release 0.5.6

README.md

@@ -29,8 +29,8 @@ have [docs](https://pywick.readthedocs.io/en/latest/)! They're still a
 work in progress though so apologies for anything that's broken.
 
 ## What's New (highlights)
-- **Jan. 15, 2020**
-  - New release: 0.5.5
+- **Jan. 20, 2020**
+  - New release: 0.5.6 (minor fix from 0.5.5 for pypi)
   - Mish activation function (SoTA)
   - [rwightman's](https://github.com/rwightman/gen-efficientnet-pytorch) models of pretrained/ported variants for classification (44 total)
     - efficientnet Tensorflow port b0-b8, with and without AP, el/em/es, cc
@@ -46,11 +46,13 @@ work in progress though so apologies for anything that's broken.
     - General bug fixes and code improvements 
 
 ## Install
+Pywick requires **pytorch >= 1.0**
+
 `pip install pywick`
 
 or specific version from git:
 
-`pip install git+https://github.com/achaiah/[email protected].5`
+`pip install git+https://github.com/achaiah/[email protected].6`
 
 ## ModuleTrainer
 The `ModuleTrainer` class provides a high-level training interface which abstracts
@@ -192,10 +194,15 @@ trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100)
   - [**SqueezeNet**](https://arxiv.org/abs/1602.07360)
   - [**VGG**](https://arxiv.org/abs/1409.1556)
 - [**BatchNorm Inception**](https://arxiv.org/pdf/1502.03167.pdf)
-- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629/)
+- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629)
+- [**EfficientNet variants b0-b8**](https://arxiv.org/abs/1905.11946)
 - [**FBResnet**](https://github.com/facebook/fb.resnet.torch)
+- [**FBNet-C**](https://arxiv.org/abs/1812.03443)
 - [**Inception v4**](http://arxiv.org/abs/1602.07261)
 - [**InceptionResnet v2**](https://arxiv.org/abs/1602.07261)
+- [**Mixnet L/M/S**](https://arxiv.org/abs/1907.09595)
+- [**MnasNet**](https://arxiv.org/abs/1807.11626)
+- [**MobileNet V3**](https://arxiv.org/abs/1905.02244)
 - [**NasNet and NasNet Mobile**](https://arxiv.org/abs/1707.07012)
 - [**PNASNet**](https://arxiv.org/abs/1712.00559)
 - [**Polynet**](https://arxiv.org/abs/1611.05725)
@@ -204,6 +211,7 @@ trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100)
 - [**ResNext**](https://arxiv.org/abs/1611.05431)
 - [**SE Net**](https://arxiv.org/pdf/1709.01507.pdf)
 - **SE Inception**
+- [**Single-Pass NAS Net**](https://arxiv.org/abs/1904.02877)
 - [**Wide Resnet**](https://arxiv.org/abs/1605.07146)
 - [**XCeption**](https://arxiv.org/pdf/1610.02357.pdf)
 

docs/source/README.md

@@ -29,8 +29,8 @@ have [docs](https://pywick.readthedocs.io/en/latest/)! They're still a
 work in progress though so apologies for anything that's broken.
 
 ## What's New (highlights)
-- **Jan. 15, 2020**
-  - New release: 0.5.5
+- **Jan. 20, 2020**
+  - New release: 0.5.6 (minor fix from 0.5.5 for pypi)
   - Mish activation function (SoTA)
   - [rwightman's](https://github.com/rwightman/gen-efficientnet-pytorch) models of pretrained/ported variants for classification (44 total)
     - efficientnet Tensorflow port b0-b8, with and without AP, el/em/es, cc
@@ -46,11 +46,13 @@ work in progress though so apologies for anything that's broken.
     - General bug fixes and code improvements 
 
 ## Install
+Pywick requires **pytorch >= 1.0**
+
 `pip install pywick`
 
 or specific version from git:
 
-`pip install git+https://github.com/achaiah/[email protected].5`
+`pip install git+https://github.com/achaiah/[email protected].6`
 
 ## ModuleTrainer
 The `ModuleTrainer` class provides a high-level training interface which abstracts
@@ -192,10 +194,15 @@ trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100)
   - [**SqueezeNet**](https://arxiv.org/abs/1602.07360)
   - [**VGG**](https://arxiv.org/abs/1409.1556)
 - [**BatchNorm Inception**](https://arxiv.org/pdf/1502.03167.pdf)
-- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629/)
+- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629)
+- [**EfficientNet variants b0-b8**](https://arxiv.org/abs/1905.11946)
 - [**FBResnet**](https://github.com/facebook/fb.resnet.torch)
+- [**FBNet-C**](https://arxiv.org/abs/1812.03443)
 - [**Inception v4**](http://arxiv.org/abs/1602.07261)
 - [**InceptionResnet v2**](https://arxiv.org/abs/1602.07261)
+- [**Mixnet L/M/S**](https://arxiv.org/abs/1907.09595)
+- [**MnasNet**](https://arxiv.org/abs/1807.11626)
+- [**MobileNet V3**](https://arxiv.org/abs/1905.02244)
 - [**NasNet and NasNet Mobile**](https://arxiv.org/abs/1707.07012)
 - [**PNASNet**](https://arxiv.org/abs/1712.00559)
 - [**Polynet**](https://arxiv.org/abs/1611.05725)
@@ -204,6 +211,7 @@ trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100)
 - [**ResNext**](https://arxiv.org/abs/1611.05431)
 - [**SE Net**](https://arxiv.org/pdf/1709.01507.pdf)
 - **SE Inception**
+- [**Single-Pass NAS Net**](https://arxiv.org/abs/1904.02877)
 - [**Wide Resnet**](https://arxiv.org/abs/1605.07146)
 - [**XCeption**](https://arxiv.org/pdf/1610.02357.pdf)
 

setup.py

@@ -2,9 +2,17 @@
 
 from setuptools import setup, find_packages
 
+# read the contents of your README file per https://packaging.python.org/guides/making-a-pypi-friendly-readme/
+from os import path
+this_directory = path.abspath(path.dirname(__file__))
+with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
+    long_description = f.read()
+
 setup(name='pywick',
-      version='0.5.5',
+      version='0.5.6',
       description='High-level batteries-included training framework for Pytorch',
+      long_description=long_description,
+      long_description_content_type='text/markdown',
       author='Achaiah',
       install_requires=[
                   'h5py',
@@ -19,7 +27,7 @@
             ],
       packages=find_packages(),
       url='https://github.com/achaiah/pywick',
-      download_url='https://github.com/achaiah/pywick/archive/v0.5.3.tar.gz',
+      download_url='https://github.com/achaiah/pywick/archive/v0.5.6.tar.gz',
       keywords=['pytorch', 'classification', 'deep learning', 'neural networks', 'semantic-segmentation', 'framework'],
       classifiers=['Development Status :: 4 - Beta',
                    'Intended Audience :: Developers',