add generic precommit hooks

.github/dependabot.yml

@@ -8,4 +8,4 @@ updates:
     open-pull-requests-limit: 2
     allow:
       - dependency-name: "timm"
-        dependency-type: "all"
+        dependency-type: "all"

.github/workflows/tests.yml

@@ -11,7 +11,7 @@ on:
     branches: [ master ]
 
 jobs:
-  
+
   lint:
     runs-on: ubuntu-latest
     steps:

.gitignore

@@ -103,4 +103,4 @@ venv.bak/
 /site
 
 # mypy
-.mypy_cache/
+.mypy_cache/

.pre-commit-config.yaml

@@ -1,4 +1,16 @@
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-json
+      - id: check-yaml
+      - id: check-toml
+      - id: check-executables-have-shebangs
+      - id: check-case-conflict
+      - id: check-added-large-files
+
   - repo: https://github.com/psf/black
     rev: 22.3.0
     hooks:
@@ -9,4 +21,4 @@ repos:
     hooks:
     - id: flake8
       args: [ --config=.flake8 ]
-      additional_dependencies: [ flake8-docstrings==1.6.0 ]
+      additional_dependencies: [ flake8-docstrings==1.6.0 ]

HALLOFFAME.md

@@ -5,87 +5,87 @@ Here you can find competitions, names of the winners and links to their solution
 
 Please, follow these rules, when adding a solution to the "Hall of Fame":
 
-1. Solution should be high rated (e.g. for Kaggle gold or silver medal) 
+1. Solution should be high rated (e.g. for Kaggle gold or silver medal)
 2. There should be a description of the solution (post at the forum / code / blog post / paper / pre-print)
 
 
 ## Kaggle
 
 ### [Severstal: Steel Defect Detection](https://www.kaggle.com/c/severstal-steel-defect-detection)
 
-- 1st place. 
-[Wuxi Jiangsu](https://www.kaggle.com/rguo97), 
-[Hongbo Zhu](https://www.kaggle.com/zhuhongbo), 
-[Yizhuo Yu](https://www.kaggle.com/paffpaffyu)  
+- 1st place.
+[Wuxi Jiangsu](https://www.kaggle.com/rguo97),
+[Hongbo Zhu](https://www.kaggle.com/zhuhongbo),
+[Yizhuo Yu](https://www.kaggle.com/paffpaffyu)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114254#latest-675874)]
 
-- 5th place. 
-[Guanshuo Xu](https://www.kaggle.com/wowfattie)  
+- 5th place.
+[Guanshuo Xu](https://www.kaggle.com/wowfattie)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/117208#latest-675385)]
 
-- 9th place. 
-[Jacek Poplawski](https://www.linkedin.com/in/jacekpoplawski/)  
+- 9th place.
+[Jacek Poplawski](https://www.linkedin.com/in/jacekpoplawski/)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114297#latest-660842)]
 
 - 10th place.
-[Alexey Rozhkov](https://www.linkedin.com/in/alexisrozhkov)  
+[Alexey Rozhkov](https://www.linkedin.com/in/alexisrozhkov)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114465#latest-659615)]
 
-- 12th place. 
-[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/), 
-[Ilya Dobrynin](https://www.linkedin.com/in/ilya-dobrynin-79a89b106/), 
-[Denis Kolpakov](https://www.linkedin.com/in/denis-kolpakov-ab3137197/)  
+- 12th place.
+[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/),
+[Ilya Dobrynin](https://www.linkedin.com/in/ilya-dobrynin-79a89b106/),
+[Denis Kolpakov](https://www.linkedin.com/in/denis-kolpakov-ab3137197/)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114309#latest-661404)]
 
