ -223,8 +223,7 @@ def forward(self, x: Tensor) -> Tensor:
             x = decoder(xs.pop(), x)
 
         # Classifier
-        x = self.classifier(x)
-        return x
+        return self.classifier(x)
 
 
 class UBlock(nn.Module):
@@ -368,8 +367,7 @@ def forward(self, x: Tensor) -> Tensor:
             x = self.upsample(x)
 
         # Classifier
-        x = self.classifier(x)
-        return x
+        return self.classifier(x)
 
 
 def _unet(arch: str, pretrained: bool, progress: bool, **kwargs: Any) -> UNet:

holocron/models/segmentation/unet3p.py

@@ -155,8 +155,7 @@ def forward(self, x: Tensor) -> Tensor:
             xs[idx] = self.decoder[idx](xs[:idx], xs[idx], xs[idx + 1 :])
 
         # Classifier
-        x = self.classifier(xs[0])
-        return x
+        return self.classifier(xs[0])
 
 
 def _unet(arch: str, pretrained: bool, progress: bool, **kwargs: Any) -> nn.Module:

holocron/nn/functional.py

@@ -133,9 +133,7 @@ def concat_downsample2d(x: Tensor, scale_factor: int) -> Tensor:
     # N * C * H * W --> N * C * (H/scale_factor) * scale_factor * (W/scale_factor) * scale_factor
     x = x.view(b, c, h // scale_factor, scale_factor, w // scale_factor, scale_factor)
     x = x.permute(0, 3, 5, 1, 2, 4).contiguous()
-    x = x.view(b, int(c * scale_factor**2), h // scale_factor, w // scale_factor)
-
-    return x
+    return x.view(b, int(c * scale_factor**2), h // scale_factor, w // scale_factor)
 
 
 def z_pool(x: Tensor, dim: int) -> Tensor:
@@ -364,9 +362,7 @@ def _xcorr2d(
     h = floor((h + (2 * padding[0]) - (dilation[0] * (weight.shape[-2] - 1)) - 1) / stride[0] + 1)
     w = floor((w + (2 * padding[1]) - (dilation[1] * (weight.shape[-1] - 1)) - 1) / stride[1] + 1)
 
-    x = x.view(-1, weight.shape[0], h, w)
-
-    return x
+    return x.view(-1, weight.shape[0], h, w)
 
 
 def _convNd(x: Tensor, weight: Tensor) -> Tensor:

holocron/nn/modules/activation.py

@@ -22,8 +22,7 @@ def __init__(self, inplace: bool = False) -> None:
         self.inplace = inplace
 
     def extra_repr(self) -> str:
-        inplace_str = "inplace=True" if self.inplace else ""
-        return inplace_str
+        return "inplace=True" if self.inplace else ""
 
 
 class HardMish(_Activation):
@@ -80,5 +79,4 @@ def __init__(self, in_channels: int, kernel_size: int = 3) -> None:
     def forward(self, x: Tensor) -> Tensor:
         out = self.conv(x)
         out = self.bn(out)
-        x = torch.max(x, out)
-        return x
+        return torch.max(x, out)

holocron/nn/modules/attention.py

@@ -74,5 +74,4 @@ def forward(self, x: Tensor) -> Tensor:
         x_h = cast(Tensor, self.h_branch(x))
         x_w = cast(Tensor, self.w_branch(x))
 
-        out = (x_c + x_h + x_w) / 3
-        return out
+        return (x_c + x_h + x_w) / 3

holocron/nn/modules/conv.py

@@ -496,6 +496,4 @@ def forward(self, x: Tensor) -> Tensor:
 
         # Multiply-Add operation
         # --> (N, C, H // s, W // s)
-        out = (kernel * x_unfolded).sum(dim=3).view(*x.shape[:2], *kernel.shape[-2:])
-
-        return out
+        return (kernel * x_unfolded).sum(dim=3).view(*x.shape[:2], *kernel.shape[-2:])

holocron/nn/modules/downsample.py

@@ -74,8 +74,7 @@ def forward(self, x: Tensor) -> Tensor:
         return x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)
 
     def extra_repr(self) -> str:
-        inplace_str = "flatten=True" if self.flatten else ""
-        return inplace_str
+        return "flatten=True" if self.flatten else ""
 
 
 class GlobalMaxPool2d(nn.Module):
@@ -97,13 +96,11 @@ def forward(self, x: Tensor) -> Tensor:
         return x.view(x.size(0), x.size(1), -1).max(-1).values.view(x.size(0), x.size(1), 1, 1)
 
     def extra_repr(self) -> str:
-        inplace_str = "flatten=True" if self.flatten else ""
-        return inplace_str
+        return "flatten=True" if self.flatten else ""
 
 
 def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1) -> int:
-    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
-    return padding
+    return ((stride - 1) + dilation * (kernel_size - 1)) // 2
 
 
 class BlurPool2d(nn.Module):

holocron/nn/modules/lambda_layer.py

@@ -105,5 +105,4 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         Y = Yc + Yp
         # B x (H * W) x num_heads x dim_v -> B x (num_heads * dim_v) x H x W
-        out = Y.permute(0, 2, 3, 1).reshape(b, self.num_heads * v.shape[2], h, w)
-        return out
+        return Y.permute(0, 2, 3, 1).reshape(b, self.num_heads * v.shape[2], h, w)

holocron/trainer/core.py

@@ -362,8 +362,7 @@ def find_lr(
             if torch.isnan(batch_loss) or torch.isinf(batch_loss):
                 if batch_idx == 0:
                     raise ValueError("loss value is NaN or inf.")
-                else:
-                    break
+                break
             self.loss_recorder.append(batch_loss.item())
             # Stop after the number of iterations
             if batch_idx + 1 == num_it:

holocron/transforms/interpolation.py

@@ -87,14 +87,13 @@ def get_params(self, image: Union[Image.Image, torch.Tensor]) -> Tuple[int, int]
     def forward(self, image: Union[Image.Image, torch.Tensor]) -> Union[Image.Image, torch.Tensor]:
         if self.mode == ResizeMethod.SQUISH:
             return super().forward(image)
-        else:
-            h, w = self.get_params(image)
-            img = resize(image, (h, w), self.interpolation)
-            # get the padding
-            h_pad, w_pad = self.size[0] - h, self.size[1] - w
-            _padding = w_pad // 2, h_pad // 2, w_pad - w_pad // 2, h_pad - h_pad // 2
-            # Fill the rest up to target_size
-            return pad(img, _padding, padding_mode=self.pad_mode)
+        h, w = self.get_params(image)
+        img = resize(image, (h, w), self.interpolation)
+        # get the padding
+        h_pad, w_pad = self.size[0] - h, self.size[1] - w
+        _padding = w_pad // 2, h_pad // 2, w_pad - w_pad // 2, h_pad - h_pad // 2
+        # Fill the rest up to target_size
+        return pad(img, _padding, padding_mode=self.pad_mode)
 
 
 class RandomZoomOut(nn.Module):