Commits on Aug 29, 2023

1. Enable AutoAugment and modernize DALI pipeline for ConvNets …

   Update DALI implementation to use modern "fn" API
   instead of old class approach.
   
   Add a codepath using AutoAugment in DALI training pipeline.
   It can be easily extended to use other Automatic Augmentations.
   
   The integration of DALI Pipeline with PyTorch additionally skips
   the transposition when exposing NHWC data.
   
   Extract the DALI implementation to separate file.
   Update the readme and some configuration files for EfficientNet:
   * dali-gpu is the default one, instead of PyTorch
   * DALI supports AutoAugment (+ a mention of other Automatic Augmentations)
   
   Fix a typo in the readme files:
   --data-backends -> --data-backend
   
   This PR is a backport of the changes made to this example, when it was
   introduced into DALI codebase:
   https://github.com/NVIDIA/DALI/tree/main/docs/examples/use_cases/pytorch/efficientnet
   
   The changes were tested with the smallest EfficientNet only.
   
   The usage od DALI GPU pipeline in the training can remove the CPU bottlneck
   on both DGX-1V and DGX-A100 when running using AMP which was covered
   in the blogpost:
   https://developer.nvidia.com/blog/why-automatic-augmentation-matters/
   
   Signed-off-by: Krzysztof Lecki <[email protected]>

   

   klecki committed Aug 29, 2023
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