Merge pull request #76 from kozistr/refactor/hubconf

[Docs, Refactor] torch.hub usage

Author: kozistr

Makefile

@@ -5,15 +5,15 @@ init:
 	python -m poetry install
 
 format:
-	isort --profile black -l 119 pytorch_optimizer tests lint.py
-	black -S -l 119 pytorch_optimizer tests lint.py
+	isort --profile black -l 119 pytorch_optimizer tests lint.py hubconf.py
+	black -S -l 119 pytorch_optimizer tests lint.py hubconf.py
 
 test:
 	python -m pytest -sv -vv --cov=pytorch_optimizer --cov-report=xml ./tests
 
 check:
-	isort --check-only --profile black -l 119 pytorch_optimizer tests lint.py
-	black -S -l 119 --check pytorch_optimizer tests lint.py
+	isort --check-only --profile black -l 119 pytorch_optimizer tests lint.py hubconf.py
+	black -S -l 119 --check pytorch_optimizer tests lint.py hubconf.py
 	python lint.py
 
 requirements:

README.rst

@@ -38,22 +38,27 @@ Simple Usage
 
     from pytorch_optimizer import AdamP
 
-    ...
     model = YourModel()
     optimizer = AdamP(model.parameters())
-    ...
 
-or you can use optimizer loader, simply passing a name of the optimizer.
-
-::
+    # or you can use optimizer loader, simply passing a name of the optimizer.
 
     from pytorch_optimizer import load_optimizer
 
-    ...
     model = YourModel()
     opt = load_optimizer(optimizer='adamp')
     optimizer = opt(model.parameters())
-    ...
+
+Also, you can load the optimizer via `torch.hub`
+
+::
+
+    import torch
+
+    model = YourModel()
+    opt = torch.hub.load('kozistr/pytorch_optimizer', 'adamp')
+    optimizer = opt(model.parameters())
+
 
 Supported Optimizers
 --------------------

hubconf.py

@@ -1,15 +1,20 @@
-dependencies = ['torch']
+"""
+PyTorch Hub models
+Usage:
+    import torch
+    optimizer = torch.hub.load('kozistr/pytorch_optimizer', 'adamp')
+"""
+from functools import partial as _partial
+from functools import update_wrapper as _update_wrapper
 
-from functools import partial as _partial, update_wrapper as _update_wrapper
+from pytorch_optimizer import get_supported_optimizers as _get_supported_optimizers
+from pytorch_optimizer import load_optimizer as _load_optimizer
 
-from pytorch_optimizer import (
-    get_supported_optimizers as _get_supported_optimizers,
-    load_optimizer as _load_optimizer,
-)
+dependencies = ['torch']
 
 for optimizer in _get_supported_optimizers():
-    name = optimizer.__name__
-    for n in (name, name.lower()):
+    name: str = optimizer.__name__
+    for n in (name, name.lower(), name.upper()):
         func = _partial(_load_optimizer, optimizer=n)
         _update_wrapper(func, optimizer)
         globals()[n] = func