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Description: - As suggested by Nikita, created `torch.backends.cpu` submodule and exposed `get_cpu_capability`. - In torchvision Resize method we want to know current cpu capability in order to pick appropriate codepath depending on cpu capablities Newly coded vectorized resize of uint8 images on AVX2 supported CPUs is now faster than older way (uint8->float->resize->uint8). However, on non-avx hardware (e.g. Mac M1) certain configs are slower using native uint8. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100164 Approved by: https://github.com/albanD, https://github.com/malfet |
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| .. | ||
| _coreml | ||
| _nnapi | ||
| cpu | ||
| cuda | ||
| cudnn | ||
| mkl | ||
| mkldnn | ||
| mps | ||
| openmp | ||
| opt_einsum | ||
| quantized | ||
| xeon | ||
| xnnpack | ||
| __init__.py | ||