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Update S3D weights#6537

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datumbox merged 3 commits intopytorch:mainfrom
datumbox:models/s3d_new_weights
Sep 5, 2022
Merged

Update S3D weights#6537
datumbox merged 3 commits intopytorch:mainfrom
datumbox:models/s3d_new_weights

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@datumbox datumbox commented Sep 5, 2022

Continuation from work at #6412

Trained from scratch using the following recipe:

PYTHONPATH=$PYTHONPATH:`pwd` python -u run_with_submitit.py --ngpus 8 --nodes 8 --cache-dataset --batch-size=12 --lr 0.2 --clip-len 64 --clips-per-video 5 --sync-bn --model s3d --train-resize-size 256 256 --train-crop-size 224 224 --val-resize-size 256 256 --val-crop-size 224 224 --data-path="/datasets01_ontap_isolated/kinetics/070618/400/"
Submitted job_id: 59255

Validated with:

torchrun --nproc_per_node=8 train.py --data-path="/datasets01_ontap/kinetics/070618/400/" --batch-size=16 --test-only --cache-dataset --clip-len 128 --clips-per-video 1 --model s3d --weights S3D_Weights.KINETICS400_V1
 * Clip Acc@1 68.368 Clip Acc@5 88.050

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@YosuaMichael YosuaMichael left a comment

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Thanks for the PR @datumbox ! Overall looks good to me, just a single minor NIT.

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datumbox commented Sep 5, 2022

The failing test is unrelated. It's a memory issue on Windows that started popping up recently. If it doesn't go away on itsown we may have to disable the mvit* GPU tests on windows.

@datumbox datumbox merged commit 9b432d0 into pytorch:main Sep 5, 2022
@datumbox datumbox deleted the models/s3d_new_weights branch September 5, 2022 13:18
facebook-github-bot pushed a commit that referenced this pull request Sep 9, 2022
Summary:
* S3D weight deployment

* Update accuracies.

* Address review comments.

Reviewed By: YosuaMichael

Differential Revision: D39381954

fbshipit-source-id: f62e69e8fc7aee2e4fa1334e2319f3a14645f159
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3 participants