Release update 0.3.2 (#705)

* Update timm encoders list

* Update flake8 config

* Update author name

* Bump version

Author: qubvel

.flake8

@@ -1,5 +1,5 @@
 [flake8]
 max-line-length = 119
-exclude =.git,__pycache__,docs/conf.py,build,dist,setup.py,tests
+exclude =.git,__pycache__,docs/conf.py,build,dist,setup.py,tests,.venv
 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 = "

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)

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

README.md

@@ -20,7 +20,7 @@ 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)
- - 119 available encoders (and 400+ encoders from [timm](https://github.com/rwightman/pytorch-image-models))
+ - 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
 
@@ -403,10 +403,10 @@ Note: In the official github repo the s0 variant has additional num_conv_branche
 
 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>

docs/conf.py

@@ -23,8 +23,8 @@
 # -- Project information -----------------------------------------------------
 
 project = 'Segmentation Models'
-copyright = '{}, Pavel Yakubovskiy'.format(datetime.datetime.now().year)
-author = 'Pavel Yakubovskiy'
+copyright = '{}, Pavel Iakubovskii'.format(datetime.datetime.now().year)
+author = 'Pavel Iakubovskii'
 
 def get_version():
     sys.path.append('../segmentation_models_pytorch')

docs/encoders_timm.rst

@@ -51,4 +51,6 @@ def make_table(data):
 
     table = make_table(supported_models)
     print(table)
+    with open("timm_encoders.txt", "w") as f:
+        print(table, file=f)
     print(f"Total encoders: {len(supported_models.keys())}")

misc/generate_table_timm.py

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