Pandinosaurus · pull · Jan 5, 2022 · Jan 5, 2022 · Jan 6, 2022 · Jan 6, 2022
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
@@ -0,0 +1,8 @@
+{
+  "image": "mcr.microsoft.com/devcontainers/universal:2",
+  "features": {
+    "ghcr.io/devcontainers/features/python:1": {
+      "verison": 3.6
+    }
+  }
+}
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
@@ -0,0 +1,11 @@
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    # Allow up to 2 open pull requests at a time
+    open-pull-requests-limit: 2
+    allow:
+      - dependency-name: "timm"
+        dependency-type: "all"
diff --git a/.github/stale.yml b/.github/stale.yml
diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
@@ -12,7 +12,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v2
       with:
-        python-version: '3.6'
+        python-version: '3.7'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml
@@ -0,0 +1,21 @@
+name: 'Close stale issues and PRs'
+on:
+  schedule:
+    - cron: '30 1 * * *'
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v4
+        with:
+          stale-issue-message: 'This issue is stale because it has been open 60 days with no activity. Remove stale label or comment or this will be closed in 7 days.'
+          stale-pr-message: 'This PR is stale because it has been open 60 days with no activity. Remove stale label or comment or this will be closed in 15 days.'
+          close-issue-message: 'This issue was closed because it has been stalled for 7 days with no activity.'
+          close-pr-message: 'This PR was closed because it has been stalled for 15 days with no activity.'
+          days-before-issue-stale: 60
+          days-before-pr-stale: 60
+          days-before-issue-close: 7
+          days-before-pr-close: 15
+          operations-per-run: 100
+          ascending: true
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -11,24 +11,39 @@ on:
     branches: [ master ]
 
 jobs:
-  test:
 
-    runs-on: ubuntu-18.04
+  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 ${{ matrix.python-version }}
       uses: actions/setup-python@v2
       with:
-        python-version: 3.6
-
+        python-version: 3.7
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install codecov pytest mock
-        pip3 install torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
-        pip install .
-    - name: Test
-      run: |
-        python -m pytest -s tests
+        pip install torch==1.9.0+cpu torchvision==0.10.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        make install_dev
+    - name: Test with pytest
+      run: make test
+
diff --git a/.gitignore b/.gitignore
@@ -3,6 +3,9 @@ __pycache__/
 *.py[cod]
 *$py.class
 .idea/
+.venv*
+examples/images*
+examples/annotations*
 
 # C extensions
 *.so
@@ -102,4 +105,7 @@ venv.bak/
 /site
 
 # mypy
-.mypy_cache/
+.mypy_cache/
+
+# ruff
+.ruff_cache/
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
@@ -0,0 +1,22 @@
+# Read the Docs configuration file for Sphinx projects
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+  install:
+    - requirements: docs/requirements.txt
diff --git a/HALLOFFAME.md b/HALLOFFAME.md
@@ -32,7 +32,7 @@ Please, follow these rules, when adding a solution to the "Hall of Fame":
 [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114465#latest-659615)]
 
 - 12th place. 
-[Pavel Yakubovskiy](https://www.linkedin.com/in/pavel-yakubovskiy/), 
+[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)]
@@ -72,7 +72,7 @@ Please, follow these rules, when adding a solution to the "Hall of Fame":
 [[code](https://github.com/khornlund/understanding-cloud-organization)]
 
 - 55th place.
-[Pavel Yakubovskiy](https://www.linkedin.com/in/pavel-yakubovskiy/)  
+[Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/)  
 [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118019#latest-678626)]
 
 ## Other platforms
@@ -85,7 +85,7 @@ Please, follow these rules, when adding a solution to the "Hall of Fame":
 
 ### [Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/)
  - 1st place.
-[Pavel Yakubovskiy](https://www.linkedin.com/in/pavel-yakubovskiy/).  
+[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)
@@ -95,3 +95,9 @@ Please, follow these rules, when adding a solution to the "Hall of Fame":
 [Aarsh chaube](https://github.com/Aarsh2001)
 [[code](https://github.com/Aarsh2001/ML_Challenge_NRSC)]
 [[Pre-Print](https://github.com/Aarsh2001/ML_Challenge_NRSC/blob/main/3rd%20Rank%20Submission.pdf)]
+
+### [NeurIPS2022 Cell Segmentation Challenge](https://neurips22-cellseg.grand-challenge.org/)
+
+- 1st place. [Gihun Lee](https://github.com/Lee-Gihun), [Sangmook Kim](https://github.com/ElvinKim), [Joonkee Kim](https://github.com/joonkeekim)
+- [[code](https://github.com/Lee-Gihun/MEDIAR)]
+[[Paper](https://arxiv.org/abs/2212.03465)]
diff --git a/LICENSE b/LICENSE
@@ -1,6 +1,6 @@
 The MIT License
 
-Copyright (c) 2019, Pavel Yakubovskiy
+Copyright (c) 2019, Pavel Iakubovskii
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

diff --git a/Makefile b/Makefile
@@ -0,0 +1,23 @@
+.PHONY: test
+
+.venv:
+	python3 -m venv .venv
+
+install_dev: .venv
+	.venv/bin/pip install -e ".[test]"
+
+test: .venv
+	.venv/bin/pytest -p no:cacheprovider tests/
+
+table:
+	.venv/bin/python misc/generate_table.py
+
+table_timm:
+	.venv/bin/python misc/generate_table_timm.py
+
+fixup:
+	.venv/bin/ruff check --fix
+	.venv/bin/ruff format
+
+all: fixup test
+
diff --git a/README.md b/README.md
@@ -4,16 +4,25 @@
 **Python library with Neural Networks for Image  
 Segmentation based on [PyTorch](https://pytorch.org/).**  
 
