Add Ruff for formatting and linting

.github/workflows/tests.yml

@@ -12,8 +12,27 @@ on:
 
 jobs:
 
+  style:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install ruff==0.4.6
+      # Update output format to enable automatic inline annotations.
+      - name: Run Ruff Linter
+        run: ruff check --output-format=github
+      - name: Run Ruff Formatter
+        run: ruff format --check
+
   test:
     runs-on: ubuntu-latest
+    needs: [style]
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
@@ -25,3 +44,6 @@ jobs:
         python -m pip install --upgrade pip
         pip install torch==1.9.0+cpu torchvision==0.10.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
         make install_dev
+    - name: Test with pytest
+      run: make test
+

.gitignore

@@ -105,4 +105,7 @@ venv.bak/
 /site
 
 # mypy
-.mypy_cache/
+.mypy_cache/
+
+# ruff
+.ruff_cache/

Makefile

@@ -4,8 +4,7 @@
 	python3 -m venv .venv
 
 install_dev: .venv
-	.venv/bin/pip install -e .[test]
-	.venv/bin/pre-commit install
+	.venv/bin/pip install -e ".[test]"
 
 test: .venv
 	.venv/bin/pytest -p no:cacheprovider tests/
@@ -16,7 +15,9 @@ table:
 table_timm:
 	.venv/bin/python misc/generate_table_timm.py
 
-precommit: install_dev
-	.venv/bin/pre-commit run --all-files
+fixup:
+	.venv/bin/ruff check --fix
+	.venv/bin/ruff format
+
+all: fixup test
 
-all: precommit test

README.md

@@ -478,7 +478,7 @@ make install_dev  # create .venv, install SMP in dev mode
 #### Run tests and code checks
 
 ```bash
-make all          # run precommit, tests
+make fixup         # Ruff for formatting and lint checks
 ```
 
 #### Update table with encoders  

docs/conf.py

@@ -14,10 +14,9 @@
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
 
-import os
-import re
 import sys
 import datetime
+import sphinx_rtd_theme
 
 sys.path.append("..")
 
@@ -68,14 +67,11 @@ def get_version():
 # a list of builtin themes.
 #
 
-import sphinx_rtd_theme
-
 html_theme = "sphinx_rtd_theme"
 html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 
 # import karma_sphinx_theme
 # html_theme = "karma_sphinx_theme"
-import faculty_sphinx_theme
 
 html_theme = "faculty_sphinx_theme"
 

misc/generate_table.py

@@ -4,19 +4,17 @@
 
 
 WIDTH = 32
-COLUMNS = [
-    "Encoder",
-    "Weights",
-    "Params, M",
-]
+COLUMNS = ["Encoder", "Weights", "Params, M"]
 
 
 def wrap_row(r):
     return "|{}|".format(r)
 
 
 header = "|".join([column.ljust(WIDTH, " ") for column in COLUMNS])
-separator = "|".join(["-" * WIDTH] + [":" + "-" * (WIDTH - 2) + ":"] * (len(COLUMNS) - 1))
+separator = "|".join(
+    ["-" * WIDTH] + [":" + "-" * (WIDTH - 2) + ":"] * (len(COLUMNS) - 1)
+)
 
 print(wrap_row(header))
 print(wrap_row(separator))

misc/generate_table_timm.py

@@ -24,20 +24,29 @@ def make_table(data):
 
     l1 = "+" + "-" * max_len1 + "+" + "-" * max_len2 + "+\n"
     l2 = "+" + "=" * max_len1 + "+" + "=" * max_len2 + "+\n"
-    top = "| " + "Encoder name".ljust(max_len1 - 2) + " | " + "Support dilation".center(max_len2 - 2) + " |\n"
+    top = (
+        "| "
+        + "Encoder name".ljust(max_len1 - 2)
+        + " | "
+        + "Support dilation".center(max_len2 - 2)
+        + " |\n"
+    )
 
     table = l1 + top + l2
 
     for k in sorted(data.keys()):
-        support = "✅".center(max_len2 - 3) if data[k]["has_dilation"] else " ".center(max_len2 - 2)
+        support = (
+            "✅".center(max_len2 - 3)
+            if data[k]["has_dilation"]
+            else " ".center(max_len2 - 2)
+        )
         table += "| " + k.ljust(max_len1 - 2) + " | " + support + " |\n"
         table += l1
 
     return table
 
 
 if __name__ == "__main__":
-
     supported_models = {}
 
     with tqdm(timm.list_models()) as names:

pyproject.toml

@@ -1,19 +0,0 @@
-[tool.black]
-line-length = 119
-target-version = ['py37', 'py38']
-include = '\.pyi?$'
-exclude = '''
-/(
-    \.eggs
-  | \.git
-  | \.hg
-  | \.mypy_cache
-  | \.tox
-  | \.venv
-  | docs
-  | _build
-  | buck-out
-  | build
-  | dist
-)/
-'''

segmentation_models_pytorch/__init__.py

@@ -50,8 +50,7 @@ def create_model(
     except KeyError:
         raise KeyError(
             "Wrong architecture type `{}`. Available options are: {}".format(
-                arch,
-                list(archs_dict.keys()),
+                arch, list(archs_dict.keys())
             )
         )
     return model_class(
@@ -61,3 +60,24 @@ def create_model(
         classes=classes,
         **kwargs,
     )
+
+
+__all__ = [
+    "datasets",
+    "encoders",
+    "decoders",
+    "losses",
+    "metrics",
+    "Unet",
+    "UnetPlusPlus",
+    "MAnet",
+    "Linknet",
+    "FPN",
+    "PSPNet",
+    "DeepLabV3",
+    "DeepLabV3Plus",
+    "PAN",
+    "from_pretrained",
+    "create_model",
+    "__version__",
+]

segmentation_models_pytorch/base/__init__.py

@@ -1,11 +1,13 @@
 from .model import SegmentationModel
 
-from .modules import (
-    Conv2dReLU,
-    Attention,
-)
+from .modules import Conv2dReLU, Attention
 
