Bump torch from 1.13.1 to 2.2.0 in /Adaptive Frequency Filters #20
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Bumps [torch](https://github.com/pytorch/pytorch) from 1.13.1 to 2.2.0. - [Release notes](https://github.com/pytorch/pytorch/releases) - [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md) - [Commits](pytorch/pytorch@v1.13.1...v2.2.0) --- updated-dependencies: - dependency-name: torch dependency-type: direct:production ... Signed-off-by: dependabot[bot] <[email protected]>
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Bumps torch from 1.13.1 to 2.2.0.
Release notes
Sourced from torch's releases.
... (truncated)
Commits
8ac9b20Run docker release build on final tag (#117131) (#117182)2490352Fix cuInit test on Windows (#117095)3a44bb7[CI] Test that cuInit is not called during import (#117043)1c8ba38[CI] Use jemalloc for CUDA builds (#116900) (#116988)96d2ddbStore user model to simplify ONNXProgram.{adapt_torch_*,call} APIs (#1152...738b4a5Update ONNX's IO Adapter to support FakeTensor with ExportedProgram (#114407)...4cf10bf[Cherry-pick] [Quant] [PT2] Enable batchnorm in _move_exported_model_to_eval ...7e97e4b[AARCH64] Fall back to GEMM if mkldnn_matmul fails (#115936) (#116666)1a3e3c7[CUDA] baddmm should fall back to addmm for batch=1 (#114992) (#116518)ab7505fFix broken PyYAML 6.0 on MacOS x86 (#115956) (#116551)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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