Add black and flake8 (#532)

* Add black and flake8
* Fix test losses
* Fix pre-commit 
* Update README

Author: qubvel

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+max-line-length = 119
+exclude =.git,__pycache__,docs/conf.py,build,dist,setup.py,tests
+ignore = I101,I201,F401,F403,S001,D100,D101,D102,D103,D104,D105,D106,D107,D200,D205,D400,W504,D202,E203,W503,B006,D412
+inline-quotes = "

.github/workflows/tests.yml

@@ -12,23 +12,39 @@ on:
 
 jobs:
   test:
-
     runs-on: ubuntu-18.04
-
     steps:
     - uses: actions/checkout@v2
-    
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2
       with:
         python-version: 3.6
-        
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install codecov pytest mock
-        pip3 install torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
-        pip install .
-    - name: Test
-      run: |
-        python -m pytest -s tests
+        pip install torch==1.9.0+cpu torchvision==0.10.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        pip install .[test]
+    - name: Run Tests
+      run: python -m pytest -s tests
+    - name: Run Flake8
+      run: flake8 --config=.flake8
+
+  check_code_formatting:
+    name: Check code formatting with Black
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.8]
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Update pip
+      run: python -m pip install --upgrade pip
+    - name: Install Black
+      run: pip install black==21.9b0
+    - name: Run Black
+      run: black --config=pyproject.toml --check .

.pre-commit-config.yaml

@@ -0,0 +1,12 @@
+repos:
+  - repo: https://github.com/psf/black
+    rev: 21.12b0
+    hooks:
+      - id: black
+        args: [ --config=pyproject.toml ]
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: 4.0.1
+    hooks:
+    - id: flake8
+      args: [ --config=.flake8 ]
+      additional_dependencies: [ flake8-docstrings==1.6.0 ]

README.md

@@ -58,7 +58,7 @@ model = smp.Unet(
 
 #### 2. Configure data preprocessing
 
-All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and **not necessary** in case you train the whole model, not only decoder.
+All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). It is **not necessary** in case you train the whole model, not only decoder.
 
 ```python
 from segmentation_models_pytorch.encoders import get_preprocessing_fn
@@ -419,11 +419,23 @@ $ pip install git+https://github.com/qubvel/segmentation_models.pytorch
 
 ### 🤝 Contributing
 
-##### Run test
+##### Install linting and formatting pre-commit hooks
+```bash
+pip install pre-commit black flake8
+pre-commit install
+```
+
+##### Run tests
+```bash
+pytest -p no:cacheprovider
+```
+
+##### Run tests in docker
 ```bash
 $ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev pytest -p no:cacheprovider
 ```
-##### Generate table
+
+##### Generate table with encoders (in case you add a new encoder)
 ```bash
 $ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py
 ```

__init__.py

@@ -10,10 +10,12 @@
     "Params, M",
 ]
 
+
 def wrap_row(r):
     return "|{}|".format(r)
 
-header = "|".join([column.ljust(WIDTH, ' ') for column in COLUMNS])
+
+header = "|".join([column.ljust(WIDTH, " ") for column in COLUMNS])
 separator = "|".join(["-" * WIDTH] + [":" + "-" * (WIDTH - 2) + ":"] * (len(COLUMNS) - 1))
 
 print(wrap_row(header))

misc/generate_table.py

@@ -7,32 +7,34 @@ def check_features_and_reduction(name):
     if not encoder.feature_info.reduction() == [2, 4, 8, 16, 32]:
         raise ValueError
 
+
 def has_dilation_support(name):
     try:
         timm.create_model(name, features_only=True, output_stride=8, pretrained=False)
         timm.create_model(name, features_only=True, output_stride=16, pretrained=False)
         return True
-    except Exception as e:
+    except Exception:
         return False
 
+
 def make_table(data):
-    names = supported.keys()
+    names = data.keys()
     max_len1 = max([len(x) for x in names]) + 2
     max_len2 = len("support dilation") + 2
-    
+
     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"
-    
+
     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)
         table += "| " + k.ljust(max_len1 - 2) + " | " + support + " |\n"
         table += l1
-    
+
     return table
-    
+
 
 if __name__ == "__main__":
 

misc/generate_table_timm.py

@@ -0,0 +1,19 @@
+[tool.black]
+line-length = 119
+target-version = ['py36', 'py37', 'py38']
+include = '\.pyi?$'
+exclude = '''
+/(
+    \.eggs
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | docs
+  | _build
+  | buck-out
+  | build
+  | dist
+)/
+'''

pyproject.toml

@@ -28,17 +28,31 @@ def create_model(
     classes: int = 1,
     **kwargs,
 ) -> _torch.nn.Module:
-    """Models entrypoint, allows to create any model architecture just with 
-    parameters, without using its class"""
+    """Models entrypoint, allows to create any model architecture just with
+    parameters, without using its class
+    """
 
-    archs = [Unet, UnetPlusPlus, MAnet, Linknet, FPN, PSPNet, DeepLabV3, DeepLabV3Plus, PAN]
+    archs = [
+        Unet,
+        UnetPlusPlus,
+        MAnet,
+        Linknet,
+        FPN,
+        PSPNet,
+        DeepLabV3,
+        DeepLabV3Plus,
+        PAN,
+    ]
     archs_dict = {a.__name__.lower(): a for a in archs}
     try:
         model_class = archs_dict[arch.lower()]
     except KeyError:
-        raise KeyError("Wrong architecture type `{}`. Available options are: {}".format(
-            arch, list(archs_dict.keys()),
-        ))
+        raise KeyError(
+            "Wrong architecture type `{}`. Available options are: {}".format(
+                arch,
+                list(archs_dict.keys()),
+            )
+        )
     return model_class(
         encoder_name=encoder_name,
         encoder_weights=encoder_weights,

segmentation_models_pytorch/__init__.py

@@ -1,3 +1,3 @@
 VERSION = (0, 2, 1)
 
-__version__ = '.'.join(map(str, VERSION))
+__version__ = ".".join(map(str, VERSION))