Hub (#639)

* init hub.py

* rename hub.py -> hub_info.py

* torchvision_hub->torchvision; use alexnet as example since it's smaller

* alexnet -> resnet18

* update repo example structure

* address comment

* add readme as comment

* address comment

* fix lint

hubconf.py

+'''
+This is an example hubconf.py for pytorch/vision repo
+
+## Users can get this published model by calling:
+hub_model = hub.load(
+    'pytorch/vision:master', # repo_owner/repo_name:branch
+    'resnet18', # entrypoint
+    1234, # args for callable [not applicable to resnet]
+    pretrained=True) # kwargs for callable
+
+## Protocol on repo owner side
+1. The "published" models should be at least in a branch/tag. It can't be a random commit.
+2. Repo owner should have the following field defined in hubconf.py
+  2.1 Function/entrypoint with function signature "def resnet18(pretrained=False, *args, **kwargs):"
+  2.2 Pretrained allows users to load pretrained weights from repo owner.
+  2.3 Args and kwargs are passed to the callable _resnet18,
+  2.4 Docstring of function works as a help message, explaining what does the model do and what's
+      the allowed arguments.
+  2.5 Dependencies is a list optionally provided by repo owner, to specify what packages are required
+      to run the model.
+
+## Hub_dir
+
+hub_dir specifies where the intermediate files/folders will be saved. By default this is ~/.torch/hub.
+Users can change it by either setting the environment variable TORCH_HUB_DIR or calling hub.set_dir(PATH_TO_HUB_DIR).
+By default, we don't cleanup files after loading so that users can use cache next time.
+
+## Cache logic
+
+We used the cache by default if it exists in hub_dir.
+Users can force a fresh reload by calling hub.load(..., force_reload=True).
+'''
+
+import torch.utils.model_zoo as model_zoo
+
+# Optional list of dependencies required by the package
+dependencies = ['torch', 'math']
+
+
+def resnet18(pretrained=False, *args, **kwargs):
+    """
+    Resnet18 model
+    pretrained (bool): a recommended kwargs for all entrypoints
+    args & kwargs are arguments for the function
+    """
+    from torchvision.models.resnet import resnet18 as _resnet18
+    model = _resnet18(*args, **kwargs)
+    checkpoint = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(checkpoint, progress=False))
+    return model
+
+
+def resnet50(pretrained=False, *args, **kwargs):
+    """
+    Resnet50 model
+    pretrained (bool): a recommended kwargs for all entrypoints
+    args & kwargs are arguments for the function
+    """
+    from torchvision.models.resnet import resnet50 as _resnet50
+    model = _resnet50(*args, **kwargs)
+    checkpoint = 'https://download.pytorch.org/models/resnet50-19c8e357.pth'
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(checkpoint, progress=False))
+    return model