-- 31st place. 
-[Insaf Ashrapov](https://www.linkedin.com/in/iashrapov/), 
-[Igor Krashenyi](https://www.linkedin.com/in/igor-krashenyi-38b89b98), 
-[Pavel Pleskov](https://www.linkedin.com/in/ppleskov), 
-[Anton Zakharenkov](https://www.linkedin.com/in/anton-zakharenkov/), 
-[Nikolai Popov](https://www.linkedin.com/in/nikolai-popov-b2157370/)  
+- 31st place.
+[Insaf Ashrapov](https://www.linkedin.com/in/iashrapov/),
+[Igor Krashenyi](https://www.linkedin.com/in/igor-krashenyi-38b89b98),
+[Pavel Pleskov](https://www.linkedin.com/in/ppleskov),
+[Anton Zakharenkov](https://www.linkedin.com/in/anton-zakharenkov/),
+[Nikolai Popov](https://www.linkedin.com/in/nikolai-popov-b2157370/)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114383#latest-658438)]
 [[code](https://github.com/Diyago/Severstal-Steel-Defect-Detection)]
 
-- 55th place. 
-[Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/)  
+- 55th place.
+[Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114410#latest-672682)]
 [[code](https://github.com/khornlund/severstal-steel-defect-detection)]
 
 - Efficiency round 1st place.
-[Stefan Stefanov](https://www.linkedin.com/in/stefan-stefanov-63a77b1)  
+[Stefan Stefanov](https://www.linkedin.com/in/stefan-stefanov-63a77b1)
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/117486#latest-674229)]
 
 
 ### [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization)
 
 - 2nd place.
-[Andrey Kiryasov](https://www.kaggle.com/ekydna)  
+[Andrey Kiryasov](https://www.kaggle.com/ekydna)
 [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118255#latest-678189)]
 
 - 4th place.
-[Ching-Loong Seow](https://www.linkedin.com/in/clseow/)  
+[Ching-Loong Seow](https://www.linkedin.com/in/clseow/)
 [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118016#latest-677333)]
 
 - 34th place.
-[Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/)  
+[Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/)
 [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118250#latest-678176)]
 [[code](https://github.com/khornlund/understanding-cloud-organization)]
 
 - 55th place.
-[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/)  
+[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/)
 [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118019#latest-678626)]
 
 ## Other platforms
 
-### [MICCAI 2020 TN-SCUI challenge](https://tn-scui2020.grand-challenge.org/Home/)  
+### [MICCAI 2020 TN-SCUI challenge](https://tn-scui2020.grand-challenge.org/Home/)
 - 1st place.
-[Mingyu Wang](https://github.com/WAMAWAMA)  
+[Mingyu Wang](https://github.com/WAMAWAMA)
 [[description](https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st)]
 [[code](https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st)]
 
 ### [Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/)
  - 1st place.
-[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/).  
+[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/).
 [[code and description](https://github.com/qubvel/open-cities-challenge)]
 
 ### [Machine Learning based feature extraction of Electrical Substations from Satellite Data ](https://competitions.codalab.org/competitions/32132#learn_the_details)

