Changed from torch.cuda.amp.autocast to torch.amp.autocast

[jamesmuking5]

torch.cuda.amp.autocast to be deprecated

[pytorch-bot]

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[NicolasHug]

Thanks a lot for the PR @jamesmuking5 .

The changes LGTM but I would suggest to just replace with torch.amp.autocast("cuda", ...) since none of those are expected to be on CPU.

If you can, it'd be great to also address the rest of the occurrences or torch.cuda.amp.* (I could find the ones below), but I can do that in a follow-up PR if you prefer. Thanks!

~/dev/vision (main*) » git grep torch.cuda.amp                                                                                          nicolashug@nicolashug-fedora-PF372ZT9
references/classification/README.md:Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [torch.cuda.amp](https://pytorch.org/docs/stable/amp.html?highlight=amp#module-torch.cuda.amp).
references/classification/train.py:        with torch.cuda.amp.autocast(enabled=scaler is not None):
references/classification/train.py:    scaler = torch.cuda.amp.GradScaler() if args.amp else None
references/classification/train.py:    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
references/depth/stereo/cascade_evaluation.py:    with torch.cuda.amp.autocast(enabled=args.mixed_precision, dtype=torch.float16):
references/depth/stereo/train.py:    with torch.cuda.amp.autocast(enabled=args.mixed_precision, dtype=torch.float16):
references/depth/stereo/train.py:        with torch.cuda.amp.autocast(enabled=args.mixed_precision, dtype=torch.float16):
references/depth/stereo/train.py:    scaler = torch.cuda.amp.GradScaler() if args.mixed_precision else None
references/detection/engine.py:        with torch.cuda.amp.autocast(enabled=scaler is not None):
references/detection/train.py:    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
references/detection/train.py:    scaler = torch.cuda.amp.GradScaler() if args.amp else None
references/segmentation/train.py:        with torch.cuda.amp.autocast(enabled=scaler is not None):
references/segmentation/train.py:    scaler = torch.cuda.amp.GradScaler() if args.amp else None
references/segmentation/train.py:    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
references/video_classification/train.py:        with torch.cuda.amp.autocast(enabled=scaler is not None):
references/video_classification/train.py:    scaler = torch.cuda.amp.GradScaler() if args.amp else None
references/video_classification/train.py:    parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
test/test_models.py:    with torch.cuda.amp.autocast():
test/test_models.py:        with torch.cuda.amp.autocast():
test/test_models.py:        with torch.cuda.amp.autocast(), torch.no_grad(), freeze_rng_state():
test/test_models.py:        with torch.cuda.amp.autocast(), torch.no_grad(), freeze_rng_state():
test/test_models.py:        with torch.cuda.amp.autocast():
test/test_ops.py:        with torch.cuda.amp.autocast():
test/test_ops.py:        with torch.cuda.amp.autocast():
test/test_ops.py:        with torch.cuda.amp.autocast():
test/test_ops.py:        with torch.cuda.amp.autocast():