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* testing dataloader * matching dataloader implementation for unet3d * remove comments * clean up dataloader * add cookie and cleanup * use shm_path when creating SharedMemory * add support for testing resnet and unet3d dataloaders * update dataset test to return preprocesed data directory in prep for dataloader testing * pass preprocessed dataset directory properly * update loader function for dataloader * add shuffling on indices * update shm name * more cleanup for unet3d dataloader * remove changes to tests --------- Co-authored-by: chenyu <chenyu@fastmail.com>
Each model should be a clean single file. They are imported from the top level `models` directory It should be capable of loading weights from the reference imp. We will focus on these 5 models: # Resnet50-v1.5 (classic) -- 8.2 GOPS/input # Retinanet # 3D UNET (upconvs) # RNNT # BERT-large (transformer) They are used in both the training and inference benchmark: https://mlcommons.org/en/training-normal-21/ https://mlcommons.org/en/inference-edge-30/ And we will submit to both. NOTE: we are Edge since we don't have ECC RAM