README.md

@@ -1,18 +1,18 @@
 <div align="center">
- 
-![logo](https://i.ibb.co/dc1XdhT/Segmentation-Models-V2-Side-1-1.png)  
-**Python library with Neural Networks for Image  
-Segmentation based on [PyTorch](https://pytorch.org/).**  
-
-[![Generic badge](https://img.shields.io/badge/License-MIT-<COLOR>.svg?style=for-the-badge)](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE) 
-[![GitHub Workflow Status (branch)](https://img.shields.io/github/actions/workflow/status/qubvel/segmentation_models.pytorch/tests.yml?branch=master&style=for-the-badge)](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml) 
-[![Read the Docs](https://img.shields.io/readthedocs/smp?style=for-the-badge&logo=readthedocs&logoColor=white)](https://smp.readthedocs.io/en/latest/) 
+
+![logo](https://i.ibb.co/dc1XdhT/Segmentation-Models-V2-Side-1-1.png)
+**Python library with Neural Networks for Image
+Segmentation based on [PyTorch](https://pytorch.org/).**
+
+[![Generic badge](https://img.shields.io/badge/License-MIT-<COLOR>.svg?style=for-the-badge)](https://github.com/qubvel/segmentation_models.pytorch/blob/master/LICENSE)
+[![GitHub Workflow Status (branch)](https://img.shields.io/github/actions/workflow/status/qubvel/segmentation_models.pytorch/tests.yml?branch=master&style=for-the-badge)](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml)
+[![Read the Docs](https://img.shields.io/readthedocs/smp?style=for-the-badge&logo=readthedocs&logoColor=white)](https://smp.readthedocs.io/en/latest/)
 <br>
-[![PyPI](https://img.shields.io/pypi/v/segmentation-models-pytorch?color=blue&style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/segmentation-models-pytorch/) 
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/segmentation-models-pytorch?style=for-the-badge&color=blue)](https://pepy.tech/project/segmentation-models-pytorch) 
+[![PyPI](https://img.shields.io/pypi/v/segmentation-models-pytorch?color=blue&style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/segmentation-models-pytorch/)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/segmentation-models-pytorch?style=for-the-badge&color=blue)](https://pepy.tech/project/segmentation-models-pytorch)
 <br>
-[![PyTorch - Version](https://img.shields.io/badge/PYTORCH-1.4+-red?style=for-the-badge&logo=pytorch)](https://pepy.tech/project/segmentation-models-pytorch) 
-[![Python - Version](https://img.shields.io/badge/PYTHON-3.7+-red?style=for-the-badge&logo=python&logoColor=white)](https://pepy.tech/project/segmentation-models-pytorch) 
+[![PyTorch - Version](https://img.shields.io/badge/PYTORCH-1.4+-red?style=for-the-badge&logo=pytorch)](https://pepy.tech/project/segmentation-models-pytorch)
+[![Python - Version](https://img.shields.io/badge/PYTHON-3.7+-red?style=for-the-badge&logo=python&logoColor=white)](https://pepy.tech/project/segmentation-models-pytorch)
 
 </div>
 
@@ -23,7 +23,7 @@ The main features of this library are:
  - 124 available encoders (and 500+ encoders from [timm](https://github.com/rwightman/pytorch-image-models))
  - All encoders have pre-trained weights for faster and better convergence
  - Popular metrics and losses for training routines
- 
+
 ### [📚 Project Documentation 📚](http://smp.readthedocs.io/)
 
 Visit [Read The Docs Project Page](https://smp.readthedocs.io/) or read following README to know more about Segmentation Models Pytorch (SMP for short) library
@@ -358,7 +358,7 @@ The following is a list of supported encoders in the SMP. Select the appropriate
 
 Backbone from SegFormer pretrained on Imagenet! Can be used with other decoders from package, you can combine Mix Vision Transformer with Unet, FPN and others!
 
-Limitations:  
+Limitations:
 
    - encoder is **not** supported by Linknet, Unet++
    - encoder is supported by FPN only for encoder **depth = 5**
@@ -420,18 +420,18 @@ Total number of supported encoders: 549
 ##### Input channels
 Input channels parameter allows you to create models, which process tensors with arbitrary number of channels.
 If you use pretrained weights from imagenet - weights of first convolution will be reused. For
-1-channel case it would be a sum of weights of first convolution layer, otherwise channels would be 
+1-channel case it would be a sum of weights of first convolution layer, otherwise channels would be
 populated with weights like `new_weight[:, i] = pretrained_weight[:, i % 3]` and than scaled with `new_weight * 3 / new_in_channels`.
 ```python
 model = smp.FPN('resnet34', in_channels=1)
 mask = model(torch.ones([1, 1, 64, 64]))
 ```
 
-##### Auxiliary classification output  
-All models support `aux_params` parameters, which is default set to `None`. 
+##### Auxiliary classification output
+All models support `aux_params` parameters, which is default set to `None`.
 If `aux_params = None` then classification auxiliary output is not created, else
 model produce not only `mask`, but also `label` output with shape `NC`.
-Classification head consists of GlobalPooling->Dropout(optional)->Linear->Activation(optional) layers, which can be 
+Classification head consists of GlobalPooling->Dropout(optional)->Linear->Activation(optional) layers, which can be
 configured by `aux_params` as follows:
 ```python
 aux_params=dict(
@@ -469,19 +469,19 @@ $ pip install git+https://github.com/qubvel/segmentation_models.pytorch
 
 ### 🤝 Contributing
 
-#### Install SMP  
+#### Install SMP
 