-from .heads import (
-    SegmentationHead,
-    ClassificationHead,
-)
+from .heads import SegmentationHead, ClassificationHead
+
+__all__ = [
+    "SegmentationModel",
+    "Conv2dReLU",
+    "Attention",
+    "SegmentationHead",
+    "ClassificationHead",
+]

segmentation_models_pytorch/base/heads.py

@@ -3,17 +3,29 @@
 
 
 class SegmentationHead(nn.Sequential):
-    def __init__(self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1):
-        conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
-        upsampling = nn.UpsamplingBilinear2d(scale_factor=upsampling) if upsampling > 1 else nn.Identity()
+    def __init__(
+        self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1
+    ):
+        conv2d = nn.Conv2d(
+            in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2
+        )
+        upsampling = (
+            nn.UpsamplingBilinear2d(scale_factor=upsampling)
+            if upsampling > 1
+            else nn.Identity()
+        )
         activation = Activation(activation)
         super().__init__(conv2d, upsampling, activation)
 
 
 class ClassificationHead(nn.Sequential):
-    def __init__(self, in_channels, classes, pooling="avg", dropout=0.2, activation=None):
+    def __init__(
+        self, in_channels, classes, pooling="avg", dropout=0.2, activation=None
+    ):
         if pooling not in ("max", "avg"):
-            raise ValueError("Pooling should be one of ('max', 'avg'), got {}.".format(pooling))
+            raise ValueError(
+                "Pooling should be one of ('max', 'avg'), got {}.".format(pooling)
+            )
         pool = nn.AdaptiveAvgPool2d(1) if pooling == "avg" else nn.AdaptiveMaxPool2d(1)
         flatten = nn.Flatten()
         dropout = nn.Dropout(p=dropout, inplace=True) if dropout else nn.Identity()

segmentation_models_pytorch/base/hub_mixin.py

@@ -2,7 +2,12 @@
 from pathlib import Path
 from typing import Optional, Union
 from functools import wraps
-from huggingface_hub import PyTorchModelHubMixin, ModelCard, ModelCardData, hf_hub_download
+from huggingface_hub import (
+    PyTorchModelHubMixin,
+    ModelCard,
+    ModelCardData,
+    hf_hub_download,
+)
 
 
 MODEL_CARD = """
@@ -45,15 +50,17 @@
 
 def _format_parameters(parameters: dict):
     params = {k: v for k, v in parameters.items() if not k.startswith("_")}
-    params = [f'"{k}": {v}' if not isinstance(v, str) else f'"{k}": "{v}"' for k, v in params.items()]
+    params = [
+        f'"{k}": {v}' if not isinstance(v, str) else f'"{k}": "{v}"'
+        for k, v in params.items()
+    ]
     params = ",\n".join([f"    {param}" for param in params])
     params = "{\n" + f"{params}" + "\n}"
     return params
 
 
 class SMPHubMixin(PyTorchModelHubMixin):
     def generate_model_card(self, *args, **kwargs) -> ModelCard:
-
         model_parameters_json = _format_parameters(self._hub_mixin_config)
         directory = self._save_directory if hasattr(self, "_save_directory") else None
         repo_id = self._repo_id if hasattr(self, "_repo_id") else None
@@ -97,8 +104,9 @@ def _del_attrs(self, attrs):
                 delattr(self, f"_{attr}")
 
     @wraps(PyTorchModelHubMixin.save_pretrained)
-    def save_pretrained(self, save_directory: Union[str, Path], *args, **kwargs) -> Optional[str]:
-
+    def save_pretrained(
+        self, save_directory: Union[str, Path], *args, **kwargs
+    ) -> Optional[str]:
         # set additional attributes to be used in generate_model_card
         self._save_directory = save_directory
         self._set_attrs_from_kwargs(["metrics", "dataset"], kwargs)
@@ -132,7 +140,9 @@ def config(self):
 @wraps(PyTorchModelHubMixin.from_pretrained)
 def from_pretrained(pretrained_model_name_or_path: str, *args, **kwargs):
     config_path = hf_hub_download(
-        pretrained_model_name_or_path, filename="config.json", revision=kwargs.get("revision", None)
+        pretrained_model_name_or_path,
+        filename="config.json",
+        revision=kwargs.get("revision", None),
     )
     with open(config_path, "r") as f:
         config = json.load(f)

segmentation_models_pytorch/base/initialization.py

@@ -3,7 +3,6 @@
 
 def initialize_decoder(module):
     for m in module.modules():
-
         if isinstance(m, nn.Conv2d):
             nn.init.kaiming_uniform_(m.weight, mode="fan_in", nonlinearity="relu")
             if m.bias is not None: