Pre-trained weights for raft-large, fine-tuned on sintel

[NicolasHug]

Towards #4644

This PR adds our own pre-trained weights as well as the original weights from the paper.

I evaluated these on Sintel test set, and we do slightly better than the original weights.

There is a limited amount of submissions we can make to sintel test, so I decided to just submit C_T_V2 after fine-tuning it on sintel with the "S+K+H" schedule (--train-dataset sintel_skh).

Results below are on sintel train, just to demonstrate that it works. They're not incredibly relevant since we trained on sintel train.

(raft) ➜  vision git:(raft_large_sintel) ✗ torchrun --nproc_per_node 8 --nnodes 1 references/optical_flow/train.py --val-dataset sintel --batch-size 10 --model raft_large --dataset-root ../downloads --weights Raft_Large_Weights.C_T_SKHT_V1

Sintel val clean epe: 0.7647	1px: 0.9233	3px: 0.9687	5px: 0.9793	per_image_epe: 0.7626	f1: 2.8538
Sintel val final epe: 1.2172	1px: 0.8862	3px: 0.9461	5px: 0.9628	per_image_epe: 1.2111	f1: 4.9317


(raft) ➜  vision git:(raft_large_sintel) ✗ torchrun --nproc_per_node 8 --nnodes 1 references/optical_flow/train.py --val-dataset sintel --batch-size 10 --model raft_large --dataset-root ../downloads --weights Raft_Large_Weights.C_T_SKHT_V2

Sintel val clean epe: 0.6482	1px: 0.9255	3px: 0.9701	5px: 0.9805	per_image_epe: 0.6470	f1: 2.6793
Sintel val final epe: 1.0197	1px: 0.8898	3px: 0.9493	5px: 0.9660	per_image_epe: 1.0151	f1: 4.5754

cc @datumbox

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[datumbox]

LGTM, thanks!