Summary:
Added a few examples and explains to how publish/load models.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14862
Differential Revision: D13384790
Pulled By: ailzhang
fbshipit-source-id: 008166e84e59dcb62c0be38a87982579524fb20e
Summary:
Fix#14610
I can repro the test failure following the steps provided, and this fixes the issue for me. Seems the timing of inserting has to happen after the downloading.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14742
Differential Revision: D13318533
Pulled By: ailzhang
fbshipit-source-id: b9207b4572d5a9443e516d9a84632e3d7b68e477
Summary:
[Edit: after applied colesbury 's suggestions]
* Hub module enable users to share code + pretrained weights through github repos.
Example usage:
```
hub_model = hub.load(
'ailzhang/vision:hub', # repo_owner/repo_name:branch
'wrapper1', # entrypoint
1234, # args for callable [not applicable to resnet18]
pretrained=True) # kwargs for callable
```
* Protocol on repo owner side: example https://github.com/ailzhang/vision/tree/hub
* The "published" models should be at least in a branch/tag. It can't be a random commit.
* Repo owner should have the following field defined in `hubconf.py`
* function/entrypoint with function signature `def wrapper1(pretrained=False, *args, **kwargs):`
* `pretrained` allows users to load pretrained weights from repo owner.
* `args` and `kwargs` are passed to the callable `resnet18`, repo owner should clearly specify their help message in the docstring
```
def wrapper1(pretrained=False, *args, **kwargs):
"""
pretrained (bool): a recommended kwargs for all entrypoints
args & kwargs are arguments for the function
"""
from torchvision.models.resnet import 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
```
* 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)`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12228
Differential Revision: D10511470
Pulled By: ailzhang
fbshipit-source-id: 12ac27f01d33653f06b2483655546492f82cce38
