Expose raft_large and raft_small to torchhub

[NicolasHug]

Minor PR to add raft_large() and raft_small() to our hubconf.py file so that they can be exposed through torchhub.

[facebook-github-bot]

💊 CI failures summary and remediations

As of commit b7121d8 (more details on the Dr. CI page):

---

• 1/1 failures introduced in this PR

---

🕵️ 1 new failure recognized by patterns

The following CI failures do not appear to be due to upstream breakages:

unittest_prototype (1/1)

Step: "Download model weights" (full log | diagnosis details | 🔁 rerun)

/home/circleci/project/test/test_prototype_feat...ead to errors or silently give incorrect results).

    state_dict = load_state_dict_from_url(model_urls[weights_name], progress=progress)
  File "/home/circleci/.local/lib/python3.7/site-packages/torch/hub.py", line 595, in load_state_dict_from_url
    return torch.load(cached_file, map_location=map_location)
  File "/home/circleci/.local/lib/python3.7/site-packages/torch/serialization.py", line 713, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "/home/circleci/.local/lib/python3.7/site-packages/torch/serialization.py", line 920, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
EOFError: Ran out of input
�[33m=============================== warnings summary ===============================�[0m
test/test_prototype_features.py::TestJit::test_bounding_box
  /home/circleci/project/test/test_prototype_features.py:150: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
    new_height, new_width = size

test/test_prototype_features.py::TestJit::test_bounding_box
  /home/circleci/project/torchvision/prototype/features/_feature.py:78: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
    return torch.as_tensor(data, dtype=dtype, device=device)

test/test_prototype_features.py::TestJit::test_bounding_box
  /home/circleci/project/test/test_prototype_features.py:164: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
    new_x1, new_y1, new_x2, new_y2, like=input, format="xyxy", image_size=tuple(size.tolist())

---

This comment was automatically generated by Dr. CI (expand for details).

Please report bugs/suggestions to the (internal) Dr. CI Users group.

Click here to manually regenerate this comment.

[datumbox]

LGTM.

[github-actions]

Hey @NicolasHug!

You merged this PR, but no labels were added. The list of valid labels is available at https://github.com/pytorch/vision/blob/main/.github/process_commit.py