-![PyPI version](https://badge.fury.io/py/segmentation-models-pytorch.svg) [![Build Status](https://travis-ci.com/qubvel/segmentation_models.pytorch.svg?branch=master)](https://travis-ci.com/qubvel/segmentation_models.pytorch) [![Documentation Status](https://readthedocs.org/projects/smp/badge/?version=latest)](https://smp.readthedocs.io/en/latest/?badge=latest) <br> ![Downloads](https://pepy.tech/badge/segmentation-models-pytorch) [![Generic badge](https://img.shields.io/badge/License-MIT-<COLOR>.svg)](https://shields.io/)
+[![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) 
+<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) 
 
 </div>
 
 The main features of this library are:
 
  - High level API (just two lines to create a neural network)
  - 9 models architectures for binary and multi class segmentation (including legendary Unet)
- - 113 available encoders
+ - 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/)
 
@@ -57,7 +66,7 @@ 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 you 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
@@ -68,9 +77,10 @@ preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
 Congratulations! You are done! Now you can train your model with your favorite framework!
 
 ### 💡 Examples <a name="examples"></a>
+ - Training model for pets binary segmentation with Pytorch-Lightning [notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb) and [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb)
  - Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/cars%20segmentation%20(camvid).ipynb).
- - Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/master/examples/notebooks/segmentation-tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/segmentation-tutorial.ipynb)
- - Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@teranus](https://github.com/ternaus)).
+ - Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb)
+ - Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@ternaus](https://github.com/ternaus)).
 
 ### 📦 Models <a name="models"></a>
 
@@ -342,6 +352,48 @@ The following is a list of supported encoders in the SMP. Select the appropriate
 </div>
 </details>
 
+<details>
+<summary style="margin-left: 25px;">Mix Vision Transformer</summary>
+<div style="margin-left: 25px;">
+
+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:  
+
+   - encoder is **not** supported by Linknet, Unet++
+   - encoder is supported by FPN only for encoder **depth = 5**
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|mit_b0                          |imagenet                        |3M                              |
+|mit_b1                          |imagenet                        |13M                             |
+|mit_b2                          |imagenet                        |24M                             |
+|mit_b3                          |imagenet                        |44M                             |
+|mit_b4                          |imagenet                        |60M                             |
+|mit_b5                          |imagenet                        |81M                             |
+
+</div>
+</details>
+
+<details>
+<summary style="margin-left: 25px;">MobileOne</summary>
+<div style="margin-left: 25px;">
+
+Apple's "sub-one-ms" Backbone pretrained on Imagenet! Can be used with all decoders.
+
+Note: In the official github repo the s0 variant has additional num_conv_branches, leading to more params than s1.
+
+|Encoder                         |Weights                         |Params, M                       |
+|--------------------------------|:------------------------------:|:------------------------------:|
+|mobileone_s0                    |imagenet                        |4.6M                              |
+|mobileone_s1                    |imagenet                        |4.0M                              |
+|mobileone_s2                    |imagenet                        |6.5M                              |
+|mobileone_s3                    |imagenet                        |8.8M                              |
+|mobileone_s4                    |imagenet                        |13.6M                             |
+
+</div>
+</details>
+
 
 \* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)).
 
@@ -351,10 +403,10 @@ The following is a list of supported encoders in the SMP. Select the appropriate
 
 Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported
 
- - transformer models do not have ``features_only`` functionality implemented
- - some models do not have appropriate strides
+ - not all transformer models have ``features_only`` functionality implemented that is required for encoder
+ - some models have inappropriate strides
 
-Total number of supported encoders: 467
+Total number of supported encoders: 549
  - [table with available encoders](https://smp.readthedocs.io/en/latest/encoders_timm.html)
 
 ### 🔁 Models API <a name="api"></a>
@@ -417,21 +469,30 @@ $ pip install git+https://github.com/qubvel/segmentation_models.pytorch
 
 ### 🤝 Contributing
 
-##### Run test
+#### Install SMP  
+
 ```bash
-$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev pytest -p no:cacheprovider
+make install_dev  # create .venv, install SMP in dev mode
 ```
-##### Generate table
+
+#### Run tests and code checks
+
+```bash
+make fixup         # Ruff for formatting and lint checks
+```
+
+#### Update table with encoders  
+
 ```bash
-$ docker build -f docker/Dockerfile.dev -t smp:dev . && docker run --rm smp:dev python misc/generate_table.py
+make table        # generate table with encoders and print to stdout
 ```
 
 ### 📝 Citing
 ```
-@misc{Yakubovskiy:2019,
-  Author = {Pavel Yakubovskiy},
+@misc{Iakubovskii:2019,
+  Author = {Pavel Iakubovskii},
   Title = {Segmentation Models Pytorch},
-  Year = {2020},
+  Year = {2019},
   Publisher = {GitHub},
   Journal = {GitHub repository},
   Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}}

diff --git a/__init__.py b/__init__.py