Add InceptionResnetV1 model.

[zacharydenton]

This is a port of https://github.com/timesler/facenet-pytorch/blob/master/models/inception_resnet_v1.py.

I'm using this to generate facenet512 embeddings for face recognition:

{:ok, facenet_model_info} =
  Bumblebee.load_model({:hf, "zacharydenton/facenet-vggface2"}, module: Bumblebee.Vision.InceptionResnetV1))

{:ok, facenet_featurizer} =
  Bumblebee.load_featurizer({:hf, "zacharydenton/facenet-vggface2"})

facenet_serving =
  Bumblebee.Vision.image_embedding(facenet_model_info, facenet_featurizer,
    embedding_processor: :l2_norm,
    defn_options: [compiler: EXLA],
  )

# use face detection (e.g. yunet from evision) to get x/y/w/h
face_img = extract_face!(img, x, y, w, h)
{:ok, facenet512} = Nx.Serving.batched_run(FacenetServing, face_img)

I've tested this implementation and compared to the embeddings generated by the facenet-pytorch library. The embeddings match the pytorch implementation with 0.9995 average cosine similarity (with latest Axon - see elixir-nx/axon#622 for details).

n.b. I want to add unit tests but I didn't find any suitable weights in https://huggingface.co/hf-internal-testing - open to advice!

[zacharydenton]

I'm not sure what to do with hidden_state and hidden_states

[zacharydenton]

This model isn't part of the transformers library so there's no valid architecture for config.json on huggingface. Thus I'm not sure if it makes sense to include this in Bumblebee or if it should be in a separate library?