 ```bash
 make install_dev  # create .venv, install SMP in dev mode
 ```
 
-#### Run tests and code checks  
+#### Run tests and code checks
 
 ```bash
 make all          # run flake8, black, tests
 ```
 
-#### Update table with encoders  
+#### Update table with encoders
 
 ```bash
 make table        # generate table with encoders and print to stdout

docker/Dockerfile

@@ -1,3 +1,3 @@
 FROM anibali/pytorch:cuda-9.0
 
-RUN pip install segmentation-models-pytorch
+RUN pip install segmentation-models-pytorch

docs/encoders_timm.rst

@@ -1085,4 +1085,3 @@ Total number of encoders: 549
 +----------------------------------+------------------+
 | xception71                       |        ✅        |
 +----------------------------------+------------------+
-

docs/insights.rst

@@ -21,20 +21,20 @@ Each encoder should have following attributes and methods and be inherited from
 .. code-block:: python
 
     class MyEncoder(torch.nn.Module, EncoderMixin):
-        
+
         def __init__(self, **kwargs):
             super().__init__()
-            
+
             # A number of channels for each encoder feature tensor, list of integers
             self._out_channels: List[int] = [3, 16, 64, 128, 256, 512]
 
             # A number of stages in decoder (in other words number of downsampling operations), integer
             # use in in forward pass to reduce number of returning features
-            self._depth: int = 5 
+            self._depth: int = 5
 
             # Default number of input channels in first Conv2d layer for encoder (usually 3)
-            self._in_channels: int = 3 
-            
+            self._in_channels: int = 3
+
             # Define encoder modules below
             ...
 
@@ -90,12 +90,12 @@ For better understanding see more examples of encoder in smp.encoders module.
 3. Aux classification output
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-All models support ``aux_params`` parameter, which is default set to ``None``. 
+All models support ``aux_params`` parameter, which is default set to ``None``.
 If ``aux_params = None`` than classification auxiliary output is not created, else
 model produce not only ``mask``, but also ``label`` output with shape ``(N, C)``.
 
 Classification head consist of following layers:
-    
+
 1. GlobalPooling
 2. Dropout (optional)
 3. Linear
@@ -104,7 +104,7 @@ Classification head consist of following layers:
 Example:
 
 .. code-block:: python
-    
+
     aux_params=dict(
         pooling='avg',             # one of 'avg', 'max'
         dropout=0.5,               # dropout ratio, default is None

docs/install.rst

@@ -12,4 +12,4 @@ Latest version from source:
 
 .. code-block:: bash
 
-    $ pip install -U git+https://github.com/qubvel/segmentation_models.pytorch
+    $ pip install -U git+https://github.com/qubvel/segmentation_models.pytorch

docs/losses.rst

@@ -1,7 +1,7 @@
 📉 Losses
 =========
 
-Collection of popular semantic segmentation losses. Adapted from 
+Collection of popular semantic segmentation losses. Adapted from
 an awesome repo with pytorch utils https://github.com/BloodAxe/pytorch-toolbelt
 
 Constants

docs/models.rst

@@ -36,5 +36,3 @@ DeepLabV3
 DeepLabV3+
 ~~~~~~~~~~
 .. autoclass:: segmentation_models_pytorch.DeepLabV3Plus
-
-

docs/quickstart.rst

@@ -6,7 +6,7 @@
 Segmentation model is just a PyTorch nn.Module, which can be created as easy as:
 
 .. code-block:: python
-    
+
     import segmentation_models_pytorch as smp
 
     model = smp.Unet(

docs/requirements.txt

@@ -1,3 +1,3 @@
 faculty-sphinx-theme==0.2.2
 six==1.15.0
-autodocsumm
+autodocsumm